EPA/620/R-93/002
November 1993
FOREST HEALTH MONITORING
1992 ACTIVITIES PLAN
Project Officer
Daniel Heggem
Environmental Monitoring Systems Laboratory
U.S. Environmental Protection Agency
Las Vegas, Nevada 89193-3478
Technical Director
Samuel A. Alexander
U.S. Environmental Protection Agency
Office of Research and Development
Washington, DC 20460
and
Program Manager
Joseph E. Barnard
U.S.D.A. Forest Service
U.S. Forest Service Laboratory
Research Triangle Park, NC 27709
Environmental Monitoring Systems Laboratory - Las Vegas, NV 89119
Environmental Research Laboratory - Corvallis, OR 97333
Atmospheric Research and Exposure Assessment Laboratory - Research Triangle Park, NC 27711
Printed on Recycled Paper
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FOREST HEALTH MONITORING
1992 ACTIVITIES PLAN
Approved by
Joseph E. Barnard
National Program Manager
Forest Health Monitoring
Samuel A. Alexander
Technical Director
Forest Health Monitoring
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NOTICE
The information in this document has been funded in part by the U.S. Environmental Protection Agency
under Interagency Agreement Number DW12934170-5 to the U.S. Forest Service, under Contract
Numbers 68-DO-0106 (RTF) and 68-C8-0006 (Corvallis) to ManTech Environmental Technology, Inc., under
Contract Number 68-CO-0049 to Lockheed Engineering & Sciences Company, under Cooperative
Agreement Number CR818526-01-0 to the University of Nevada-Las Vegas, and under Cooperative
Agreement Number 58-6645-0-002 to North Carolina State University. It has been subjected to the
Agency's peer and administrative review, and it has been approved for publication as an EPA document.
Mention of trade names or commercial products does not constitute endorsement or recommendation for
use.
Mentton of trade names is for the information of the reader and does not constitute endorsement by the
U.S. Government.
Proper citation of this document is:
Alexander, S.A. and J.E. Barnard. 1992. Forest Monitoring Monitoring 1992 Activities Plan; EPA/602/R-
93/002. U.S. Environmental Protection Agency, Washingtion, DC.
IV
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ABSTRACT
Forests, which cover approximately one-third of the United States, are an important part of the U.S.
economy, culture, and ecology. In response to legislative mandate and concerns for our environment,
several government agencies have been working together to develop a program to monitor the condition
of the Nation's forests. This multiagency program is called the Forest Health Monitoring (FHM) program.
The U.S. Department of Agriculture Forest Service has contributed to this initiative under the auspices of
their Forest Health Monitoring program. The U.S. Environmental Protection Agency has participated
through the forest component of the Environmental Monitoring and Assessment Program. Other
contributing agencies include the National Association of State Foresters and individual state forestry
agencies, the Tennessee Valley Authority, the Soil Conservation Service, the Bureau of Land Management
the Fish and Wildlife Service, and the National Park Service.
This report is designed to serve two purposes for FHM. The first is to provide a description of
major FHM activities planned for the fiscal year 1992. These activities range from the initial planning
stages of field work to the assessment and reporting activities. The second is to provide background
information about the FHM program organization, the indicator development process, and other activities
within FHM.
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Table of Contents
Notice - IV
Abstract v
Figures ix
Tables *
Contributors • • • *j
Acknowledgements *»'
Acronyms x'v
1. Introduction 1-1
1.1 Overview 1-1
1.2 Forest Health Monitoring Design 1-2
1.3 History of FHM Field Activities ... 1-3
1.4 1992 Activities • • • 1~*
2. Detection Monitoring 2-1
2.1 Overview 2-1
2.2 1992 Activities 2-2
3. Evaluation Monitoring 3-1
4. Intensive Site Ecosystem Monitoring • • - 4-1
4.1 Introduction • 4-1
4.2 Intensive Ecosystem Monitoring Sites 4-2
4.3 Implementation Schedule and Budget 4-2
4.4 Relationship with Other Programs 4-3
4.5 1992 FHM ISEM Activities - 4-3
5. Indicator Evaluation 5-1
5.1 Southeast Lobtolly/Shortleaf Pine Demonstration Summary 5-1
5.2 Western Pilot Summary 5-3
5.3 Southern Appalachian Man And Biosphere (SAMAB)
Demonstration 5-6
6. Indicator Development 6.1-1
6.1 Fndicator Selection, Evaluation, and Conceptual Strategy 6.1-1
6.2 Site Classification, Growth, and Regeneration 6.2-1
6.3 Crown Classification , 6.3-1
6.4 Damage and Mortality Assessment 6.4-1
6.5 Branch Evaluation : 6.5-1
6.6 Soil Classification & Physiochemistry 6.6-1
6.7 Foliar Chemistry Indicator - - 6.7-1
6.8 Stemwood (Tree Core) Chemistry Indicator 6.8-1
6.9 Dendrochronology Indicator 6.9-1
6.10 Root Evaluation 6.10-1
6.11 Photosynthetically Active Radiation (PAR) Indicator . 6.11-1
6.12 Vegetation Structure Indicator 6.12-1
6.t3 Wildlife Habitat and Bird Population Estimates 6.13-1
VI
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6.14 Air Pollution Bioindicator Plants Indicator 6.14-1
6.15 Lichen Communities and Elemental Content 6.15-1
»
7. Design Overview 7_1
7.1 The Forest Health Monitoring (FHM) Design 7-1
7.2 The Flexibility of the FHM Design .. 7.4
8. Assessment Overview 6-1
8.1 Introduction 8-1
8.2 Regional Forest Health Monitoring Assessments 8-1
8.3 National Forest Health Monitoring Assessments 8-2
8.4 The National Forest Health Monitoring Assessment Group 8-2
8.5 Relationship to Field Pilot and Demonstration Projects . 8-4
9. Reporting Overview g_1
9.1 Activities Plan 9_1
9.2 Quality Assurance Project Plan 9-1
9.3 Methods Guides and Manuals '.'. 9-1
9.4 Statistical Summaries 9-1
9.5 Demonstration and Pilot Reports 9-2
9.6 Miscellaneous Reports 9-2
10. Quality Assurance Overview 10-1
10.1 Quality Assurance Documents 10-1
10.2 Quality Assurance Responsibilities 10-2
.10.3 Data Quality Objectives :..... 10-7
,10.4 Documentation of Data Collection 10-11
10.5 Quality Assurance Reports to Management 10-12
11. Field Logistics Overview H-1
11.1 Field Crew Leader 11-1
11.2 Foresters H-3
11.3 Field Crew ..'.'.'.'.'.'.'.'. 11-3
11.4 Logistical Aide 11-5
12. Information Management Overview 12-1
12.1 PDR System . . ,,, 12-1
12.2 Laptop PC Systems 12-1
12.3 VAX System 12-3
12.4 Soil Discrepancy System 12-3
12.5 Preparation Laboratory System 12-3
12.6 Analytical Laboratory System 12-4
12.7 Field Software Testing 12-4
12.8 Training and Support 12-4
13. Global Positioning System 13-1
13.1 Objectives 13-2
13.2 Design 13-2
13.3 Logistics 13-2
13.4 Information Management 13-3
14.
References
14-1
VII
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Appendix A.
Appendix B.
Appendix C.
Appendix D.
Appendix E.
Appendix F.
Appendix G.
Appendix H.
National Plan Forest Pest Management and Associated State Component
National Forest Health Monitoring Program, January, 1992 ................. A-1
Working Plan, Integration of Forest Pest Management and Forest Health
Protection Activities With Forest Health Monitoring for the Eastern United
States, Draft: June 25, 1992
B'1
Field Logistics .................................................. ^"1
Off-Frame Indicator Development .................................... D'1
California FHM State Plan ...................... ................... E"1
Operations Plan, Colorado, Forest Health Monitoring ...................... F-1
Southern Appalachian Man And Biosphere (SAMAB) Demonstration Plan ....... G-1
Table of Contents, Environmental Monitoring and Assessment Program FHM
Quality Assurance Project Plan
H-1
Appendix I. Table of Contents, FHM Field Methods Guide
VIII
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Figures
Figure 1-1
Figure 6.1-1
Figure 6.11-1
Figure 6.11-2
Figure 6.12-1
Figure 6.12-2
Figure 6.12-3
Figure 6.12-4
Figure 6.12-5
Figure 6.12-6
Figure 6.12-7
Figure 6.15-1
Figure 6.15-2
Figure 7-1
Figure 10-1
Figure 10-2
Figure 11-1
Figure 12-1
Organizational structure of the FHM national office 1-2
Tree components and processes associated with those components .... 6.1-4
PAR sampling scheme for Western Pilot study in 1992 6.11-2
Beam fraction for four different dates 6.11-4
Biodiversity defined at several levels 6.12-2
Forest health monitoring ecological assessment model for
biotic diversity 6.12-3
Mean age class forest profiles of naturally regenerated loblolly
pine stands, combined data set 6.12-10
Mean age class forest profiles of naturally regenerated oak/hickory
stands, combined data set 6.12-11
Height class vs % foliage occupancy for age class 30-39 with
no disturbance on planted and natural loblolly pine sites 6.12-12
Height class vs % foliage occupancy for age class 40-49, natural
on planted and natural loblolly pine sites 6.12-13
Hypothetical regional response of plant communities (as measured
by equitability [J']) to expanding human population . 6.12-14
Final map of the epiphytic lichen vegetation in the Netherlands
(based on classification T4) 6.15-4
Sulphur concentration in C. rangiferina in eastern Canada 6.15-8
The FHM field plot design 7-5
Proposed organizational structure for FHM quality assurance
staff 10-6
The organizational structure for FHM quality assurance staff
for 1992 10-8
Example of a plot layout procedure 11-4
Forest Health Monitoring data flow 12-2
IX
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Tables
Table
Table
Table
Table
Table
Table
Table
Table
Table
6.2-1
6.2-2
6.2-3
6.2-4
6.2-5
6.2-6
6.2-7
6.2-8
6.2-9
Table 6.6-1
Table 6.6-2
Table 6.6-3
Table 6.7-1
Table 6.7-2
Table 6.7-3
Table 6.11-1
Table 6.11-2
Table 6.11-3
Table 6.11-4
Table 6.12-1
Table 6.12-2
Table 6.12-3
Table 6.12-4
Table 10-1
Table 10-2
Table 11-1
Plot identification 6-2"1
Condition classification 6.2-1
Point description • 6-2"2
Boundary information 6.2-2
Microplot understory vegetation 6.2-2
Microplot seedlings - 6-2"2
Microplot saplings 6.2-2
Subplot trees • 6.2-3
Sequoia/redwood trees (California) 6.2-3
Soil classification parameters for habitat/exposure 6.6-3
Soil physiochemistry parameters for exposure/response assessment.... 6.6-6
Possible soil assessment scenarios for FHM 6.6-10
The DRIS norms for assessing the nutrition of 4-year-old loblolly pine
growing in the South Carolina and Georgia Piedmont. 6.7-2
Comparison of DRIS-derived optima and critical levels for loblolly pine
foliar-nutrient concentrations for the 20/20 Study with those reported
in the literature 6.7-3
Comparison of average nutrient ratios and coefficients of variation from
the 20/20, Study with literature-reported values for loblolly pine 6.7-4
Plot summary for PAR indicator - EMAP FHM 1991 Georgia pilot 6.11-5
EMAP FHM-1991 Georgia Pilot means of % transmitted PAR
(19 pts vs 7 pts) 6.11-6
EMAP FHM 1991 Western pilot-plot summary 6.11-7
EMAP-FHM-1991 Western pilot means of% transmitted PAR
(19 pts vs 7 pts) - 6.11-8
Response indicators of biotic diversity 6.12-4
Relationship of response indicators for different organizational levels
of biotic diversity 6.12-6
Definition and calculation of plot-level values for each element of
heterogeneity and complexity of vegetation 6.12-7
Results of pole point remeasurement during 1990 training ... . 6.12-9
Agency, responsibilities in 1992 FHM activities having quality :
assurance concerns 1u'3
FHM 1992 personnel responsibilities in related to QA concerns 10-4
Example of operations and responsibilities "11-1
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Contributors
(listed alphabetically by organization, location, and name)
Harry Reid Center for Environmental Studies, University of Nevada-Las Vegas, 4505 S. Maryland
PKWY, Las Vegas, NV 89154
C.I. Lift
Lockheed Engineering & Sciences Company, 1050 E. Flamingo Rd., Las Vegas NV 89119
T.E. Lewis
R.L. Tidwell
R.D. Van Remortel
ManTech Environmental Technologies, Inc., 200 SW 35th St., Corvallis, OR 97333
D.L. Cassell
S. Cline
T. Droessler
ManTech Environmental Technologies, Inc., Environmental Sciences, P.O. Box 12313, 2 Triangle
Drive, Research Triangle Park, NC 27709
K. Hermann
R. Kucera
Oregon State University, Dept. of General Science, Weniger 355, OSU, Corvallis OR 97331-6505
B. McCune
University of Arkansas, Fish & Wildlife Research, 627 Science and Engineering BLVD., Fayettevllle,
AR 72701
T. Martin
D. Petit
L. Petit
USDA Forest Service, Anchorage Forest Sciences Lab, 201 E. 9th, Suite 303, Anchorage, AK 99501
V.J. LaBau
USDA Forest Service, Rocky Mountain Station, 240 W. Prospect Road, Ft. Collins, CO 80526
M. Schomaker
T. Shaw
USDA Forest Service, Forest Inventory and Analysis, 200 Weaver Blvd, Southeastern Experiment
Station, Asheville, NC 29804
W. Bechtold
USDA Forest Service, Forest Pest Management, RT 3 Box 1249A, Asheville, NC 28806
R.L. Anderson
W. Hoffard
XI
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USDA Forest Service, U.S. Forest Service Lab, 3041 Cornwallis Rd, Research Triangle Park, NC
27709
J. Barnard
B.C. Loomis
K.W. Stolte
USDA Forest Service, 5 Radnor Corporation Center, 100 Matsomford Rd., Suite 200, Radnor, PA
19087
W. Burkman
USDA Forest Service Forest Science Lab, 507 25th St. Ogden, UT 84401
W. McLain
R. O'Brien
P. Rogers
USDA Forest Service Research Lab, 5985 Highway K, Rhinelander, Wl 54501
J.G. Isebrands
S. Steele
U.S. EPA, U.S. Forest Service Lab, 3041 Cornwallis Rd, Research Triangle Park, NC 27709
S.A. Alexander
U.S. EPA, Environmental Monitoring Systems Laboratory, EAD, 4220 S. Maryland Parkway, Las
Vegas, NV 89119
C.J. Palmer
Kit
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ACKNOWLEDGEMENTS
The authors thank the external reviewers of this document, Dr. Robert Brooks and Dr. Louis
Iverson, for their time in reading the document and their constructive comments.
The following individuals are acknowledged as the EPA Work Assignment Managers: Ralph
Baumgardner, Daniel Heggem, and Spencer Peterson.
The authors gratefully acknowledge the additional review comments from Bill McLain and John
Vissage.
Appreciation goes to Barbara Conkling and Elizabeth Eastman for their contributions as editors.
Appreciation also goes to the following persons: Leslie Gorr and Cheryl Simmons for their
assistance with word processing, and Shirley Burns for her excellent graphics assistance.
XIII
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ACRONYMS AND ABBREVIATIONS
3-D
MS
AD
Al
BA
BLM
C
Ca
CBC
Cd
CDF
CEC
C!
CNS
Cr
Cu
CV
DBH
DQO
DRC
DRIS
ELVES
EMAP
EMSL-LV
EPA
ERL
EQO
F
FAA
Fe
FAO
FIA
three-dimensional
atomic absorption spectrometry
Associate Director
the element aluminum
the element baruim
Bureau of Land Management
the element carbon
the element calcium
calcium binding capacity
the element cadmium
cumulative distribution function
cation exchange capacity
the element chlorine
the carbon, nitrogen and sulfur elemental parameters
the element chromium
the element copper
coefficient of variation
diameter at breast height (1.37 meters)
data quality objectives
diameter at root collar
Diagnosis and Recommendation Integrated System
Environmental Laboratory Verification and Entry System
Environmental Monitoring and Assessment Program
EPA Environmental Monitoring Systems Laboratory in Las Vegas, NV
U.S. Environmental Protection Agency
EPA Environmental Research Laboratory in Corvallis, OR
Ecosystem-level DQO
the element fluorine
flame atomic absorption spectrometry
the element iron
Food and Agriculture Organization
Forest Inventory and Analysis
XIV
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FHM
FPM
FS
FWS
FY
GIS
GMT
GPS
GSMNP
Hg
ICP
ICP-MS
ICP-OES
ID
IM
IQO
IRC
ISEM
K
LAI
LTER
MEI
Mg
MLRA
Mn
MQO ,
MSDR
MSFD
N
NASF
NCSFNC
NCSS
Ni
NIST
NPS
Forest Health Monitoring
Forest Pest Management
Forest Service
Fish and Wildlife Service
fiscal year
geographic information system
Greenwich Mean Time
global positioning system
Great Smoky Mountains National Park
the element mercury
inductively coupled plasma
inductively coupled plasma-mass spectrometer
inductively coupled plasma optical emission spectroscopy
identification
information management
indicator-level DQO
independent regional coordinator
Intensive Site Ecosystem Monitoring
the element potassium
leaf area index
National Science Foundation's Long-Term Ecological Research
maximum expression of injury
the element magnesium
major land resource area
the element manganese
measurement quality objective
measurement system detection reference samples
measurement system field duplicate samples
the element nitrogen
National Association of State Foresters
North Carolina State Forest Nutrition Cooperative
National Cooperative Soil Survey
the element nickel
National Institute of Standards and Testing
National Park Service
xv
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p
PAR
Pb
PC
PD
PDR
PEA
PL
PVC
QA
QMRW
QAC-FHM
QAMS
QAO
QAPjP
QC
Ri
RQO
RTP
S
SA
SAMAB
SCS
SOP
Sr
SRM
TD
Tl
TQM
TSA
TVA
USDA-FS
USGS
V
VPI&SU
the element phosphoais
photosynthetically active radiation
the element lead
personal computer
pilot/demonstration crews
portable data recorder
performance evaluation audits
preparation laboratory
polyvinyl chloride
quality assurance
Quality Assurance Annual Report and Workplan
national Quality Assurance Coordinator for Forest Health Monitoring
Quality Assurance Management Staff
Quality Assurance Officer
Quality Assurance Project Plan
quality control
retrospective indicator
Resouce Group-level DQO
Research Triangle Park, NC
the element sulfur
selective availability
Southern Appalachian Man and the Biosphere program
Soil Conservation Service
standard operating procedure
the element strontium
standard reference material
Technical Director
the element titanium
Total Quality Management
technical systems audit
Tennessee Valley Authority
United States Department of Agriculture Forest Service
United States Geological Survey
the element vanadium
Virginia Polytechnic Institute and State University
XVI
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vs
XRF
Zn
vegetation structure
X-ray fluorescence spectroscopy
the element zinc
XVII
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1. Introduction
S.A. Alexander and J.E.
Barnard
1.1 Overview
Forests, which cover approximately one-
third of the United States, are an important part
of the U.S. economy, culture, and ecology. In
response to legislative mandate and concerns
for our environment, several government
agencies have been working together to
develop a program to monitor the condition of
the Nation's forests. This multiagency program
is called the Forest Health Monitoring (FHM)
program. The U.S. Department of Agriculture
(USDA) Forest Service (FS) has contributed to
this initiative under the auspices of their Forest
Health Monitoring program. The U.S.
Environmental Protection Agency (EPA) has
participated through the forest component of
the Environmental Monitoring and Assessment
Program (EMAP-Forests). Other contributing
agencies include the National Association of
State Foresters (NASF) and individual state
forestry agencies, the Tennessee Valley
Authority (TVA), the Soil Conservation Service
(SCS), the Bureau of Land Management
(BLM), the Fish and Wildlife Service (FWS),
and the National Park Service (NPS).
A major impetus behind the development of
this program has been increasing concern
about documented and potential effects of air
pollutants, global climate change, and a variety
of insect, disease, and other interacting
stressors on forested ecosystems. To help
address these concerns, the FHM program is
designed to assist resource managers and
policy makers in managing the Nation's forest
resources, allocating funds for research and
development, and evaluating environmental
policy for forest resources. The specific
objectives of the program are to:
1. Estimate the current status, extent,
changes, and trends in indicators of the
condition of the nation's forest resources
on a regional basis with known
confidence,
2. Monitor indicators of pollutant exposure
and habitat condition and identify
associations between natural and
human-induced stresses and forest
condition, and
3. Provide periodic statistical summaries
and interpretive reports on forest status
and trends to resource managers and
the public.
The organizational structure for planning,
managing, and implementing the FHM program
has evolved gradually and is still being
developed. An overview of the current
organizational structure is provided in Figure
1-1. National Technical Coordinating
Committees are or will be established for each
major program area listed in the bottom tier of
Figure 1-1. In addition, regional coordination of
detection monitoring and other field activities
will be accomplished through Regional
Technical Committees that are being
established for each of the four FHM mega-
regions: North, South, Intermountain, and West
Coast. The National Technical Coordinating
CommttteeSy and the Regional Technical
Committees are staffed by personnel from the
FS, EPA, state forestry agencies, and other
participating federal agencies.
This report is designed to serve two
purposes for the FHM program. The first is to
provide a description of major FHM activities
planned for the fiscal year 1992. These
activities range from the initial planning stages
of field work to the assessment and reporting
activities. The second is to provide
background information about the FHM
program organization, the indicator
development process, and other "activities
within FHM. The FHM program includes three
levels of monitoring activities which are
summarized below and described in more
detail in Chapters 2, 3, and 4. Chapter 5
describes the purpose, scope, and plans for
each of three field studies (two demonstration
1-1
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USDA Forest Service
National Forest System
Research
State and Private Forestry
U.S. Environmental
Protection Agency
Environmental Monitoring and
Assessment Program (EMAP)
Other Agencies
National Association of
State Foresters
Tennessee Valley Authority
Fish and Wildlife Service
Bureau of Land Management
Soil Conservation Service
National Park Service
FOREST HEALTH MONITORING
NATIONAL OFFICE
Joseph E. Barnard
National Program Manager
(USDA FS)
Samuel A. Alexander
Technical Director
(U.S. EPA/EMAP)
Robert C. Loomis
Deputy, Applications
(USDA FS)
Logistics
Assessment
Forest Pest Management
Data
Regional Coordination of
Detection Monitoring
Kenneth W. Stolte
Deputy, Research
(USDA FS)
indicator Development
Geographic Information
System Support
Evaluation Monitoring
Intensive Site Ecosystem
Monitoring
Research on Monitoring
Techniques
Craig J. Palmer
Deputy Technical Director
(U.S. EPA/EMAP)
Design and Statistics
Information Management
Quality Assurance
Reporting
Figure 1-1. Organizational structure of the FHM National Office.
projects and one pilot study) to be conducted
this year. The rest of the chapters summarize
plans in the major program areas that support
indicator development, field, and assessment
activities.
1.2 Forest Health Monitoring
Design
Initially, both the FS and EPA, working
more or less independently, designed programs
to monitor forest health. The programs
designed by both agencies are comprised of
multiple tiers; each tier constitutes a level or
type of monitoring activity related to assessing
ecological condition. The FS program identifies
three tiers while the EPA EMAP comprises
four tiers. The EMAP and FS tier structures
are very similar and this has facilitated
development of the jointly sponsored FHM
program.
Within EMAP, Tier 1 is the broadest level of
monitoring and focuses on landscape
characterization to estimate the extent, type,
and geographic distribution of forests based
primarily on remotely sensed data. Tier 2
activities focus on estimating status and trends
in forest condition. Measures of chemical,
physical and biological indicators are collected
from a subset of Tier 1 sites and aggregated to
assess the status and trends of forests on a
regional basis. The two main functions of Tier
3 are to determine whether or not current
conditions warrant additional evaluation and to
1-2
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diagnose problems in order to recommend
appropriate actions. The data needed to
accomplish these purposes may be obtained by
intensifying the sampling grid in a specific area
or by collecting additional data at established
plots. Tier 4 is essentially a research
component which supports the monitoring
activities of Tiers 2 and 3. The objectives of
Tier 4 are: (1) to provide a conceptual
framework for testing, implementing, and
refining forest health indicators for Tiers 2 and
3; and (2) to evaluate experimentally whether
or not indicators adequately reflect ecosystem
conditions.
In the three-tiered structure developed by
the FS,each successive tier represents a more
detailed level of monitoring. The three tiers are
Detection Monitoring, Evaluation Monitoring,
and Intensive Site Ecosystem Monitoring, each
of which is described in more detail in the
following chapters (2, 3, 4). Detection
Monitoring, comparable to the EMAP Tier 2, is
designed to collect data on the extent and
condition of forested ecosystems, estimate
baseline (normal) conditions, and detect
changes over time. Data are collected from a
network of permanent plots and augmented
with information from air and other ground
reconnaissance studies. The purpose of
Evaluation Monitoring, comparable to EMAP
Tier 3, is to determine the extent, severity,
specific nature, and, if possible, the causes of
unexplained changes found through Detection
Monitoring. Studies are conducted to identify
causal agents or develop testable hypotheses.
Intensive site Ecosystem Monitoring is
designed to provide very high quality, detailed
information on fundamental processes that
shape forest ecosystems. At this level,
hypotheses regarding cause/effect relationships
are assessed in experimental studies
conducted at a small number of sites
representing important forest ecosystems.
Research on monitoring techniques, is an
important component of the FS program and
has been a primary focus of the jointly
sponsored FHM program during the past two
years. Research on the biological, statistical,
and analytical aspects of monitoring forest
health is a necessary prerequisite for FHM and,
in the future, will continue to support all tiers of
the program.
Currently, research on monitoring
techniques is focused on identifying and
selecting indicators of forest condition. An
indicator is defined as "a characteristic of the
environment that, when measured, quantifies
the magnitude of stress, habitat characteristics,
degree of exposure to the stressor, or degree
of ecological response to the exposure"
(Hunsaker and Carpenter 1990). Indicators are
categorized depending on their stage of
development:
In the first stage of development,
candidate indicators were identified through
a combination of literature review, expert
workshops, and interviews with scientists and
environmental managers. In the second stage,
candidate indicators are evaluated against
specific criteria to select research indicators -
- those that are believed to be the most
promising for future evaluation. Procedures
for evaluating the expected performance of
research indicators include analysis of existing
data, simulation studies, and limited field tests
or pilot studies.1 Indicators that pass
evaluation for expected performance and, with
the concurrence of scientific peer reviewers,
are deemed suitable for actual performance
testing in regional demonstration projects are
called developmental indicators. Finally,
developmental indicators that are selected for
long-term implementation are termed core
indicators (Hunsaker and Carpenter 1990).
1.3 History of FHM
Activities
Field
A pilot project, called the Visual Damage
Survey, was conducted in 1988 and 1989
1 Field studies in which data are collected
from a subset of FHM plots selected according
to the sample design described in Section 7,
are referred to "on-frame" or "on-plot" studies.
Off-frame or off-plot studies do not use the
FHM sample design for selecting plots.
1-3
-------
under the auspices of the National Vegetation
Survey in the Forest Response Program, part
of the National Acid Precipitation Assessment
Program. A select group of indicators for
determining forest condition were identified in
workshops in 1987 and implemented during
pilot tests in 1988 and 1989. Visual damage
survey plots were established in mixed
hardwood forests (128 plots), high elevation
spruce fir forests (31 plots), and natural loblolly
pine stands in the piedmont (157 plots) and
coastal plain (222 plots) regions. The
objectives of this project were: (1) to assess the
condition of selected areas of the eastern forest
through the, application of visual and
sample collection procedures and evaluations,
(2) to determine the proportion of trees that
show damage symptoms which might have
been caused by exposure to atmospheric
pollutants and/or other agents, and (3) to
determine the severity of damage as
determined and grouped in damage classes of
symptoms that may have been caused from
exposure to atmospheric pollutants and/or other
agents.
Based on the success of these and other
studies, forest health monitoring was initiated in
six new England states in the summer of 1990.
The participating states included Connecticut,
Maine, Massachusetts, New Hampshire, Rhode
Island, and Vermont. Over 200 plots were
established in this project by the FS and state
forestry agencies; EMAP-Forests staff provided
assistance in quality assurance and information
management. Certain visual symptoms
indicator measurements, along with standard
forest mensuration measurements were made.
A second field project was undertaken
during the 1990 field season to evaluate
several additional indicators (Palmer et al.
1990). These additional indicators were
identified during interagency workshops and
peer reviews of the EMAP ecological indicators
document (Hunsaker and Carpenter 1990).
Twenty plots were established in northern
hardwood forests of New, England and 20 plots
were situated in loblolly pine stands in Virginia
on sites that would not become FHMi sites.
This project is referred to as the 20/20 pilot
study. In addition to visual symptoms and
growth measurements, indicators of soil
productivity, foliar nutrients, vertical vegetation
structure, and percent transmitted
photosynthetically active radiation (PAR) were
measured.
In 1991, detection monitoring was
continued in the six New England states and
initiated in six additional states: Alabama,
Delaware, Georgia, Maryland, New Jersey, and
Virginia. As in 1990, standard forest
mensuration measurements and selected visual
symptoms indicator data were collected.
Two pilot studies were conducted in 1991:
one in Georgia and one in Colorado and
California. The Georgia pilot study was
designed to evaluate, indicators related to
ecological nutrient cycling (soil chemistry, foliar
chemistry, tree core elemental analysis, and
the presence and taxonomy of selected root
fungi) and to examine relationships between
selected field measurements (vegetation and
habitat structure and intercepted PAR) and
data interpreted from high-resolution aerial
photography.
Prior to 1991, most FHM indicators were
developed and tested in eastern forests. The
purpose of the pilot study in California and
Colorado was to develop measurement
procedures.and evaluate research indicators in
western forests.
The FHM staff also began developing
detailed plans for Evaluation Monitoring and
Intensive Site Ecosystem Monitoring in 1991.
A draft plan for Intensive Site Ecosystem
Monitoring was prepared and is currently
undergoing internal review and revision.
1.4 1992 Activities
Detection Monitoring activities in 1992 will
include revisiting the plots that have been
established in the 12 participating eastern
states and establishing plots in 2 western
states, California and Colorado. The same site
condition, growth, regeneration, and visual
1-4
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symptoms indicator data that were collected in
1990 and 1991 will be collected from FHM
plots in 1992.
Two demonstration projects and one pilot
study will be conducted in 1992 to further
develop and evaluate indicators of forest
health. The Southeast Regional Demonstration
will be carried out in the loblolly/shortleaf pine
forest-type group of the Atlantic coastal plain in
Virginia, North Carolina, South Carolina, and
Georgia. This forest-type group represents one
kind of forest ecosystem. The purpose of this
study is to test the regional forest health
assessment potential of a broad suite of
indicators across a major forest type.
Indicators that will be evaluated include:
• Standard mensuration measurements.
• Visual crown ratings.
• Soils (characterization and physiochemistry).
• Habitat structure for wildlife.
• Foliar chemistry.
• Tree ring growth and chemistry.
• Lichen diversity.
• Branch evaluation.
• Photosynthetically active radiation (PAR).
• Root diseased evaluation.
• Air pollution bioindicator plants.
The second demonstration project will be
conducted in the portions of Virginia,
Tennessee, North Carolina, South Carolina,
Georgia, and Alabama that comprise the
Southern Appalachian Man and Biosphere
(SAMAB) region, principally an oak-hickory
forest. This study will provide an opportunity to
evaluate the developmental indicators listed
above and selected research indicators in a
second type of forest.ecosystem. The pilot
study to be conducted in Colorado and
California will test a similar suite of indicators in
western forests. These three field studies
aredescribed in more detail in Chapter 5.
In addition to field studies, other important
activities in 1992 are related to:
• Development of a conceptual strategy for
selecting and evaluating indicators.
• Design and statistics.
• Assessment.
• Reporting.
• Quality assurance/quality control methods.
• Logistics.
• Information management.
• Use of global positioning system equipment.
The 1992 activities in each of these areas are
discussed in separate chapters later in this
document.
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2. Detection Monitoring
R.C. Loomis
2.1 Overview
Detection Monitoring is the most extensive
component of the national Forest Health
Monitoring Program (FHM) and it currently is
the most developed monitoring level. Detection
Monitoring field implementation was initiated in
1990 and is conducted jointly by the USDA
Forest Service (FS), the U.S. Environmental
Protection Agency's Environmental Monitoring
and Assessment Program for Forests (EMAP-
Forests), and state forestry and other federal
agencies. The FS, EMAP-Forests, and other
partners work together in the following program
areas:
• Policy and goal development.
• Assessment endpoint definition.
• Quality assurance and control.
• Logistics.
• Training.
• Indicator development and testing.
• Plot establishment.
• Data analysis and reporting.
• Assessments.
In FHM, Detection Monitoring covers all
forested lands and consists of (1) a plot
component which is a network of permanent
plots, and (2) a survey component which
includes aerial and other surveys of forest pest
and other stressor effects coupled with reports
of forest damage. Together, these two
components monitor and report the condition of
forest ecosystems by estimating baseline
(normal) conditions, measuring changes from
those baselines over time, and determining if
changes are normal or are cause for concern
and warrant additional evaluation. The
schedule for implementing Detection Monitoring
is developed jointly by FHM partners. The 14
states now participating in FHM Detection
Monitoring are Alabama, California, Colorado,
Connecticut, Delaware, Georgia, Maine,
Maryland, Massachusetts, New Hampshire,
New Jersey, Rhode Island, Vermont, and
Virginia.
The FHM program addresses the needs of
participating agencies to monitor the status and
trends of the Nation's forests. To accomplish
this, FHM has identified a number of societal
concerns that the long-term monitoring is
designed to address. These concerns include:
• Productivity.
• Sustainability.
• Biodiversity.
• Aesthetics.
• Extent.
• Damage.
Societal concerns are described in terms of a
.number of assessment endpoints which
address one or more of the concerns.
Assessment endpoints are in turn described by
a number of ecological indicators which
describe ecological condition. Specific field
measurements quantify the ecological
indicators.
Proposed ecological indicators are tested
and improved in pilot and demonstration
studies and must address the following criteria
before they are recommended to become
"core" indicators for incorporation into FHM
long-term monitoring (see Chapter 6):
• Interpretability.
• Quantification.
• Regional responsiveness.
• Index-period stability.
• Signal-to-noise ratio.
• Environmental impact.
To date, two indicators have been
provisionally accepted by FHM as "core"
indicators. They have been implemented in 12
eastern states (see Chapter 1), and their
performance will continue to be evaluated.
These indicators are:
• Mensuration:
Site condition
Stand condition
Seedling condition
Sapling condition
Understory condition
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• Crown/bole assessments:
Visual Crown Rating
Damage Evaluation
A number of proposed indicators have been
tested in several pilot studies in the East and
one pitot study in the West. These indicators
will be further tested in 1992 in two regional
demonstration studies in the southeastern U.S.
and in a pilot study in the West (California and
Colorado). The southeastern demonstration
studies include portions of the loblolly/shortleaf
pine ecosystem and portions of the oak-hickory
ecosystem. The indicators that are being
evaluated in these studies are:
• Soils (characterization & physiochemistry).
• Habitat structure for wildlife.
• Foliar chemistry.
• Tree ring growth and chemistry.
• Lichen diversity.
• Damage evaluation (for western
conditions).
• Mortality evaluation (for western
conditions).
• Branch evaluation.
• Photosynthetically active radiation (PAR).
• Root disease.
• Air pollution bioindicator plants.
In addition to the evaluation of these FHM
plot-based indicators, off-plot data are collected
and evaluated on pest and other stressor
effects, climate, and air pollution.
The FHM design provides for national and
regional assessments, although sampling can
be intensified to answer more localized
questions. The FHM plot selection is designed
to link FHM and the FS Forest Inventory and
Analysis (FIA) plot networks and to provide
data to fulfill FHM partner mandates. Statistical
design and ground plot selection are discussed
in Chapter 7. There are an estimated 4,000
forested sample plots nationwide. In addition
to ground sampling, the initial EMAP strategy
includes characterizing land cover and land use
in each 40 km2 hexagon. Work is currently in
progress using various forms of remotely
sensed data.
2.2 1992 Activities
2.2.1 FHM Plot Network
Twelve eastern and two western states
comprise the 1992 FHM program. The eastern
states (Alabama, Connecticut, Delaware,
Georgia, Maine, Maryland, Massachusetts,
New Hampshire, New Jersey, Rhode Island,
Vermont, Virginia) have 925 plots on the EMAP
sampling frame. Of these, there are 628
forested plots on which remeasurements will be
made. The measurements referred to here
mean measurements taken on plots which
were established in previous years. The
western states (California and Colorado) have
1045 plots on the EMAP sampling frame. Of
these, there are an estimated 384 forested
plots. Western field activities include: (1)
determining which plots are forested on the
specified one-fourth (262) of the sampling
frame plots in accordance with EMAP
interpenetrating design specifications, and (2)
establishing plots and taking first year
measurements on the forested plots.
In the east, field crews are mostly state or
state contract personnel who usually work in
two-person crews. Field work on an individual
plot is normally completed in one day.
Western crews will likely need more time to
establish the plot and collect initial data. Data
are recorded on Portable Data Recorders
(PDRs) and periodically downloaded and sent
to FHM regional coordinators. Communication
with field crew personnel is largely through
state coordinators who are also in contact with
FHM regional coordinators.
Regional training and certification of field
crew members is scheduled for June in Utah,
North Carolina, and New Hampshire with
additional follow-up training done in individual
states. Quality assurance/quality control
(QA/QC) checks and remeasurements are
made shortly after field activities commence
and during the field season as specified in
QA/QC guidelines. Field crews and FHM
regional and national staff will have post-
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season debriefings to identify ways of
improving field procedures and coordination for
1993.
Plot establishment and data collection
procedures for FHM are specified in the FHM
Field Methods Guide (Conkling and Byers (eds)
1992). Eastern remeasurements will focus on
the following:
• Trees 5 inches DBH and greater - crown
measurements of crown ratio, diameter,
density, dieback, and foliage transparency;
tree damage measurements; and tree
mortality measurements.
• Saplings (trees with DBH between 1.0 and
4.9 inches) - measurements of tree vigor,
crown ratio, mortality, and damage.
• Measurements of symptoms of ozone
effects on biological indicator plants in the
Northeast.
• Soil measurements on a subset of New
England plots and in conjunction with pilot
and demonstration activities in the South
and West.
• Confirmation of 1990 and 1991 data where
data were lost due to PDR malfunction or
when errors in the data are suspected.
Western plot establishment and initial data
collection include all variables specified in the
national FHM Field Methods Guide. These
variables are listed under the following broad
categories:
• Site condition, growth, and regeneration (up
to 71 variables).
• Crown classification (up to 16 variables).
• Damage and mortality assessment (up to 23
variables).
2.2.2 Off Frame
Three important factors influencing forest
health are measured off the EMAP sampling
frame: (1) climate, (2) air quality and
deposition, and (3) forest insects and diseases.
In 1992, FHM will support work to improve
the consistency, reliability, and usefulness of
current forest pest and other forest stressor
effects reporting. The eastwide workplan,
Integration of Forest Pest Management and
Forest Health Protection Activities with Forest
Health Monitoring for the Eastern United States
(see Appendix B), outlines activities underway
to do this. It is based on the FHM national
plan for integrating the Forest Pest
Management component into FHM.
The eastwide plan identifies two aspects
that need updating to improve the usefulness
of currently available pest and stressor effect
data. The first is survey standardization; the
second is establishing reporting standards and
guidelines for particular insects, diseases, and
other stressor effects.
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3. Evaluation Monitoring
R.C. Loomis and
W. Burkman
Evaluation Monitoring is being designed to
determine or hypothesize likely causal
relationships for unusual or unexpected
changes in forest health and to recommend
management responses. Evaluation Monitoring
is activated by Detection Monitoring results and
is designed to estimate the extent, severity, and
possible causes for changes in forest health
status beyond those initially identified in
Detection Monitoring.
When Detection Monitoring or other reports
identify areas or problems of concern for which
more specific information is needed, FHM will
assess the situation and determine specific
Evaluation Monitoring needs. These could
include additional surveys, site- or area-specific
evaluations, and more detailed monitoring. In
some cases, specific research studies may be
required. Evaluation Monitoring project design,
planning, and funding is on a project basis.
Criteria for project design and selection are
being developed by the FHM program.
One example of an evaluation project
undertaken by FHM is the continuation of the
North American Sugar Maple Decline Project.
This project was initiated in 1987 as a joint
effort between the United States and Canada
because of concern about sugar maple decline
in sugarbushes and northern hardwood stands.
Initial program management and support in the
United States came from the Eastern
Hardwood Research Cooperative, the FS
Northeastern Forest Experiment Station, and
the National Acid Precipitation Assessment
Program. This project is now the responsibility
of the FS Northeastern Area, and state and
private forestry. Program management and
support in Canada is provided by Forestry
Canada.
The North American Sugar Maple Decline
Project is scheduled for review in August 1992.
Recommendations from that review will guide
future project activities.
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4. Intensive Site Ecosystem
Monitoring
K.W. Stolte and C.G. Shaw
4.1 Introduction
Intensive Site Ecosystem Monitoring is the
third component of the Forest Health
Monitoring (FHM) program. Intensive Site
Ecosystem Monitoring (ISEM) will provide
high-quality, detailed information on ecosystem
structure and processes through long-term
monitoring at a limited number of sites
representing important forest ecosystems.
Intensive Site Ecosystem Monitoring is defined
in the FHM program as:
Intensive, continuous measurement
and analysis of forest ecosystem
attributes and processes at selected
biologically representative sites. The
purpose of this monitoring is to provide
detailed baseline information on key
components of the selected
ecosystems and to further our
understanding of the processes and
mechanisms responsible for adverse or
unexpected changes in forest health.
Intensive Ecosystem Monitoring Sites are
very similar to the concept of the National
Science Foundation's Long-Term Ecological
Research (LTER) sites. Information from
Intensive Ecosystem Monitoring sites can be
augmented with additional short-term
measurements or it can be expanded by
measuring new variables during specific
studies.
The primary goals of Intensive Site Ecosystem
Monitoring are to:
1. Understand the natural variability of forest
ecosystems, and
2. Understand the response of forest
ecosystems to human environmental
influences.
Intensive Site Ecosystem Monitoring will
accomplish these goals by:
• Developing hypotheses of cause-effect
relationships.
• Providing information to interpret the
baseline and trends in forest condition
observed in the Detection Monitoring
component of FHM.
• Confirming or refuting causes of change
indicated in the Evaluation Monitoring
component of FHM.
• Supporting mechanistic research to identify,
describe, or model tree and forest
processes.
• Enhancing the understanding of the
mechanisms underlying change in forest
ecosystems by developing a sufficient
understanding of forest ecosystem structure
and function to enable explanation of
ecosystem processes and their relationships
to changes in forest health over time.
Intensive Site Ecosystem Monitoring has five
purposes:
1. To help anticipate changes in forest health,
2. To increase basic understanding of causal
relationships,
3. To provide long-term, detailed
measurements that will support experimental
research conducted by programs external to
FHM at the Intensive Ecosystem Monitoring
Sites,
4. To provide indicators and methods for
Research on Monitoring Techniques that
will enable explanations or projections of
observations, and
5. To provide the understanding necessary
to develop management responses to
adverse or unexpected changes.
Intensive Site Ecosystem Monitoring will
provide long-term data and the sampling
infrastructure that will facilitate research
onmechanisms and processes that shape
forest ecosystems, such as:
• Genetic selection and diversity.
• Compartmentalization of carbon and other
nutrients.
• Forest community succession.
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Indicators of forest ecosystem health and
the specific measurements to be made as part
of ISEM remain to be developed during future
planning meetings. The following general
indicators are considered important to address:
• Forest fertility.
• Forest composition.
• Physical environment.
• Aquatic systems.
The Forest Health Monitoring Program
is concerned literally with ecosystems in which
trees comprise a significant part of the
dominant vegetation. Sixteen criteria were
Identified to guide in the selection of specific
Intensive Ecosystem Monitoring Sites
representing important forest ecosystems:
1. Site integrity,
2. Representative of an extensive forest
ecosystem,
3. Sensitive to change in environmental
conditions,
4. Maximally different from other sites in
ISEM,
5. Availability of existing long-term data sets,
6. Potential to support multidisciplinary team
research,
7. Supported financially, logistically, and
philosophically by host facility,
8. Politically supported (Federal, State,
University, etc.),
9. Accurate site history information,
10. Ecotonal (related to #3; in conflict with #2),
11. Maximum wrthin-site variability,
12. Capability to identify and track changes in
ecosystem conditions,
13. Representative of an important forest
ecosystem,
14. Freedom from inertia of existing programs
at site,
15. Provide information on near-term effects of
global change, and
16. Potential to attract support external to the
Forest Service.
4.2 Intensive
Monitoring Sites
Ecosystem
process exists to form the basis of observation
and study. In ISEM, the organism to be
monitored is a purposefully selected,
intensively monitored site. Each site
represents the abstract concept of a particular
kind of forest ecosystem, and becomes the
basis for making the repeated observations
necessary to understand the structure and
function of that ecosystem.
There has been considerable discussion
about whether the Intensive Ecosystem
Monitoring Sites should be chosen to:
• represent the modal conditions of particular
ecosystems (the "basics" of the ecosystems)
where processes observed most commonly
occur in the ecosystem or
• represent ecotones (the "edges" of
ecosystems) where processes are in a less
stable equilibrium and are therefore more
responsive to environmental changes.
The ISEM plan opts for a design that
captures the desirable features of all of these
approaches. By evaluating modal and ecotonal
sites in important forest ecosystems, it will be
possible to:
1. Characterize the conditions that are most
representative of each ecosystem,
2. Characterize the range of conditions
inherent in each ecosystem, and
3. Identify ecosystem conditions and
processes that are most sensitive to
change.
4.3 Implementation Schedule
and Budget
Intensive Site Ecosystem Monitoring will be
conducted at 20 regional groups, composed of
the modal and ecotonal sites and identified by
the core site of each group. These 20 regional
groups will be selected according to site-
selection criteria and phased in over time.
Because ISEM will initially involve only 20
regional groups, the number of forest
ecosystems covered will be limited.
The study of ecosystems poses a challenge
because no convenient, specific component or
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Full funding of ISEM will be $500,000
(estimates are in 1990 dollars) per core group
per year. This includes the core site and any
associated satellite sites (modal or ecotonal).
The total operational funding is $10 million for
the full 20 regional group network. The
recommended implementation schedule is (in
1990 dollars):
1992 No funding.
1993 $0.42 million
1994 $3 million
1995 $8 million
1996 $10 million
Review, revise, and
complete plan.
Initiate 2 regional
groups at 40% funding
Implement 2 regional
groups at 100%
funding. Initiate 6
regional groups at
50% plus funding.
Implement 8 regional
groups at 100%
funding. Initiate 6
funding groups at 50%
plus funding.
Implement 20 regional
groups at 100%
funding.
4.4 Relationship with Other
Programs
Many Forest Service research programs
are closely related to forest health and,
therefore, to the Forest Health Monitoring
Program. All components of Forest Health
Monitoring and especially ISEM, will support
these other programs, (e.g., the Global Change
Program) by providing long-term data sets and
logistical infrastructure for experimental
research.
managers, other federal agencies, university
personnel, and industry personnel. An
internal/external peer review workshop is
scheduled for late July 1992 to provide an
interchange of information on unresolved
issues of the plan.
The initial implementation plan will involve
funding of two regional groups in 1992-1993
(FY93). The funding will be allocated among
the potential sites (core and satellite sites)
within the site groups according to a consensus
of station directors representing the sites:
Year 1993 - Regional Group 1:
Core Site: Hubbard Brook Experimental
Forest (NH)
Satellite Sites: Howland, Mt. Mansfield (VT)
Year 1993 - Regional Group 2:
Core Site: Coweeta Hydrologic
Laboratory (GA)
Satellite Sites: Great Smoky Mountains
National Park (TN), Clingman's
Dome (NC)
Comments on the ISEM plan should be
directed to:
Kenneth W. Stolte
Deputy Program Manager, Research
FOREST HEALTH MONITORING PROGRAM
Southeastern Forest Experiment Station
Forestry Sciences Laboratory
3041 Cornwallis Road
Research Triangle Park, NC 27709
919-549-4020
DG-K.Stolte:S29L03A
4.5 1992 FHM ISEM Activities
The ISEM plan will be finalized in 1992.
The plan has been internally (primarily within
USFS) reviewed twice by a group of
participants that first outlined the plan at a
workshop in Salt Lake City, Utah, in 1991. A
third draft of the plan is scheduled for
completion and internal and external peer
review in July 1992. The external peer review
group will consist of other federal land
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5. Indicator Evaluation
R. Kucera, C.J. Palmer, and
E.R. Smith
5.1 Southeast Loblolly /
Shortleaf Pine Demonstration
Summary
The objectives of the Loblolly/Shortleaf Pine
Demonstration objectives are:
1. To demonstrate the regional forest health
assessment potential of selected indicators
through the application of statistical and
interpretive methods, and
2. To demonstrate the ability to measure
selected indicators across a major forest
type.
5.1.1 Design-Plot Selection and
Location
The selection of the hexagons and plots
has been made (see Section 7 for a more
detailed description of plot selection and
design). The Demonstration will be conducted
in the region that includes the entire states of
Virginia (VA), North Carolina (NC), South
Carolina (SC), and Georgia (GA) over a 2-year
period (1992 and 1993). The plots selected for
measurement in 1992 are those which are
characterized as forested (stocked and with at
least 10 cubic feet of trees), "loblolly/shortleaf"
forest types of the 1991 and 1992 rotation of
the EMAP Tier I Sample Frame within the
states of VA, NC, SC, and GA. The plots to be
selected for measurement in 1993 are those
which are characterized as above and included
in the 1992 and 1993 rotations of the
interpenetrating design within the four states.
The locations of the plots are at regular
intervals in a regular grid which overlays the
EMAP Tier I hexagons. The plots have been
located by geographical information system
(GIS) computer applications. The GIS has
printed out correctly scaled overlay maps
showing the plots. These maps have been
used to directly transfer the locations to
planimetrically correct aerial photographs and
topographic maps (see Section 7).
Implementation of the demonstration requires
field activities of reconnaissance, pretraining,
training, field measurements, and debriefing.
5.1.2 Organization
Forest Health Monitoring (FHM) staff
develop the required documentation of the QA
project plan, methods guides and handbooks,
and the Activities Plan. The FHM staff train
field personnel and facilitate technical direction
and quality assurance; cooperating agencies
assist in procurement of personnel,
transportation, and equipment. Planning and
implementation of the demonstration is a
cooperative effort.
Forest Health Monitoring develops
agreements with State and Federal agencies to
structure and fund the logistical requirements of
staffing, transportation, and purchasing. Forest
Health Monitoring cooperators' arrange to
employ personnel, obtain and pay expenses for
transportation, and obtain equipment. The
Technical Director for FHM has approved the
strategy of developing agreements with FHM
cooperators in the Southern Appalachian Man
and Biosphere (SAMAB) program. The
Tennessee Valley Authority (TVA) currently
serves as the FHM liaison for the SAMAB
committee with contacts for the USDA Forest
Service National Forests in NC, USDA Forest
Service Region 8 Forest Pest Management, the
TVA, and the National Park Service (NPS)
being emphasized. The states of NC, GA, VA
and SC are important to the Demonstration as
cooperators and are currently participating at
different levels on a state-by-state basis
through their state forestry agencies.
From 65 to 75 plots will be measured
between June 15 and August 21, 1992.
The measurements which will be made
require a crew which consists of two foresters,
a botanist, a soil classifier, tree climber, and an
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aide. One crew member will be designated as
the crew leader responsible for on-plot and
day-to-day coordination of the crew activities.
An operational goal is to complete all of the
measurements on a plot in one day. Plans are
to travel and work for ten consecutive days in
two weeks and take four days off at the home
station. With the crews measuring plots at an
average rate of 7.5 in the 10-day periods, 75
plots can be measured by two crews in a 10-
week field season.
A pretraining workshop is scheduled March
30 to April 3 in the Duke Forest and Research
Triangle Park, NC. The workshop will provide
a field test of the portable data recorders, field
methods guide, and field crew activities.
Reconnaissance is required in NC and SC
on approximately 60 plots in each state. It was
expected that approximately 20 plots in each
state would be identified as loblolly/shortleaf
pine plots through the reconnaissance process.
The state of NC has agreed to establish the
plots, identify their forest type classification, and
obtain landowner permission for the plots which
require measurement. Other cooperators could
utilize their field operational capabilities to
conduct these activities in SC.
Training will be conducted the week of June
8,1992, at the Bent Creek Experimental Forest
in cooperation with the USDA FS Southeastern
Forest Experiment Station and the Region 8
Forest Pest Management Unit as well as FHM
staff and contractors. Two crews of the
demonstration field personnel will be trained.
There will be a debriefing and quality
assurance measurement workshop in Asheville,
NC, after the field season.
5.7.3 Personnel
Forest Health Monitoring will create two
crews for the field measurements taken as part
of the Southeastern Demonstration project.
Each crew will consist of a lead forester,
another forester, a botanist, soil classifier, a
tree climber, and an aide.
At least two and preferably 4 to 8 foresters
will develop files of aerial photography, maps,
and diagrams for plots located in NC and SC.
These people will locate the plots and obtain
landowner permission to make measurements
prior to the field crew's arrival.
The personnel for the demonstration will be
obtained by the most effective means. The
personnel, their qualifications, and the options
for their employment are:
• Lead forester - a veteran, graduate forester;
services to be obtained via agreement with
state, the Soil Conservation Service (SCS),
EPA Region, the National Park Service
(NPS), or consultant.
• Forester - a senior forestry student or
graduate; services to be obtained via
agreement with state, SCS, EPA Region,
NPS, or consultant.
• Botanist - a senior botany student or
graduate; services to be obtained via
agreement with state, SCS, EPA Region,
NPS, or consultant.
• Soil classifier - a soil scientist able to classify
soils of the region; to be obtained via
agreement with SCS or consultant.
• Tree climber - experienced individuals with
ability to climb trees without spikes, and
capable of using overnight mail services, and
computerized data recording and data
transfer procedures; to be obtained via
contract with tree surgery company by EPA.
• Aide - A forestry (or related discipline)
student capable of using overnight mail
services, and computerized data recording
and electronic data transfer procedures; to
be obtained via agreement with the
Tennessee Valley Authority.
There is an FHM staff and working group of
scientists who develop different aspects of the
program. The demonstration is utilizing
technical information from:
• QA Specialist - G.E. Byers/C.J. Palmer
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• Logistics - R. Kucera, R.L. Tidwell.
• Indicator Leads - S. Cline, R.D. Van
Remodel, B. McCune, K.W. Stolte, W.
Burkman, W. Bechtold, R.L. Anderson, W.
Hoffard, S.A. Alexander, T. Martin, I.E.
Lewis, T. Droessler.
• Statistics - D.L. Cassell, J. Hazard.
• Reporting - B.L. Conkling.
• Assessment - K. Riitters (with team).
• Information Management - C.I. Liff, T.
Hastings, V.C. Rogers, B. Cordova.
5.7.4 Communications
A chain of communications will be
established to allow consistent technical
communication of procedures and to allow
timely communication between employees.
Electronic mail and phone communications will
be used predominantly.
5.1.5 Sampling Schedule
• Reconnaissance. April 3, 1992, to June 8,
1992.
• Pretraining. March 30, 1992, to April 3,
1992.
• Training. June 8, 1992, to June 12, 1992.
• Field Season. June 15,1992, to August 21,
1992.
• Debriefing. August 23-25, 1992.
5.1.6 Site
Reconnaissance
Access and
In GA and VA the demonstration plots were
established in 1991. In NC and SC the plots to
be measured will be established during
reconnaissance in 1992. The sequence of field
activities is outlined in Appendix C.
5.1.7 Procurement
• PDRs, laptop computers, printers, bar code
readers from EMSL-LV.
• Indicator measurement and general forestry
equipment and supplies will be purchased
by:
a. FHM USDA Forest Service Southeastern
Forest Experiment Station.
b. States.
c. Other cooperators.
d. EPA.
e. ManTech Environmental Technology, Inc.
5.1.8 Lab Operations
Several measurements require shipment of
samples to analytical laboratories. Laboratory
operations will be arranged by contract by FHM
staff at EMSL-LV.
5.1.9 Information Management
The FHM Information Management
Coordinator would like regional and state
cooperators to develop the necessary skills and
provide assistance with the PDRs and
telecommunications.
5.1.10 Quality Assurance
A QA plan is being written and will be
managed by the FHM QA Technical
Coordinator. Discussions have indicated that
the field QA would consist of measurements
and remeasurements at training and debriefing
and of field audits on the plots.
5.2 Western Pilot Summary
5.2.1 Background and Objectives
The decision by the FHM program to begin
detection monitoring in California and Colorado
in 1992 has provided an opportunity for
indicator testing during this field season in
conjunction with the establishment of FHM
plots. This study has been named the
"Western Pilot."
The Western Pilot will be an on-frame pilot
since measurements will be made on the FHM
plots being established on the sampling frame.
The purpose of the pilot will be to provide key
information to the indicator evaluation process
with respect to design, logistics, spatial and
temporal variability, and effectiveness of
5-3
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measurement techniques. An on-frame pilot
has the advantage of providing a representative
sample of the range of conditions that will be
ultimately encountered on FHM plots. The
Western Pitot will provide the first opportunity to
evaluate many of the FHM indicators in an
operational mode for forest types in the
western United States. A second advantage of
an on-frame pitot is that portions of the pilot
data collected this year may be used for
assessment purposes overtime. Several of the
pitot indicators have been tested in other
regions and may ultimately become core
indicators.
In anticipation of the Western Pilot, an off-
frame pilot was conducted at seven sites in
California and seven sites in Colorado during
August of 1991 (Papp et al. 1992). Due to
limited funding and the limited timeframe given
to implement that study, the objectives were to
conduct a logistics and feasibility study for a
variety of indicators and establish training plots
and demonstration areas for future FHM efforts.
Several indicators, including soil microbiology,
root sampling and branch extraction, were
determined not to be ready for further testing
due to feasibility problems. These will be
developed in additional off-frame studies in
1992 and 1993. Some suggestions for
improving indicators resulted from the study.
For example, the understory vegetation
indicator was found to underrepresent the
species composition on the plots. This method
has now been improved for the 1992 pilot
study.
Logistical issues were found to be very
important in the 1991 study. One
recommendation was that a party chief be
made available to support the field crews and
that a logistics person assist crew members
with on-ptot and off-plot responsibilities (e.g.,
data handling and sample shipping)."Improved
training of field crews was also suggested,
particularly for the portable data recorders. All
of these suggestions are being implemented in
the 1992 Western Pilot.
The objectives of the 1992 Western Pilot
are:
1. To obtain information for the evaluation of
FHM research indicators in western
forest types, and
2. To foster multi-agency participation in the
establishment of the FHM program in the
West.
5.2.2 Design and Indicators
5.2.2.1 Plot Selection and Location
The Western Pilot will be conducted on all
FHM plots established this year in California
and Colorado. In summary, the one-fourth
EMAP interpenetrating grid was used to select
candidate 635 km2 hexagons for monitoring. In
these hexagons, the nearest Forest Inventory
and Analysis (FIA) photo point to the hexagon
center will be evaluated for the presence of a
forest. If a forest is present, an FHM plot will
be established. It is expected that, following
this protocol, about 70 and 40 plots will be
identified in California and Colorado,
respectively. Since both states have two
crews, contingencies have been developed to
reduce the number of plots in California and
augment the number of plots in Colorado.
The first step in locating a plot is
determining the location of the FIA photo point
nearest to the center of a candidate hexagon.
The landowner or government agency
responsible for this location is then contacted
and permission requested for access to the plot
location. When permission is granted, the
photo point is visited and if criteria are met
regarding the presence of a forest, a plot
center established. The photograph, along with
plot access instructions, is then given to the
field crews.
5.2.2.2 Indicators
The following indicators will be evaluated in
the Western Pilot:
• Soils.
• Tree crown assessments.
• Tree damage - insect, disease, and other
indicator plants.
• Photosynthetically active radiation (PAR).
5-4
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• Understory vegetation.
• Lichen communities and chemistry.
• Tree mortality.
Additional research will be encouraged in
off-plot pilot modes to enhance methodologies
for specific aspects of the following indicators:
root disease, understory vegetation sampling,
soil microbiology, PAR, tree height,
dendrochronology, branch extraction, foliage
sampling, and soils. This research will be
separate from the Western Pilot and is
discussed further in Appendix D.
5.2.3 Organization
The Western Pilot is a cooperative study.
Funding for the study is being provided from
the research, state and private, and National
Forest System components of the Forest
Service. Funding is also being provided
through the EMAP-Forests program of the
Office of Research and Development in the
U.S. Environmental Protection Agency (EPA),
the Colorado State Forest Service, and the
California Department of Forestry. The Bureau
of Land Management (BLM) and the Soil
Conservation Service (SCS) are providing one
crew member on each crew through
interagency agreements with EPA.
Each state has established an FHM
coordinating committee. The leader of the
California FHM project is Vernon (Jim) LaBau
from the Anchorage Forestry Sciences
Laboratory of the Pacific Northwest Research
Station. The leader of the Colorado FHM
project is Bill McLain from the Forest Inventory
group at the Intermountain Research Station in
Ogden, Utah. Each of these leaders has
prepared a detailed operations plan for their
states (see Appendices E and F). Input from
other agencies is being coordinated by Craig
Palmer (EPA), Don Perkins (California
Department of Forestry), Mike Schomaker
(Colorado State Forest Service), Jim Francis
(BLM), Carol Wettstein (SCS-Colorado), and
David Smith (SCS-Colorado).
Training of field crews will be conducted
June 1 to 5 in Logan, Utah. A pre-training
workshop will be held the previous week to
establish training plots and provide crew leader
training. Additional state-specific training will
be conducted in California and Colorado June
8 to 12. Training of the crews will be the
responsibility of national indicator leads.
Training has been planned so as not to conflict
with the training of crews for the
Loblolly/Shortleaf Pine and SAMAB
demonstrations. A debriefing session is
planned for the last week in August in Logan,
UT.
5.2.4 Personnel
Each state will have two crews. The basic
crew consists of a lead forester, a forester
(forest pest specialist), a botanist, a soil
scientist, and an aide. In Colorado, the basic
crew will be supplemented by two additional
forest pest specialists conducting pest surveys
near the FHM plots. The required
qualifications for these individuals are identified
in Section 5.1.3.
The field crews are supported by a national
FHM team. In addition, the California crews
will be supported by three foresters with
previous experience in FHM. These individuals
will be responsible for plot reconnaissance,
crew coordination, and quality assurance.
5.2.5 Communications
Each crew will be supplied with three PDRs
and a portable computer. The logistics crew
member will be responsible for downloading
data to the FHM data base. • Updating of crew
status and requirements will be the
responsibility of the crew leader.
5.2.6 Sampling Schedule
Reconnaissance
Crew Leader Training
Training: Joint
Apr. 3 - June 1.
May 27 - May 29,
Logan, UT.
June 1 - June 5,
Logan, UT.
CA and CO
June 8 -June 12.
5-5
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• Field Season
• Debriefing Session
5.2.7 Site
Reconnaissance
June 15-Aug. 28.
Aug. 29- Aug. 31.
Access and
Site access will be requested by the Forest
Service reconnaissance staff in each state.
Assistance will be provided by federal and state
staff for access on government lands.
5.2.8 Procurement
Major indicator and information
management equipment is being provided by
the EPA. Funding for minor equipment is being
provided by the Forest Service and EPA and
will be purchased according to local
agreements.
5.2.9 Laboratory Operations
Soil and lichen samples will be sent
overnight in coolers to the FHM sample
preparation laboratory in Las Vegas, NV. After
preparation, soil samples will be sent for
analysis to qualified analytical laboratories.
Lichen samples will be forwarded to Dr. Bruce
McCune at Oregon State University.
5.2.10 Information Management
Information management protocols have
been established for all data collection efforts in
the FHM program. These protocols will be
followed in the Western Pilot. Individuals from
each state monitoring activity will receive
special information management training in
Asheville, NC, during mid-May.
5.2.11 Quality Assurance
A quality assurance (QA) plan has been
prepared for the overall FHM program by the
FHM QA Technical Coordinator. Key
components of this plan include the
establishment of measurement quality
objectives, methods guide, and training,
auditing, remeasurement, and debriefing
requirements. Each national indicator lead is
responsible to see that these QA requirements
are met for measurements in the Western Pilot.
5.3 Southern Appalachian Man
And Biosphere (SAMAB)
Demonstration
The Southern Appalachian Man and
Biosphere (SAMAB) .Forest Health Monitoring
(FHM) Demonstration planned for the summer
of 1992 will be a multiagency effort made
possible largely through personnel, equipment,
and monetary contributions from the SAMAB
member organizations. As such, indicator
selection is based upon resource availability
rather than a prioritization of the candidate
indicators.
Five states will be participating in the
SAMAB Demonstration: Virginia, Tennessee,
North Carolina, South Carolina, and Georgia.
Twenty-five plots are spread among these five
states.
The indicators which will be used in the
SAMAB Demonstration project will be the same
as for the Southeast Loblolly/Shortleaf Pine
Demonstration project, with the exceptions that
root samples will not be collected in the
SAMAB region and an on-frame pilot using
photosynthetically active radiation (PAR)
measurements will be conducted. The project
logistical information presented in section 5.1
above can be referenced for more details.
There will be one field crew participating in
the SAMAB Demonstration. The crew will
consist of:
• Two foresters - TVA.
• One botanist - TVA and NPS.
• One soil scientist - TVA and SCS.
• One tree climber - by contract with the FS.
• One aide - TVA.
The required qualifications for these individuals
are identified in Section 5.1.3.
5-6
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6. Indicator Development
This chapter is organized so that each
major section contains a different indicator
discussion and has the author's name(s)
associated with it.
Selection,
Conceptual
6.1 Indicator
Evaluation, and
Strategy
T. Droessler
6.1.1 Introduction
On October 3, 1991, the Forest Health
Monitoring (FHM) Indicator Selection
Committee was formed in Asheville, North
Carolina, to discuss a process for selecting
indicators to be used in 1992 field work. The
assigned goal was to provide an objective
process for selecting indicators for a
demonstration project in the Southeast.
Potential indicators were subsequently
evaluated using a prioritized list of selection
criteria that were agreed upon in Asheville.
These criteria, presented by Knapp et al.
(1991) in The Indicator Development Strategy
For The Environmental Monitoring And
Assessment Program, include:
1. Unambiguously interpretable,
2. Simple quantification,
3. High signal-to-noise ratio,
4. Regionally responsive,
5. Index period stability, and
6. Low environmental impact.
6.1.2 Target Population
The FHM target population is defined as
the areal extent of a forested ecosystem about
which estimates of conditions will be made.
Target populations can be defined by a region
or attribute. For example, the population of
interest might be the forests of the Northeast as
defined by the Forest Service Forest Inventory
and Analysis (FIA) units, only high elevation
spruce-fir forests, or all stands of sugar maple
in New England. At the broadest level, the
target population for FHM is all forest
ecosystems in the United States (Palmer et al.
1991).
The implication of the target population is
that any field measurement must be
aggregated up to and be interpretable at the
subplot, plot, hexagon, and regional level. A
measurement that is only informative at the
individual tree level is not appropriate as an
indicator for FHM. The FHM program is
interested in monitoring at the regional scale to
provide quantitative and unbiased estimates of
the status and trends in ecological condition
(Palmer et al. 1991). For example, a mean
subplot diameter, a mean diameter by species,
a diameter distribution by species, or basal
area by species are all appropriate
aggregations. Once measurements are
aggregated to the subplot or plot level, they
can be taken to a regional level through
expansion factors.
6.1.3 Critical Criteria
6.1.3.1 Unambiguously Interpretable
The indicator must be related
unambiguously to a policy-relevant assessment
endpoint or relevant exposure or habitat
variable that forms part of the ecological
resource group's overall conceptual model of
ecological structure and function. An
assessment endpoint is a forest feature in
which a U.S. Environmental Protection Agency
(EPA) or Forest Service client or the general
public is interested. A conceptual model of
ecological structure and function for FHM is
currently being developed.
There must be a way for each indicator to
aggregate all measurements up to plot level.
Plot level data will be used in cumulative
distribution functions (CDFs) and graphs in the
assessment reports. A method for determining
a nominal/subnominal boundary for the CDF
should also be included.
6.1-1
-------
6.1.3.2 Simple Quantification
Each indicator must be quantified by
synoptic monitoring or by cost-effective
automated monitoring.
It should be possible to collect the
measurements for the indicator in one day (or
to gather them in one day for later analysis).
An alternative would be a cost-effective
automated monitoring method to gather the
data overtime. Related considerations include:
• Reliability of the instruments.
• Complexity/difficulty of the measurement
process.
• Time required to collect measurements.
• Measurement error.
Measurement error in this context is the total
amount of expected variability in repeated
measurements on the same material.
Measurement error in the visual symptoms
Indicator is usually attributable to differences
between observers. Measurement error for
PAR is related to both instrument variability and
cloud conditions. Measurement error for soil
chemistry is the combination of variability due
to sampling protocol violations, preparatory
laboratory variability, and analytical laboratory
variability.
The subject of measurement error must be
carefully considered, since measurement error
decreases the accuracy of measures of current
status and extent, adds biases to the tails of
CDFs, and decreases the ability to detect
changes and trends over time.
6.1.3.3 High Slgnal-to-Noise Ratio
Each indicator must have sufficiently high
signal strength (when compared' to natural
annual or seasonal variation) to allow detection
of ecologically significant changes within a
reasonable time frame (which may vary among
the indicators). It is important to determine
whether or not the natural annual variability of
the indicator will mask an important shift in the
value of the indicator. Documentation for each
indicator should address the indicator's ability
to detect a trend or change, the amount of
change that the indicator can detect, and the
period of time over which that change would
occur.
With repeated visits over time, site-to-site
differences do not matter nearly as much as
annual variability. Repeat visits allow analysis
analogous to a paired t-test or a repeated
measures analysis. With a large number of
sites, the ability to detect a trend regionally is
different than the ability to detect a change at
one site.
As an example, consider the desire to
detect a mean change of 10 percent in a
measurement that has a remeasurement error
of 20 percent. For a single site at two different
times, there is no chance of detecting a
change. For 35 sites with the same change
and remeasurement error, a mean change can
be detected with a significance level of 0.05
(analogous to a 95 percent confidence interval)
and a power of over 80 percent. For 200 sites,
a change of 10 percent and a remeasurement
error of 50 percent, a mean change can be
detected with a significance level of 0.05 and
roughly the same power.
6.1.3.4 Regionally Responsive
Each indicator must reflect changes in
ecological condition, pollutant exposure, or
habitat condition, and respond to stressors
across most pertinent habitats within a regional
resource class. This criterion refers to the
importance of a strong relationship between the
field measurement and the environmental
index. A difference in the value of the indicator
must reflect a difference in site characteristics,
either across time or space. This difference
does not have to be independent of external
variables such as climate. A visual crown
rating that shows regional increases in
defoliation during drought years is not
defective. It is showing real climatic effects.
As long as climatic information is available, the
difference can be explained.
6.1-2
-------
6.1.3.5 Index Period Stability
Each indicator must exhibit low
measurement error and stability (low temporal
variation) during an index period. The index
period is the window in which samples will be
collected. For example, if data are collected
between June and August, then the indicator
needs to be relatively stable during that time
window. Diurnal and seasonal variability are
both important considerations. Effects on the
indicator measurements due to phenologic and
climatic changes during the sampling period
also affect index period stability.
High index-period variability adds noise to
the indicator, thus making it more difficult to
accurately assess current status or to detect
trends overtime. In addition, high index-period
variability, like measurement error, causes
biases in the tails of the CDFs to be used in
assessments.
Individual measurements need not be
stable over the same period. For example, a
measurement of PAR that does not exhibit
diurnal or seasonal effects would rate more
highly on this criterion than a PAR
measurement that is very sensitive to sun
angle. Available data from studies or the
literature can be used in documenting an
indicator's index period stability.
6.1.3.6 Low Environmental Impact
Documentation for each indicator should
address the environmental impact of all data
collection and sampling procedures. All
potential impacts on the site and other
indicators, even trampling of the understory,
should be considered. The goal is for
measurement of indicators to cause the least
amount of environmental damage possible.
6.1.4 Evaluation Procedure
In December 1991, indicator leads
submitted evaluations of their indicators using
existing or manufactured data and relevant
literature to demonstrate how their indicator
met the above criteria. An indicator selection
panel reviewed and rated each candidate
indicator using the criteria described above.
The process was intended to be tractable and
provide an objective process for selecting or
rejecting indicators for field use and
re-evaluating Indicator performance following
demonstration studies.
6.1.5 Conceptual Strategy
The FHM program has implemented an
indicator selection and development strategy
proposed by Knapp et al. (1991), "...
conceptual models are an important tool for
formalizing possible relations among indicators,
assessment endpoints, and stressors, and for
identifying data or knowledge gaps that could
be filled through the selection and development
of additional indicators."
A primary goal is to develop a theoretical
framework for indicator selection, integration,
and interpretation and to quantitatively link
each field measurement to a public value. The
conceptual model should identify components
of the forest ecosystem with interdependences,
duplication, etc. One strategy is to include all
energy pathways so that critical energy flows
and implications of interruptions due to
environmental pollution and disturbances can
be identified. Any number of above- or
below-ground factors affect energy flow,
including nutrient deficit or surplus and
drought. Using this strategy, indicators suitable
for monitoring the condition of critical energy
flows would be the most useful for monitoring.
6.1.6 Towards a Draft Tree-Level
Conceptual Model
Trees are the most conspicuous aspect of
a forest ecosystem. While trees are necessary,
they are insufficient for defining the ecosystem.
A ."stand" is an arbitrary delineation often
imposed for operational harvesting, but usually
represents a contiguous group of trees with
some homogeneous characteristics. The use
of stands and their dynamics is appropriate
6.1-3
-------
for monitoring and assessing impacts on
trees for the following reasons (as given by
K5ester1988):
1. The stand is an important integrator of
most biological effects of pollutants on
forests (see Assmann [1970] for a
comprehensive discussion)
2. The stand may be the only level of
organization at which an effect can be
detected in the field. Regardless, the
interaction between stand processes and
pollutant effects must be disentangled
before any claim of effect can be made
(Hyink and Zedaker 1987).
3. The stand is an important economic unit.
One can predict the volume a stand can
produce, but one cannot predict it (with
any certainty) from trees considered as
noninteracting entities. Most of the theory of
forest economics deals with stands because
of the first reason.
4. The stand is customarily the unit used
in assessing the effects of other deleterious
environmental processes such as the action
of disease and pests. Meineke (1917 and
1928) was an early proponent of stand-level
analyses in forestry pathology research. A
tree and stand of trees can be divided into
the components of canopy, bole and roots.
The associated functions and processes of
each can be detailed as shown in Figure
6.1-1.
The production of biomass in forests
depends on the interception of light by leaves
and on the conversion of intercepted light
energy into carbohydrates. The efficiency of
this conversion depends on the availability of
water since leaves under drought stress
intercept light, but fail to produce
carbohydrates. The productivities of forests
CANOPY
Variable Slolchlomelry
Translocalion
Nutrient Fixation
and Growth
Enhanced Capture of
Dry Deposition
Foliar Exudation
Foliar Leaching
Soln. Equilibration ol
Interception Storage
unerfatl
BOLE
Variable Slolchtomolry
Nutrient Fixation
and Growth
ROOTS
Nutrient Uptake
Nulrient Fixation
and Growth
Respiration
Figure 6.1-1. Tree components and processes associated with those components (see Gherini et al. in
Kiester et al. 1990).
6.1-4
-------
differ because of differences in stand leaf area
and resultant light interception and in the
efficiency of producing carbohydrates from
intercepted light. These differences depend on
climate, soils, species composition, and history
of such factors as disturbance. (Binkley et al. in
press).
6.1.7 Current Indicators and
Draft Conceptual Model
the
Using Figure 6.1-1 as a guide, current
indicators cover aspects of the forest canopy,
bole, and roots. The visual condition rating,
photosynthetically active radiation, and foliar
chemistry indicators describe physical, spectral,
and chemical aspects of the canopy. The DBH
increment and wood chemistry indicators
measure physical and chemical features of the
bole. The root disease and soil indicators
measure structural, biological, and chemical
aspects of the rooting environment.
The vegetation diversity indicator describes
the vertical and horizontal distribution of floral
and faunal habitat. The vegetation diversity
indicator is not comprehensive in its attempt to
define potential habitat for all species.
6.1-5
-------
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6.2 Site Classification,
Growth, and Regeneration
Indicator
6.2.1 Introduction
Site classification, growth, and
regeneration includes 113 measurements and
other data to describe plots and plot vegetation
as part of the Detection Monitoring phase of
the FHM program.
6.2.2 Indicator Evaluation
The site classification, growth, and
regeneration measurements were chosen for
initial use based on research results, the Forest
Service Forest Inventory procedures, and the
consensus of experienced foresters. The
measurements are being evaluated using the
indicator evaluation process described in
Section 6.1 and a more detailed presentation of
how these indicator measurements meet the
selection criteria will be included in the next
revision of this document.
6.2.3 Field Measurements
The following tables provide a
complete listing of all variables currently
measured in Detection Monitoring. The tables
show the different variables collected for plot
identification (Table 6.2-1), condition
classification (Table 6.2-2), point description
(Table 6.2-3), boundary information (Table
6.2-4) microplot understory vegetation (Table
6.2-5), microplot seedlings (Table 6.2-6),
microplot saplings (Table 6.2-7), subplot trees
(Table 6.2-8), and sequoia/redwoods (in
California) (Table 6.2-9).
.TABLE 6.2-1. PLOT IDENTIFICATION
TABLE 6.2-2. CONDITION CLASSIFICATION
Measurement Variable
State
County
Plot Number
Measurement Type
Old Plot Status
Current Plot Status
Photo Year (West)
Month
Day
Year
Elevation
Terrain Position
Measurement Variable
Condition Class
Land Use Class
Forest Type
Stand Origin
Stand Size
Past Disturbance 1
Disturbance Year 1
Past Disturbance 2
Disturbance Year 2
Past Disturbance 3
Disturbance Year 3
Site-Tree Species (WEST. SE)
Site-Tree Height (WEST. SE)
Site-Tree Age (WEST. SE)
Stand Age (WEST, SE)
6.2-1
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TABLE 6.2-3. POINT DESCRIPTION
TABLE 6.2-6. MICROPLOT SEEDLINGS
Measurement Variable
Stope Correction
Percent Slope
Aspect
Microrelief
Subplot Cond'rtfon List
Subpfot Center Condition
Microptot Center Condition
Subplot Offset
Microptot Offset
Measurement Variable
Species
Condition Class
Crown Class
Seedling Count
TABLE 6.2-7. MICROPLOT SAPLINGS
TABLE 6.2-4. BOUNDARY INFORMATION
Measurement Variable
Ptot Type
Offset Point
Contrasting Condition
Left Azimuth
Left Distance
Comer Azimuth
Corner Distance
Right Azimuth
Right Distance
TABLE 6.2-5. MICROPLOT UNDERSTORY
VEGETATION
Measurement Variable
Tree Number
Offset Point
Old Tree History
Current Tree History
Species
OW Diameter at Breast Height (DBH)*
Diameter at Breast Height
Diameter at Breast Height Check
Old Stem Count
Stem Count
Old Diameter at Root Collar(W)
Diameter at Ro6t CoIlar(W)
Old Diameter Largest Stem(W)
Diameter Largest Stem(W)
Horizontal Distance
Azimuth
Mortality Year
Condition Class
Crown Class
Tree Height (CA)
Measurement Variable
Percent Moss
Percent Lichens (West)
Percent Ferns
Percent Herbs
Percent Shrubs
Percent Seedlings
' DBH is the diameter measured at a
height of 1.37 m.
6.2-2
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TABLE 6.2-8. SUBPLOT TREES
Measurement Variable
Tree Number
Offset Point
Old Tree History
Current Tree History
Species
Old Diameter at Breast Height
Diameter at Breast Height
Old Stem Count
Stem Count
Diameter at Breast Height Check
Old Diameter at Root Collar(W)
Diameter at Root Collar(W)
Old Diameter Largest Stem(W)
Diameter Largest Stem(W)
Horizontal Distance
Azimuth
Mortality Year
Ground Year
Condition Class
Crown Class
Tree Height (CA)
TABLE 6.2-9. SEQUOIA/REDWOOD TREES
(CALIFORNIA)
Measurement Variable
Tree Number
Offset Point
Old Tree History
Current Tree History
Species
Old Diameter at Breast Height
Diameter at Breast Height
Diameter at Breast Height Check
Old Diameter at Root Collar
Diameter at Root Collar
Horizontal Distance
Azimuth
Mortality Year
Ground Year
Condition Class
Crown Class
Tree Height
The FHM program is working toward
remeasurement of all variables on plots on at
least a 4-year cycle. This is consistent with the
EMAP interpenetrating design (see Chapter 7).
Full .remeasurement includes reconciling all
land use changes, remeasuring diameters for
growth, and remeasuring the plots for ingrowth,
cutting, and mortality. In areas where annual
measurements are made, as part of detection
monitoring (eastern United States), the crown
ratings are the focus. Complete
remeasurement will be done only every 4
years.
6.2.4 Data Analysis
Data are verified, validated, and made
available to analysts on both regional and
national scales. The data are used in regional
reporting activities and appear in the annual
FHM national Statistical Summary. Information
about stand characteristics appears in these
reports. The plot identification data are
archived for use at the next remeasurement
visit. Data from previous years are also used
as part of the quality assurance and quality
control procedures by means of the portable
data recorders (hand-held computers) that are
used for recording data. The portable data
recorders can also store and display a limited
amount of data from previous years.
6.2.5 1992 Activities
Site classification, growth, and
regeneration data will be collected on the new
Detection Monitoring plots which will be
established in California and Colorado.
Selected crown and damage measurements
will be taken on established plots in the 12
participating states in the eastern United States
(Alabama, Connecticut, Delaware, Georgia,
6.2-3
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Maine, Maryland, Massachusetts, New
Hampshire, New Jersey, Rhode Island,
Vermont, and Virginia) (see Chapter 2 for more
details). These measurements will also be
collected as part of the Southeast
Demonstration project (participating states are
Virginia, North Carolina, South Carolina, and
Georgia) and the SAMAB Demonstration
project (participating states are Virginia, North
Carolina, Tennessee, South Carolina, and
Georgia).
6.2-4
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6.3 Crown Classification
R. Anderson and
W. Burkman
6.3.1 Introduction
The crown classification indicator consists
of several measurements which are combined
when evaluating the status of seedling, sapling,
and tree crowns. The measurements may be
evaluated individually or combined using a
visual crown rating model (Anderson et al.
1992). The five crown variables can be
aggregated into the visual crown rating. This is
accomplished by defining good, average and
poor classes for each variable, aggregating the
variables, and subsequently defining good,
average, poor and very poor ratings for each
tree. Tree level data are then averaged to give
a plot-level score. Each of the individual
measurements are discussed separately in the
following sections.
6.3.2 Indicator Evaluation
This section discusses the indicator
selection criteria described in Section 6.1 as
they apply to the measurements for the crown
classification indicator.
6.3.2.1 Crown Ratio
Crown ratio is the percent of total tree
height that supports live green foliage that
contributes effectively to tree growth, the
crown ratio is determined by the ratio of crown
length to total height of the tree. Crown length
is determined from the top of the crown with
the last live branch to the base of the obvious
live crown. When live branches occur below
the obvious live crown, these branches are
included only if their diameter at the base is
larger than 1 inch and they are located within 5
feet of the obvious base of the crown or the
previous branch above used for this
determination. The point on the main bole is
perpendicular to the foliage on the last branch
included in the crown estimate. The base of
the crown is determined by the foliage and not
the point of branch intersection with the main
bole. Dead branches in the top of the crown
are not included in the ratio estimate.
Crown ratio is recorded in 5-percent
classes from 0 to 100. The ratio is estimated
by two field crew members to the nearest 5-
percent class. The highest class, 100 percent
is recorded as 99-percent class. A tree with no
obvious crown is recorded as a 0.
All the crown raters must participate in
annual crown-rating training and must provide
quantitative evaluation of their ability (such as
a test) that they can perform at an acceptable
level. Visual standards and written definitions
are provided to reduce annual drift in rating
values. Two persons are required to rate a
tree from opposite sides; the two estimates are
averaged and recorded in the 5-percent class.
Interpretability -
Recorded measurements must be within 2
percentage classes (+10 percent) when
remeasured. Greater differences are
considered errors; however, a third
measurement is required to determine which
measurement is in error. The error frequency
must be 10 percent or less for the .data to be
acceptable unless confirmation is made that
the original measurements were correct (i.e.,
the remeasurement was in error).
Measurement errors may occur from
several sources. The top of the tree may not
be clearly visible, or when rounded, clearly
identifiable. The branch structure of some
trees is such that a clear "obvious1 base of the
crown is difficult to establish. One should also
be aware that a small tree, with a 5-inch
diameter at breast height (DBH) tree, such as
hemlock after a clear-cut, might have a high
crown ratio, while a large oak in a dense stand
may have a small crown ratio.
Live crown ratio can be directly related to
tree vigor and DBH growth. For example, the
larger the ratio is, the greater the diameter
growth of the crown. What effect it has on total
volume growth of trees is not certain. The
6.3-1
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reduction of growth due to declining crown ratio
may be the result of increased distance
between the bottom of the live crown and the
DBH area where growth is measured. It is
known that diameter growth declines as the
distance increases.
Quantification —
This measurement can be collected for all
trees on a plot in one day. The measurements
are an estimation to the nearest 5 percent of
the total tree height that is in live crown.
Clinometers are used to measure test trees for
training purposes. The instruments are
accurate to the nearest foot, but it is often hard
to see the top of the tree. Data from training in
1991 show the crew measurements were within
±10 percent of the instrument measured value
94 percent of the time and they were never
more than 20 percent off in the remaining 6
percent The estimate is easy to make and
takes 2, to 3 minutes per tree.
Signal-to-Noise Ratio -
The signal-to-noise ratio is very high for this
indicator. Estimates of crown ratio are
relatively easy to make and determination of
live crown gives a definite indicator (signal) of
the vigor of the tree crown.
Regional Responsiveness -
This indicator is very responsive at the
local, state, and regional Fever. An example is
that crown ratio typically changes with stand
density. At the state or regional revel', it would
be possible to identify species and areas that
had different average crowa ratios. There are
considerable data which show that the growth
rate and vigor of a tree are related to its crown
ratio.
Index Period Stability -
The estimates are very stable for the same
person evaluating the same tree and for
different people rating the same tree. Unless
the tree is damaged by storms, for example,
the variable is very stable. This measurement
has almost no change from June to August and
little change from year to year. The live crown
ratio is a clear, specific measure that is not
confused by other factors.
This measure is very stable during the day
and over the sampling season. Crown ratio,
however, may decline for many reasons. The
base of the crown may move upward or the top
be lost, thus decreasing the crown length.
Common causes for this are:
1. Self-pruning with increasing age,
characteristic of a particular species,
2. Shading from neighboring trees as a result
of increasing crown closure,
3. Mechanical damage from adjoining tree
branches,
4. Defoliation caused by loss of lower
branches, and
5. Loss of tree top from breakage or
mortality.
Crown ratio normally changes slowly and
the usual trend is to decline, unless some
mechanical pruning or damage occurred.
Changes may go in either direction. Increasing
stand density is likely to reduce crown ratio,
while opening of a stand, coupled with' vigorous
growth, could increase the ratio.
Environmental Impact -
This measurement only has minor impact
and is of short duration. Minor damage to the
understory may result from two people,
approximately one tree-length away from the
tree, making their estimate.
6.3.2.2 Crown Diameter
Crown diameter is measured on the;
perimeter of the crown;, or the drfpline. Two
measurements to the nearest 1 ft (± 3 ft) are
taken, one at the widest point and another on
a line perpendicular to the widest point line.
The two measurements are averaged for the
crown diameter.
The expected use is to classify trees into
three to four groups, such as large, average,
moderate, and poor crown diameter: Crown
6.3-2
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diameter is measured vertically down from the
branches spaced furthest apart in the crown.
The second measurement is taken at a
perpendicular angle, again at points vertical to
the out-most branch tips. The two
measurements are recorded and then the
average crown width calculated on the
computer by finding the average of the two
instruments.
All crew members who rate crowns must
participate in annual crown-rating training and
must provide quantitative evaluation of their
ability (such as a test) that they can perform at
an acceptable level. Visual standards and
written definitions are provided to reduce
annual drift in measurements. A second
person standing back to confirm vertical lines
down from branches or the use of a device to
measure a vertical line from the ground to
crown edge is recommended.
Interpretability -
Measurement errors may originate from
several sources. The side branches may be
intermingled with the neighboring trees or
shielded by lower canopy, making it difficult to
determine the end of the branch. The vertical
line may not be correct, giving an incorrect
distance between branch tips.
Crown diameters are associated with vigor
and DBH growth of loblolly and shortleaf pine.
Simply stated, the larger the crown is, the more
growth expected. This assumption may not be
valid for mature and over-mature trees that
have slowed growth, but may be developing
wider, short crowns. Data are needed to
compare young and old trees of various
species.
Crown diameter measurements are
sometimes related to crown density
measurements. When two trees are close
together, one side also tends to have a
reduced or even non-existent crown. As a
result, one crown measurement may be one-
half of normal. A similar reduction is recorded
in crown density for the missing one-half of the
crown. Indirectly, crown diameters are related
to crown position because of the effect of stand
density on crown diameter growth.
Quantification -
This measurement can be collected in one
day. A tape, accurate to the nearest inch, is
used to measure the distances. For training, a
clinometer is used to determine the point on
the ground to measure the crown width. It is
accurate to the nearest degree. Data from
training studies show that the crew
measurements were within ± 3 feet of the tree
crown diameter for 95 percent of the time. The
remaining time their measurements were within
± 5 feet. The measure is easy to make and
takes about 3 minutes per tree.
Signal-to-Noise Ratio -
The signal-to-noise ratio is very high for this
indicator. Crown diameter can be directly
related to the growth of tree species (Frances
1986; Sprinz and Burkhart 1987). In an
analysis of 14,296 trees, crown diameter was
always highly correlated (r > 0.60) with bole
diameter at breast height measurments for
most tree species (Stolte et al. 1992).
Regional Responsiveness -
This indicator is very responsive at the
local, state, and regional level. An example is
that crown diameter normally changes with
stand density. At the state or regional level, it
would be possible to identify species and areas
that had different average crown diameters.
Data from our loblolly pine studies show that
there is a correlation between crown diameters
and tree vigor, expressed in growth at DBH.
Index Period Stability -
The estimates are very stable for the same
person making remeasurements on the same
tree and for different people rating the same
tree. Unless the tree is damaged by storms,
for example, the measures are very stable. It
has almost no change from June to August and
change slowly from year to year.
This measure is very stable during the day
and over the sampling season. Crown
diameters are likely to increase as a tree
assumes higher canopy position and with
6.3-3
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decreasing stand density where space is
available for lateral branch growth. Crown
diameters decline with increased competition
with neighboring trees. Mortality of lower
branches from competition, snow and ice
damage, or insect or disease damage reduces
crown diameter. Shock response after logging
and similar disturbances may result in loss of
large branches. Crown diameters are not likely
to change from one year to the next unless
some injury has occurred. Measurements
every 5 to 10 years may be needed to detect
significant changes.
Environmental Impact -
This measurement only has a minor impact
and is of short duration. Minor damage is
caused to the understory when two people hold
a tape under the tree to determine the crown
diameter.
6.3.2.3 Crown Density
Crown density estimates the condition of
the tree crown in relation to a normal, healthy
forest-grown tree and also is an indicator of
expected growth in the near future. Crown
density represents the amount of branches,
foliage, and reproductive structures that block
light visibility through the crown. This
measurement was devleoped on loblolly and
shortleaf pines where growth correlations were
observed.
Crown density is recorded in 21 5-percent
classes. The recorded value is the average of
two estimates made by observers from
opposite sides of the tree, upgraded to the next
full 5-percent class. The lowest class, 0
percent, is recorded when the tree does not
have a defined crown (live branches larger than
1 inch at the base). The highest class, 100
percent, is recorded as the 99-percent class.
Interpretability -
Recorded measurements must be within 2
percentage classes (+10 percent) when
remeasured. Greater differences are
considered errors; however, a third
measurement is required to determine which
measurement is in error. The error frequency
must be 10 percent or less for the data to be
acceptable unless confirmation is made that
the original measurements were correct (i.e.,
the remeasurement was in error).
Measurement errors may occur from
several sources. The major cause of variation
is the assumed normal symmetrical crown of
the tree and then the estimated branch area
missing, particularly for a tree closely adjoining
another tree. The top of the tree or sides may
not be clearly visible at the same time and
errors may occur from "piecing the crown
together" from various viewing points. The
branch structure of some trees is such that a
clear "obvious" base of the crown is difficult to
establish.
Crown density relates to vigor and DBH
growth on loblolly and shortleaf pine. Although
not confirmed, one suspects that crown density
has similar relationships with growth for all
trees. Although the measurements are
recorded in 5-percent classes, the most likely
use of the data is broader groupings which
indicate good, average, and poor crown.
Crown density includes several other crown
characteristics measured separately:
1. Branch transparency as influenced by
changes in foliage abundance; however,
the woody parts, branches and twigs remain
to reduce light penetration,
2. Dieback as reflected by complete absence
of foliage, and
3. Crown diameter as influenced by changed
crown shape from neighboring trees.
Quantification —
This measurement can be collected for
trees on a plot in one day. The measurements
are an estimation to the nearest 5 percent of
the density of foliage, branches, and
reproductive structures in the crown. A density
card is used to make the estimates.
6.3-4
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Photographs of examples are also provided to
the crews. Data from the 1991 training shows
the crews were within ±10 percent of the
predetermined estimate 90 percent of the time
and they were never more than 20 percent off
crown density in the remaining 10 percent of
the time. The estimate is easy to make and
takes 2 to 3 minutes per tree!
Signal-to-Noise Ratio -
The signal-to-noise ratio for this indicator is
relatively good, but not as good as for crown
ratio and diameter. Low crown density can
indicate low numbers of branches with full
foliage or high numbers of branches with thin
foliage. The latter would probably affect tree
growth to a greater rate.
Regional Responsiveness -
This indicator is very responsive at the
local, state, and regional levels. An example is
that the crown density typically changes with
site quality. At the state or regional levels, it
would be possible to identify species and areas
that had different average crown densities.
Data from our loblolly and shortleaf pine
studies show a relationship between crown
density and tree growth at DBH (r2 of 0.37 to
0.61 depending on stand age and species):
Index Period Stability -
The estimates are very stable for the same
person evaluating the same tree and for
different people rating the same tree. Unless
the tree is damaged by storms or defoliated,
the variable is very stable. It has very little
natural change from June to August and
changes slowly from year to year. For conifers,
the measures change little from summer to
winter. This measure is specific to the crown of
the tree. Overlapping branches can cause
some measurement errors.
Normally, crown density is expected to
decline over time as a result of competition in
the stands and aging of the tree. Occasionally,
however, crown density may improve (increase)
when disturbances in the stand occur such as:
1. Increased growing space as a result of
death or removal of neighboring trees,
2. Rebuilding crown structure after loss of
major branches,
3. Increased foliage after a poor foliage year,
and
4. Loss of branches causing redefinition of
crown perimeter, such as dead branches
in the top of the tree, or death of widely
separated branches in the lower crown.
Various damages, changes in stand density,
and reduced growth associated with aging may
cause a decline in crown density:
1.
or
Loss of branches from mortality
breakage, creating gaps in the crown,
Increased transparency from poor foliage
production or from defoliation,
Shading of branches by neighboring trees
when stand density increases,
Foliage stunting that accompanies injury
or follows refoliation,
Crown form changes as a result of
increased stand density, and
6. Gaps increase with age of trees.
2.
3.
4.
5.
Crown density usually changes slowly as
the stand density changes. Defoliation may
change crown density within a growing season,
but repeated defoliation followed by branch
dieback is needed for permanent change in
crown density. Branch mortality, such as wind
and ice damage, creates rapid change in crown
density.
Environmental Impact -
This measure has only a minor impact and
it is of short duration. The minor damage is
caused to the understory as two people
distance themselves approximately one tree-
length away from the tree to make their
estimates.
6.3-5
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6.3.2.4 Crown Dleback
Crown dieback is defined as branch
mortality that begins at the terminal portion of
the branch and proceeds toward the trunk.
When whole branches are dead in the upper
crown, without obvious signs of damage such
as animal damage or breakage, the branch is
assumed to have died from the terminal down.
Dead branches in the center and lower portion
of the crown or below the live crown are
assumed to have died from competition or
shading and are not considered crown dieback.
Crown dieback estimates reflect the severity
of recent stresses on the tree. A tree with
Increasing crown dieback is likely to have
reduced growth. However, a tree may be
recovering from past stresses as growing
conditions improve. Therefore, the
measurement serves as an early indicator of
tree response to environmental stresses or
damage. The probable use of the
measurements is aggregation into several
crown dieback severity classes.
Interpretability -
Crown dieback is recorded in 21 5-percent
classes in multiples of 5, with the code being
the upper range of the class. The zero-class
was created to indicate total absence of crown
dieback. One dead twig seen in the crown is
recorded in the 5-percent class. When all the
branches are dead, but the tree is still alive,
class-99 is recorded. The percentages are
based on the estimated size of the affected
area in relation to the total crown. Estimates
are made by two crown raters on the opposite
sides of the tree. Then the percentages are
averaged and recorded in the 5-percent
classes.
Measurement errors may occur from
several sources. The top of the tree may not
be clearly visible, or when rounded, clearly
identifiable. The branch structure of some
trees is such that a clear "obvious" base of the
crown is difficult to establish. Heavy defoliation
(foliage transparency greater than 50 percent),
makes it difficult to separate dead twigs and
living branches that have been defoliated.
Recorded measurements must be within 2
classes when remeasured. Greater differences
are considered errors; a third measurement is
required to determine which measurement is in
error. The error frequency must be 10 percent
or less for the data to be acceptable, unless
confirmation is ,made that the original
measurements were correct.
None of the other crown attributes are
included in the crown dieback measurements.
Crown dieback is included in the crown density
measurement. Extent of crown dieback may
affect crown ratio, because of loss to the top of
the crown.
Increase in crown dieback is a disease
symptom in response to severe stresses. It
may be initiated from damage to the roots,
damage on the trunk that interferes with water
and nutrient transport to the crown, and direct
injury in the crown. Complete defoliation of
conifer twigs is likely to cause immediate crown
dieback. Likewise, some hardwoods respond
to severe defoliation with crown dieback, but
the time of the growing season when it occurs
seems to be important. Excessive seed
production on some species causes crown
dieback of small twigs. Frequently, hardwoods
released from competition, such as after
logging, have crown dieback. A few
hardwoods left after a commercial clearcut may
have a period of severe crown dieback.
Quantification -
This measurement can be collected in one
day. Advanced or specialized education is not
required for this measurement, although
training is required. In fact, high school
students have successfully collected this data
for the North American Sugar Maple Decline
Project.
All the crown raters must participate in
crown rating training and must provide
quantitative evaluation of their ability (such as
a test) that they can perform at acceptable
levels. Training is required annually. Visual
standards and written definitions are provided
to reduce annual drift in rating values. Two
persons are required to rate a tree from
6.3-6
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opposite sides; the two estimates are averaged
and recorded in the 5-percent classes.
Remeasurement data from 1990 in New
England, show that 92 percent of the
remeasurements were within the measurement
quality limits (Burkman and Alerich in press).
Data from the North American Sugar Maple
Decline Project show a 97 percent agreement
(Burkman 1990) for 1989 results.
Signal-to-Noise Ratio -
Crown dieback directly indicates an external
stressor to the tree originating in the soil, air, or
from pathogens. It is a direct estimation of loss
of the upper portions of a tree crown that does
not occur on trees under optimal conditions.
Regional Responsiveness -
This indicator is very responsive at the
local, state, and regional level. In 1990 FHM
results, this measurement related to the
presence of the beech-bark disease complex
on American Beech (Faqus qrandifolia Ehrh.) in
New England (Brooks et al. 1991).
Index Period Stability -
This measure is very stable during the field
season except during situations of high wind or
excessive, heavy rainfall. Small dead twigs do
not persist very long and may drop annually.
Larger dead branches persist longer.
Therefore, annual fluctuations of crown dieback
are expected, unless severe stresses cause
large amounts of branch mortality that require
many years of recovery. Crown dieback can
decrease through the loss of the dead twigs
and branches. Trees frequently rebuild their
crowns after dieback unless continuation or
additional new stresses impact the trees.
Environmental Impact -
This measurement only has a minor impact
on site conditions. The impact on the site is
related to the traffic on the site to make the
measurement and will only affect the microplot
measurements. This effect can be reduced by
collecting this measurement after the collection
of the microplot information.
6.3.2.5 Foliage Transparency
Foliage transparency is defined as the
amount of skylight visible through the foliated
portion of the crown or branch. On closed
crown trees, foliage transparency is estimated
for the whole crown, excluding large openings
where foliage can not be present because of
the absence of branches. On open crown
trees, foliage transparency is measured on the
foliated portions of the branches. The primary
purpose of the measurement is to standardize
measurements of foliage loss from disease or
insect defoliation. Tree species and age seem
to affect foliage • transparency because of
characteristic branch overlap and foliage
placement on the branches. Poorly growing
trees also have sparse branches and foliage.
Foliage transparency is used as a
measurement of defoliation. Usually, serious
adverse effects on the tree are not expected
until more than one-half of the foliage is lost.
Thus, broad classes are used to describe
stresses from defoliation.
Interpretability -
Foliage transparency is recorded in 21 5-
percent classes in multiples of 5, with the code
being the upper range of the class. Foliage
transparency is estimated from several sections
of the crown and averaged. Estimates are
made by two crown raters on the opposite
sides of the tree, then percentages are
averaged and recorded in the 5-percent
classes. Complete defoliation is recorded in
the 99-class.
Measurement errors may occur from
several sources. Portions of the crown may
not be clearly visible. As defoliation increases,
branches and seeds may interfere with foliage
estimates. Some- variation occurs from
judgement when to include or exclude holes,
areas without branches, in the overall estimate.
Recorded measurements must be within 2
classes when remeasured. Greater differences
6.3-7
-------
are considered errors; a third measurement is
required to determine which measurement is in
error. The error frequency must be 10 percent
or less for the data to be acceptable, unless
confirmation is made that the original
measurements were correct.
Foliage transparency does not include any
other crown measurement. However, it is
included in the crown density estimate. Since
foliage blocks transmission of light through the
crown, the attribute measured in crown density,
it contributes heavily to the measurement.
Quantification -
This measurement can be collected in one
day. Advanced or specialized education is not
required for this measurement, although
training is required. In fact, high school
students have successfully collected this data
for the North American Sugar Maple Decline
Project.
All the crown raters must participate in
crown rating training and must provide
quantitative evaluation of their ability (such as
a test) that they can perform at acceptable
levels. Training is required annually. Visual
standards and written definitions are provided
to reduce annual drift in rating values. Two
persons are required to rate a tree from
opposite sides; the two estimates are averaged
and recorded in the 5-percent classes.
Remeasurement data from 1990 in New
England, show that 87 percent of the
remeasurements were within the measurement
quality limits (Burkman and Alerich in press).
Data from the North American Sugar Maple
Decline Project show a 92 percent agreement
(Burkman 1990) for 1989 results.
Signal-to-Noise Ratio
Crown transparency is a direct estimation of
defoliation of the tree crown. Therefore, the
slgnal-to-noise ratio for this indicator is high.
Reductions in crown transparency indicate loss
of foliage.
Regional Responsiveness -
This indicator is very responsive at the
local, state, and regional level. In the North
American Sugar Maple Decline Project, this
measurement was highly associated with areas
of drought during the summer of 1988 in
Wisconsin, Michigan, and Ontario (Allen and
Barnett unpublished data).
Index Period Stability -
Foliage transparency is potentially one of
the most rapidly changing variables measured
in the crown of the tree. Wind, frost, and hail
can change foliage transparency within hours.
Insect defoliation changes may be measurable
days apart. Diseases may contribute to steady
decline of foliage during a growing season.
Defoliation of evergreens may contribute to
foliage transparency change for more than a
year. Foliage transparency may be associated
with poor sites and persist for the life of the
tree.
Most frequent changes occur from direct
damage and removal of foliage. Foliage
transparency increases, that is, foliage is
reduced from:
1. Insect defoliation,
2. Diseases,
3. Previous stresses may reduce foliage
production in the following year,
4. Declining tree vigor reduces foliage
abundance (logging, drought, etc.),
5. Decreased needle retention of conifers,
6. Decreased size of leaves or needles from
previous stresses, and
7. Loss of branches decreases foliage overlap.
Foliage transparency may decrease with
reduction of stand density and followed by
addition of new branches to create branch
overlap. Of course, foliage transparency will
increase following previous defoliation if the
tree does not die.
6.3-8
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Environmental Impact -
This measurement only has a minor impact
on site conditions. The impact on the site is
related to the traffic on the site to make the
measurement and will only affect the microplot
measurements. This effect can be reduced by
collecting this measurement after the collection
of the microplot information.
6.3.3 1992 Activities
Crown classification measurements will be
made as part of Detection Monitoring activities
in the 12 eastern states, as part of the
Southeastern and SAMAB Demonstration
projects, and as part of the Western Pilot
project.
6.3-9
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6.4 Damage and Mortality
Assessment
W. Hoffard, V.J. LaBau,
and M. Schomaker
6.4.1 Introduction
Damage caused by diseases, insects, air
pollution, and natural and manmade activities
can affect the growth and development of the
forests. Any of these agents, either singly or in
combination, can cause significant decline in
forest tree health. Identification of such agents
can provide valuable information concerning
the status of forest condition and indicate
possible causes of deviation from the expected
norm. Signs and symptoms are recorded if, in
the estimate of the observers, the damage
could kill the tree, cause growth reduction in
the tree, or provide entry points for other
damaging agents.
Mortality caused by agents as discussed
above can also affect the structure and
development of forests. Identification of such
agents is important in monitoring forest health.
Combined with damage information, it will be
possible to piece together the events that
ultimately lead to the death of a tree.
6.4.2 Indicator Evaluation
This section discusses the indicator
selection criteria described in Section 6.1 as
they apply to damage and mortality
assessment.
6.4.2.1 Interpretability
Damage assessment describes current
stresses such as defoliation, broken limbs, and
other damage. All these stressors can affect
the growth and development of trees and
forests. Alone or in combination, they can
cause significant declines in forest tree health.
Signs and symptoms are recorded if, in the
opinion of the recorder, the causal agent could
kill the tree, cause growth reduction, or provide
entry points for other destructive agents such
as fungi.
The tree is observed from all sides and any
identifiable symptoms and signs of damage
noted. The procedure allows for the coding of
up to three different damages in any of nine all-
inclusive locations. If the symptoms and signs
are attributable to specific causal agents, they
are recorded also.
All data collectors must participate in
damage coding training consisting of both
indoor (35-mm slide show) and field sessions.
Trainees are tested, and error frequency must
be < 10 percent before trainees are certified as
data collectors.
Measurement error may occur from several
sources. Portions of the trees (especially tops)
may not be clearly visible. Damage causal
agents can sometimes be difficult to determine,
even for professional pathologists and
entomologists. Seasonality influences the
frequency and abundance of several insects
and diseases, especially defoliators. (This
shortcoming is largely offset by spring plot
visits which are designed to capture early
spring data which may be difficult to discern
later in the growing season.) Finally,
"significant" is a highly relative term. Some
data collectors may consider a causal agent
significant, whereas others may consider it
unworthy of mention.
6.4.2.2
Quantification
The measurements are collected in one
day. Occasionally, binoculars are used to
achieve a better view of the crowns and high
limbs. The biggest challenge to crews is to
decide whether or not to record the damage.
Damage recording is a relatively simple
process and requires no more than three
minutes per tree. Data from the summer of
1991 shows crews were within the 90 percent
accuracy requirement. . *
6.4-1
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6.4-2.3 Slgnat-to-Noise Ratio
The estimates are very stable for the same
person evaluating the same and different trees,
but can vary between people. The damage
classifications have a very high ability to detect
trends over a given physical range of forests.
6.4.2.4 Regional Responsiveness
This indicator is very responsive at the
local, state, and regional level. An example
would be an outbreak of orange-striped
oakworm such as recently occurred in northern
Georgia. These epidemics often cover millions
of acres and would evidence themselves in a
number of Forest Health Monitoring (FHM)
plots throughout the outbreak region.
6.4.2.5 Index Period Stability
Measurement stability varies from high to
tow, depending on the time frame in question.
On a daily basis, it is high, but this consistency
diminishes due to longer term phonological
effects. For example, insect life cycles are
typfcalty linked with flowering or budbreak.
Climatic effects such as storm damage can
influence data or invalidate information
collected earlier in the sampling season.
6.4.2.6 Environmental Impact
The measurement has minimal impact over
a relatively short time frame. Most impact is
confined to trampling of vegetation as crews
maneuver for position and circle the tree for
optimum views of the various tree structures.
6.4.3 Field Measurements
The following damages are recorded:
• Dead (describes part of a living tree).
• Cfosed wounds.
• Small holes or pinholes.
• Cracks and seams.
« Broken.
• Conks.
• Removed or missing (other than defoliation by
insects).
• Defoliation (from insect feeding).
• Resinosus.
• Bleeding.
• Crook or sweep severe enough to impede
growth or survival.
• Swelling (greater than Vz. the diameter of the
stem).
• Excessive branching (indicator of past injury
such as top kill).
• Abundance of epicormic branching.
• Rotten branch stubs.
• Stunted or dwarfed.
• Deformed, twisted, curled.
• Embedded foreign objects.
• Leaning from partial windthrow.
• General discoloration.
• Pale green foliage.
• Yellow-green foliage.
• Spotted leaves.
• Damaged leaves.
• Distorted foliage.
• Stunted foliage.
• Dwarfed foliage.
• Other.
These damages are recorded and coded
for the following locations: crown stem, upper
bole, lower bole, roots, whole trunk, branches,
buds and shoots, foliage, and whole crown.
The purpose of damage coding is to
explain probable causes for mortality and
growth impact. The mere presence of damage
should not necessarily be recorded. Multiple
damage coding should not occur frequently.
For mortality, the cause of death, ,is
recorded for all saplings 1.0-inch in diameter at
breast height (DBH) and larger on the micropfot
and all trees with a 5.0-inch DBH and larger on
the subplot that were recorded as live trees
during the previous inventory.
In addition, in California a full-hectare
cruise is used on all plots to tally mortality and
"fader" trees 10 cm and larger. Mortality and
"facfer" trees, trees with green but fading
needles or leaves resulting from such causes
as insects, disease, and drought, less than
to cm measured at DBH or root collar are
recorded only on the four sub-plots.
6.4-2
-------
6.4.4 1992 Activities
Damage and mortality assessments will be
made as part of Detection Monitoring activities,
for the Southeast and SAMAB Demonstration
projects and as part of the Western Pilot
project.
6.4-3
-------
-------
6.5 Branch Evaluation
K.W. Stolte
6.5.1 Introduction
The evaluation of the condition of tree
crowns is performed in FHM by estimation of
crown variables (ratio, density, transparency,
dieback, diameter - see Section 6.3) made
visually by ground observers. These remote
estimations of crown condition are
complemented by evaluation of tree branches
obtained from the upper crowns of dominant or
codominant trees in the same condition class
as found in the subplots. The purpose is to
obtain more specific tree vigor data and identify
early crown symptoms caused by air pollutants,
disease, or insects that require close-up
examination of foliage. The method of
evaluation described here is based on
procedures developed and tested at The
Pennsylvania State University for the Eastern
Hardwood Research Cooperative (Nash et al.
1989), the Visual Damage Survey for the
National Vegetation Survey (Alexander and
Carlson 1989), and the Western Pine Plot
Workshop (Stolte and Miller 1991).
On four randomly selected trees in the
annular section of two subplots (see Section 7),
two branches exposed to direct sunlight are
extracted from the upper tree crowns by tree
climbers or by severing with shotguns or pole
pruners (forest with lower canopies). These
branches are brought to the ground and are
measured for growth parameters and examined
for damage symptoms. This method of leaf
collection provides excellent control for sample
selection. Climbers can be directed to a
particular branch. Leaves collected by this
technique provide samples that are in excellent
condition. Many insects can be recovered
following falls from heights in excess of 25 m
(Nash et al. 1989). This collection method
provides branch material for examination. In
addition, climbers are able to report or sample
any unusual symptoms, insects, or diseases
that are encountered during the climb. The
major disadvantages to this system are that
(1) some trees are not safe to climb, (2) the
weight and bulk involved with the climbing
gear, and (3) any tree injury caused by
climbing and severing branches.
The variables measured or evaluated by a
trained crew person are wood and foliage
damage, number of leaves (representative
shoot on deciduous species) or whorls of
needles or foliated length of branch (conifers),
needle and leaf length, and length of previous
year's branch node. After visual evaluation of
the branch, the branch is then labeled, bagged,
and mailed to the preparation laboratory for
foliar chemistry analysis (see Section 6.7).
Evaluation of branch growth addresses the
societal value of forest productivity, and
evaluation of branch damage addresses forest
contamination and sustainability.
6.5.2 Indicator Evaluation
This selection discusses the indicator
selection criteria described in Section 6.1 as
they apply to branch evaluation.
6.5.2.1 tnterpretability
Branch evaluations are highly interpretable
because measurement of growth variables
gives direct indication of foliage production and
wood (branch) growth. Also, estimation of
damage variables gives direct indication of the
condition of the wood (branch) and foliage.
Growth measurements (previous year's node
length, number of leaves or whorls of needles,
leaf length, previous year's modal needle
length) give definitive information on how well
the branches are growing and producing
foliage. Damage estimates (branch and
foliage) give definitive information on the
condition of the branches and foliage including
descriptions and severity of symptoms, and
suspected causal agents.
Aggregation to the Plot Level -
The branch growth measurements can be
aggregated into an index of growth by the
following formulas, where the maximum score
6.5-1
-------
possible for a branch is 100. The scores are
based on scaling the measurement of each
variable to the 90th percentile of the population
value for each variable. The variables are
weighted in each branch growth determination.
Formulas vary for branches from deciduous
trees, conifers with distinct whorls of needles,
and conifers without distinct whorls of needles.
The general form of the growth index for each
branch type is:
NL(45) + NLVS(45) + LL(10)
NL(40) + NW(25) + FL(25)
NL (40) + FL (50) + NDL (10)
branch growth
node length
number of leaves
leaf length
number of needle
whorls
foliated length of
branch
needle length
weighting for each
variable
NDL(10)
where BG =
NL
NLVS =
LL
NW
FL
NDL -
The damage estimates for the branch and
foliage can be aggregated in some cases within
forest types and species. This aggregation
would depend on the symptoms observed and
the tree species selected for evaluation.
Cumulative Distribution Functions and
Nominal-Subnomlnal Boundaries --
Identifying nominal and subnominal
proportions of the population will be possible
for the growth index of this indicator. We will
set concern thresholds at a plot-level index
score of 50, which will proportion the population
into the nominal (>50) and subnominal (<50)
classes. We will divide the nominal class by
setting an optimal threshold at an index score
of 75, producing a nominal subclass (50 < x <
75) and an optimal subclass (75 < x <. 100).
Similarly, we will divide the subnominal class by
setting a poor threshold at an index score of
25, producing a subnominal subclass (50 > x >
25) and a poor subclass (25 > x > 0).
A more narrow delineation of the population
with respect to branch and foliage damage is
possible since the damages are recorded in
three classes (1 = highest injury; 2 =
moderate; 3 = slight). The highest plot-level
injury class will be considered poor, the second
highest subnominal, and the lowest injury class
will be considered nominal.
6.5.2.2 Quantification
The growth variables for this indicator are
readily quantifiable with the use of a ruler to
measure internode length, length of foliage on
the branch, and needle or leaf length.
Quantification of the .number of whorls and
number of needles is done through counting.
These measurements are relatively simple and
require very little time to make.
The damage estimations are also relatively
easy to quantify. Foliar damage is more
precise than branch damage, since it is based
on the percentage of leaf area affected (in 3
broad classes based on Horsfall and Barratt
[1945]. Damage on branches is more
subjective, and involves an estimation as to
whether the observed damage threatens the
survival of the branch (damagel), injures the
branch but is not likely to kill it (damage2), or
is present on the branch but does not appear
to be impeding any biological processes
(damages).
6.5.2.3 Signal-to-Nolse Ratio
The signal (branch growth/damage)-to-
noise (unrecognizable nodes, whorls,
etc./unknown damages) ratio is relatively high
for this indicator. The growth variables
selected are generic for any species of tree
and consequently can be measured readily,
and the damage assessments on the branch
and foliage are broad and include categories
for unknown symptoms or causal agents. The
scaling of the growth variables to the 90th
percentile for the population enables a relative
comparison of an individual of any tree species
6.5-2
-------
with other individuals of the same species.
The damage determinations are relatively
direct; either damage is present on the branch
or foliage or it is not. What is undetermined at
this time is the representativeness of the signal
from two branches on a tree compared to the
signal from the whole tree.
6.5.2.4 Regional Responsiveness
This indicator is regionally responsive since
branch growth and damage are two variables
that respond to changes in environmental or
biological conditions and can be monitored over
large areas. Branch and foliage damage have
been shown in a number of Forest Pest
Management and air pollution studies (Stolte et
al. 1992; Pronos et al. 1978) to be responsive
over large areas. Although branch growth may
not have been demonstrated previously for
regional responsiveness, some of the
components of branch growth (number of
whorls retained, needle length) have been
shown to respond to environmental influences
over large areas (Duriscoe 1990; Pronos et al.
1978).
6.5.2.5 Index Period Stability
Growth measurements are relatively stable
over the index period (May to September)
when measurements are taken. We specifically
selected growth measurements that would not
change over the growing season, for example
last year's internode length and needle length
of previous year's whorl of needles. These
variables are likely to be 'fixed' and change
very little, if any, over the summer season.
Other variables such as number of needle
whorls and length of foliage on branch
(conifers), and number of leaves (deciduous
trees) may change slightly over the summer as
new leaves are added or old conifer needles
are abscised. In general, however, these
variables are not likely to change greatly after
the spring initiation of leaves and needles and
prior to the fall abscission of leaves and
needles. Most, if not all, of the field work is
conducted between these phenological events.
Estimates of damage to foliage are likely to
have lower index period stability than growth
measurements. Insects, diseases, air pollution,
and physical damage (e.g., wind) are likely to
be either episodic or cumulative events that will
affect damage estimates over the summer
season. Estimates of damage to branches
(wood) may be seasonally more stable than for
foliage because of the more systemic nature of
some branch stressors (e.g., fungus, mistletoe).
6.5.2.6 Environmental Impact
The environmental impacts from measuring
this indicator are relatively minor, but some
damage might occur to the tree due to branch
extraction. Tree climbers may cause some
damage, although all climbing is done without
spikes. Branch extraction, by pole pruner or
shotgun, will cause some minor damage to the
tree, but since only two branches are taken,
the overall effects on tree vigor are expected to
be negligible. Some trampling of the
understory vegetation will also occur as
branches are collected, analyzed for growth
and damage variables, and packaged for
mailing for chemical analysis.
6.5.3 Field Measurements and Data
Use
Branch growth measurements and damage
estimations are intended to quantify the
condition of the wood and foliage on the
branch. Procedures rely on the in-hand
evaluation of branch samples to conduct a
detailed examination of wood and foliage
condition. Branches of conifers obtained for
visual symptom evaluation, whether extracted
by tree climbers, firearms, or pole pruners,
should contain a full complement of annual
needle whorls; for deciduous trees, the
branches should contain a full complement of
leaves of all ages. In practice this means small
terminal or side branches will suffice for an
adequate sample. Branches should be kept in
the shade until on-site visible symptom
evaluation can be performed.
Growth of branches is measured by
recording the number of annual needle whorls
of conifers (>25 percent of fascicles retained
equals a whorl present), the length of a branch
6.5-3
-------
of conifers w'rth foliage (to nearest centimeter),
length of needles in previous year's whorl (to
nearest centimeter), length (petiole axis) of
deciduous leaf (to nearest centimeter), and the
length of previous year's intemode growth (to
nearest 0.5 centimeter). These measurements
are recorded separately and are aggregated
into a plot-level value for branch growth for
each species using the methods described
above in Section 6.5.2.1.
Damage to branches is recorded separately
for wood (branch and side shoots) and foliage
(leaves or needles). The wood symptoms are
recorded in order of severity, with "wood
damage 1" indicating damage symptoms that
are likely to cause death of the branch (e.g.,
canker), "wood damage 2" indicating symptoms
that are impeding normal branch processes but
are not likely to result in branch mortality (e.g.,
animal damage), and "wood damage 3"
indicating symptoms that are present but not
likely to cause branch mortality or impede
normal process (e.g., healed wound). There
are eight possible damage codes, including
"none", and an additional ninth code for "other"
that can be written in. Damage on foliage is
recorded as a percentage of the total leaf
surface area affected. The damage estimates
are divided into three broad classes: 1-25
percent; 26-50 percent; and >50 percent.
Twenty-two foliar damage codes, including
"none", and a 23rd code for "other" can be
.described. In addition, there are seven foliar
discoloration codes, including "none" and an
eighth "other" code. There are seven
probable causes possible for both branch and
foliar symptoms, and an eighth "other" code.
This information is analyzed as described
above in Section 6.5.2.1.
In addition to the data collected on the
branches and foliage, information is collected
on the tree and its immediate environs.
Species, crown position, crown and bole
condition variables, and DBH information is
collected similarly to the trees in the subplots
detection monitoring (see Section 6.2). Data
are also collected on the microrelief
characteristics where the tree is growing
(planar, convex, concave, complex), percent
bare rock in a radius (20 times the DBH of the
tree) around the tree, and associated vascular
plant species. This information can be used to
help to interpret conditions or trends observed
in the branch samples. For example, poor
needle retention may be due to the site having
poor soil moisture, at the site. Soil moisture
can be determined by looking at the
percentage of bare rock surrounding the tree
and by examining the composition of the
vascular plant community in the immediate
vicinity.
6.5.4 1992 Activities
The branch sample indicator will be part of
the Southeast and SAMAB demonstration
projects.
6.5-4
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6.6 Soil Classification and
Physiochemistry
R. D. Van Remortel
6.6.1 Overview
In its infancy, the soils indicator
documentation consisted of general fact sheets
that provided a rationale for monitoring "soil
nutrients" and "soil toxins" (Hunsaker and
Carpenter 1990). These "exposure-category
indicators" were intended for use in
documenting the status and trends of regional
forest soil condition and in identifying
associations with other types of indicators.
Since that time, the scope has broadened to
facilitate the integration of all essential soil-
related parameters influencing forest condition
or health. This concept of "soil productivity" is
an appropriate and useful strategy for
addressing the Forest Health Monitoring (FHM)
program objectives.
The primary focus has been on those
parameters which have been determined by
consensus to be important for assessing soil
productivity with respect to forest health, and
which are also economically and logistically
feasible at this initial stage of implementation.
Although limited research has been devoted to
identifying the effect of these individual soil-
related components on forest ecosystems,
considerable advances have been made on
identifying the soil processes that are important
in vegetative response (Bouma 1989). The
necessary components, however, have not yet
been brought together into an index or model
that is suitable for regional or national
monitoring. This lack of a suitable model is
primarily because of the limited number of
forest soil data bases that contain detailed
exposure and response data upon which to
build. Ultimately, the interpretive goals for
regional evaluation of status, trends, and
associations are the driving factors in the
continuing development of appropriate
assessment tools.
There are many reasons to monitor soils as
part of forest health assessment. For example,
assessment scenarios of possible relevance for
soil monitoring in the diverse forested regions
of the U.S. demonstrate interest in soils as:
1. The essential mechanism for ensuring
sustainable nutrient cycling, forest
sustenance, and soil productivity into the
future; linkages with other nutrient cycling
indicators; for habitat/response
assessments,
2. A key linkage, e.g., with respect to
buffering effects, for monitoring acidic
deposition, cation leaching, other water
quality concerns; for exposure/response
assessments,
3. A comprehensive predictor of risk or
susceptibility, and a means for assessing
regional abatement strategies; for
stressor/exposu re/habitat/response
assessments,
4. A sink for airborne metal contaminants or
deleterious organic compounds; for
exposure/response assessments, and
5. A primary classification medium for EMAP
tiers 1 and 2 landscape characterization
and geographic information system (GIS)
layering; for exposure/habitat
assessments.
A combined examination of these scenarios is
important to establish a solid baseline
characterization of soils for assessing forest
health.
As implied in the scenarios described
above, some of the necessary soil
measurements are intended primarily for
ancillary Environmental Monitoring and
Assessment Program (EMAP) classification
needs, while others are intended for long-term
evaluation and interpretation of status and
trends in forest health. Hence, the soil
productivity indicator has been subdivided into
two main categories to address the different
EMAP categories of indicators (Hunsaker and
Carpenter 1990) where appropriate. The two
categories are: "Soil Classification for
Habitat/Exposure" and "Soil Physiochemistry
for Exposure/Response," as described below in
6.6-1
-------
subsections 6.6.2 and 6.6.3, respectively. In
these subsections the rationale for measuring
these indicator categories is specified according
to the six indicator selection criteria described
in Section 6.1.
6.6.2 Indicator Evaluation-Soil
Classification for Habitat/Exposure
Assessments
This category encompasses a widely
accepted baseline field characterization and
sampling procedure that is deeply rooted in the
soils literature. Included in soil classification
are methods regarding plot and hole location,
soil excavation, profile description, sample
collection, and plot restoration (Van Remortel
1992). As many as 25 measurements of field
site, pedon, and horizon characteristics are
made, when appropriate, on each plot (see
Table 6.6-1). This soil classification category
also includes soil sample preparation steps and
certain soil physical analyses. The
measurements are intended to define specific
physical properties of the organic and mineral
soil samples collected in the field, such as
forest floor biomass on a mass per unit area
basis, particle size (sand, silt, and clay), and
bulk density of replicate core samples collected
in the field (see Table 6.6-1).
Soil physical characteristics are unlikely to
vary in any significant manner on a within-
season, among-season, or among-year basis in
the short term, so repeat-cycle measurements
will not need to be made for at least a decade.
Hence, all soil classification measurements can
be considered to be one-time baseline
measurements.
6.6.2.1 Interpretability
Classification information must be collected
which is ancillary to FHM needs but important
in the assessment of forest soil health from
habitat and exposure standpoints. This
process logically begins with field soil
measurements and sample collection for later
analysis. Detailed taxonomic information is a
necessary step in properly classifying soils for
descriptive and interpretive purposes, and is
essential if FHM is to be a long-term program
established -on a permanent set of plots.
Regardless of whether the intent is for soil data
to serve as inputs to a stressor-, exposure-,
habitat-, or response-category assessment,
field soil characterization and the preparation
and physical characterization of soil samples
are fundamental baseline activities that will
support all other FHM measurements.
Physiographic features such as slope,
aspect, and elevation have been incorporated
successfully into models to predict stand
composition (Fralish 1988) and have been
shown to influence Douglas-fir growth
responses (Steinbrenner 1963). It is not
unexpected that these parameters would affect
forest growth response because they contribute
to the overall hydrologic characteristics of a
site. Soil drainage characterization, along with
other moisture characteristics, has long been
recognized as vital information in estimating
soil productivity (Green et al. 1989; Hamilton
and Krause 1985; Mader 1976; Storie and
Weislander 1948). Physiography, forest floor
biomass, particle size, and bulk density are
important parameters in estimating the
hydrologic contributions of runoff and lateral
water flow (Hewlett 1961) and for their effects
on nutriept availability (Mader 1976; Carter and
Lowe 1986), aeration (Mader 1976;
Steinbrenner 1963), and root distribution (Hillel
1980; Blanchar et al. 1978), all of which directly
affect vegetative response.
Long-established, well-documented, and
unambiguous protocols (SCS 1983, 1984,
1985) established for the National Cooperative
Soil Survey (NCSS) serve as the basis and
justification for the field measurements (Van
Remortel 1992), Suggested changes have
been incorporated where necessary as a result
of experience gained from previous pilot and
demonstration studies in New England,
Virginia, Georgia, California, and Colorado,
6.6-2
-------
TABLE 6.6-1. SOIL CLASSIFICATION PARAMETERS FOR HABITAT/EXPOSURE
Field Parameters
Taxonomy: series, order, suborder, great group, subgroup, particle
size class, mineralogy class, reaction class, temperature
regime, other class, moisture regime
Major land resource area
Slope: percent, shape, geomorphic position, hillslope position,
aspect
Physiography: regional and local
Water table: depth, days, kind
Land use class
Surface stoniness class
Hydraulic conductivity class
Drainage class
Parent material: bedrock inclination, mode of deposition, origin,
bedrock fracture
Hydrologic group
Water erosion class
Water runoff class
Flooding frequency
Ponding frequency
Particle size control section depths
Diagnostic feature: depths, kind
Horizon: depths, discontinuity, master and suffix designations
Moist color: location, percent, hue, value, chroma
Boundary: distinctness, topography
Texture: class, modifier
Structure: grade, size, shape
Mottles: quantity, size, contrast, hue, value, chroma
Field property: quantity, kind
Roots: quantity, size, location
Pores: quantity, size, continuity, shape
Concentration: quantity, size, shape, kind
Rock fragments: volume percent, roundness, kind, size
Laboratory Physical Parameters
Core bulk density: the oven-dry density of the <2-mm soil fraction (minus rock fragments) from replicate core samples, measured
gravimetrically; (mineral soils only).
Forest floor biomass: total mass of organic constituents in a given area of forest floor, measured gravimetrically and by loss-on-ignition; (organic
soils only).
Total sand: particle diameter between 0.05 mm and 2.0 mm, determined by wet sieving; (mineral soils only).
Total silt: particle diameter between 0.002 mm and 0.05 mm, determined by pipetting; (mineral soils only).
Total day: particle diameter less than 0.002 mm, determined by pipetting; (mineral soils only).
distribution functions (CDFs) can be derived
easily. Appropriate nominal/subnominal
boundaries for particular responses are easily
defined for all parameters. Around the world,
the methods can be universally extended
across regional (and ecosystem) boundaries by
virtue of comparable National Cooperative Soil
Survey/Food & Agriculture Organization
(NCSS/FAO) frameworks.
Good accuracy and precision are ensured
when the measurements are made by soil
scientists experienced in these protocols. The
codes used to describe soil characteristics are
uniformly applied across the U.S. With the
exception of the biomass measurement, long-
established and unambiguous protocols exist
for sample preparation and physical analyses
(Byers and Van Remortel 1991). These
methods can also be applied uniformly across
the U.S.
A nationwide network of data bases, part
of the interagency soil survey coverage,
assures good comparability and interpretability
with respect to other soils programs.
Aggregating to the plot level is very
straightforward when the entire suite of
component depth, gravimetric, and volumetric
measurements are made; thus cumulative
6.6.2.2 Quantification
The data collection phase is detailed, yet
straightforward and cost-effective. The
proposed field characterization data can be
collected in a standard 6-hour day on the plot,
as demonstrated in the pilots and
demonstration work of the past 2 years. A
minimum of one soil scientist, regionally
experienced in NCSS procedures and the
6.6-3
-------
Forest Health Monitoring field guide protocols,
is needed to perform soil measurements and
sampling on each plot. On some plots,
assistance from another crew member is
required to excavate the holes and transport
the soil samples from the plot. The use of a
uniform set of NCSS descriptive codes applied
in a consistent and repeatable manner ensures
that the field data are easily quantifiable and
comparable. Consistent application of
descriptive codes will constrain measurement
uncertainty to a minuscule part of the overall
data uncertainty. (NOTE: The term
"uncertainty" describes the sum of all
quantifiable errors associated with a particular
portion of a measurement system or population
of interest [Byers and Palmer 1992].)
Organic matter measured by loss-on-
Ign'rtion and, subsequently, forest floor biomass,
are easily quantified using specific field
measurements in conjunction with data
collected at the soil sample preparation
laboratory (Ballard and Carter 1985; David
1988; Byers and Van Remortel 1991; Van
Remortel 1992). Bulk density estimated from
core samples consists of a straightforward
sample collection technique (Van Remortel
1992) and a simple gravimetric measurement
procedure at the preparation laboratory (Byers
and Van Remortel 1991). The determination of
particle size is straightforward and may
possibly be undertaken at the preparation
laboratory using a hydrometer method;
alternatively, a more detailed particle size
analysis can be performed at the analytical
laboratory (Byers and Van Remortel 1991).
These procedures require preparation
laboratory staff on a seasonal basis. One or
more analytical laboratories may be needed on
a seasonal basis for sample particle size
analysis.
Measurement uncertainty in the field phase
is controllable and quantifiable. Estimates of
this uncertainty may be derived by using
existing field quality assurance (QA) data for
regional soil surveys (Coffey et al. 1987; Kern
and Lee 1990). In these studies, observer
differences were minor and, in almost all cases,
considered to be nonsignificant. This finding
can be attributed to the capability and
experience of the participating SCS soil
scientists. Similar high quality in FHM can be
expected if the staffing specifications are
satisfied. Estimates of measurement
uncertainty for the laboratory parameters may
be derived by using existing QA data for
regional soil surveys in which the data passed
stringent QA acceptance criteria (Van Remortel
et al. 1988; Byers et al. 1989; Papp and Van
Remortel 1990; Byers et al. 1990). For each of
these measurements, an average coefficient of
variation (CV) of 10 percent or less is typical
for replicate samples introduced at the
laboratory. The expected laboratory bias is
± 5 percent or less of the reference value. For
the sample measurement system as a whole
(e.g., sampling, preparation, and analysis), an
average CV of 20 percent or less is typical.
Careful collection, preparation, and analysis of
samples will minimize measurement uncertainty
relative to overall measurement uncertainty.
6.6.2.3 Signal-to-Noise Ratio
The field and laboratory measurements for
soil classification have few if any interferences
(see Section 6.6.3.3). There is very little
unexplained or uncontrolled variability in the
classification properties at a particular sampling
site. The laboratory measurements have
negligible interference from instrumentation and
stock solutions. Hence, the signal-to-noise
ratio is very high. Previous assessments of soil
scientist precision and accuracy during EPA
field activities of the last decade support this
conclusion (Coffey et al. 1987; Kern and Lee
1990). Variability is generally contingent on the
spatial form and distribution of the specific
parameter in a specific landform or region, or
on the ability and experience of the scientist
making the measurements. In either case, this
variability is readily quantifiable.
Within the 'soil sample measurement
system, there can be variability due to arhong-
scientist, within-run, among-ruh, within-batcn,
amohg-batch, or amohg-laboratory differences.
Each of these possible sources of uncertainty
can be kept within acceptable bounds. It is
highly likely that noise may be further reduced
by adjusting the data for climatic effects (e.g.,
available soil moisture in a growing season) or
for covariate parameters (e.g., particle size).
6.6-4
-------
6.6.2.4 Regional Responsiveness
The soil classification component
measurements are particularly useful and
regionally responsive for classification and
aggregation purposes and may also be
sensitive to different regional environmental
stresses. A repository of soil characterization
data such as the USDA-SCS Soil Pedon File
data base exists for over 20,000 phases of soil
series across the United States. The data
base includes comparable information on
organic matter, bulk density, rock fragments,
and particle size fractions. Using this data
base and others such as the U.S.
Environmental Protection Agency (EPA)
Direct/Delayed Response Project data bases
(Church et al. 1989), it is possible to provide a
characterization basis for simulation and
geostatistical interpretation within and among
regions. The data report from the 1990 20/20
pilot study (Riitters et al. 1991 b) shows that the
sample design used for soil sampling satisfies
cost-benefit optimization criteria set by the FHM
team and that the measurements are
appropriate for a regional monitoring program
of this nature.
6.6.2.5 Index Period Stability
Except for the seasonal fluctuations
characteristic of soil water tables, all of the field
and laboratory physical measurements are
extremely stable (within ±1 percent) throughout
the projected index period. For interpretive
purposes, soil sampling should be performed
concurrently with any vegetative measurement
or sampling on the plots.
6.6.2.6 Environmental Impact
Compared with many other types of soil
sampling, FHM field soil characterization
procedures have a minimal effect on the long-
term integrity of plots. All soils work is carried
out in an annular zone well outside the
vegetative measurement areas of a plot. Each
soil hole is neatly and carefully backfilled in the
original order of horizons excavated. Future
soil holes may be excavated without danger of
contamination from the previous holes. Other
indicators will not be adversely affected by the
presence of the holes.
The laboratory analyses have a negligible
environmental effect as the result of strict
controls on the handling of stock solutions and
the disposal of sample material.
6.6.3 Indicator Evaluation-Soil
Physiochemistry for
Exposure/Response Assessments
The proposed soil physiochemical
parameters (see Table 6.6-2) have been
identified as a result of an intensive review of
laboratory methods in collaboration with
researchers and laboratory chemists across the
United States, Canada, and Europe. The
recommendations of many previous committees
and investigators relating to similar types of
projects have also been incorporated
(Anderson 1987; Blume et al. 1990; Morrison
1988; NCASI 1983; Robarge and Fernandez
1987; SCS 1984; UN-ECE 1987).
All soil physiochemistry measurements
should be made during the first plot visitation
cycle to assess baseline condition. Detectable
changes in soil chemical characteristics may be
significant on an intraseason, interseason, or
interannual basis in the short term; thus repeat-
cycle measurements of the surface soil
horizons should be considered.
6.6.3.1 Interpretability
Historically, soil pH data have been
incorporated into response studies with species
such as jack pine (Conyers and Davey 1990;
Hamilton and Krause 1985; Pawluk and
Arneman 1961) and Douglas-fir (Green et al.
1989). A measure of electrical conductivity is
useful for estimating the ionic strength of the
soil solution (Griffen and Jurinak 1973). Ionic
strength is used to calculate the activity of ions
in solution, allowing the monitoring of chemical
equilibria in soil samples and modeling of long-
term chemical weathering of soil minerals
(Lindsay 1979). Changes in carbon
sequestering may also occur in some forests
as a result of heavy disturbance (Harmon et al.
6.6-5
-------
TABLE 6 6-2 SOIL PHYSIOCHEMISTRY PARAMETERS FOR EXPOSURE/RESPONSE
ASSESSMENT
Soil Reaction and Elemental CNS Parameters
Electrical conductivity and pH: determined in deionized water using 1:1 mineral soil to solution ratio (.1:4 organic), measured with an electrical
conductivity meter and a pH meter with combination electrode, respectively.
Tola! elemental carbon, nitrogen, and sulfur: determined by rapid oxidation through dry combustion followed by infrared or thermal detection
using an automated CNS analyzer.
Soil Exchange and Extraction Chemistry Parameters
Exchangeable calcium, magnesium, potassium, and sodium: determined in a buffered (pH 7.0) 1M ammonium acetate solution using a 1:13
mineral soil to solution ratio (1:52 organic) by atomic absorption spectrometry or inductively coupled argon plasma atomic emission
*sfiA ct rom st rv
Cation exchange capacity: determined in a buffered (pH 7.0) 1M ammonium acetate solution using a 1:13 mineral soil to solution ratio (1:52
organic}; this is the effective CEC which occurs at approximately the field pH when combined with the acidity component; samples are
analyzed for ammonium content by one of three methods: automated distillation/titration; manual distillation/automated titratton; or
ammonium displacement/flow injection analysis. .
Total exchangeable acidity: determined in a buffered (pH 8.2) barium chloride triethanolamine solution using a 1:30 soil to solution ratio and
a back titratfon procedure. .
Effective exchangeable acidity and exchangeable aluminum: determined in an unbuffered 1M potassium chloride solution using a 1:20 soil
to solution ratio and a direct titration procedure;
Mineralizabte nitrogen: a predictor of soil nitrogen availability due to biological activity; an incubation technique is specified for the determination
of anaerobic nitrogen as ammonium-nitrogen.
Extractabte phosphorus: determined in a Bray & Kurtz No. 1 extractant (acid soils only) using a 1:13 mineral soil to solution ratio (1:52 organic)
and a cotorimetric procedure and autoanalyzer.
Extractabte sutfate: determined in a deionized water extractant and in a sodium phosphate extractant using a 1:20 soil to solution ratio by ion
chromatography.
Soil Total Elemental Chemistry Parameters
Total elemental phosphorus, calcium, magnesium, potassium, sodium, iron, manganese, copper, zinc, boron, chromium, aluminum, lead,
cadmium, nickel, vanadium, arsenic, and mercury: determined by initial microwave digestion followed by dilution and multi-elemental
readout by inductively coupled argon plasma atomic emission spectrometry (organic soil horizons only).
1990). Total carbon, nitrogen, and sulfur can
be used to characterize soil organic matter
which is a critical component of nutrient cycling
within the forest ecosystem' (Mader 1976;
Wikie 1964).
Soil productivity in forests is affected by the
sufficiency or deficiency of essential plant-
available nutrients (Edmonds et al. 1989). In
the Douglas-fir forests of the Pacific Northwest,
for instance, available nitrogen is the nutrient
most likely to limit site production (McNabb et
al. 1986). Productivity can also be disrupted by
a decline in the population of certain
microorganisms essential to biological cycling
processes within the forest floor nutrient
reserve zone. These effects may be caused
by long-term natural perturbations or short-term
changes due to human activity, either of which
can be manifested in low-level plant stress.
For instance, whole-tree harvesting in
commercial forests can affect macronutrient
cycling (McColl and Powers 1984; Johnson et
al. 1988). Likewise, a low ambient level of
magnesium in some localized forest soils is an
example of a naturally-occurring stress that
potentially could be aggravated by certain
management practices (Ballard and Carter
1985). Timber harvesting can aggravate the
depletion of nutrients on already nutrient-poor
6.6-6
-------
sites (Entry et al. 1987; Schulze 1989). Forest
floor disturbances can interfere with nitrogen
cycling (Peterson et al. 1984). Burning
(Debano and Klopatek 1988) and disruption of
the soil mycorrhizal fungi on tree roots (Vogt
and Persson 1990) are other known stresses.
Parameters such as the exchangeable
cations, cation exchange capacity, available
phosphorus, and exchangeable acidity have
historically been incorporated into acid
deposition studies (Church et al. 1989) and into
response studies with species such as jack
pine (Hamilton and Krause 1985; Pawluk and
Arneman 1961) and Douglas-fir (Green et al.
1989). Extractable sulfate and phosphate are
important constituents of the soil solution and
can be measured easily on the same extract
using ion chromatography. These
measurements, together with electrical
conductivity, can be used to estimate ionic
activity of the soil solution (Griffen and Jurinak
1973).
Soil productivity can also be affected by
micronutrient availability and by the presence of
toxic substances and contaminants in the soil
(Smith 1991). The latter presence can indicate
exposure to potentially detrimental chemical
compounds and elements possibly resulting
from land use practices (e.g., application of
pesticides, mine spoils, sewage sludge),
atmospheric deposition (e.g., aluminum
mobilization resulting from acidic precipitation),
or naturally occurring phenomena (e.g.,
overabundance of magnesium in serpentinitic
parent materials). Iron, manganese, copper,
zinc, and boron are essential to tree growth
and should be measured (Bonneau 1991).
However, iron and aluminum, as well as lead
(Johnson et al. 1982), cadmium, nickel,
chromium, and vanadium, can damage root
systems and are detrimental to plant growth
and forest systems as a whole (Driscoll et al.
1983; Johnson and Henderson 1989; Ulrich et
al. 1980). Plant metabolic processes can be
disrupted either directly, through uptake of the
substances, or indirectly, through impairment of
soil nutrient availability (Zedaker et al. 1987).
In the first case, the substances can affect
physiological processes and internal physical
structure, thereby lowering the rate of
photosynthesis, growth, and resistance to
secondary stresses (Mclaughlin 1985; Miller
1983). In the second case, mobile substances
bind with soil nutrients and migrate to
subsurface soil horizons.
Chemical toxicity can also reduce the
number and variety of soil decomposer
microorganisms, thereby decreasing the rate at
which nutrients become available for plant
uptake (VDIKRL1987) and effectively lowering
site productivity. This phenomenon has direct
implications for management considerations
with respect to mineral extraction, pesticide
applications, and atmospheric emissions. The
degree of toxic effects on plant tissues and
growth is related to the duration of exposure,
concentration, exposure regime, and chemical
dynamics of forested systems. Discovery of
such substances in the soil could warrant close
monitoring of areas exhibiting exposure.
Monitoring the concentrations of ions, both
those known to be nutrients and those which
act as toxic substances, is an important
measure of the potential for good forest
nutrition (Edmonds 1989). However, factors
which influence soil moisture imports and
exports must be evaluated because of their
effects on the availability of nutrients and toxic
substances. Evaluation of these factors is a
developmental aspect of FHM soil monitoring
because it requires the use of sometimes
scanty ancillary moisture, climate, and
exposure data from sources other than FHM.
Soil characterization using the parameters
in Table 6.6-2 is a fundamental baseline
activity that gives a broad, if incomplete, picture
of soil physiochemical "status" and is
particularly useful for soil habitat, exposure,
and response assessments. , These
measurements are essential to any serious
classification effort for risk assessment,
monitoring soil as a sink for metals, evaluation
of soil "potential," or other interpretive
purposes, and if this program is to be carried
out long-term on a permanent set of plots. The
parameters are of critical importance in
monitoring nutrient cycling characteristics of
forest soils. The total elemental parameters
are proposed for measurement in foliage and
6.6-7
-------
stemwood samples as well as soils to increase
interpretive power.
Soil pH, electrical conductivity, and the total
elemental parameters are easily and
unambiguously interpretable forthe purposes of
baseline soil chemistry characterization. The
proposed methodology is accepted
internationally and has been tested and used in
FHM. Also, the results of many large-scale
surveys of the recent past (e.g., EPA acid
deposition studies such as the Direct/Delayed
Response Project [Church et. al. 1989]) and
research-level surveys can be used to establish
reference data for comparability purposes.
When the measurements are made by
laboratories experienced in the protocols and
subject to strict QA guidelines, good accuracy
and precision can be achieved.
It can be argued that the soil exchange
chemistry parameters are somewhat
ambiguous because of the dizzying array of soil
exchange and extraction chemistry
methods. However, the proposed FHM
extraction methods are generally accepted by
a large cross-section of the soil chemistry
community, and have already been tested and
used for previous FHM soil analyses. Recent
efforts to establish standard or reference
methods has made the selection process much
more coherent. Following more methods
comparisons, the above list of exchange and
extraction parameters can probably be
consolidated into fewer extraction steps thus
reducing the cost of analysis without sacrificing
interpretability.
Aggregating to the plot level is very
straightforward; hence, cumulative distribution
functions can be derived. Appropriate
nominal/subnominal boundaries for particular
responses can be defined. Worldwide, most of
these parameters can be extended across
regional and ecosystem boundaries by virtue of
comparable methods. In a few cases, between
regions of the eastern U.S. and western U.S.,
different soil methods are necessary to address
acid/alkaline soil extraction efficiencies. This
difference in methods is easily manageable.
6.6.3.2 Quantification
The specified instrumentation used to
measure the soil physiochemistry parameters
is well documented in the literature. A detailed,
but straightforward, computer program
containing algorithms organizes, quantifies, and
reports all of the laboratory data. The program
has been used successfully in several previous
EPA-sponsored analyses (e.g., DDRP and
EMAP/FHM). As a result, only limited
refinements will be needed as FHM
progresses. Specific step-by-step protocols are
available for each parameter (Byers and Van
Remortel 1991). A major benefit of the total
elemental analysis is that a large amount of
data can be collected for numerous parameters
using only one or two instrument loadings.
Staffing needs are minimal and include a soil
scientist experienced in the field guide
protocols to perform seasonal soil sampling on
each plot; a preparation laboratory staff to
process samples on a seasonal basis; and
one or more analytical laboratories to perform
sample analyses on a seasonal basis.
6.6.3.3 Signal-to-Noise Ratio
Overall variability is low to moderate forthe
proposed physiochemistry parameters,
although chemical concentrations can vary
within seasons, within years, between years,
within plots, and between plots. Temporal or
spatial variability is generally contingent on the
form, mobility, and concentration range of the
parameter. Each of these possible sources of
uncertainty can be controlled to varying
degrees. By virtue of the inherent destructive
nature and unrepeatability of soil sample
collection, temporal and spatial uncertainties
are usually confounded, and cannot be strictly
partitioned and controlled individually.
Within the sample measurement system,
there can be variability due to between-crew,
within-run, between-run, within-batch, between-
batch, and between-laboratory differences.
Each of these possible sources of uncertainty
can be controlled within acceptable and known
limits. Estimates of measurement uncertainty
for the physiochemistry parameters may be
6.6-8
-------
derived using soil survey data that have
satisfied especially stringent QA criteria (Van
Remortel et al. 1988; Byers et al. 1989; Byers
et al. 1990; Lewis and Byers 1992). For
replicate samples introduced at the analytical
laboratory, an average CV of 5 percent or less
is typical for each of the soil reaction and
elemental carbon, nitrogen, and sulfur (CNS)
parameters and 10 percent or less for each of
the soil exchange and extraction parameters
and the other elemental parameters. The
expected laboratory bias is ± 5 percent or less
of the reference value. For the sample
measurement system as a whole (e.g.,
sampling, preparation, and analysis), an
average CV of 10 percent or less is typical for
each of the soil reaction and elemental CNS
parameters and 20 percent or less for each of
the soil exchange and extraction parameters
and the other elemental parameters. Careful
collection, preparation, and analysis of the
samples will constrain measurement uncertainty
to be a minuscule part of the overall data
uncertainty.
Interferences will be controlled through
the application of strict protocols and selection
of the best available instrumentation. It is
highly likely that noise may be further reduced
by adjusting the data for climatic effects (e.g.,
available soil moisture in a growing season) or
for covariate parameters (e.g., carbon and
nitrogen).
6.6.3.4 Regional Responsiveness
The soil physiochemistry measurements are
responsive to regional changes in condition or
exposure and can be extremely sensitive to
different environmental stresses (Smith 1991).
Repositories of soil characterization data, e.g.,
the USDA-SCS Soil Pedon File database, exist
for thousands of soil series across the United
States. Using this data base and others such
as the EPA Direct/Delayed Response Project
data bases (Church et al. 1989), it is possible
to provide a characterization basis for
simulations and geostatistical interpretation
within and among regions. Several research-
level data bases are available which contain
significant amounts of data documenting heavy
metal depositional gradients.
To date in the FHM program, evidence for
regional responsiveness in soils is being
gathered through evaluation of the Diagnosis
and Recommendation Integrated System
(DRIS) (Beaufils 1973; Walworth and Sumner
1987) techniques using soil chemistry and
dendrochronology data from a Southern
Appalachian spruce-fir data base (Kelly and
Mays 1989; Van Deusen 1988). Previously, an
acidic deposition gradient data base from the
north-central United States (Ohmann et al.
1989; Riitters and Van Remortel 1991 a) was
evaluated using DRIS techniques. Exploratory
DRIS evaluations have been done on the 1990
20/20 pilot study soil, foliar, and mensuration
data to examine nutrient cycling assessment
scenarios.
6.6.3.5 Index Period Stability
It is extremely difficult to verify the temporal
patterns of soil chemical variability in forest
systems because it is virtually impossible to
resample soils (with any certainty) from a
destructively sampled zone. Any such
estimates of variability are confounded and
generally cannot be attributed to temporal
versus other sources of variability. Based on
some research studies taking in situ
measurements with lysimeters, temporal
variability within a seasonal index period is
expected to be within ±10 to 15 percent.
Among years, variability is expected to be ±5 to
10 percent for any given annual increment.
Some of the more mobile soil nutrients such as
N-fractions may be more variable. The
potential effect of temporal variability can be
minimized by remeasuring each plot at about
the same time within the index period.
6.6.3.6 Environmental Impact
Under the present FHM field plot design, the
soil sampling activities have little or no effect
on the vegetative measurement subplots and,
therefore, on the long-term integrity of the
plots. Furthermore, all soil sampling work is
carried out in an annular zone well outside the
vegetative measurement areas of a plot. Any
resampling in future years will be conducted a
sufficient distance away from the original soil
holes to aviod contamination.
6.6-9
-------
The laboratory analyses have a negligible
environmental effect due to strict controls on
stock solutions and sample disposal.
6.6.4 Selection of Measurement
Parameters by Assessment
Scenarios
If funding is insufficient to measure all soil
classification and physiochemistry parameters,
it may be useful to prioritize the assessment
scenarios presented in Table 6.6-3 to
determine which data to collect. It should be
noted and understood that certain data of
importance or relevance to the data users may
be unavailable if all parameters are not
selected for measurement. Also, Table 6.6-3
does not present an exhaustive list of
appropriate soil parameters or possible
scenarios that have relevance. The present list
of parameters for baseline forest monitoring is
lean, but comprehensive.
6.6.5 1992 Activities
Soil samples will be collected as part of the
Southeastern Demonstration, SAMAB
Demonstration, and Western Pilot projects.
Chemical analyses will be performed as
described previously in this section.
Classification measurements will be taken on
the same plots from which soil samples are
collected.
TABLE 6.6-3. POSSIBLE SOIL ASSESSMENT SCENARIOS FOR FHM
1. Soil as the essential mechanism for ensuring sustainable nutrient cycling, forest sustenance, and soil productivity into the future;
linkages with other nutrient cycling indicators; from a habitat/response standpoint. To make this assessment, data must be collected
for all of the measurements under both the soil classification and physiochemistry categories.
2. Soil as a key linkage, e.g., with respect to buffering effects, for monitoring acidic deposition effects, cation leaching, or certain other
water quality concerns; from an exposure/response standpoint. To make this assessment, data must be collected for all of the
measurements under the classification category, and the soil reaction/CNS and soil exchange/extraction measurements under the
physfochemislry category.
3, Soil as a comprehensive predictor of risk or susceptibility, and as a means for assessing regional abatement strategies; from a
stressof/exposure/habitat/resDonse standpoint. To make this assessment, data must be collected for all of the measurements under
the classification category and the soil reaction/CNS measurements under the physiochemistry category.
4, Soil as a s'rnk for airborne metal contaminants or deleterious organic compounds; from an exposure/response standpoint. To make
portions of this assessment, we will need to collect data for ail of the measurements under the classification category and the soil total
elemental chemistry measurements under the physiochemistry category.
5. Soil as a primary classification medium for Tier 1 & 2 landscape characterization and GIS layering; from an exposure/habitat standpoint.
To make this assessment, data must be collected for all of the measurements under the soil classification category.
6.6-10
-------
6.7 Foliar Chemistry Indicator
6.7.2.1 Interpretability
T.E. Lewis
6.7.1 Introduction
Foliar chemistry is the quantitative and
qualitative measurement of the levels of micro
and macronutrients, as well as certain trace
elements in foliage. These measurements
indicate the nutritional or pollutant status of a
forest stand. In previous Forest Health
Monitoring (FHM) studies, foliar chemistry
consisted of two separate parts, "foliar
nutrients" and "foliar contaminants," the latter
encompassing elements not considered
essential to plant growth. Since both indicators
are based on analyses of the same foliar tissue
samples, the two will henceforth be referred to
as the foliar chemistry indicator.
Foliar chemistry is an exposure-habitat
indicator. This class of indicator is designed to
quantify stressors which may be associated
with changes in forest condition. The foliar
chemistry indicator is also a key component in
the suite of indicators that addresses the
nutrient cycling assessment endpoint. Foliar
analysis can be used as an indicator of the
current nutritional status of a stand, elements
available in soil, and atmospheric deposition.
Foliar nutrient levels can be influenced by
factors other than availability. These influences
may include site factors, availability of other
nutrients, genotype, and sampling protocol.
6.7.2 Indicator Evaluation
The rationale for measuring foliar
chemical attributes is addressed in light of the
six indicator selection criteria described in
Section 6.1.
Foliar chemistry is subject to many sources
of variability such as time of year when
samples are collected, crown position on the
tree, and available water regime. Considering
single nutrient levels at one point in time
provides little information about nutrient status
or growth dynamics. However, by determining
some type of index, such as the Diagnosis and
Recommendation Integrated System (DRIS)
index (Beaufils 1973), that considers all
nutrients in harmony, a relationship between
nutrient status and growth (e.g., basal area
growth) may be established.
The DRIS indices were calculated for the
loblolly foliar chemistry data obtained from the
FHM 1990 20/20 pilot study. The DRIS indices
were calculated for each sample tree by
comparing the 20/20 data to a reference
population using DRIS norms from Needham et
al. (1990) for 4-year old loblolly pine growing in
the South Carolina and Georgia Piedmont.
These DRIS norms are shown in Table 6.7-1.
The DRIS index for each nutrient was
calculated by first determining intermediate
functions for each nutrient ratio, using N/P as
an example.
f(N/P) = ([(N/P)/(N/P)] -1) (100/CV^p)
if N/P > N/P (1)
f(N/P) = (1 - [(N/P)/(N/P)]) (lOO/CVwp)
if N/P < N/P (2)
where the boldface type refers to the Needham
etal. (1990) reference-population nutrient ratio,
regular type refers to the 20/20 sample
population being compared, f is the functional
relationship between the sample and reference
population values, and CV is the coefficient of
variation of the nutrient ratio of the reference
population.
67-1
-------
TABLE 6.7-1. THE DRIS NORMS FOR ASSESSING THE NUTRITION OF 4-YEAR-OLD LOBLOLLY
PINE GROWING IN THE SOUTH CAROLINA AND GEORGIA PIEDMONT (FROM NEEDHAM ET
AL 1990).
Nutrient ratio
Mean
Standard Deviation Coefficient of variation
N/P
N/K
Ca/N
Mg/N
P/K
Ca/P
Mg/P
Ca/K
Mg/K
10.426
2.361
0.154
0.066
0.227
1.589
0.696
0.362
0.158
1.209
0.427
0.037
0.026
0.037
0.452
0.197
0.126
0.050
11.6
18.1
24.5
24.5
16.6
28.4
28.5
35.2
25.25
The DRIS index for each nutrient was
calculated as follows:
N index- f(N/P) + f(N/K) - f(Ca/N) - f(Mg/N)
4 (3)
p index- -f(N/P) + ffP/K) - f(Ca/P) - f(Mq/P)
4 (4)
K index- -f(N/K) - f(P/K) - f(Ca/K) - f(Mg/K)
4 (5)
Ca inrim^KCa/N) +f(Ca/P) +f(Ca/K)+f(Ca/Mg)
4 (6)
Mg Inrtfly-ffMa/N) + f(Mq/P) +f(Mg/K) f(Ca/Mg)
4 (7)
The data compared in Table 6.7-2
demonstrate that the sampling strategy
employed during the 20/20 study provided foliar
chemistry data that were directly comparable to
those reported in the literature for loblolly pine.
There is close agreement between all values
except for potassium (K), which was
considerably higher in the DRIS derivation. In
theory, DRIS-derived optimum nutrient levels
should be slightly higher (about 10 percent)
than traditionally derived critical levels because
of differences in their definition.
The question remains: how does the FHM
sampling strategy compensate for or reduce
the variability due to seasonal variability and
crown-position effects? While individual foliar
nutrients are known to vary temporally and
spatially on the bole of the tree, the use of an
index, such as DRIS, may reduce such sources
of variability. Intraseasonal foliar nutrient
concentrations and contents in conifers depict
a pattern similar to deciduous trees, with
seasonal maxima occurring in the current-year
foliage in the summer followed by generally
stable or slightly decreasing levels in the
autumn (Nommik 1966; Nommik and Popovic
1968). Foliar sampling in FHM is performed
during the summer months.
From an interpretative standpoint it may at
first seem convenient to sample during the time
of year when nutrients are most stable, autumn
and winter for conifers and one month prior to
autumn coloration for deciduous species.
6.7-2
-------
TABLE 6.7-2. COMPARISON OF DR1S-DERIVED OPTIMA AND CRITICAL LEVELS FOR LOBLOLLY
PINE FOLIAR-NUTRIENT CONCENTRATIONS FOR THE 20/20 STUDY WITH THOSE REPORTED IN
THE LITERATURE
Foliar nutrient concentration
• (g/kg dry weight)
Parameter
N
K
Mg
nutrient level when DRIS
index = 0
DRIS optimum
critical levels
1 1 .31
12.02
12.02
11.03
12.04
111
1-22
1.12
1.03
1.04
1.16
4.61
S.22
S.22
3.53
2.6s
0.71
0.82
0.82
0.73
0.85
1Based on fofiage collected in the summer of 1990 during the 20/20 Study in Virginia; DRIS optima and critical levels could not be calculated from
such a small sample.
2Needhametal.(1990)
"Hockman and Allen (1989)
4Fowells and Krauss (1959)
5Sucoff (1961)
'Wells and Crutchfiek) (1969)
However, the biological justification for such an
index period is tenuous. This dormant period
follows translocation of the mobile nutrients out
of the foliage in preparation for next year's
growth and deposition of the nonmobile
elements. Thus, measuring foliar nutrients at
the most stable time of year does not provide
any information as to the nutritional status of
the plant at the most physiologically important
use period (Armson 1965). By sampling during
the most physiologically active period, as is
currently done in FHM studies, critical nutrient
demands may be more detectible than during
dormant periods. It is also logistically easier to
collect foliage samples at the same time all
other measurements are made on the plot.
Table 6.7-3 compares the average ratios
and coefficients of variation from the 20/20
Study with those from Needham et al. (1990)
and Hockman and Allen (1989). The authors
sampled previous-year growth loblolly foliage in
February and December and January,
respectively. In the 20/20 Study
previous-year growth loblolly foliage was
collected in June and July. Despite the
differences in index period between the three
studies, the nutrient ratios are similar (Table
6.7-3). When evaluated within the context of
the DRIS approach, seasonal differences may
be factored out by comparing all nutrient ratios
simultaneously.
DRIS has also been applied to deciduous
species. Lozano and Huynh (1989) examined
DRIS indices for sugar maple. They found that
DRIS diagnoses were identical irrespective of
sampling date (month) and nearly identical
diagnoses were made by DRIS irrespective of
the position on the crown where foliage was
collected.
6.7.2.2 Quantification
Foliage can be collected from each plot in
a single day by either a tree climber, rifle, or
pole pruner. Analysis of foliar tissue for micro-
and macronutrients and trace elements has
been performed for decades. Advances in
microwave digestion and analytical
instrumentation have improved accuracy and
6.7-3
-------
TABLE 6.7-3. COMPARISON OF AVERAGE NUTRIENT RATIOS AND COEFFICIENTS OF VARIATION
FROM THE 20/20 STUDY WITH LITERATURE REPORTED VALUES FOR LOBLOLLY PINE
nutrient
ratio
N/P
N/K
Ca/N
Mg/N
P/K
Ca/P
Mg/P
Ca/K
Mg/K
Ca/Mg
20/20
12.363
2.143
0.161
0.060
0.183
1.973
0.739
0.343
0.125
2.770
means
N2
10.426
2.361
0.154
0.066
0.227
1.589
0.696
0.362
0.158
2.355
HA3
10.909
3.076
0.133
0.075
0.282
1.545
0.818
2.437
0.231
1.778
20/20
26.8
24.3
43.2
31.5
32.5
49.3
39.2
49.7
32.0
40.4
CV1
N
11.6
18.1
24.5
24.5
16.6
28.4
28.5
35.2
31.9
25.3
HA
__4
-
—
--
—
--
~
—
-
—
'Coefficient of variation
'Needham el al. (1990)
"Hodman and Alten (1989)
4Authors did not report CV
precision. The use of National Institute of
Standards and Testing (NIST) and in-house
audit samples will also improve within-batch
and between-batch precision and accuracy.
The calculation of DRIS ratios and norms from
the foliar chemistry data is a simple arithmetic
operation.
6.7.2.3 Slgnal-to-Noise Ratio
The foliar nutrient ratios observed from the
1990 20/20 Study are in agreement with those
reported by Needham et al. (1990) and
Hockman and Allen (1989). This is despite the
fact that the loblolly in the 20/20 Study were
sampled during the summer, when nutrient
levels are known to fluctuate, and the other two
studies collected foliage in the winter. The CVs
are greater for the 20/20 Study data due to
large variability in the analytical procedures.
The variability in foliar nutrient ratios and their
relative magnitudes are not different from those
reported by other investigators studying nutrient
status in loblolly pine (Table 6.7-3). It should
be reiterated that in equations 1 and 2 the
DRIS nutrient ratios are weighted by the
inverse of the CV. Thus, for those ratios
exhibiting greater CVs in the 20/20 Study
loblolly data set, the resulting intermediate
function is given less weight in the DRIS index.
This weighting procedure serves to increase
the signal-to-noise ratio.
6.7.2.4 Regional Responsiveness
Needham et al. (1990) obtained foliage
from 504 randomly selected 4-year-old loblolly
pines in 8 plantations in the South Carolina and
the Georgia Piedmont. The sites represented
diverse cultivation practices. Hockman and
Allen (1989) collected foliage from loblolly
pines spanning 11 states and 52 soil series
from 3 control plots of the North Carolina State
Forest Nutrition Cooperative regionwide trial
series. Geographically, their study extended
from Delaware to southern GeorgiaHhd west to
Louisiana and Arkansas. Loblolly pine and
loblolly pine-hardwood forest types were
sampled in Virginia during the 1990 20/20
Study. These stands occurred primarily in the
Atlantic coastal plain and Piedmont.
Table 6.7-2. compares the DRIS optima,
critical levels, and foliar nutrient concentrations
6.7-4
-------
when DRIS indices = 0 from the three studies.
The values obtained from the 20/20 Study,
notwithstanding its inherent problems with
limited regional representativeness and
analytical shortcomings, compared quite
remarkably to the literature reported values.
These similarities indicate that, at least on a
species level, comparisons can be made with
respect to the major nutrients in loblolly pine
foliage. Some fine tuning of the DRIS norms
may be necessary to obtain estimates that are
more physiogeographically representative.
Greater trend detection accuracy and sensitivity
would be obtained if region- or soil-group
specific DRIS norms are developed.
How is foliar chemistry regionally
responsive to air pollutants such as ozone,
sulfate deposition, heavy metal deposition,
organics, pests, and other regional stressors?
Trees are one of the greatest interceptors of
contaminants because of their large surface
area. The surface area of the above ground
parts usually far exceeds the surface area of
the ground in which they are growing. The leaf
surface area represents the bulk of the surface
area both in herbaceous and woody plants, and
leaf area indices up to 20 have been observed
(Schulze 1982). Since most leaves have a
lower and upper surface, there may be up to
40 m2 leaf surface area on every m2 of soil.
Therefore, most airborne pollutants will be
intercepted by leaf surfaces rather than by the
soil surface. The use of plant material that
accumulates pollutants is convenient for
demonstrating the geographical distribution of
a stressor. The levels of trace elements in the
20/20 Study were generally at or below the
method detection limit. This is useful
information regarding the deposition of trace
elements in plots sampled during the 20/20
Study. Obviously, no regional estimates of
trace element deposition can be made from the
small sampling conducted during the 20/20
Study. However, the data indicate that, for the
area sampled, heavy metal deposition was low.
Foliage has been demonstrated to be an
excellent integrator of inorganic pollutant
exposure (Blauel and Hocking 1974; Hogan
and Wotton 1984). Recently, the impact of
organic contaminants on forest ecosystems has
become a matter of concern. From leaf area
indices and area weights of cuticles, Riederer
and Schonherr (1984) estimated that, for most
plant stocks, the quantity of lipophilic cuticle
per hectare amounts to between 180 and 1,500
kg. The quantity of cuticular mass in forests is
probably greater, suggesting that lipophilic
compounds accumulate in significant amounts
in forests (Sherby and Gould 1990). Lipophilic
compounds include PCBs, PAHs, chlorinated
pesticides, and- volatile organic compounds
(e.g., trichloroethane, perchloroethene).
6.7.2.5 Index Period Stability
It is apparent from a cursory review of the
literature that foliar nutrients fluctuate over the
course of the growing period and even during
the dormant period. However, if sampling
protocols are standardized so that foliage is
collected at approximately the same time of
year, from the same position on the crown, and
from the same age class, then trends in foliar
chemistry can be detected. By linking the FHM
data base with other data bases that are
available for similar species, DRIS indices may
be computed and compared with reasonable
confidence using analyses of foliage collected
within the index period. Furthermore, the study
on sugar maple by Lozano and Huynh (1989)
suggest that DRIS indices are stable during the
entire growing season.
DRIS indices are not limited to nutrient
ratios. Other influential factors such as leaf
area index, fascile weight, available water,
temperature, and toxic elements (e.g., Ca/AI
ratio) can be incorporated into the DRIS
equations.
6.7.2.6 Environmental Impact
The amount of foliage collected from the
sample trees during FHM surveys is not
considered to affect the health of the tree,
given a 4-year interval for resampling. The
foliar sampling activities do not adversely affect
other indicator or potential indicator
measurements. Tree climbers do not use
spikes when obtaining foliage from the crown.
6.7-5
-------
6.7.3 Summary of Foliar Sampling
and Analysis
In brief, samples are collected from four
trees on each plot by collecting current leaves
of deciduous species and previous year (one-
year old) needles from coniferous species from
the upper third of the live crown of dominant or
codominant trees. Specimens are obtained by
one of three methods: (1) climbing the tree with
ropes and ascenders (e.g., jumars, gibbs), (2)
pole pruner, or (3) rifle. Most reputable tree
companies prohibit the use of climbing spurs or
spikes except when the tree is to be felled.
Spikes vyjn notDe used on any sample tree.
The specimens are shipped to the
preparation laboratory where the samples are
oven dried, ground to less-than-one-millimeter
particle size, homogenized, and shipped to the
analytical laboratory. The samples ,are
analyzed using methods described in Part IV of
the FHM Analytical Methods Handbook (Byers
and Van Remortel 1991). The dried foliar
material is analyzed for the following groups of
analytes:
Group I. Micro- and macronutrients and trace
elements - Block digestion of foliage and forest
f(oor litter followed by elemental analysis of the
digest by inductively coupled plasma optical
emission spectroscopy (ICP-OES) for the
following micro-and macro-nutrients and certain
trace elements:
ICP
Aluminum
Calcium
Copperlron
Manganese
Phosphorus
Strontium
Vanadium
Boron
Chromium
Lead
Molybdenum
Potassium
Sulfur
Zinc
Cadmium
Cobalt
Magnesium
Nickel
Sodium
Titanium
Group II. Mercury - by cold vapor ICP.
Forest floor samples to be analyzed only.
Group HI. Arsenic--by hydride generation ICP.
Forest floor samples to be analyzed only.
Group IV. Total A/~combustion of the foliar
material in the furnace of total CNS analyzer.
6.7.4 Data Analysis
The DRIS approach will be used to
aggregate data down to the plot level.
Macronutrient, and possibly micronutrient,
ratios will be produced and DRIS indices
calculated using literature-reported DRIS norms
for "healthy" species. If literature values are
not readily available for certain species, then
FHM or FIA growth-related measurements will
be used to segregate foliar nutrient ratios into
nominal and subnominal populations for
calculating DRIS norms.
For trace elements such as Pb, Cd, and
Hg, cumulative distribution frequency (CDF)
plots will be generated to illustrate the regional
trends in contamination levels of these
elements. Comparisons will be made to
literature values for similar species growing in
contaminated and "pristine" environments.
Foliar, soil, and stemwood elemental
concentrations will be compared to detect
correlations between elemental trends in the
various forest ecosystem components.
Ultimately, a compartmentalized model will be
constructed to assist in the interpretation of
regional trends in trace element deposition and
cycling in various forest types.
6.7.5 1992 Activities
The foliar chemistry of samples collected
during the Southeastern Demonstration will be
measured. This will include samples from the
Georgia Demonstration conducted in 1991 and
samples collected during the 1992
Southeastern Demonstration. An off-frame
pilot study will be conducted in Portland,
Oregon, to develop an optimal branch
extraction procedure that uses rifles. The use
of rifles will reduce the costs of tree climbers
and allow easier implementation of the foliar
chemistry indicator in other regional
demonstrations and pilots.
6.7-6
-------
6.8 Stemwood (Tree Core)
Chemistry indicator
T. E. Lewis
6.8.1 Introduction
Stemwood chemistry is the quantitative and
qualitative measurement of the levels of micro-
and macronutrients, as well as certain trace
elements, in Stemwood that indicates
retrospective changes in uptake and
translocation. Stemwood chemistry is an
exposure-habitat indicator. This class of
indicator is designed to quantify stressors which
may be associated with changes in forest
condition. The Stemwood chemistry indicator is
a key component in the suite of indicators that
address the nutrient cycling assessment
endpoint.
Stemwood chemistry is also a retrospective
indicator (Rl). The RIs place current
observations, such as soil chemistry, foliar
chemistry, leaf area index, and basal area, into
the context of a longer time frame, thereby
providing the opportunity to evaluate slowly
operating processes, past cyclic changes or
unusual events, disturbance regimes, and
historically constrained phenomena
(Schoomaker and Foster 1991). Many of the
current Forest Health Monitoring (FHM)
indicators are nonretrospective. They provide
extensive geographic coverage, but lack the
temporal aspect of evaluating seasonal, annual,
decadal, or longer trends. Current
measurements of foliar or soil solution
chemistry cannot indicate historical changes in
dynamic equilibrium of soil chemistry. The
current FHM indicators operate in a wait-and-
see context. The first year of monitoring yields
a reference point, but not a baseline indicative
of long-term trends. With historical data
gathered from chemical analysis of tree cores,
future trends starting at current values of non-
retrospective indicator measurements can be
predicted. The Stemwood record from any
particular position is essentially a summary of
the changes in the chemistry of the sap in
contact with the wood at that position. The
greatest value in the use of this record is to
indicate the direction and the comparative rate
of change in sap chemistry (Bondietti et al.
1990). This is distinct from the direct
measurement of branch sap chemistry, which
provides a useful picture of nutrient availability
at the moment of sampling. The chemistry of
branch sap depends on the time of day and
position of the branch in the crown, as well as
nutrient availability in the soil (Stark et al.
1985).
The analysis of elemental concentrations in
tree cores may provide evidence of historical
trends in nutrient cycling. Most studies of
elemental chemistry of tree cores have
examined distinct tracers of anthropogenic
origins (e.g., lead (Pb) from leaded gasoline,
strontium-90 (90Sr) from atomic weapons
testing, heavy metal emissions from smelters).
Following is a summary of the results of some
of these studies.
The initial observations of Ault et al. (1970)
demonstrated distinct increases in Pb content
in the more recently formed annual growth
rings which could be related to parallel
increases in traffic density. Ward et al. (1974)
produced a more detailed study on Pb content
of annual growth rings. Distinct patterns in Pb
content were observed. The authors reported
that changes in Pb levels could be correlated
with changes in traffic density, even with
changes in levels of a Pb anti-knocking agent
in gasoline marketed in New Zealand during
World War II.
Bondietti et al. (1990) found that trends in
^Sr levels in tree cores approximated historical
deposition patterns in the biosphere during
aboveground testing of atomic devices.
Close correlation between elemental
patterns in shortleaf pine Stemwood and
historical sulfate emissions from the Copper Hill
Smelter in eastern Tennessee (Baes and
Mclaughlin 1984) provided early evidence that
changes in tree ring chemistry reflect changing
inputs of regional pollutants in forests.
Increasing levels of iron were found in those
tree cores during the 50 years of open pit
6.8-1
-------
smelting operations (1860 to 1910). After
emissions were reduced to preindustrial levels
In 1910, levels of iron were significantly lower
for 40 years. The levels of iron have again
increased during the last 30 years, in response
to increasing acidic deposition. Bowers and
Melhuish (1988) observed a similar pattern in
tree cores collected from loblolly and red oak
growing near the Chromasco Smelter outside
of Memphis, Tennessee.
Examining the relationship between tree
ring chemistry and changes in soil chemistry
during the life of a tree is a more recent
approach (Legge et al. 1984; McClenahen et
al. 1987; Guyette and McGinnes 1987;
Bondietti et al. 1989). Bondietti et al. (1989)
observed a significant increase in the ratio of
aluminum (Al) to calcium (Ca) in tree rings of
red spruce and eastern hemlock in the Great
Smoky Mountains of Tennessee. The increase
was attributed to increased mobilization of Al
and leaching of Ca in the soil as a result of
acidic deposition. The increased ratio of Al to
Ca corresponded to a decrease in the radial
growth of the species. Bondietti et al. (1990)
also sampled red spruce and other species in
New England and North Carolina. The
researchers observed an increase in divalent
cations present in red spruce wood formed in
the mid-1900's that was coincident with rapid
increases in sulfate and nitrate deposition in
eastern North America and with increases in
radial growth. A decrease in divalent cation
content was noted in the red spruce wood
formed in the late 1900's with a concomitant
decrease in radial growth. Shortle and Smith
(1988) have proposed that increasing Al to Ca
ratios in soil solution and stemwood may be
partly responsible for the regionally expansive
decline in red spruce in the Northeastern
United States.
6.8.2 Indicator Evaluation
The rationale for measuring stemwood
chemical attributes is addressed in light of the
six indicator selection criteria described in
Section 6.1.
6.8.2.1 Interpretability
Dendrochronological analysis coupled with
chemical analysis of stemwood samples
provides a valuable method for gauging forest
responses to temporal and spatial changes in
atmospheric deposition of pollutants and
climate change (Lepp 1975; Scherbatskoy and
Bliss 1982; Baes and Mclaughlin 1984;
Mclaughlin 1985; Hpldaway 1991).
Two factors are believed to affect the
interpretation of trends in elemental
concentrations in tree cores: (1) translocation
may occur across rings, and (2) calcium
binding capacity (CBC) is reduced by changes
in sap solution pH and by innate composition of
wood formed at increasing distances from the
pith. These factors require care in the
interpretation of trends in elemental levels in
tree increment cores. However, various
researchers have made adjustments in
analytical procedures and data evaluation to
compensate for these factors. It should be
stressed that stemwood chemistry is a
relativistic detection monitoring tool. It does
not provide absolute values for elemental
concentrations in a single year's growth ring.
The issue of translocation and how it may
add ambiguity to the interpretation of trends in
stemwood chemistry is addressed in the
following discussion. One perspective of how
red spruce stemwood records reflect changes
in cation availability can be obtained by
considering the fallout of ^Sr, a calcium
analog, with a known input history into soils.
Bondietti et al. (1989) examined the ^Sr
concentration in red spruce stemwood samples
collected from the Great Smoky Mountains
National Park. The deposition of ^Sr began
around 1954. Although strontium appeared in
tree rings prior to the beginning of the fallout
period, both the number of rings affected and
the amount of ^Sr present were relatively
small. This and other measurements of ^Sr in
different tree species indicate that 5- to 10-year
increments are reasonable intervals for
digestion and subsequent instrumental analysis
for determining changes in cation availability
(Bondietti et al. 1989). The maximum level of
6.8-2
-------
fallout occurred about 1964. High levels of ^Sr
were measured in wood formed 10 years
earlier and a few years after. However, relative
trends in ^Sr are easily discemable given 5- to
10-yr increments. The questions remain, how
can the high levels of 90Sr in wood predate the
period of maximum fallout and how can ^Sr
levels be found in wood as far back as 1920?
The answer is that xylem transport of solutes
from the soil solution occurs in wood as old as
approximately 30 years. Thus, when the initial
releases of 90Sr occurred in 1954, the actively
transporting sapwood would have extended
back to around 1924. The continued increase
in 90Sr after steadily decreasing fallout levels
indicates that 90Sr is still available for uptake
from soil.
The way in which samples are prepared for
chemical analysis by inductively coupled
plasma (ICP) or inductively coupled plasma-
mass spectrometer (ICP-MS) will compensate
for the apparent translocation of elements.
Five- to 10-year increments are digested and
provide evidence of relative increases or
decreases over time in macro- and
micronutrients, as well as trace elements.
The second factor believed to contribute
uncertainty in the interpretation of stemwood
chemistry data is the decrease in CBC from the
pith to the cambium. Interpreting the historical
record of successive equilibria between sap
chemistry and stemwood elemental levels
requires recognition that the availability of ion
exchange sites decreases with increasing radial
distance from the pith (Momoshima and
Bondietti 1990). They found a decrease in the
CBC of red spruce bolewood as a function of
the year the wood was formed and the radial
distance from the pith. The CBC was highest
near the pith and decreased toward the
cambium. The similar CBC values near the
center of the trees indicate that the number of
binding sites was similar and that production of
binding sites is independent of environment,
since trees grew at sites in Tennessee, North
Carolina, and Maine. The decline in CBC is
apparently more a function of radius than
chronological age. Red spruce from the Great
Smoky Mountain National Park Site #32
(GSMNP-32 site) had slightly higher CBC
values, but declined with the same slope as the
other trees from other sites.
Presently, the reason for the decrease in
CBC is not fully understood. Since CBC shows
a geometric dependency, changes in tracheid
morphology may be involved. Tracheids
become longer with increasing tree radius
(Bailey 1957), allowing an increase in the
amount of water movement to accommodate
the higher demand and capillary lift in mature
trees. Tracheid elongation is accompanied by
an increase in the number of tracheid end
connections per unit weight. The radial decline
in CBC compensates for what otherwise would
be a geometric increase in calcium and other
divalent cation storage in the bole if CBC and
pH were constant. The decrease in CBC in the
outer radius allows more transport of cation
nutrients to the upper portions of the mature
tree. Recognizing this phenomenon, Bondietti
et al. (1990) plotted CBC and the actual
divalent cation concentration in individual red
spruce wood from trees sampled in New
Hampshire and Maine. Divalent cation
concentrations paralleled the trend of CBC for
the New Hampshire trees during the 1960s,
followed by a sharp decrease relative to the
CBC from the early 1970s through to 1985.
The Maine trees showed a parallel relationship
between cation concentration and CBC up until
the 1980s, where a sharp decrease was
observed. The sharp decreases in divalent
cation concentration may be due to leaching of
nutrients from the rooting zone of the trees by
acidic deposition (Bondietti et al. 1990).
Analysis of changes in wood chemistry from
samples across several sites in the eastern
United States (Bondietti et al. 1990) indicates
that over the past 30 to 40 years there have
been some substantial departures from the
expected linear decrease in CBC and expected
Ca accumulation patterns in wood.
A study by Arp and Manasc (1988) is an
example of how the decrease in CBC with
increasing radius from the pith may not
contribute to ambiguity in the interpretation of
stemwood chemistry data. They examined the
yearly trend in nutrient and trace element
concentrations in red spruce stemwood
sampled near a coal-burning power plant.
6.8-3
-------
Trees were separated into two groups-trees
greater than 35 years old and trees less than
35 years old. At any point in time, the tree ring
in a mature tree (greater than 35 yr)
corresponding to a given year's growth would
be at a greater distance from the pith than the
same year's ring in a younger tree (less than
35 yr). The older tree's ring would thereby
have a lower CBC than the younger tree's.
Even with the CBC phenomenon reported by
Momoshima and Bondietti (1990) acting on
elemental uptake and translocation, similar
trends are noted in young and mature trees.
Relative trends can be discerned,
notwithstanding differences in CBC with
increasing radial distance from the pith for
mature and young trees. During FHM studies
an increment core can be collected from a
younger tree of the same species as the
dominant or codominant tree. An estimate of
the change in CBC with distance from the pith
can be obtained by analysis of both a mature
and young tree.
6.8.2.2 Quantification
Increment boring of trees is a simple
procedure that can be performed by trained
field technicians. Cores are collected presently
in FHM for dendrochronological evaluation. All
cores are collected at breast height to
standardize the procedure across plots and
regions.
Baes and Mclaughlin (1984) have shown
accuracy and precision to be very good for
stemwood elemental analysis. Trees were
sampled with increment borers at several sites
near Oak Ridge, Tennessee and in the Great
Smoky Mountain National Park, and
multielement analysis was performed by
inductively coupled plasma-optical emission
spectrometry (ICP-OES) (Baes and McLaughlin
1984). For most elements, analytical accuracy
was at least 80 percent. Their recovery of iron
(46 percent) and aluminum (71 percent) from
National Institute of Standards and Testing
(NIST) standard reference materials (SRMs),
(i.e., citrus and orchard leaves and pine
needles), was incomplete. Since their study, a
new microwave digestion procedure has
become available which has made recoveries
for most metals nearly 100 percent efficient.
Baes and McLaughlin (1984) found precision to
be very good for all metals in their study.
Because precision was good and because
temporal patterns of trace elements in wood
were very similar among trees of a stand,
confidence in temporal patterns was high. With
use of the new microwave digestion procedure
(Sah and Miller 1992), accuracy will be
considerably increased.
6.8.2.3 Signal-to-Noise Ratio
Year-to-year (ring-to-ring) variability will be
reduced by compositing 5- to 10-year
increments for ICP or ICP-MS analysis.
Bondietti et ai. (1989) have shown that this
increment size is appropriate for detecting
trends in trace element (e.g., ^Sr) and nutrient
(e.g., Ca) concentrations in stemwood samples.
Arp and Manasc (1988) discerned elemental
trends in greater than 35-year-old and less
than 35-year-old red spruce by compositing 5-
year increments.
Periods of growth decline, both recent and
historical, often correspond to increased
concentrations of metals such as iron (Fe) and
titanium (Ti) in stemwood (Baes, and
McLaughlin 1984). The increase may be due
to a decrease in the amount of tissue laid down
in a given year. This noise can be factored out
by multiplying elemental concentration and
growth rate (microgram of element per gram of
wood x gram of wood per year = micrograms
of element per year), yielding "xylem
accumulation rates" of elements into ring
segments. This measure represents annual
ring content or burden (total annual
accumulation) and is useful in evaluating
historical patterns.
Baes and McLaughlin (1986) found that AI
levels in wood began increasing in the mid-
19008 in conifers sampled in the GSMNP.
Concentrations of alkaline earth elements,
particularly Ca, were often declining during this
same period. When the AI:Ca ratios were
calculated (Bondietti et al. 1989), the 1940s
stood out as a period when wood Al usually
began increasing at various high-elevation
locations in the park. The use of AI:Ca
ratios normalizes fluctuations in cation
6.8-4
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concentrations and accentuates periods when
polyvalent cations changed at different rates
from, for example, calcium, thereby increasing
the signal-to-noise ratio.
The Diagnosis and Recommendation
Integrated System (DRIS) has been used
successfully for fertilizer treatments to achieve
high yields. It was employed by Hockman and
Allen (1989) and Needham et al. (1990) to
evaluate nutritional status of loblolly pine by
foliar analysis. Recently, DRIS has been
applied to yet another plant tissue, stemwood,
for determining forest health (Riitters and Van
Remortel 1991). The DRIS techniques use the
inverse of the coefficient of variation (CV) to
weight each of the intermediate elemental
functions. Thus, if an element exhibits large
variability in concentration over a region, its
contribution to the DRIS index of any given
element is reduced. This weighting procedure
serves to increase the signal-to-noise ratio.
6.8.2.4 Regional Responsiveness
Bondietti et al. (1989) found that the AI:Ca
ratio measured in the stemwood of trees
growing at the GSMNP have increased in the
last four decades. However, the same trends
in AI:Ca ratios have been observed in other
regions and in different species: New
Brunswick red spruce (Aip and Manasc 1988),
red spruce and sugar maple in Vermont
(Matusiewicz and Barnes 1985), shortleaf pine
in Tennessee (Braker et al. 1985; Baes and
Mclaughlin 1984), and hickory in North
Carolina (Berish and Ragsdale 1985). What
factors other than increased Al availability due
to increased mobilization could account for this
trend? Cation concentration in wood could
increase as more exchange sites are produced
or if sap chemistry changes. Increased
numbers of exchange sites and increased total
cation concentration occur in wood that is
wounded (Shevenell and Shortle 1986).
However, the widespread, simultaneous
occurrence of these anomalies in the AI:Ca
ratios argue against synchronous episodes of
physical wounding, including damage by fire.
Also, fungal infections that would have resulted
from wounding typically are associated with
large increases in potassium (K). Elevated
levels of K were not observed. Site-specific
phenomena such as fire or other disturbances
that affect soil chemistry have not occurred
simultaneously throughout the region. The
chemical analysis of stemwood from several
species of trees collected by many different
investigators throughout the region all indicate
a similar pattern in the AI:Ca ratio. Stemwood
chemistry is obviously regionally responsive.
However, the cause of this trend cannot be
definitively determined from stemwood
chemistry data alone. In conjunction with other
indicator data, such as soil chemistry, a
possible link to acidic deposition could be
established. Guyette (1991) found a significant
inverse relationship between Ba and Mn trends
in red cedar tree rings and soil sulfate activity.
The Ba/Mn signal in stemwood may be useful
in differentiating between changes in soil
acidity due to anthropomorphic sulfur emissions
and those due to natural, non-sulfate,
acidification processes such as climate, base
cation sequestering in woody vegetation, fire
supression, and species change. This
technique may not be applicable to sites with
pH values close to 7 and high soil Ca levels.
6.8.2.5 Index Period Stability
Stemwood chemistry is distinct from the
direct measurement of branch sap chemistry,
which provides a useful picture of nutrient
availability at the moment of sampling. The
chemistry of branch sap depends on the time
of day and position of the branch in the crown,
as well as nutrient availability in the soil (Stark
et al. 1985) which will vary seasonally. Thus,
in the cambium, changes in sap chemistry may
be detected when stemwood samples are
collected and analyzed. However, since 5- to
10-year increments are composited, this
source of variability during the index period will
be smoothed out. In conifers, most wood
cations are bound to uronic acid polymers in
various structures in the cell wall. Saka and
Goring (1983) used energy-dispersive X-ray
analysis to show that the middle lamella, torus,
and other structures in black spruce wood
contained the highest concentrations of calcium
and other cations. The secondary walls of
tracheids contained lower concentrations but
accounted for the bulk of the cation inventory.
6.8-5
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A coniferous stem can thus be thought of as a
renewable ion exchange column in which sap
cations are in continuous equilibrium with the
walls of the functioning tracheids (Momoshima
and Bondietti 1990). Only a small percentage
(1 percent to 5 percent) of the bound cations in
the wood are in an exchangeable form. Any
short-term temporal changes in soil solution
chemistry will have a minor influence on the
total percentage of nonexchangeable, bound
forms in the tracheids that are actively involved
in nutrient transport in the outer rings (cambium
back 10 years). As tracheids in the annual
rings lose function (i.e., greater than 10-yr old),
they in effect isolate a relative record of sap
chemistry. Thus, short-term temporal variability
will not have any effect on the older rings
during the index period (i.e., June to August) in
which the trees are sampled during FHM
surveys.
6.8.2.6 Environmental Impact
The collection of increment cores is not
thought to affect the tree health. Although the
wound caused by boring can provide an entry
point for bacterial and fungal infection, the risk
of infection appears to be minimal for most tree
species (Phipps 1985) (see also Section
6.9.2.6). Trees which have a living bark layer
such as aspen may be affected. These
species will not be bored during FHM surveys.
Also, small diameter trees may be harmed by
boring. Only trees over 5 inches in diameter at
breast height (DBH) are sampled in FHM
studies.
The collection of increment cores does not
interfere or adversely affect any other indicators
currently in use in FHM surveys.
6.8.3 Summary of Foliar Sampling
and Analysis
In brief, the procedure calls for the
sampling of two trees on each of two of four
subplots (subplots 2 and 4), i.e., 4 trees on
each plot. Two increment cores are collected
from deciduous or coniferous species of
dominant or codominant trees. Specimens are
obtained by pushing and turning an increment
borer into the sample tree at breast height
(1.37 meters above ground). An extractor is
used to remove the core from the bit, while the
bit is still in the tree. The core is placed in a
plastic straw for storage and shipment to the
preparation laboratory. Samples are prepared
for subsequent ICP or ICP-MS elemental
analysis.
6.8.4 Data Analysis
Relative changes in elemental
concentrations will be plotted over time to
detect regional trends. Low calcium-to-
aluminum ratios have been exhibited by red
spruce undergoing a decline in growth (Shortle
and Smith 1988). This ratio will be examined
in relation to certain growth parameters such
as increment growth and basal area growth at
the plot level.
Riitters et al. (1991 a) have examined DRIS
indices in stemwood samples. This approach
will also be used in the Southeast
Demonstration and Western Pilot studies to
detect trends in elemental ratios.
6.8.5 1992 Activities
Stemwood samples for stemwood
chemistry analyses will be collected during the
Southeast Demonstration and Western Pilot
(California and Colorado) studies. The same
procedures and data analysis approaches will
be used in each study.
6.8-6
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6.9 Dendrochronology
Indicator
T. Droessler
6.9.1 Introduction
Dendrochronology is the systematic study
of growth rings in trees to date past events that
affect growth. The growth rings are obtained
by extracting one or more cores from a tree at
a.fixed height, usually 1.37 meters, using an
increment borer. The sample tree selection
and core extraction site protocols are
dependent on the goals of the sampling and
critical to the success of the sampling (e.g., for
tree core chemistry; contamination, and for
dendrochronology; dominant or codominant
tree). The cores are usually stored in labelled
straws and the straws stored in a tube until
they can be slowly dried in the laboratory. The
preparation includes drying, stabilizing the core
by mounting it in a wood strip, and machine
and hand sanding of the core so the growth
rings are clearly visible on an even, flat
surface. Stokes and Smiley (1968) present a
formal description of tree core preparation.
Cook and Kairiukstis (1990) contains several
sections dealing with core handling.
6.9.2 Indicator Evaluation
This section discusses the indicator
selection criteria described in Section 6.1 as
they apply to the dendrochronology indicator.
6.9.2.1 Interpretability
The policy-relevant assessment endpoints
are diameter or volume growth trends. Further,
growth and volume relate directly to economic
value (timber production, biomass). Annual or
periodic growth is measured directly from the
cores from raw ring widths. Volume can be
calculated if a local growth/yield model is
available. A typical volume equation is:
Volume = aDbHc
where D is diameter at breast height, H is total
tree height and a, b, and c are regression
coefficients. Tree height may be estimated
using a local height model (usually a function
of diameter at breast height [DBH] and age).
As long as the pith is located on the core, tree
age at DBH can be reliably obtained. Total
tree height will also be collected for the current
year. These data can also be used to test the
height estimates from a height growth equation
for the current year.
Aggregation to plot level --
If the cores are from the same species and
are approximately equal in age and subject to
similar growing conditions (evident from the
growth trends), the increment data can be
pooled and an average annual increment for
the plot and species calculated. Master
chronologies are frequently developed by
pooling data from many trees over a small
geographic region. The merits of pooling must
be considered on a case by case basis; there
is always a danger of confounding a real but
weak trend in one or two cores by averaging
with cores that do not show the trend.
Cumulative distribution functions and nominal/
subnominal boundaries -
Identifying nominal/subnominal boundaries
over a tree core record may be possible. High
and low frequency trends are characteristics
often used to delineate growth patterns in a
tree. Low frequency growth trends occur over
several decades, most often associated with
stand dynamics (gap dynamics) and long-term
trends in climate. High frequency trends are
year-to-year changes in growth, most often
associated with year-to-year variation in
climate. Nominal and subnominal, however.^
must be considered over decades and
centuries of growth records. The benefit of
tree core records is that they often cover
decades to centuries.
6.9.2.2. Quantification
A record of annual tree growth over the life
of the tree can be obtained from one sampling
6.9-1
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(assuming a solid core to the center of the tree
can be extracted).
Obtaining good quality cores can
dramatically improve measurement accuracy
and precision and save considerable time and
expense. Common quality defects in most
increment cores are:
1. Rough, broken core surfaces because of
dull borers,
2. Corkscrewing for the first 1 to 2 cm near
the bark from a wobbly, free-hand start
and directional change, often resulting in
broken cores and missing outer rings, and
3. Discoloration and decay because of
improper storage.
Dramatic improvements in core quality are
possible by following simple instructions,
provided the information is comprehensive and
includes increment borer selection, the
anatomy of increment borers, the basics of the
coring procedure, maintenance, and sharpening
(Jozsa 1988). Comprehensive training is the
key.
A sharp borer, a starting aid, and a core
storage container that is easily labelled for
each core help minimize the field sampling time
and effort required. Two cores 180° apart
should be extracted from each sample tree.
The tree ring measurement equipment may
vary. The following describes a typical
incremental measuring machine used for tree
ring width measurement. The measuring
equipment consists of a moveable stage on
which the mounted, sanded, and crossdated
core is placed. Stage movement is controlled
by turning a dial. A combination binocular
microscope and video camera with cross hairs
allows magnification on a monitor screen to aid
alignment with the beginning of a growth ring.
The starting position is automatically
recorded through a RS232 connection to a
personal computer. The stage is moved by
turning a dial until the cross hairs align with the
end of a growth ring. The end of the .growth
ring is recorded and the stage travel distance is
computed and recorded as the ring width. The
end of one growth ring forms the beginning of
the next and measurement proceeds until the
core is measured from pith to bark.
Cleaning and lubrication of the incremental
measuring machine will ensure smooth stage
movement for locating beginning and ending
ring width positions. Smooth stage movement
can improve measurement accuracy and
precision and save considerable time and
expense.
Standard descriptive statistics will be
calculated for each chronology. These include:
1. Mean ring width,
2. Percent missing rings,
3. Standard deviation of ring-width indices,
4. First order autocorrelation (a measure
of correlation between adjacent indices),
and
5. Mean sensitivity (a measure of relative
difference in index values of adjacent rings).
6.9.2.3. High Signal-to-Noise Ratio
Trend detection -
Tree cores can be used to document forest
disturbance history and growth trends and
interpret recent growth trends as an indicator of
forest condition. The interpretation of growth
trends includes identification of periodic
(low-frequency), yearly (high-frequency), and
disturbance patterns. Disturbance can be
classified as endogenous and exogenous.
Within-stand competition (gap dynamics) or
disturbance that causes asynchronous changes
in short-term or long-term growth trends is
classified as endogenous. Region-level
disturbances such as logging, fire, insect, and
disease outbreaks that result in synchronous
changes in ring width throughout the region are
classified as exogenous.
Undisturbed tree growth follows expected
growth trends based on age and stand
dynamics. Departures from expected growth
trends may indicate a change in the condition
of the forest. Disturbances also modify
expected growth trends of trees and need to be
documented before interpretations of departure
from expected growth trends can be made.
6.9-2
-------
Disturbance histories have only recently been
recorded for many stands and it will take
decades before a long disturbance history can
be compiled for interpretation of growth trends.
Tree cores provide an immediate, long-term
disturbance record which allows disturbance
effects to be identified as well as interpretation
of departures from expected growth trends.
Tree growth can be correlated with climate,
management, and other disturbances (positive
and negative correlation and the subsequent
growth patterns that follow).
Natural Annual and Seasonal Variability -
Trends in annual increment are commonly
correlated, for example, with stand dynamics,
temperature and moisture patterns, and
disturbances. Tree rings are often used to
reconstruct temperature and moisture patterns.
6.9.2.4. Responsiveness
If there is a common regional signal that
affects growth, such as drought or high ozone,
the pattern should be present in trees across
the region. Much work has been done in
reconstructing climate from tree cores,
especially in arid and polar regions. A regional
climate signal is recorded in tree rings and has
been exploited for a variety of species in
several regions (Jacoby and Cook 1981;
Jacoby and D'Arrigo 1989; Lev 1987; Swetnam
et al. 1985).
Climate is known to limit tree species
distribution and growth (Ritchie 1985; Currie
and Paquin 1987). Limiting climatic factors
may vary, but light, moisture, and heat or their
interactions are often discussed (Arno and
Hammerly 1984; Zeide 1980). A striking and
sensitive example of climatic control of species
distribution is altitudinal and latitudinal tree line.
Tree rings provide chronologies useful for
detecting growth changes in response to
climate. The annual variation of radial
increment, expressed as the variation
coefficient or mean sensitivity, increases in
higher latitudes and reaches a maximum at
the tree line (Mikola 1962). Garfinkel and
Brubaker (1980) used ring-width sequences of
white spruce to define climatic limitations on
radial growth at the tree line and to reconstruct
past climatic variables for Fairbanks, Alaska.
Jacoby and D'Arrigo (1989) reconstructed
annual northern hemisphere surface
temperature departures for the last 300 years
using 11 tree-ring chronologies from
high-latitude, boreal sites in Canada and
Alaska. Hari and Arovaara (1988) document
growth changes in northern Finland. Edwards
and Dunwiddie (1985) used balsam popular
(Populus balsamifera) chronologies from the
north slope of the Brooks Range to study
response to climate. Lev (1987) used cores
from balsam popular from three sites in Alaska,
including tree line in the Brooks Range, to
relate diameter increment to temperature and
precipitation. LaMarche et al. (1984) present
tree-ring evidence for growth enhancement in
natural vegetation in alpine environments in the
western United States.
6.9.2.5. Index Period Stability
An increment core is stable over a day for
all but the current year's growth. Dendrometer
studies have shown that trees shrink and swell
(millimeter) over a day as a response to
moisture availability (dendrometers are
instruments that record instantaneous growth).
Since the current year growth is not measured
or used in data analysis, the diurnal variability
is not a problem.
An increment core is stable over a growing
season for all years except the present. The
current year increment would not be measured
(or, if measured, not used in data analysis or
interpretation). All previous annual increments
are fixed, so the current year variability is not a
problem.
. 6.9.2.6. Low Environmental Impact
Increment boring is not a completely
harmless sampling technique. The general
consensus is that healing of the sample tree
progressed satisfactorily when holes were left
unplugged. The first stage of the healing
process is almost immediate through resin
exudation. Next, callus tissue will be formed
over the wound with new growth. The dimpled
6.9-3
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surface of the stem will become more straight
grained with each succeeding growth
increment. Repeat borings, however, even
years apart, may have a cumulative effect and
should be avoided (Jozsa 1988) (see also
Section 6.8.2.6).
6.9.3 1992 Activities
The dendrochronology indicator will be
used in the Southeast Demonstration project,
the SAMAB Demonstration project, and the
Western Pilot project.
6.9-4
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6.10 Root Evaluation Indicator
S.A. Alexander
6.10.1 Introduction
Root diseases are significant contributors to
the decline and mortality of forests. Growth
loss due to root diseases has been
demonstrated in both conifers and hardwoods
(Bradford et al. 1978; Skelly 1974). The
pathogens that cause root disease may act
alone or in combination with other factors such
as drought, insects, and air pollution. Trees
under stress are more susceptible to invasion
by certain root pathogens (James et al. 1980;
Wargo and Houston 1974). A regional
increase in root disease over time may indicate
widespread environmental or habitat changes.
For this reason it is important to establish
baseline values for the prevalence of root
disease in forests. Unlike aboveground pests,
root pathogens are difficult to detect and are
often overlooked as contributors to forest
condition.
6.10.2 Indicator Evaluation
The following discussion considers the
indicator selection criteria described in Section
6.1 as they apply to the root evaluation
indicator.
6.10.2.1 Interpretability
Root disease plays an important role in the
decline and mortality of forest trees. Trees
under stress are more susceptible to invasion
by certain root pathogens (James et al. 1980)
and an increase in root disease may indicate
the occurrence of a long-term environmental
stress such as air pollution.
6.10.2.2 Quantification
Root disease is quantified on a stand or
plot basis as the proportion of sample trees
from which the pathogen is isolated.
6.10.2.3 Signal-to-Noise Ratio
There is no background noise in this
indicator. Presence of a root pathogenic
fungus within root tissue is an unequivocal
indication of disease. There is virtually no
seasonal or annual variation associated with
root disease in forest trees.
6.10.2.4 Regional Responsiveness
Trees under stress from air pollution or
defoliation have been shown to be more
susceptible to attack by root disease pathogens
(James et al. 1980; Wargo and Houston 1974).
Regional increases in root disease incidence
over time may indicate widespread
environmental or habitat changes.
6.10.2.5 Index Period Stability
Cultural isolation of root disease pathogens
is not dependent on seasonal fruiting or
sporulation. The vegetative mycelium from
which the isolations are made is present
throughout the year.
6.10.2.6 Environmental Impact
Some soil disturbance is caused by root
excavation. Wounds made on the root during
sampling of healthy trees will heal.
6.10.3 Field Measurements
The following method, adapted from
Alexander and Skelly (1974), has been used to
determine the presence of root disease in a
survey mode from 1988-1991 in the Visual
Damage Survey and the Forest Health
Monitoring (FHM) program (Alexander and
Carlson 1988, 1989, 1991).
The two-root method involves direct
sampling of two roots of a sample tree. Root
samples are collected from one pair of sample
trees at each of two sub-plots per hexagon.
The sample trees are the same trees from
which increment cores, and branch and foliar
6.10-1
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nutrient samples are taken. Two root samples
from each of two roots per tree are collected.
Two primary roots on opposite sides of the
tree are excavated to a length of one meter.
The bark is examined for signs and symptoms
of disease and injury which are recorded. A
knife is used to peel back several 2.5-cm
sections of bark so the wood underneath can
be examined and symptoms recorded. If
symptomatic roots are found, a wedge of wood
approximately 3 x 3 x 3 cm is taken from the
symptomatic area. If no symptoms are found,
the samples are taken 15 cm and 30 cm from
the root collar. The equipment is disinfected
with a bleach solution between each tree. All
soil removed from the roots is replaced. The
root samples are bagged, labeled, and shipped,
on ice, to the Forest Pathology Laboratory
at Virginia Polytechnic Institute and State
University (VPI&SU).
6.10.4 Data Analysis
Root samples are logged in when received
at the Forest Pathology Laboratory at VPI&SU
and refrigerated until processed. Samples are
washed for five minutes under running tap
water to remove dirt and debris. Blocks of
wood 1 x 1 x 1 cm are aseptically removed and
plated onto general and specific media. After
21 and 60 days of incubation, the plates are
examined for root pathogens.
6.10.4 1992 Activities
Plans for the 1992 field season include
sampling approximately 70 plots during the
1992 Southeast Demonstration project.
•16.10-2
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6.11 Photosynthetically Active
Radiation (PAR) Indicator
J.G. Isebrands and S.J.
Steele
6.11.1 Introduction
Measurements of solar radiation
intercepted by the canopy are fundamental to
the interpretation of the productivity and
function of plant communities (Norman and
Campbell 1989). Photosynthetically active
radiation (PAR) is the quantity of light between
the 400-700 nm wavelengths of the spectrum,
and is the part of the spectrum used by plants
for photosynthesis. The percentage of PAR
transmitted by a plant canopy can be estimated
by the ratio of PAR under the canopy to
ambient incoming PAR. This ratio can be
related to canopy condition and combined with
growth measurements to estimate growth
efficiency, an important indicator of forest
health (Waring and Schlesinger 1985).
Photosynthetically active radiation can also be
combined with companion measurements of
vegetation structure (VS), crown transparency,
and/or remote-sensing data to assess canopy
condition with a multivariate indicator approach.
In the past, reliable measurements of
transmitted solar radiation were difficult to
achieve on the ground, and were typically
characterized by significant temporal and
spatial variability. For example, ambient PAR
measurements can vary depending upon cloud
conditions, time of day, and solar angle (i.e.,
location and season). However, this variability
can be minimized by following a protocol that
consists of a narrow sampling window
combined with adjustments for cloudiness.
6.11.2 Definitions
Photosynthetically active radiation
(PAR) is the visible portion of the light spectrum
between 400 - 700 nm and is that part of the
spectrum utilized by plants for photosynthesis.
PAR is expressed in umol m~2s~1 (SI unit).
Transmitted PAR is the quantity of PAR
measured under the canopy divided by the
quantity of PAR measured in the open (or
above the canopy). Transmitted PAR is
expressed as a percentage, and is the
percentage of incoming radiation transmitted
through the canopy.
Beam fraction is defined as 1 minus
(shaded PAR in the open/unshaded PAR in the
open). Beam fraction is used here as an index
of cloudiness.
Leaf area index (LAI) is the quantity of
leaf surface area of a plant canopy per unit of
land area. LAI is expressed in m2/m2 (or
without units). The LAI is often used in
ecological modeling as a quantitative measure
of canopy structure.
6.11.3 Summary of Method
Photosynthetically active radiation is
measured with specialized quantum sensors
that measure solar radiation in the 400-700 nm
waveband. Ambient PAR is measured with two
independent quantum sensors placed in a
nearby open area and connected to a
datalogger. One quantum sensor is shaded,
and one is left in full sun. The shaded
quantum sensor measures diffuse PAR used to
determine beam fraction, an indicator of
cloudiness. Under-canopy PAR is measured
with the Ceptometer, which has a wand with a
linear array of 80 quantum sensors coupled to
an internal integrating datalogger.
Under-canopy and ambient PAR are measured
in synchrony in order to estimate transmitted
PAR and leaf area index.
The PAR measurements are made
during a standard sampling window from 1100
hrs to 1300 hrs (standard zone time, i.e., 1200
and 1400 daylight time). This measurement
window ensures accurate measurement of the
percentage of transmitted PAR that depends
on solar angle and is a function of time of day.
The PAR measurements are made on
a grid of sample points on a 4 fixed area
subplot cluster (Figure 6.11-1) for several
6.11-1
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SAMPLE #/LOCATION
1-7 SUBPLOT 1
8-14 SUBPLOT 2
15-21 SUBPLOTS
22-28 SUBPLOT 4
Rgure 6.11-1. PAR sampling scheme for Western Pilot Study in 1992. Subplot example on left shows
sampling point layout according to azimuth. Diagram on right shows 7 point grid for each
subplot of the 4 fixed area subplot cluster. A total of 28 points will be sampled per location.
reasons. Rrst, a grid allows remeasurement of
sample points for quality control (QC)
comparisons. Second, other indicator
measurements such as VS are made at the
same grid locations so that these different
measurements can be compared and
correlated. The grid system also allows the
estimation of spatial and temporal variability of
PAR, and scaling up from the subplot level to
the plot level.
6.11.4 Indicator Selection Criteria
6.11.4.1 Interpretability
Transmitted PAR as defined above
(hereafter, PAR) is a very versatile indicator in
that it can be used as an indicator of canopy
condition and to estimate LAI, an indicator of
crown structure (Burton et al. 1991). In
addition, PAR can be combined with a suite of
canopy indicators to produce a "canopy
condition index". Photosynthetically active
radiation has also been used as an indicator of
"growth efficiency" on plots that are
remeasured during subsequent years (Waring
and Schlesinger 1985).
Photosynthetically active radiation is
easily aggregated to the plot level, but also has
utility for assessing variability on a site through
examination of subplot means and variances.
Photosynthefically active radiation is useful in
assessing canopy condition related to stressors
such as air pollution, water stress, insect and
diseases, wind, and other crown-related
stresses. Any increase in transmitted PAR
over time at a subplot or plot level signals a
change in canopy condition.
6.11.4.2. Quantification
Photosynthetically active radiation is
measured with fully automated data logging
equipment on a minute-by-minute basis for a 2
hr measurement window in one day.
Transmitted PAR, expressed as a percent, is
easily calculated by dividing PAR measured
with a ceptometer under the canopy by PAR
measured in the open (ambient) with a
quantum sensor and multiplying by 100
6.11-2
-------
(Russell et al. 1989). The two independent
measures of PAR are measured synchronously
on site and then transferred to a data file at the
end of the day. In fact, transmitted PAR and its
variance could easily be available at the end of
each sample day using a simple software
program.
Photosynthetically active radiation
equipment is reliable when standard factory
equipment is used. Audits of PAR during the
1991 Georgia and Western Pilots showed that
measurement error is 10% or less when an
auditor remeasured PAR immediately after the
crew member. Some of this error may be
related to temporal differences. Potential errors
in PAR are associated with variable cloudiness,
but PAR values can be adjusted for cloudiness
and sun angle to improve data reliability.
Photosynthetically active radiation, however,
cannot be measured in heavy rain.
6.11.4.3. Signal-to-Noise Ratio
Presently, transmitted PAR has a high
signal-to-noise ratio when standard factory
equipment is used during the 12:00 to 2:00
p.m. measurement window on clear days
during the standard field season between June
1 and September 1. However, cloudiness and
sun angle affect PAR over an annual cycle.
Cloudiness effects, which may vary with site
depending upon the quantity of diffuse light,
and sun angle effects are well documented
(Larcher 1980). Cloudiness index is
automatically measured with the ambient PAR
station by simply integrating ambient PAR over
the measurement period. This index can be
used as a covariate for analysis or regression
can be used to adjust PAR to improve
signal-to-noise ratio. Another avenue greatly
reduces signal-to-noise ratio. This approach
uses transmitted PAR, beam fraction (Figure
6.11-2) (a measure of cloudiness) and sun
angle to estimate LAI for each Forest Health
Monitoring (FHM) plot. The LAI is an important
measure of canopy structure that can be used
to assess stressor effects on canopies (Norman
and Campbell 1989). We have investigated
using an estimate of LAI for a diurnal and
annual cycle in off-frame studies and found
that LAI estimated from PAR is very stable over
the day and season and with different cloud
conditions (see Tables 6.11-1, 6.11-2, 6.11-3,
and 6.11-4).
6.11.4.4. Regional Responsiveness
In 1991 PAR was tested on-frame on
a large number of forest types in Georgia in the
Landscape Pilot Field Study and in the
Western Pilot Study in Colorado and California
(see attached supporting data). It was also
studied in Wisconsin, Michigan, North Carolina,
Tennessee, and Idaho at interregional off-frame
research sites. Standard PAR equipment was
reliable at all of the above sites. Adjustments
for cloudiness and sun angle make PAR
responsive to all locations in the country at any
time during the field season.
Photosynthetically active radiation can be
quantified easily at the subplot, plot, state,
subregional, or regional levels. Similarly, LAI
estimated from PAR can be aggregated to any
of these levels.
6.11.4.5. Index Period Stability
Presently, PAR is measured between
12:00 and 2:00 p.m. to ensure stability over the
"index period". The window centered around
solar noon ensures minimal sun angle effects
on the PAR measurements. Measuring PAR
between June 1 and September 1 may be
necessary to ensure that full canopy
development has occurred for the season. Any
indicator of canopy condition will naturally have
to be measured during the summer field
season to minimize effects of autumnal leaf
abscission. If one measures transmitted PAR
outside the daily and seasonal index periods,
errors can result.
6.11.4.6. Environmental Impact
The PAR indicator measurement has
minimal environmental impact, except where
the PAR crew member tramples the understory
vegetation while making the measurement. In
1991 there were 19 sampling points per
6.11-3
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TABLE 6.11-3. EMAP FHM 1991 WESTERN PILOT-PLOT SUMMARY
PLOT*
1
2
3
4
5
6
1
7
8
9
10
i
11
12
HEX*
9111111
2222222
3812062
3812041
3812021
3711983
3333333
3710731
3710724
3710743
3710844
3710832
3710728
3710799
DATE
SAMPLED
8-1
8-2
8-5
8-7
8-9
_8-12
8-13
8-20
8-21
8-22
8-23
8-26
8-27
8-28
NEAREST TOWN
Georgetown, CA
Georgetown, CA
Kirkwood. CA
Kirkwood, CA
Strawberry, CA
Tioga Pass, CA
Crane Flat, CA
Pagosa Springs, CO
Piedra, CO
Vallecito, CO
Delores, CO
Mancos, CO
Durango, CO
Purgatory, CO
FOREST TYPE
Ponderosa Pine
Mixed Evergreen
Jeffrey Pine
California Red Fir
Ponderosa Pine/
California Black Oak
Lodgepole Pine
Sequoia
Ponderosa Pine
Doug. Fir/Pond. Pine
White Fir
Ponderosa Pine
Aspen/Scrub Oak
Pinyon Pine
Spruce/Fir
CLOUDINESS INDEX
INTEGRATED
PAR/HOUR
1200-1400 HRS ,
118849
117367
118184
120801
114586
46474
40735
116360
116677
17305
121678
-
125792
24427
'Complete PAR not measured.
subplot for a total of 76 sample points per plot,
thereby creating the potential for understory
damage. In 1992 the number of PAR sampling
points has been reduced to 7 per subplot and
a total of 28 per plot without seriously affecting
the estimate (see attached supporting data).
This change will substantially reduce the
amount of time spent making PAR
measurements and greatly reduce the number
of sample points affected.
6.11.5 Data Analysis
Photosynthetically active radiation is a
promising indicator. It is attractive because it
can be used simultaneously as an indicator of
canopy condition in the form of transmitted
PAR or it can be used to estimate LAI while
taking into account cloudiness and sun angle.
The LAI estimates can be used as ground truth
for aerial photography or remote-sensing data.
Photosynthetically active radiation can also be
used to estimate growth efficiency if there is
remeasurement of sites in subsequent years.
Another promising approach might be to
combine PAR with other crown-related
, indicators such as vegetation structure, crown
transparency, or mensurational variables to
produce a "crown index" indicator of forest
health.
6.11.6 1992 Activities
The PAR indicator will be a part of the
SAMAB Demonstration project and the
Western Pilot project.
6.11-7
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6.12 Vegetation Structure
Indicator
S. Cline
6.12.1 Introduction
Development of a vegetation and habitat
structure indicator is part of an ongoing effort to
select, develop, and test indicators of the status
and trends of biodiversity in US forests.
Biodiversity refers to the identity (composition)
of biotic and abiotic elements and their
structure (abundance and spatial arrangement),
and the associated ecological processes
(functions) at several levels of biological
organization (genetic, population and species,
community and ecosystem, and landscape and
region) (Noss 1990, Figure 6.12-1).
Biotic diversity is at risk from six major
types of threats:
1. Direct population reduction;
2. Physical alteration of habitats;
3. Chemical and solid waste pollution;
4. Global atmospheric change;
5. Introduction of alien species; and
6. Cumulative or multiplicative effects
of interactions among these major threats
(U.S. EPA 1990).
Monitoring effects on biotic diversity due to
physical alteration of habitats is an initial focus
of Forest Health Monitoring (FHM) because
physical habitat alteration is an immediate
concern (it was identified as the greatest threat
to bird diversity [U.S. EPA 1990]), and may
exacerbate the potential impacts of future
climate change.
Initial efforts to develop and test biodiversity
indicators for FHM have focused upon the plant
kingdom and in particular upon vegetation
structure and its role as a component of habitat
structure. Vegetation structure is defined as
the composition of vegetation (e.g., species,
growth forms, forest type) and its structure
(e.g., relative abundance as cover or area and
spatial arrangement such as vertical
stratification, canopy height and cover, or patch
configuration). Habitat structure is the physical
arrangement of biotic and abiotic elements in
the environment that influences the
distribution and abundance of animals at a
particular scale (McCoy and Bell 1991). For
example, a forest structure of oak-gum-cypress
in the southeastern United States would
indicate a swamp or wetland forest habitat
containing moisture-sensitive flora. This forest
structure, plus the presence of standing water
and snags near water, would constitute a
habitat structure potentially suitable for the
prothonotary warbler.
An initial ecological risk assessment model
of the effects of physical alteration of habitats
on vegetation and habitat structures has been
developed (Figure 6.12-2). In this model,
heterogeneity and complexity of vegetation is
the assessment endpointforthe environmental
and societal value of biodiversity. The model
shows that habitat alteration affects several
organizational levels and, consequently,
numerous candidate indicators might
legitimately be measured depending upon the
scale of assessment (Table 6.12-1). Figure
6.12-2 also shows that these scales are linked
hierarchically. For example, a species-rich,
multilayered forest may not have long-term
biotic integrity if it exists as a forest patch
surrounded by agricultural land; likewise a
large area of forest cover may be biologically
impoverished if it is dominated by young age
classes of single-species plantations.
6.12.2 Indicator Evaluation
Although a comprehensive assessment of
biodiversity requires measurements at several
levels of biological organization (Table 6.12-2),
this section focuses upon the evaluation of
ground-based measurements. Even though
one may be able to substitute some remote-
based measurements of vegetation and habitat
structure for ground-based measurements,
some ground-based measurements will
probably always be needed to: (1) supply data
on response indicators that cannot be
assessed remotely (e.g., species identification
is unlikely using remote data, especially in the
forest understory where most of the
species are concentrated); and (2) to
provide ground-truth as relationships between
6.12-1
-------
Genetic
processes
Demographic
processes, life
histories
Interspecific
interactions, ecosyste
processes
Landscape processes and
disturbances, land-use
trends
FUNCTIONAL
Figure 6.12-1. Biodiversity defined at several levels (Noss 1990).
6.12-2
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TABLE 6.12-1. RESPONSE INDICATIORS OF BIOTIC DIVERSITY
A. Course patch delineation - Landscape and Region level (by source and person/organization)
Remote-based variables (from small-scale [1:45,000] photos, EPA Environmental Photographic
Interpretation Center)
• Forest area by class (conifer, deciduous, mixed).
* Land area by use type.
• Landscape pattern (area, shape, juxtaposition, and conductivity of patches).
B. Fine patch delineation based on external features - Community and Ecosystem level (by source and
person/organization)
Remote-based variables (from large-scale [1:16,000 or 12,000] photos/EPA Environmental
Photographic Interpretation Center)
• Subdivision of patches by forest type.
• Subdivision of patches by tree density and height.
• Overstory cover, roughness, and patchiness.
• Location and area of ecotones.
• Number of vertical strata.
C. Fine patch characterization based on internal! features - Population, and species level (by source
and person/organization)
Ground-based variables from 24-ft radius subplots, FIA
• Tree species.
• Tree diameter and basal area.
• Tree density.
Ground-based variables from pole and quadrat methods
• Profile of understory vegetation cover.
• Patchiness of understory vegetation cover,
• Canopy cover.
• Species and growth-form composition.
• Species richness.
remote and ground measurements are
developed.
The following discussion considers .the
indicator selection criteria described in Section
6.1 as they apply to the vegetation structure
indicator.
6.12.2.1 Interpretabllity ~
Maintenance of the heterogeneity and
complexity of vegetation and habitats is a
socially relevant assessment endpoint because
significant homogenization and simplification of
vegetation structure within a region would have
a negative influence on biodiversity
conservation (Figure 6.12-2). Conservation
and maintenance of animal and plant species
diversity has been identified as an important
public value. The connection between diverse
vegetation structure and diverse wildlife
communities is reflected in numerous existing
public laws. Notably, maintenance of diverse
habitats in the form of plant communities and
6.12-4
-------
special habitat elements figure prominently in
most wildlife conservation laws (DeGraaf and
Rudis 1983).
Changes in the value of the vegetation
structure indicator (response indicator) will be
used to track the status and trends of the
heterogeneity and complexity of vegetation and
habitats (assessment endpoint). This response
indicator is closely connected to the
assessment endpoint. For example, the
elements of heterogeneity and complexity of
vegetation include:
• Richness (the number of different plant
species and habitat elements per sample).
• Evenness (the relative abundance of
species and habitats within the sample).
• Diversity (i.e., heterogeneity, synthetic
measures that are sensitive to both richness
and equitability).
• Pattern diversity (the amount of cover and its
vertical [stratification] and horizontal
[patchiness] arrangement).
Field measurements made for the vegetation
structure indicator provide the new data to
quantify each of these elements of
heterogeneity and complexity of vegetation.
This is very close to meeting the
recommendation of Chapman (1991) that
measurement endpoints (essentially response
indicators) and assessment endpoints be the
same.
The vegetation structure indicator provides
evidence of a relevant and meaningful
ecological condition (heterogeneity and
complexity of vegetation) and is subject to
environmental and anthropogenic stress.
Vegetation composition and profile is an
element of the diversity of plant communities
and:is positively correlated with animal and
plant species richness and diversity (e.g.,
MacArthur and MacArthur 1961; Willson 1974;
Dueser and Shugart 1978; August 1983).
Moreover, structurally simple forests are less
tolerant of biotic stresses such as disease and
insect attacks (Schmidt 1978; Knight and
Heikkenen 1980). Furthermore, vegetation
structure is commonly exposed to and changed
by anthropogenic stresses such as air pollution,
land use changes, and forest management
practices. For example, under high ozone
doses, ponderosa pine ecosystems have been
simplified in species composition and converted
to more resistant serai stages with little vertical
stratification (Miller and McBride 1975). Forest
profile is also routinely manipulated by forestry
practices such as logging, plantation
establishment, thinning, prescribed fire,
scarification, and herbicide application. In
some situations forest structure may be greatly
simplified, such as when mixed-species forests
are cleared and replaced by pine plantations,
resulting in impoverished flora and fauna
relative to native forests (Atkeson and Johnson
1979; Repenning and Labisky 1985; Childers et
al. 1986; Felix et al. 1986). Finally, understory
compositions have been used extensively as
indicators of environmental gradients (moisture,
temperature, nutrients) and site productivity
under both undisturbed and disturbed
conditions (Archambault et al. 1989).
Table 6.12-3 summarizes the formulations
and methods of generating plot-level values of
richness, equitability, diversity, and vertical
stratification and horizontal patchiness. All
calculations can be made from basic data on
the amount and location of plant cover by
species. The indices offer great flexibility in
application across communities, ecosystems,
and biotic regions and in the type of
measurement data that can be substituted in
the formula. For example, Romme (1982)
used similar indices to estimate landscape
diversity by using plant communities as species
taxa and the area of plant communities for
abundance. It is important to interpret index
values cautiously, because communities with
similar values may vary significantly in
composition (Peet and Christensen 1982).
Index values can be interpreted more reliably
in conjunction with other vegetation data and
site information.
Interpretations based upon individual plant
species or ecological species groups may be
particularly fruitful because the reaction of
plants to different environmental factors,
competition, and disturbance varies on a
species-specific basis (Daubenmine 1959).
Once a reliable determination of plant species
6.12-5
-------
TABLE 6.12-2. RELATIONSHIP OF RESPONSE INDICATORS FOR DIFFERENT
ORGANIZATIONAL LEVELS OF BIOTIC DIVERSITY
Organization Level
Purpose
Focus
Data source
Response
indicators
Landscape/Region
Provide extrapolation
units for region
Coarse patch
delineation and
arrangement
Satellite and small-
scale photo imagery
See Table 6.12-1
Community/Ecosystem
Provide check of
representativeness of
plot data for coarse
patches
Provide extrapolation
unit for internal
features of fine
patches
Fine patch delineation
based on external
features of overstory
Large-scale photo
imagery
See Table 6.12-1
Popu lation/Species
Provide ground-truth for
external features based
on large-scale photos
Provide data not
accessible from remote
sources
Provide data to develop
relationship between
internal and external
patch features
Fine patch
characterization based
on internal features of
overstory and understory
Ground measurements
on plots
See Table 6,1 2-1 •
6.12-6
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composition, including cryptogams, has been
made, one can derive structural and functional
aspects of vegetation based upon the species
present (Mueller-Dombois and Ellenberg 1974).
Finally, more plant species and assemblages
can be examined as potential ecological
indicators when the species composition of
sites is adequately measured. The most
abundant plant species at a site, while greatly
influencing biomass production and nutrient
cycling, are not necessarily the most sensitive
indicators of environmental conditions, stress,
or change (Poore 1955; Daubenmire 1968),
Detection monitoring is predicated, on the
concept of tracking ecosystem characteristics
that are relatively easy to measure and
sensitive to environmental and anthropogenic
stresses of interest. More detailed evaluation
monitoring is triggered if and when
predetermined levels of change are exceeded.
It is one thing to determine if ecological
condition is changing (detect a trend) and quite
another to determine the quantitative boundary
between a good and bad ecological condition
(set a nominal/subnominal boundary). Setting
such a black and white boundary for regional
biodiversity indicators is fraught with problems,
among them a lack of theoretical and
experimental guidance on one hand and the
range of human emotions regarding species
extinction on the other. Consequently, while no
definitive boundaries have been set for the
vegetation structure indicator to date,
establishment of these types of boundaries
remains as an objective of the biodiversity
indicator development process.
6.12.2.2 Quantification
Measurement of vegetation structure (i.e.,
sessile organisms) is relatively easy and
inexpensive (i.e., completed in one visit by one
person in one day). Pole measurements (using
a telescoping height pole) can be completed in
approximately 2 hr per plot and quadrat
measurements in made within aim2 quadrat in
4 hr per plot. (Complete measurement
methods are found in Section 11 of Conkling
and Byers, 1992.)
One source of error for the pole method is
leaf and branch flutter during windy conditions.
Errors induced by windy weather are not
unique to the pole method as a photographic or
laser method would experience similar
difficulties under similar conditions. The only
really effective way to limit this type of error is
to suspend data collection during poor weather
conditions. During both the 1990 20/20 and
the 1991 Georgia pilot studies, this conclusion
was necessary on several occasions.
Play in the fully extended pole is a
potentially more serious source of error that we
have tried to limit by leveling the pole with the
aid of a bubble level and positioning the bubble
level to the north to assure that pole play is
consistent among points. Results of point
remeasurements made during the 1990 training
are shown in Table 6.12-4. For any height
level +1 hits is equivalent to +6 percent since
there were 16 points (1/16 = 6.25 percent) per
subplot in the 20/20 study. This result was
judged to be satisfactory performance for pilot
testing.
The total variation in measurements of
response indicators will greatly influence the
usefulness of indicators in detecting changes in
forest condition. Regional, temporal, within-
plot, within-subplot, and measurement variation
are important components of total variation. A
preliminary estimate of the relative contribution
of measurement variation to the total variation
of pole measurements of vegetation structure
can be estimated from the 1990 20/20 study
data. Measurement variation was less than 20
percent of total variation in the first 5- or 6- foot
levels, and usually 20 to 30 percent above 6 or
7 feet up to 30 feet. These ratios must be
interpreted with caution because a realistic
estimation of total variation has not been
calculated. Total variation will undoubtedly
increase because no temporal variation was
included, and regional and within-plot variation
were underestimated since the plots in each
region were from a relatively small
geographical area and the subplots were
6.12-8
-------
TABLE 6.12-4. RESULTS OF POLE POINT REMEASUREMENT DURING 1990 TRAINING
Variable
Total hits recorded
Assignment of height class
Cumulative frequency of
no difference +1 hits
61%
71%
93%
96%
N
(No. of pairs)
41
51
rotated into the same homogenous stand
condition. In contrast, the measurement
variances are probably robust, since the
estimates were based upon a large sample
size, and procedural improvements have been
and will continue to be made. Consequently,
there is every reason to believe that the relative
contribution of measurement error to total
variation will decrease for the pole method, and
easily meet Taylor's (1987) ±10 percent
guideline for an acceptable level of
measurement error.
Data quality for the vegetation structure
indicator depends greatly upon hiring qualified
personnel. This is especially true for plant
species identification. Botanists familiar with
regional flora are required to meet data quality
requirements.
6.12.2.3 Signal-to-noise Ratio
How responsive is the vegetation structure
indicator to forestry treatments such as thinning
or planting? Can this response to
anthropogenic stress be differentiated from
natural cycles and trends due to forest type
and age? This issue has been addressed
initially by examining the shape of vegetation
profiles of major forest cover types on 3,100
plots in the Georgia Piedmont in relation to
known natural factors (site and stand
conditions) and anthropogenic stresses
(forestry practices) (Cline et al. 1990). The
vertical structure of forest vegetation is one
element of the vegetation structure indicator.
Preliminary results suggest that the vegetation
profile changes in predictable ways during
normal stand development. For example, as
expected, stand age, in 10-year age classes,
had the strongest influence upon vegetation
profile, with most mean age class profiles
differing significantly from each other. In
addition, mean profiles of similarly aged loblolly
pine and oak/hickory stands are reasonably
distinct (Figures 6.12-3 and 6.12-4). These
patterns of forest profiles might be useful as a
"baseline" from which to judge future changes
in vegetation profile across the region.
Even with these strong natural trends in
profile slope, the effects of forestry practices
can still be detected. When mean profiles from
similarly aged stands were compared, the
effects of forestry practices were quite apparent
(e.g., Figure 6.12-5, natural versus planted
loblolly pine, and Figure 6.12-6, unthinned
versus thinned loblolly pine stands). These
results suggest that observed changes in the
types and distribution of vegetation profiles
could then be used to indicate the
homogenization and simplification of vertical
structure across a region with respect to
expansion or intensification of forestry
practices.
While these results make us cautiously
optimistic about the value of vegetation profile
as an indicator of some anthropogenic
stresses, we realize the analyses to date have
a limited scope. Expanded analyses are
planned to include changes in vegetation
composition of each profile in relation to
forestry practices and to examine temporal
variation across the Georgia Piedmont region.
Until these analyses are complete, vegetation
6.12-9
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LOBLOLLY PINE
Age class 30-39
No disturbance
Planted
1—'—i—'—i—'—i—'—i—'—I—'—i—'—r
0 10 20 30 40 50 60 70 80 90 100
FOLIAGE OCCUPANCY, %
Figure 6.12-5. Height class versus % foliage occupancy for age class 30-39 with no disturbance
on planted and natural loblolly pine sites.
6.12-12
-------
LOBLOLLY PINE
1001
Age class 40—49
Natural
0 10 20 30 40 50 60 70 80 90 100
FOLIAGE OCCUPANCY, %
Figure 6.12-6. Height class versus % foliage occupancy for age class 40-49, natural on planted
and natural loblolly pine sites.
6.12-13
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profile cannot be fully evaluated as an indicator
and remains subject to being dropped. Part of
the concern is the large, inherent variation of
vegetation profiles and thus the large number
of plots that would need to be tracked to pick
up changes. This concern is lessened
somewhat by the knowledge that compared to
the pole method, the ocular method used by
the Southeast FIA to measure forest profile is
much more imprecise (CV of 31to 44 percent
for cover estimates between 3 and 18 ft [Cost
and Graver n.d.]).
6.12.4 Regional Responsiveness
The conservation of biodiversity is
threatened by the overharvest of populations,
physical alteration of habitats, pollution of
water, air, and soil, climate change, introduction
of alien species, and the cumulative or
interactive effects of these major threats (U.S.
EPA 1990). These threats or stresses are
often closely related to expansion of the human
population and associated economic
development, including agriculture,
industrialization, and urbanization. Typically the
ecological resources in an area are simplified
and homogenized by exposure to one or more
of these anthropogenic stresses. For example,
compared to native forests, forests today are
younger, less extensive, more insulated,
subject to fewer wildfires, more accessible by
road, and more structurally simple (the extreme
being monoculture). The area impacted by
these stresses continues to increase.
Urban sprawl creates a gradient of impacts
on the heterogeneity and complexity of
vegetation structure. Moving from urban-
suburban to rural to semi-natural to wild
settings, the following trends are expected:
• Fewer buildings and roads.
• Decreasing management intensity (less
cultivation, less energy and nutrient inputs).
• Increasing extent of natural vegetation cover
(fewer cultivars, horticultural varieties,
exotics and aliens).
• Increasing size and irregularity of vegetation
patches.
The response of plant communities, as
measured by one element (equitability) of the
vegetation structure indicator, to this stress
gradient is illustrated in Figure 6.12-7.
Analyses of the number and dominance of
exotic or naturalized plants or the presence of
plants with known fidelity in relation to
moisture, light, and disturbance might also be
particularly revealing.
6.12.2.5 Index-period Stability
The stability of the vegetation structure
indicator will be influenced most by annual
phenological patterns, which are regulated by
local weather conditions. Cover estimates will
vary until tree, shrub, and herbaceous leaves
are fully expanded. A June start date for
sampling should be adequate in this regard,
except perhaps in mountainous regions.
Analysis of patterns of species presence or
absence may be informative for regional
assessments of biodiversity and overcomes
problems associated with interannual changes
in plant cover. Flower and fruiting patterns are
also phenologically variable, and this has more
serious implications as most plant taxonomy
keys are based upon flowers, and to a lesser
extend fruits, of plants. Thus, while a plant
may be fully expanded vegetatively at the time
of sampling, it may be in a nonproductive
state, making it difficult to identify positively to
species. This effect may be lessened
somewhat because many plants have distinct
vegetative features useful for species
identification, and a botanist knowledgeable of
the regional flora would likely be able to use
them. In addition, many of the more common
species can be identified without flowers. Plant
specimens will be collected in the field for all
unidentified species and each "known" species.
These will be identified or verified by qualified
taxonomists and archived for documentation.
6.12.2.6 Environmental Impact
Measurement methods used for the
vegetation structure indicator have Ijttle impact
beyond the disturbance from walking: There is
no impact, as long as walking is confined to
areas outside points and plots to be measured.
People making the vegetation measurements
6.12-15
-------
are trained to minimize their own impact, but
the impact of other crew members is hard to
control. Experience indicates that people
measuring visual symptoms and heights of
trees move about the plots the most, so this is
the greatest potential impact on understory
vegetation. Also, photosynthetically active
radiation (PAR) measurements directly overthe
pole points have affected vegetation
occasionally. In 1992, the vegetation sampling
plots will be marked as early as possible during
each field day so as to minimize disturbance.
Plant specimen collection is done off-plot and
requires only plant pieces, not whole plants.
6.12.3 Field Measurements
Vegetation structure measurements will be
made in small permanent quadrats
systematically laid out on each subplot (3 per
subplot or 12 per plot). The objective of the
procedure is to identify all plant species present
in the sample quadrats and to measure the
amount and spatial pattern of their canopy
cover individually and collectively. On each
quadrat five basic types of data will be
recorded:
1. Plant species identification,
2. Reliability of plant identification,
3. Vegetation strata of plant,
4. Plant cover, and
5. Phonological status of plant.
In addition, canopy cover measurements will be
made at each subplot center and shrub stem
density will be determined within each
regeneration microplot. Specimens of unknown
and representative known plant species will be
sampled off-plot and pressed. "Unknowns" will
be uniquely coded in the field so that the data
base can be updated after identification. All
data will be recorded using the portable data
recorder. See Section 11 of the 1992 Forest
Health Monitoring Field Methods guide for
complete details of the vegetation structure
measurement method.
6.1.4 Data Analysis
Verified and validated measurement data
on the amount and spatiar location of plant
canopy cover by species will be used to
calculate plot-level indices of vegetation
heterogeneity and complexity using standard
formulas (Table 6.12-3). These values indicate
the floristic and structural diversity of
vegetation, an important element of
biodiversity.
Collectively, the indicator values will be
used to establish baseline levels of vegetation
complexity and heterogeneity by region. Plot
remeasurements over time will be used to
determine changes in vegetation structure
indicator values and to track regional trends in
the heterogeneity and complexity of vegetation
and habitats in relation to natural and
anthropogenic stresses (e.g., Figure 6.12-7).
6.12.5 1992 Activities
The vegetation structure indicator will be
measured on ground plots in the Southeast
Demonstration and in the Western Pilot in
Colorado and California.
6.12-16
-------
6.13 Wildlife Habitat Indicator
and Bird Population
Estimates
T.E. Martin, D.R. Petit,
and L.J. Petit
6.13.1 Introduction
Animal populations are important
assessment endpoints because the public and
scientific communities perceive the presence of
certain animal species to be indicative of
ecosystem health (Hunsaker and Carpenter
1990; Finch 1991). Measurements of bird
populations can be especially useful in
determining ecosystem health because birds
exhibit a high degree of habitat specialization
and are closely associated with specific
vegetation features. Changes in vegetation
features due to human-induced or natural
disturbance, therefore, will have direct impact
on suitability of habitat for birds. In addition,
the mobility of birds enables them to react to
changes in habitat quality in a measurable way,
allowing assessment of changes in ecosystem
health.
The wildlife habitat indicator consists of
measures of vegetation structure and floristics
that predict the presence and absence or
relative abundance of forest bird species or
guilds. The majority of the vegetation
measurements are collected as part of other
indicators such as vegetation structure and site
classification, growth, and regeneration.
Ecosystem health in terms of wildlife habitat
suitability is important to the public. In
particular, much public concern focuses on the
decline of neotropical migratory birds, most of
which inhabit forests. Thus, attempts to
monitor forest health must incorporate
measures relevant to wildlife. The wildlife
habitat indicator is based upon vegetative
characteristics that are currently being
measured for other FHM indicators and, thus,
integrates directly into the current plan for
forest health monitoring. This indicator also
adds an important component to the current
suite of FHM indicators by providing direct
biological relevance of vegetation
measurements with respect to bird populations.
6.13.2 Indicator Evaluation
The following discussion considers the
indicator selection criteria described in Section
6.1 as they apply to the wildlife habitat
indicator.
6.13.2.1 Interpretability
The wildlife habitat indicator is highly
interpretable because birds exhibit a high
degree of habitat specialization and are closely
tied to specific vegetation features. Measures
of vegetation structure and floristics within a
forest sampling unit can be related to types
and numbers of birds present. Predictive
models can be generated from these bird-
habitat relationships to determine suitability of
the forests for particular bird species as
indicators of forest health.
6.13.2.2 Quantification
All vegetation measurements are
straightforward and easily quantifiable by field
crews after minimal training. Methods are
patterned after standard methods for
measuring bird habitat (James and Shugart
1970).
6.13.2.3 Signal-to-Noise Ratio
Noise for this indicator should be relatively
low, as natural variation in stem densities and
vertical distribution of foliage within a forest
stand is typically low. Signal effects, however,
are high compared with natural variation
among years or sites. In 1991, variation
between human-disturbed sites and
undisturbed sites was significantly greater than
within undisturbed sites. Thus signal-to-noise
ratio is high for this indicator.
6.13-1
-------
6.13.2.4 Regional Responsiveness
Bird-habitat relationships and vegetation
characteristics are relatively consistent within
regional forest resource subclasses. Thus,
predictive models of wildlife habitat should be
regionally responsive, so long as local
variability is incorporated into the model during
development and validation.
6.13.2.5 Index-period Stability
Bird abundances are correlated with
relatively gross features of the environment.
Therefore, low measurement error and
temporal variation of this indicator are readily
attainable because of the spatial scale and
level of resolution at which vegetation structure
and composition are measured. Thus, the
indicator has high index-period stability.
6.13.2.6 Environmental Impact
Sampling techniques for the wildlife habitat
indicator have minimal environmental impact,
mainly involving soil compaction and some
trampling of herbaceous vegetation when
walking within the sampling plot. There is no
destructive sampling of vegetation.
6.13.3 Field Measurements
Relative bird abundance will be measured
on each of the Forest Health Monitoring (FHM)
Demonstration plots. An expert in bird
identification will enumerate all birds heard or
seen within an unlimited-radius point count
centered on the center of each plot. Only birds
detected within 50 m will be used in the initial
development of the wildlife indicator.
Twenty-minute censuses will be conducted on
each plot to increase accuracy of bird counts.
Vegetation measurements for the wildlife
habitat indicator are similar to those being
collected for the vegetation structure indicator
as well as to core measurements collected by
FHM personnel. Measurements used for these
indicators include percent canopy cover, foliage
cover at different height strata, tree and shrub
species composition, tree and shrub densities,
and distribution of tree diameter size classes.
Data are collected as part of the other
indicators and then used as part of the wildlife
habitat indicator assessment.
6.13.4 Data Analysis
The approach for the wildlife habitat
indicator is to develop models of bird-habitat
relationships that allow us to predict the
occurrence of bird species that are associated
with varying levels of disturbance in forest
ecosystems. Thus, the indicator being
developed has two components: (1) bird
populations and (2) vegetation measurements.
Implementation of the indicator into FHM would
include only vegetation measurements.
Changes in vegetation structure and plant
species composition due to disturbance have
direct implications for the quality of forests as
habitats for birds. Measures of vegetation
structure and floristics within a forest sampling
unit can be related to types and numbers of
bird species present. Predictive models
generated from these relationships, can be
used to determine general suitability of forest
habitats (based upon vegetative features) for
particular bird species as an indication of forest
health. The .predictive models can be
generated by .logistic regression, which
estimates the probability of a particular bird
species occurring at a given sampling location
based upon vegetation characteristics at that
site. Probabilities of occurrence can be
estimated for a wide diversity of species known
to be indicative (both positively and negatively)
of forest health. An overall index of condition
(similar to the Index of Biotic Integrity of Karr
and Dudley [1981]) then can be computed
based upon vegetation characteristics and the
health status (nominal, marginal, subnominal)
assigned to each site. Classification of forest
health, therefore, is based directly on
vegetative features and indirectly on bird
species occurrence.
Results from the FHM 1991 pilot study in
the eastern United States indicated that
6.13-2
-------
logistic models based on vegetative
characteristics had high predictive ability in
classifying forest stands as nominal and
subnominal. Overall, 80 percent of stands that
were known previously to be nominal or
subnominal (based on observed disturbance
levels) were correctly classified by derived
logistic models. This high predictive ability was
achieved even though the range of disturbance
levels sampled in the pilot study was narrow.
Predictive capability will improve with
incorporation of (1) a wider gradient of habitat
disturbance levels, (2) more intensive sampling
of bird populations (i.e., 20-min instead of
8-min counts), and (3) measures of landscape
features around forest sampling units (e.g.,
percent cover types, perimeter-to-area ratio,
connectivity).
6.13.5 1992 Activities
Data for the wildlife habitat indicator will be
collected in the Southeast Demonstration
project. Additional data will be collected off-
frame on sites in Arkansas and Ohio for model
development and validation.
6.13-3
-------
-------
6.14 Air Pollution Bioindicator
Plants Indicator
K.W. Stolte
6.14.1. Introduction
Vascular plants have been used as
bioindicators of ozone and other air pollutants
when fumigation or field studies have identified
species that are sensitive to ambient levels of
an air pollutant and respond to the air pollutant
with a distinct visible foliar injury symptom.
Foliar injury symptoms are often visible on the
upper leaf surface as distinct patterns of
discolored stipples or lesions (see Skelly et al
1988 and Jacobson and Hill 1970 for symptom
descriptions and photographs). Affected leaves
often age rapidly and acceleration of
senescence occurs. Since ozone is the only
regional gaseous air pollutant that is frequently
measured at known phytotoxic levels, the focus
of this indicator in detection plots will be on
ozone only.
The societal values addressed by this
indicator are contamination (air pollution
impacts), productivity (potential reduction in
carbon fixation),,, aesthetics (discoloration of
foliage, accelerated senescence of foliage,
reduction in density and live crown ratio of tree
crowns), and biodiversity (loss of pollution-
sensitive genotypes; change in composition of
plant communities).
6.14.2 Indicator Evaluation
We will examine ozone injury by selecting
suitable sites (open areas in the east and
mesic areas in the west) where the injury, if it
occurs, will most likely be found. At these
sites the presence or absence of foliar injury,
above a 5 percent threshold level, will be
recorded by in-hand evaluation of the foliage of
30 plants of each ozone-sensitive species. The
detection plot will be scored as positive or
negative for ozone injury. Plants must be in full
sunlight part of the day (greater than 2 hours)
in the east. Herbs and shrubs must be in
mesic areas in the west. All areas must be
free of major disturbances such as herbicide
use. Each of the plants selected at the site
must have at least 25 percent of the foliage
that can be touched. In the east, blackberry,
black cherry, tulip poplar, sweetgum, milkweed,
and white ash will be evaluated. In the west,
ponderosa pine, Jeffrey pine, California black
oak, ninebark, aspen, and Rhus sp. will be
evaluated.
Since the incidence and severity of ozone
injury has been shown to increase over the
summer season in the northeast and
southeast, reference plots (two types) will be
established in different physiographic regions to
evaluate the effectiveness of the detection plots
to meet the stated objectives. Each year the
reference plots will be used to determine the
onset of ozone injury and the maximum
severity of injury in the region. Previous pilot
studies have shown that foliar injury symptoms
first appeared in June, and the best time to
survey for the symptoms was July 15 to August
15. During this period, symptoms had
developed distinctly and there was no statistical
change in the severity of symptoms recorded.
The reference plots come under the scope of
research on monitoring techniques or
evaluation monitoring and, as such, are not a
part of detection monitoring.
The following discussion considers the
indicator selection criteria described in Section
6.1 as they apply to the air pollution
bioindicator plants indicator.
6.14.2.1. Interpretability
Air pollution indicator plants are highly
interpretable as an indication that phytotoxic
concentrations of ozone are occurring when
sensitive species are vegetatively active.
These species are selected for high sensitivity
to ozone, have unique and discernible foliar
symptoms that are indicative of ozone injury,
are evaluated at sites (on or near the detection
plot) where ozone injury is most likely to occur
if phytotoxic levels of ozone are present in the
area, and can be quantified with one plot-level
average.
6.14-1
-------
Aggregation to the plot level -
This indicator is reasonably aggregated to
the ptot level by quantification of the incidence
(percentage) of injured individuals at the site or
stand level. Thirty individuals of a sensitive
species have been shown to provide a
reasonable estimate of the degree of air
pollution stress at a site (Duriscoe 1988). This
indicator is evaluated by giving a "plus" or
"minus" score for ozone injury to each plant. If
5 percent or greater of the surface area of any
one leaf on a plant has discernible ozone
symptoms, then the plant is scored as positive
for ozone injury. In the West, the plot-level
average is the number of plants injured and is
expressed as a percent of injured plants for
each plot. In the East, we do not yet
understand the effect of different size canopy
openings on the percentage of plants injured.
In the East, plot-level averages are 0 or 1
("plus" or "minus") for ozone injury.
Cumulative Distribution Functions and
Nominal-Subnominal Boundaries ~
Cumulative distribution functions (CDFs)
and the delineation of the population into
nominal-subnominai properties can be
calculated for this indicator in the West. The
CDFs for western species are calculated as the
percentage of plants injured in or near each
detection plot (X) per the frequency of
occurrence in the population (Y). The
frequency of occurrence in the population is
adjusted by the representativeness of the
sample at the detection plot (number of plants
evaluated) and the inclusion probability for the
plot.
The percentage of injured plants per plot is
expected to range from 0 to 100 percent, with
the frequency distribution skewed towards low
injury per ptot. Distribution of nominal and
subnominal classes from the CDF for the
population will involve setting three thresholds:
poor, concern, and optimal. The poor threshold
will be set at 50 percent or more of the plants
injured. Concern thresholds will be set at 0
percent plants injured. The rationale for setting
a stringent concern threshold is recognizing
that once ozone exposures reach the point
where sensitive species are injured near the
ground, crowns of dominant and codominant
trees may be experiencing even higher ozone
exposures in the upper canopy.
In the East, no calculation of CDFs will be
possible at this time until the effect of site
opening size on the number of plants injured
can be determined. Alternately, we consider
the plot as plus or minus for injury based on
one or more plants at the plot showing 5
percent or greater foliar injury. Nominal
proportions of the population are those having
0 percent injured plants, and subnominal
proportions of the population are those showing
1 percent or more of the population with injury.
In this way, comparisons of ozone injury
through number of plots injured between
eastern and western bioindicator populations
can be partially drawn.
6.14.2.2. Quantification
This indicator can be quantified with a high
degree of accuracy. The number of plants
evaluated per species is designed to minimize
variability at the plot level. The determination
of severity of foliar injury is reduced to two
classes: plants having 5 percent or more
surface area with foliar injury and plants having
less than 5 percent foliar injury. This
determination is readily done since Horsfall and
Barratt (1945) showed that the highest
precision with foliar estimates was obtained at
high and low degrees of injury (1 to 6 percent
and 94 to 100 percent).
6.14.2.3 Signal-to-Noise Ratio
The signal (ozone injury on foliage)-to-noise
(confounding foliar symptoms; lack of response
to ambient ozone) is very high for this indicator
since we have selected plant species that have
demonstrated sensitivity to ambient
concentrations of ozone. These plant species
were also selected because these plants are
common (widespread distributions) and easily
identifiable. These plants have distinct foliar
injury symptoms that are readily recognizable
and it is relatively easy to determine where
foliar injury is more than 5 percent of the foliar
surface area.
6.14-2
-------
The signal-to-noise ratio is reduced for this
indicator in years where drought lessens the
physiological activity of the ozone-sensitive
species. Drought will be quantified through the
off-frame climatic data that is reported in FHM.
6.14.2.4 Regional Responsiveness
This indicator is very responsive at state
and ecosystem regional levels. Surveys and
long-term monitoring plots have shown that
ozone-sensitive species are very good at
delineating the spatial distribution of ozone over
large regions (Duriscoe and Stolte 1989;
Pronos et al. 1978; Miller and Millecan 1971).
By evaluating an adequate number of plants
(10 to 50) at each survey point or plot, areas
receiving high ozone exposures can be
delineated. The responsiveness of ozone-
sensitive species to ambient ozone exposures
has recently been quantified by studies where
ozone-sensitive species are evaluated near
ambient ozone monitors (Stolte et al. 1991).
6.14.2.5 Index Period Stability
Air pollution injury does increase over the
summer season (May to September), and
consequently the response of deciduous
species is stable only through a proportion of
the summer (in some years this period was
from July 15 to August 15). This is true for
both incidence and severity of injury. Although
we would like to describe the incidence of
injury (number of plants affected), we will have
to limit our determinations at the detection plots
only to whether sites have any ozone-injured
plants. With coniferous plants, the incidence of
injury is not as likely to change over the
summer, since the injury is cumulative over
years. Thus incidence of injury on conifers in
the West will be relatively stable over the
summer field season.
The onset of injury on deciduous plants
each summer will be determined by a
combination of reference plots and plants
evaluated at the detection plots. The reference
plots are selected so that conditions are
favorable for the formation of ozone injury
(large opening, mesic conditions). Once ozone
injury is observed on a species (verified by
specialists), then evaluation of that species at
the detection plots is considered valid. Prior to
the onset of injury at the reference plots or the
detection plots, plots evaluated as negative for
ozone injury will be considered as uncertain in
the analysis at the end of the season. This
indicates that, we have evaluated the plot
before ambient ozone levels have reached
phytotoxic levels. This onset-of-injury point
must be determined for each species
evaluated.
6.14.2.6 Environmental Impact
The evaluation of this indicator should have
minimal environmental impact. Most
evaluations are done without removing any
foliage; voucher specimens (a few leaves or a
small branch) are collected by the crews to
verify symptom identification, once for each
species (until the proper diagnosis is made).
The only damage will be from crews walking
around to observe plants, and in many cases
this will occur out of the detection subplots or
even the 1 -hectare detection plot.
6.14.3 Field Measurement and Data
Use
The guidelines are intended for the
evaluation of ozone air pollution injury on
sensitive vascular plant species. The focus is
determination of the presence of ozone air
pollution symptoms on sensitive plant species
in suitable sites in or near the FHM detection
plots. Suitable sites are defined as those
where air pollution injury is most likely to be
found if phytotoxic concentrations of ozone are
occurring near the FHM detection plots. A
suitable plot is generally determined by the
habitats favored by sensitive species, the
spatial conditions that allow intrusion of ozone,
and environmental factors that favor plant
response to air pollutants. Consequently, the
definition for suitable plots will be different for
different regions of the country. In the East,
(northeast and southeast), suitable sites are
those where openings in the tree canopy allow
substantial intrusions of ozone (openings
greater than 6.5 acres) and environmental
factors (particularly radiation) are not found to
6.14-3
-------
limit pollutant uptake. Suitable sites in the
West, California and Colorado to date, are
meslc areas (riparian, meadows, and slope
bottoms) where summer drought conditions are
somewhat ameliorated. This is true especially
for ozone-sensitive herb and shrub species.
Conifers (particularly) and deciduous trees
(somewhat) in the West can be evaluated at a
broader range, of sites and are not limited only
to mesic habitats.
Species evaluated are those that have
been determined through field and laboratory
studies to be highly sensitive to ozone air
pollution. The sensitive species must have a
distinct response to ozone, such as a distinct
foliar injury symptom. The plant species
proposed for this indicator are those that have
a relatively consistent sensitivity to ozone and
a distinct foliar injury symptom that is not
difficult to diagnose. Within a species,
differences in genetics between individuals
often results in differential sensitivities to air
pollutants. It is not uncommon to find an
individual of a species with severe air pollution
injury growing immediately adjacent (i.e., same
soil, light, moisture) to another individual of the
same species with few or no symptoms. This
is the norm rather than the exception. Only
when air pollution concentrations become high
are many individuals affected, and even then
there will be gradients of injury among the
individuals.
Symptoms are used to determine whether
or not an individual plant or a sensitive species
is injured. If more than 5 percent of the upper
leaf surface area is covered with symptoms,
then the plant is considered "symptomatic" and
is tallied as positive (+) for injury. No other
quantification of injury severity is required. For
example, plants with leaves with 6 percent to
100 percent foliar injury are grouped in one
class and the plants are scored as positive for
injury. Of equal importance to the type of
symptom observed on the foliage is the pattern
of foliar injury over the whole plant. Since
gaseous pollutant injury is dependent on
stomatal conductance as a normal part of the
photosynthesis process, the age of the leaves
(position on the stem, branch, or rosette)
affects the susceptibility to gaseous air
pollutants. If an air pollutant is the cause of an
observed foliar injury, then the pattern of the
injury should be consistent with the
developmental pattern of the foliage on the
plant. This can be very helpful in distinguishing
air pollution injury from other abiotic and biotic
symptoms. Glater et al (1962) provided an
excellent illustration of the effect of leaf position
and air pollutant response with their work with
tobacco. Stolte (1982) observed a similar
distinct pattern of leaf position and symptom
development due to ozone stress on a
chaparral shrub species. The pattern of air
pollution injury should be consistent with the
sensitivity of leaves according to leaf position.
In general, leaves at 75 percent full expansion
are the most sensitive and would tend to show
symptoms most definitively toward the center of
the leaf. Older leaves would show symptoms
more widespread over the leaf surface, while
younger leaves would show symptoms more
commonly near the leaf tip. It is unlikely that
only one or a few leaves would show severe
symptoms without other leaves being affected
(this result may happen if the injury on one or
a few leaves is very slight). Also, if leaves on
one branch in the sun are affected, then leaves
at a similar leaf position on another sun-
exposed branch should be affected, especially
for branches on the same side of the plant
under similar environmental conditions (sun or
shade leaves).
Voucher specimens (pressed leaves with
symptoms) are collected for each species the
first time symptoms are found on a plot. These
vouchers are pressed and mailed to the
appropriate QA/QC person (e.g., the trainer for
this indicator) to ensure proper symptom
identification. Once a species is verified for
symptom identification, no further voucher
specimens are required for that species.
Additional QA/QC measures (reference plots)
will be initiated to gauge the efficiency of this
method in detection monitoring.
6.14.4 1992 Activities
The air pollution bioindicator plants
indicator will be a part of the Southeast and
SAMAB Demonstration projects, the
. Northeastern detection monitoring, and the
Western (California and Colorado) Pilot project.
6.14-4
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6.15 Lichen Communities and
Elemental Content Indicator
B. McCune
6.15,1 Lichen Communities
A lichen community is the assemblage of
species of lichenized fungi present at a site.
The FHM program restricts the lichen
community to macrolichens occurring on living
or standing dead woody substrates.
Microlichens (i.e., crustose lichens) are
excluded because they are poorly known
taxonomically and often difficult to differentiate
in the field. Only standing woody substrates
are included to standardize the measurements
to a class of substrates that can be found on all
sites. Lichens are commonly abundant and
diverse on rocks, but many FHM plots will not
have exposed rock as an available substrate.
This component of the lichen flora, however,
may be added in the future, particularly in dry
western forests.
Lichen communities are clearly indicative of
several key assessment endpoints, including
contamination of natural resources, biodiversity,
and sustainability of timber production.
Hundreds of papers worldwide (chronicled in
the series "Literature on air pollution and
lichens" in the Lichenologist) and dozens of
review papers and books (e.g., Hawksworth
and Rose 1976), published over the last
hundred years, have documented the close
relationships between lichen communities and
air pollution, especially SO2. In a comparison
of biological responses between areas near
and far from a coal-fired power plant, lichens
gave a much clearer response (diversity, total
abundance, and community composition) than
either foliar symptoms or tree growth (Muir and
McCune 1988). The lack of ambiguity is largely
due to the possibility of standardizing the
substrate (e.g., bark or bark of a particular
group of species). Much of the sensitivity of
epiphytic lichens apparently results from the
lack of a cuticle and a total reliance on
atmospheric sources of nutrition.
To the extent that air pollution affects long-
term sustainability and biodiversity of forests,
lichens are also indicative of trends in those
assessment endpoints. In addition, lichens in
themselves form a large portion of the
macrophytic species in many forests. For
example, in Abies grandis forests in western
Montana, the number of macrolichen species in
a stand is comparable to the number of
vascular species (Lesica et al. 1991). In many
forests lichens have key roles in nutrient
cycling (especially nitrogen fixation in moist
forests; Pike 1978) and food webs. For
example; a main component of the diet of
spotted owls is flying squirrels (Dawson et al.
1987), and flying squirrels eat epiphytic lichens
during the winter (Maser et al. 1986; Maser et
al. 1985). Epiphytic lichens are also a primary
food for the endangered mountain caribou in
Idaho (Rominger and Oldemeyer 1989;
Servheen and Lyon 1989).
Various community parameters at the plot
level can be calculated from lichen species
abundance data (also collected at the plot
level, but the data are aggregated from
individual species to the community). The
most commonly used are:
• Species richness - the total number of
species recorded in the sampling unit (plot).
• Total abundance - the sum of the abundance
classes across species.
•Score on compositional gradient - the score
is calculated as (1) a weighted
average across species for a given sample
unit, the species weights being derived from
a measure of sensitivity to air pollution or any
other gradient (see McCune 1991 for a
discussion of the available methods) or (2)
scores derived from equations based on
ordination of samples varying in the quantity
to be indicated (i.e., an "assessment
endpoint," e.g. air pollution).
6.15.1.1 Indicator Evaluation
The following discussion considers the
indicator selection criteria described in Section
6.15-1
-------
6.1 as they apply to the communities part of
the lichen indicator.
Interpretability -
All of these community parameters have
been shown to be related to air quality (e.g.,
McCune 1988). Total abundance and species
diversity are simple and fairly effective, but do
not incorporate specific information on indicator
value of different species. In the only study to
compare all three approaches, compositional
scores gave the strongest correlations with air
quality data (r2 > 0.7; McCune 1988).
Lichens are also well known to be sensitive
to climatic differences. In general, however,
lichen species are more widespread over
diverse climatic regions than are terrestrial
vascular plant species. Over very long periods,
one should also anticipate successional
changes in lichens, as the forest structure
changes. These changes should be separable
from changes induced by air pollutants
because of the species involved. Certain lichen
species are known to be sensitive to air
pollutants, others are pollution tolerant (see
lists in Wetmore 1983 and Ryan and Rhoades
1991), and still others are known to increase or
decrease with forest succession (examples
from North American literature: McCune and
Antos 1981, 1982; Lesica et al. 1991). Thus
changes in lichen communities will reflect
changes in climate, the structure of forest
communities, and air quality.
In the most important case, that of air
quality, nominal/subnominal boundaries can be
drawn by comparison of known polluted areas
with otherwise similar but remote areas.
Locally polluted areas occur in essentially all
forested ecosystems of North America and the
nominal/subnominal boundary can be varied
and calibrated throughout the continent.
Quantification —
Lichen community data can be
straightforward, rapid, and inexpensive to
collect. The ultimate example of this (Gilbert
1974) is a map of SO2 pollution zones in the
British Isles that was prepared from data
collected by schoolchildren! The principal
difficulty with lichen community data is reliable
identification of species. If one includes all of
the macrolichens, significant lichenological
training is needed to name the species. In the
case of the British schoolchildren, a subset of
easily recognized indicator species was used,
along with user-friendly training materials. We
propose to solve this problem by having field
personnel collect a specimen of each species
at each plot, to be mailed to a lichenologist for
identification. In the 1991 FHM western pilot,
field work per plot required about one hour for
one person.
Quantification of lichen communities has
taken various forms (McCune 1991), all of
which have been effective to some degree.
The simplest is to record the presence or
absence of indicator species. More commonly,
and to distinct advantage, cover or abundance
classes are assigned by visual inspection of
large plots. Higher accuracy at more expense
is possible by quantitative subsampling. These
methods have been shown to provide
estimates of community parameters that are
strongly related to long-term means of air
pollutants (McCune 1988; Nimis 1990).
The primary potential cause of
measurement error will be observers. We
propose to minimize and quantify this error.
Observer error will be minimized by (1) 2-day
training sessions for field personnel, (2) using
professional lichenologists for identifying
routinely collected vouchers, mailed to them by
the field personnel, and (3) frequent field
audits. We also propose to measure the size
of this source of error by a QA study to be
completed before the 1992 field season.
Signal-to-Noise Ratio -
The high signal-to-noise ratio is evident in
the overwhelming body of literature
demonstrating strong relationships between air
quality and lichen communities, even with
crude measures (see discussion under
"Interpretability".) This signal comes through
both spatially and temporally. There are
hundreds of papers using lichen communities
6.15-2
-------
as indicators of local pollution gradients (e.g.,
McCune 1988). There are many similar papers
related to regional scale (e.g., de Wit 1976 and
other papers listed below under "Regional
responsiveness").
Temporal responsiveness has been
demonstrated both for degrading air quality
(hundreds of studies) and for improving air
quality (Rose and Hawksworth 1981; Showman
1981).
The signal can be enhanced by statistically
controlling other variables. For example, Muir
and McCune (1988) and McCune (1988) used
covariates based on tree characteristics to
enhance the air pollution signal of epiphytic
lichens. Multivariate data reduction can be
used to isolate trends related to specific
assessment endpoints (e.g., air quality, Muir
and McCune 1988).
Regional Responsiveness -
Regional responsiveness of lichen
communities has been demonstrated and
utilized by national monitoring grids in other
countries; for example:
Country References
England Looney and James (1990),
Wolseley and James (1990)
France Van Haluwyn and Lerond
(1988)
Netherlands de Wit (1976- see Figure
6.15-1, 1983)
Switzerland Ammann et al. (1988),
Liebendorfer et al. (1988)
All of these studies have demonstrated
moderate to strong relationships with directly
measured air quality data.
Index Period Stability -
Lichen communities are an ideal long-term
indicator because they show little or no
variation at time scales from diurnal to year-to-
year (in the absence of catastrophic
environmental fluctuations, e.g., fire). There is
essentially no seasonal or diurnal variation in
lichen communities. Lichen species vary little
in how apparent they are. There are no
macroscopically apparent phenological
changes. Their reproductive structures are
stable and perennial, allowing the same
opportunities for discriminating among species
throughout the field season (and throughput the
winter, for that matter). Short-term differences
in climate will cause no apparent change in
individual lichens, and thus, lichen community
data should be independent of short-term
weather anomalies.
Environmental Impact -
Environmental impacts of the field work will
be negligible. Only one specimen per species
per plot is needed for vouchers to be sent to
specialists. This sampling activity will have very
little effect on the lichen populations in the plot.
Since lichen species are collected only off
subplots, no trampling of subplot vegetation will
occur.
6.15.1.2 Field Measurements
The objective of this task is to determine
the presence and abundance of macrolichen
species on trees in each plot. Samples will be
collected and mailed to lichen experts.
The method has two parts and these are
performed at the same time.
1. Make a collection of voucher specimens,
for identification by a specialist, that
represents the species diversity of
macrolichens on the plot as fully as
possible. The population being sampled
consists of all macrolichens occurring on
woody plants, excluding the 0.5-m basal
portions of trees and shrubs.
2. Estimate the abundance of each species.
Note that the crew member responsible for
this task need not be able to accurately
assign species names to the lichens (that is
done later by a specialist), but must be able
to make distinctions among species.
6.15-3
-------
co
cr
U-
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JJD Class 5
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"a~l Class 3
I?H Class 2
S Class 1
JrDno data
i i .»»«».^>«y-<5ar^
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Figure 6.15-1. Final map of the epiphytic lichen vegetation in the Netherlands (based on
classification T4 (de Wit 1976).
6.15.1.3 Data Analysis
Data analysis will consist of:
1. Analysis of data quality by using data from
audited plots. Data collected by the field
personnel and the auditors (lichen
specialists) will be compared.
2. Derivation of synthetic composite variables
representing the major components of
variation in lichen communities. This
multivariate analysis will be done within
bioclimatic regions.
3. Description of regional patterns of lichen
community parameters.
4. Establishment of nominal/subnominal
boundaries for indications of air quality by
comparison of known polluted areas with
otherwise similar, but remote areas. Locally
polluted areas occur in essentially all
forested ecosystems of North America, and
the nominal/subnominal boundary can be
varied and calibrated throughout the
continent.
5. Analysis of the relationship between
lichen community parameters and various
off-frame spatial data (e.g., pollutant
emission data) using standard analytical
procedures provided by the FHM statistics
group.
6.15-4
-------
6.15.1.4 1992 Activities
Lichen communities will be included as an
indicator in the Southeast Demonstration and
possibly in the Western Pilot.
6.15.2 Lichen Elemental Content
Elemental content refers to the proportion
(ppm or percent on dry weight basis) of various
selected elements. Elements are selected for
analysis based on the likelihood of detectable
enrichment from anthropogenic sources.
Typically, these include S, Pb, and as well as
various other elements such as Ca, Cr, Cu, Fe,
Mn, Ni, Pb, Ti, V, Zn. Each sample must
contain only one lichen species and be free of
dirt, bark, and other contaminants. Species are
standardized by using regionally common
species as much as possible to maximize the
comparability among sites.
The lichen material is pooled in the field
collection process thus yielding data collected
directly at the plot level.
Elemental content has been determined by
various methods, most commonly flame or
furnace AA, ICP spectroscopy, and neutron
activation analysis!v A 10- to 20-g sample is
adequate for splitting into 2 to 3 analyses for
most methods. The method of processing the
samples depends on the analytical method.
However, samples are typically rinsed, oven-
dried, then ground. Dried material can be
stored indefinitely as a means of testing
between-laboratory or within-laboratory
variation over a period of years.
Lichen elemental content is clearly
indicative of a key assessment endpoint:
contamination of natural resources. Measuring
a suite of elements allows differentiation among
sources of contamination (e.g., fossil fuels,
saltwater aerosols, agricultural dusts).
6.15.2.1 Indicator Evaluation
The following discussion considers the
indicator selection criteria described in Section
6.1 as they apply to the elemental content part
of the lichen indicator.
Interpretability -
Lichens are effective absorbers of soluble
and insoluble minerals from their surroundings,
including both wet and dry deposition (Nieboer
et al. 1978). Extracellular ion-binding sites
have been demonstrated that allow lichens to
accumulate large quantities of potentially toxic
cations without serious metabolic effects.
Lichens tend to accumulate greater
concentrations of metals than the foliage on the
trees on which they grow (Bargagli et al. 1987)
and consequently may more clearly
demonstrate changes in deposition of
atmospheric pollutants.
The total sulfur content of lichens is
correlated to wet and dry sulfate deposition
(typically 0.26 < r2 < 0.64; Takala et al. 1985;
Zakshek et al. 1986). Elevated levels of many
trace elements can be ascribed to coal-fired
power plants (Showman and Hendricks 1989).
General reviews of studies of elemental
contents of lichens can be found in Bargagli
1989, Nash 1989, Nieboer et al. 1978, and
Tyler 1989.
The rate of accumulation of elements varies
by the element (see above reviews) and it
varies by species (S, Takala et al. 1985; Ti,
Takala and Olkkonen 1985)- However, for a
given species and a given element, the
elemental content is a good indicator of
atmospheric deposition of that element (see
references in preceding paragraph). Thus the
interpretability is maximized by focusing as
much as possible on a small number of
species and by analyzing the same
combination of elements over broad regions.
Elemental content can be expressed for
individual elements or for suites of covarying
elements, just as is commonly done for direct
measurements of atmospheric deposition (e.g.,
Hooper and Peters 1989). The latter case
often strengthens the signal in the data by
synthesizing information from covarying
elements, the covariance resulting from
6.15-5
-------
different elements having common sources.
This correlation structure can be fairly strong
(e.g., Rhoades 1988) and can be extracted by
various techniques, especially principal
components analysis. Plot scores on the
principal components represent the importance
of particular atmospheric sources, e.g.
combustion of fossil fuels, saltwater aerosols,
and agricultural dust and fertilizers. Each
principal component is a linear transformation
of original elemental contents into a synthetic
score of the form:
score = a(E1) + b(E2) + c(E3)... + m(EN)
where the Es represent content of a particular
element and the small letters represent
coefficients that maximize the variance
explained by that equation. A series of such
equations is produced by eigenanalysis.
Typically, the first several such equations
contain much of the variance in elemental
content and are interpretable with reference to
particular types of sources of atmospheric
contaminants.
Nominal/subnominal boundaries can be
drawn by comparison of known polluted areas
with otherwise similar but remote areas.
Locally polluted areas occur in essentially all
forested ecosystems of North America, and the
nominal/subnominal boundary can be varied
and calibrated throughout the continent. In
addition, elemental analyses of epiphytic
lichens nave been used in many areas of North
America and form a substantial data base for
different elements and different lichen species.
These data could be synthesized and used to
establish tentative nominal/subnominal
boundaries.
Quantification -
Collecting samples for analysis of elemental
content is quite simple and should take 10 to
30 minutes per plot. In the 1991 FHM western
pilot, field work per plot required about 1 hour
for one person for this task and the lichen
community sampling.
Minimal training is needed to recognize
certain target species. A small reference
collection of target species can be prepared
before training so that the collector is familiar
with the target species. This reference
collection can be consulted in the field by the
collector.
There do not appear to be any large
problems with contamination of samples in the
process of field work or in laboratory handling,
provided certain basic precautions are taken.
Protocols for analysis should be the same as
for any other plant tissue samples analyzed for
FHM.
Measurement error should be small.
Measurement error will be quantified by field
splits and laboratory splits.
Signal-to-Noise Ratio -
Lichens rely directly on atmospheric
sources of nutrition and lack a cuticle. For
these reasons, their elemental content is very
sensitive to changes in atmospheric chemistry.
The signal-to-noise ratio .is greatly enhanced by
standardizing the target species and substrate
(e.g., bark, or bark of a particular group of
species).
Temporal responsiveness of elemental
contents in concert with known changes in
industrial emissions has been shown by
Perkins et al. (1980), Showman and Hendricks
1989, and Walther et al. (1990). Lichens
transplanted from areas with low air pollution to
areas near coal-fired power plants had higher
contents of certain elements (e.g., Cr, Ni)
within 1 year (Garty 1987).
The biological mean residence time for
most elements in lichens is 2 to 5 years
(Nieboer and Richardson 1981; Walther et al.
1990). Thus one can anticipate some
smoothing of an abrupt change in elemental
deposition. However, lichens would be
expected to show a reduction in elemental
content within 1 to 2 yr of a decrease in
emission from an industrial source (Nieboer
and Richardson 1981).
6.15-6
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Regional Responsiveness -
Elemental analysis of lichens has been
useful in mapping regional air quality, providing
greater detail than is possible from the sparse
networks of direct air quality data. Some
examples of national or regional programs of
elemental analysis of lichens are listed below.
Eastern Canada
Finland
Southern Louisiana
Southwest US
Switzerland
References
Zakshek et al. 1986
Takala et al. 1985,
Takala and Olkkonen
1985
Thompson et al. 1987,
Walther et al. 1990
Nash and Sommerfeld
1981
Ammann et al. 1988,
Liebendorfer et
al. 1988
The total sulfur content of lichens is
correlated to wet and dry sulfate deposition
(typically 0.36 < r2 < 0.64; Takala et al. 1985,
Zakshek et al. 1986). Fewer data are
available for other elements in regional studies
because of the sparseness of direct deposition
data for metals. However, many studies of
metal contents around local pollution sources
have readily demonstrated corresponding
spatial differences in elemental content in
lichens (e.g., Nieboer et al. 1972; Tomassini et
al. 1976; Beckett et al. 1982).
Figure 6.15-2 illustrates the pattern of
sulphur content in a reindeer lichen, Cladina
rangiferina. in eastern Canada (Zakshek et al.
1986). In this case, the correlation coefficient
between lichen S content and wet deposition
was 0.6. The authors felt the correlation would
have been higher had the sample sites been
co-located and had dry deposition also been
included (since lichen S reflects both wet and
dry deposition).
Index Period Stability -
Lichen elemental contents have sufficient
temporal stability over the short term to be
reliable indicators of long-term changes. Good
evidence of this is the easily-observed
responsiveness of elemental contents to
changes in regional pollutant emissions, (e.g.,
Perkins et al. (1980), Puckett (1985), Showman
and Hendricks (1989), Walther et al. (1990))
and retrospective studies (based on herbarium
specimens) reviewed by Lawrey (1984).
Seasonal and short-term variability have
been studied in detail by Boonpragob and
Nash (1990) and Puckett (1985). These
studies found no consistent seasonal variation
in elemental contents of lichens. In some
cases, however, occasional significant
departures from the norm occur (e.g., Al in
reindeer lichens sampled monthly over three
years showed occasional high spikes; Puckett
1985). There is no reason to expect diurnal
variation in elemental content.
The material is rinsed before analysis,
differences in elemental content due to time
elapsed since heavy, cleansing rains should
have little or no effect on the elemental
content.
Lichens are phenologically stable
throughout the year; i.e., there are no
macroscopic phenological changes in lichens
(such as development and shedding of leaves)'.
Environmental Impact -
Environmental impacts of the field work will
be negligible. In almost all cases, the amount
of material collected for elemental analysis will
be only a small fraction of that available in the
plot, simply because target p species will be
chosen partly on the basis of their abundance.
Since lichen species are collected only off
subplots, no trampling of subplot vegetation is
likely to occur.
6.15.2.2 Field Measurements
Lichen samples, collected to determine the
elemental content of selected lichen species in
each plot, will be used to establish site
baseline for toxic elements and determine
6.15-7
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Sulphur
(ngg1)
Figure 6.15-2. Sulphur concentrations in C. ranaiferina in eastern Canada. Data are the mean
value of 5 replicates (Zakshek et ai. 1986).
regional toxic element profiles. Desirable
attributes of species to be collected for
elemental analyses are: regionally abundant,
commonly present on the plot, and easy to
collect 20 to 50 g dry weight of tissue. No
lichens are collected from within the subplots.
The samples are processed first by a
Hchenologist and then sent to an analytical lab
for determination of elemental content. The
lichen specialist processes the sample by first
checking to see that it is thoroughly air dried,
identifying the species contained in the sample,
and verifying that the sample contains only one
species. The samples are then mailed to the
laboratory where they will be cleaned, ground,
and analyzed.
6.15J2.3 Data Analysis
Data analysis will consist of:
1. Analysis of data quality by comparing
results from field splits and laboratory splits.
2. Derivation of synthetic composite variables
representing the major components of
variation in elemental content of selected
species. This multivariate analysis will be
done within bioclimatic regions.
3. Description of regional patterns of elemental
content.
4. Establishment of nominal/subnominal
boundaries for indications of air quality by
comparison of known polluted areas with
otherwise similar but remote areas.
Because locally polluted areas occur in
essentially all forested ecosystems of North
America, the nominal/subnominal boundary
can be varied and calibrated throughout the
continent.
5. Analysis of the relationship between lichen
community parameters and various off-
frame spatial data (e.g., pollutant emission
data), using the standard analytical
procedures provided by the statistics group.
6.15-8
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6.75.2.4
Activities
The relatively late consideration of lichen
elemental content analysis as an indicator has
precluded its inclusion in the 1992 field work to
date. Meanwhile, development of supporting
documentation will continue.
6.15-9
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7. Design Overview
D.L. Cassell
The U.S. Forest Service has a developing
Forest Health Monitoring program. The
Environmental Monitoring and Assessment
Program (EMAP) is a program of the U.S.
Environmental Protection Agency (EPA). As
previously discussed in this document, these
two agencies have combined their efforts with
other agencies interested in evaluating the
health of our nation's forests.
7.1 The Forest
Monitoring Design
Health
The FHM program objectives can be stated
as follows:
1. To estimate the current status, extent, and
trends in indicators of the condition of the
nation's forest resources. Furthermore, this
is to be done on a regional and
national basis, with known statistical
confidence in the estimates,
2. To monitor indicators of pollutant exposures
and habitat condition and identify
associations .between human-induced
stresses and forest conditions, and
3. To provide yearly statistical summaries and
(non-annual) interpretive reports on forest
status and trends to resource managers and
the public.
Specific design features are essential to
achieve FHM objectives. One feature is the
use of probability samples to provide a
consistent representation of environmental
reality. In addition, all ecological resources and
environmental entities must be represented.
The design must be sufficiently flexible to
accommodate post-stratification and
aggregation for many alternative
subpopulations, such as states or special areas
of interest. There must be inbuilt capacity to
respond quickly to a new question or issue.
The spatial distribution of any sample must
reflect the population distribution of the
resource. Finally, periodic revisiting of all
sampling sites is required.
These requirements led directly to the
EMAP FHM design (Overton et al. 1990). This
design has been subjected to extensive peer
review. The American Statistical Association
has a special peer review panel to evaluate the
EMAP design, as well as all the resource group
designs, including that of FHM. This peer
review committee has. found that the EMAP
design is a good choice for achieving the goals
of FHM. Furthermore, the FHM design was
favorably reviewed by Forest Service
statisticians in early 1991.
7.1.1 The Monitoring Grid
The EMAP monitoring grid design uses a
triangular grid so that the nation is tesselated
with hexagons, each covering approximately
635 square kilometers. Within each 635
square kilometer hexagon, a hexagon of
approximately 40 square kilometers represents
a 1/16th sample of the resource. Each such
"40-hex" represents the tier I sample which will
be characterized using remote sensing. Within
a 40-hex, a point can be located on the
ground. A ground plot then represents the
support for that point. Measurements are
made for indicators of exposure, habitat, and
response. In the national design,
measurement visits are made on a 4-year cycle
on specific subsets of the grid that preserve the
spatial structure, so that samples are
distributed over time and space. In statistical
terminology, this arrangement of sites across
space and time is often referred to as an
'interpenetrating' design. This permits analysis
using traditional Horvitz-Thompson estimation
(Horvitz and Thompson 1952: Overton et al.
1990).
7.1.1.1 The Triangular Grid
The grid yields approximately 12,600
hexagons, of which approximately 4,000 will
have forested ground plots. Ground plots are
classified as forest according to standard
Forest Service FIA procedures. The hexagons
7-1
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represent a systematic grid with a random start;
the centers of these larger hexes are randomly
located by selecting a single random point in
space and moving the entire pattern so that a
hexagon center is on that point. Then a
random point is located within a large hexagon,
and the entire systematic grid of 40-hexes is
fixed by locating one 40-hex center on that
point.
The triangular grid is preferable for the
FHM project because it is spatially compact, it
provides uniform spatial coverage, and it is
very flexible for altering the grid density. In
addition, a triangular grid is less likely than a
square grid to coincide with anthropogenic
features.
Regular spacing of points generally leads to
population estimates with smaller variance than
simple random sampling. With uniform spatial
coverage, resources in the landscape are
sampled in proportion to abundance, area,
spatial pattern, and grid density. If the
interpenetrating sampling scheme and the 4-
year cycle are used, this is achieved every
year. The triangular grid allows a wide variety
of grid enhancements and reductions, as well
as other options for special cases, such as
subpopulations of special interest.
7.1.1.2 Evaluating Status and Change
There are several key issues in monitoring
across both time and space. Current status is
best measured by including as many population
units as possible since greater coverage
enhances the identification of subtle
subpopulation differences. Detection of trends,
however, is best achieved with repeat
observation of the same units over time, with
the interval between observations related to the
signal-to-noise ratio of the measurements. In
addition, observer effects can be a problem;
too long a time between visits may mean that
field measurements cannot be properly
calibrated, while too short a time between visits
may lead to environmental impacts, such as
trampling of the understory.
The EMAP solution adopted by FHM is an
interpenetrating design that distributes samples
across both time and space, so that each
year's sample preserves the spatial structure of
the real resource, and sampling is always
proportional to the area! extent of the resource.
The interpenetrating design is a special form of
a class of statistical sampling designs that are
also known as rotating panel designs in the
(sociological) survey sampling literature.
For faster detection of trends in the first 4
years, a simple improvement was developed by
the EMAP-Design staff. If desired,
approximately 10 percent of the sites can be
visited annually for the first 5 years of the
project. By the beginning of the second cycle,
the statistical advantage of annual revisits to
sites is negligible. Furthermore, revisiting more
than about 10 percent of the sites, even in the
first cycle, is not cost-effective. The extra cost
and effort is not rewarded by a corresponding
improvement in trend detection ability. If there
are ecological reasons for visiting sites more
often, they need to be established scientifically
as part of FHM pilot studies.
Alternate methods for improving detection
of trends and speeding assessment of current
status involve taking larger subsets of the grid
in the first field season. For example, if one
visits all sites in the first year and then each
following year visits only the standard
interpenetrating one-quarter, then each
estimate for years two through four consists of
replacing the first year's matching one-quarter
of data with the latest year's one-quarter and
updating the estimates of status and extent. In
such an approach, by year five, one would be
back to the standard interpenetrating cycle. A
smaller 'jumpstaif can be achieved by doing
more than the standard one-quarter butjess
than all sites in the first year. The design is
flexible enough to allow any desired level of
intensity for visiting ground plots.
To answer questions about the condition of
forest ecosystems, it is necessary to specify
the area of interest explicitly. This is the target
population, the areal extent of forested
ecosystem about which estimates of conditions
7-2
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will be made. Subpopulations can be defined
for any region or attribute. Subpopulations can
serve two major purposes: (1) increasing the
precision of estimates of condition and trend by
reducing extraneous variation; and (2) targeting
specific sets of resources for assessment and
reporting.
7.1.1.3 Stratification
No stratification of the resource is being
performed. There have been discussions
about appropriate stratification methods for the
effective evaluation of status and trends in
forests, but no clear choice of stratification
method has emerged. In fact, it may be that
stratification on current resources would
provide distinctly nonoptimal sample designs for
future study of the dynamic forest resource.
In many cases, insufficient prior data would
be available to perform stratification at the
desired level. Furthermore, misclassification
errors of as little as 20 percent would invalidate
any potential gains in precision from
stratification.
7.1.1.4 Higher Grid Densities
Increasing the grid density is recommended
for increasing the sample size for any
subpopulation. For specific forest types of
special interest, it appears that higher grid
densities than the standard EMAP grid would
be needed. As an example, high-elevation
spruce/fir forest occurs only in specific elevation
contours. Based on the standard EMAP grid
density, it is estimated that less than five high-
elevation spruce/fir forest sites would be
obtained. Another case applicable to FHM is
the special interest of particular states. For the
1990 FHM field season in New England, the
state of Rhode Island asked for a denser grid
to be overlaid on their state so that they could
obtain enough sites to make statistical
evaluations.
In each case, it will be necessary to use an
iterative process to select such a tier II sample
(see Section 1.2 for a definition of the tiers).
The first step will be to determine the desired
tier II sample size. If the standard grid will not
produce a sufficient number of sites, then the
density of the grid can be iteratively increased
until the desired sample size is obtained. The
FHM design is flexible enough to meet this
need without serious problems.
7.1.2 The Field Sampling Design
Field sampling involves the collection of tier
II measurements for specified indicators. The
key features are the statistical selection of
ground plots and the design of ground plots for
multiple categories of measurements.
7.1.2.1 Selection of Ground Plots
A crucial step in collecting indicator
measurements is selecting the field plots to
ensure that the data represent a probability
sample. The current method of tier II site
selection is equivalent to selection of a single
Forest Inventory and Analysis (FIA) photo
point. This method permits a statistical linkage
to the FIA program. The FIA photo point grid
for a region is overlain on a 40-hex. Then the
closest FIA photo point to the center of the 40-
hex is selected. In cases where the selected
photo point is already an FIA plot, the FHM plot
is offset from the photo point.
In areas where FIA photo point grids do not
exist, the FIA has been willing to extend the
systematic photo point grids to cover all
forested lands.
There are other alternatives as well. With
modern GIS software, a planimetric (equal-
area) grid can be laid over a base map to
locate ground plots and accurately determine
latitude and longitude. The selected points can
then be photointerpreted along with the FIA
photo points, so that links with the FIA program
are maintained.
Another, less preferable, alternative is
feasible in places where FIA photo points are
not currently available. The ground plot can be
located at a random offset from the center of
the 40-hex, and these points can then be
added to the list of FIA photo points for
photointerpretation.
7-3
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7.1.2.2 Ground Plot Structure
The design of FHM field plots was
developed jointly by the Forest Service and the
EPA. The original plot design resulted from
research on optimally cost-effective plot
designs for FIA mensuration measurements
(Scott 1991). This design includes a circular 1-
hectare plot with four fixed-radius subplots for
field measurements (Figure 7-1). The 24-foot-
radius subplots are arranged in an equilateral
triangle with an additional subplot at the center
of the triangle. The centers of the outer
subplots are 120 feet from the center of the
central subplot.
The plot design has been evaluated for all
indicators that have reached the pilot and
demonstration stages (Riitters et al. 1991 b).
This evaluation used sampling theory formulas
(Cochran 1977) to estimate optimal numbers of
plots, subplots, and observations within
subplots. Current research suggests that the
plot design is adequate for all current FHM
indicators in previously studied regions.
The fixed-radius plots provide several key
advantages over variable-radius plots.
Although variable-radius plots are slightly more
efficient for measuring current status for
characteristics correlated with basal area, fixed-
radius plots are preferable for estimates based
on variables uncorrelated with basal area, such
as the majority of FHM measurements. In
addition, fixed area plots are easier for
measuring change over time.
Extractive measurements on trees are
performed in a 36-foot wide ring around each
subplot, with soil samples taken at the
junctures of these four rings. The subplots and
destructive sampling zones are laid out so that
there are statistical and ecological linkages
between indicators.
7.2 The Flexibility of the FHM
Design
The flexibility and responsiveness of the
FHM design is reflected in the design variations
used in the pilot, demonstration, and
implementation projects being organized across
the country.
7.2.1 The Southeast Region
In the Southeast region, a combination of
the standard design method and slight
variations has been used. The 1991
demonstration and pilot projects in Georgia
used the standard interpenetrating one-quarter
of the grid, while growth, mensuration and
visual crown ratings were performed in all the
hexes with forested ground plots. The extra
plots measured in 1991 will allow earlier
evaluation of change and trend earlier than if
no plots were revisited before the fifth year.
The 1992 Southeast demonstration project
has a slightly different objective than standard
implementation monitoring, and hence has a
slightly different design. This is a 2-year
demonstration. The focus of the project is the
loblolly and shortleaf pine forest types in a four-
state region. Thus, the subpopulation of
interest is that area of forest in loblolly pine or
shortleaf pine forest type; the domain is the
forested region bounded by the borders of the
four states.
Since the goal of this demonstration is not
all forest types in the region, early
reconnaissance will categorize the forest types
at all ground plots. Plots with loblolly and
shortleaf forest types will be visited by field
crews during the field season. This is
equivalent to a poststratification, since it is
logically equivalent to visiting all the plots and
only reporting on the forest types of interest.
In planning the Southeast demonstration, it
was calculated that at the baseline grid density
and using the one-quarter interpenetrating
schedule, only about 30 plots would be
sampled. This is not sufficient for all the
analyses planned. Therefore, several
alternatives were examined. The alternative
that will provide the most gain and still maintain
the grid density is to visit more than one
quarter of the plots. Since this is a 2-year
project, the most effective way of subsetting
the grid is the sampling during the 1992 field
7-4
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Distance between points is 120 ft.
Azimuth 1-2 36CT
Azimuth 1-3 120*
Azimuth 1-4 240*
Subplot
24.0' radius (7.32 m)
Annular Plot
58.9' radius (17.95 m)
' Mioro-nlof
6.8' radius (2.1 m)
12' @ 90" az. from
subplot center (3.66 m)
Figure 7-1. The FHM field plot design.
7-5
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season of both the 1991 and 1992 quarters of
the interpenetrating schedule and to sample
both the 1992 and 1993 quarters of the
interpenetrating schedule in 1993.
With this design, approximately 65 ground
plots will be visited in 1992 and in 1993, and
approximately 33 plots will be visited in both
years. This will permit the generation of yearly
statistical reports in both years of the study, as
well as evaluation of short-term change and
inter-annual variability.
Logistical problems led to the use of a
planirnetric grid in place of the FIA photo point
grid for locating plots in North and South
Carolina. The photo point grid was not
available for South Carolina in time for field
work. Hence, a planirnetric grid of the same
density as the FIA photo point grid was laid
over the two states and ground plots were
selected as discussed in Section 7.1.2.1.
7.2.2 The Southern Region
In the Southern region, the state of
Alabama participated in pilot activities in 1991.
This state was not able to use current photo
point grids, and so they used the previously
discussed (Section 7.1.2.1) alternative of
locating each ground plot at a random offset
from the hex center.
7.2.3 The Western Pilot and
Detection Monitoring Activities
In the Western pilot and detection
monitoring activities, the original plan was to
use the standard one-quarter of the
interpenetrating design. However, fiscal and
logistic considerations led to problems that the
overall design was flexible enough to handle.
In California, due to budget constraints, it
appears that not all of the hexes in the
standard one-quarter of the interpenetrating
schedule can be visited this year. Altering the
grid density or changing the length of time
between revisits are not reasonable options
given the need for states to be compatible with
regional analyses. A simpler approach was
developed that preserves the grid density and
the interpenetrating schedule.
A spatially clustered subset of the standard
one-quarter of the grid was selected. In
essence, this process divides the hexes within
the standard one-quarter in the state into
contiguous groups of four hexes each. Three
of the four hexes in each cluster are randomly
chosen to be visited. This reduces the number
of ground plots to an affordable level, while
preserving the spatial structure of the grid and
the four-year cycle. In addition, this design
permits easy expansion to the original
schedule.
A different problem arose in planning for
Colorado. The crews may be able to complete
their field work in less time than has been
allotted. Planners asked for approximately ten
additional plots to be visited in case of early
completion of the planned field work.
Extra plots for Colorado were selected so
that, if they were visited in 1992, they would be
revisited in 1993 for estimation of interannual
variability in the region. A fraction of the
standard one-quarter of the interpenetrating
grid for 1993 was selected, so that these sites
are a reduction of the grid. This preserves the
spatial characteristics of the FHM grid so that
the sample is taken proportional to the area of
the resource.
7.2.4 The Northeast Region
In the Northeast region, all ground plots are
currently visited annually. That is, all four
quarters of the tier II interpenetrating schedule
were visited in 1990 and 1991 and will be
revisited in 1992. However, only visual crown
ratings were taken in 1991 and only these
measurements will be taken in 1992. Under
normal circumstances this design is not a cost-
effective approach and could ultimately lead to
impacts from repeated visits.
The design, however, is flexible enough to
permit analyses with this schedule. In the
Northeast there is serious concern about
annual variability in tree crowns. In addition,
7-6
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FHM in the Northeast does not have sufficient
remote sensing capability to replace the annual
ground visits. For these reasons current plans
to continue the annual visits for measurement
of visual crown ratings until more effective
approaches are available.
In addition, the Northeast has intensified
the baseline grid density in some areas. In
1990, EMAP-Design group developed an
intensification of the standard grid for the state
of Rhode Island, so that state foresters would
have enough ground plots to make state-level
estimates. Similar plans are currently
underway for the state of Delaware.
7.2.5 The Ground Plot Design
In addition to these extensions of the
monitoring network design, the ground plot
design also permits flexibility and alterations in
measurements. The ground plot design has
proved to be flexible enough to incorporate
measurements of lichen community, stemwood
chemistry, and wildlife habitat. The adaptability
of the design has permitted alterations in the
measurements for vegetative structure. In
addition, the plot design is flexible enough to
allow extensions of the ground plot design to
incorporate off-plot measurements on air
pollution bioindicator plants and off-plot FPM
measurements.
7-7
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8. Assessment Overview
FHM National Assessment
Group
8.1 Introduction
Assessment is the process by which data
from Forest Health Monitoring (FHM) and other
sources are converted into useful information.
The immediate purpose of FHM is to prepare
forest health assessments (provide useful
information) needed by those concerned with
forest environmental and management policy.
The process of assessments in forest health
monitoring can serve a larger purpose,
enabling forest health studies and assessments
by a set of users extending beyond the
sponsoring federal agencies. This chapter
reviews critical elements of the FHM
assessment process and describes FY92
activities.
The background and prototype approaches
to forest health assessments are discussed
elsewhere (e.g., Palmer et al. 1991; Riitters et
al. 1992). The logical, multiphase and
multidimensional research approach taken is
consistent with recent National Research
Council recommendations for monitoring and
forest research (NRC1989,1990a, 1990b) and
will not be reviewed in detail here. In
comparison to other forest health assessment
processes, an exciting and unique feature of
the FHM approach is the "environmentalist"
paradigm (e.g., Leopold 1949) as
recommended by the NRC (1990b) to permit
responses to emerging public concerns about
forest health (without locking into any "preferred
observer" status). Another important feature is
the early formation of a truly cooperative
assessment group with responsible individuals
from each participating agency.
Current assessment reports are severely
limited by the amount of data presently
available from the program; only about 20
percent of the total number of plots have been
installed, only a few measurements are made
on those plots, and only 1 year of data is
typically available. These facts have led to the
current emphasis on developing assessment
tools (hardware, software, infrastructure, team-
building, and communications). As data
accumulate over time, the assessment
"machine" will be developed that can analyze
the data and to prepare increasingly relevant
assessment reports. In the meantime, reports
are being prepared based on available data.
Well over half of the assessment effort is
directed towards incorporating other data
bases, such as air pollution, climate, and forest
pest management, that will be absolutely
necessary for understanding the statistical
status and trends that the plot network
identifies.
8.2 Regional Forest Health
Monitoring Assessments
By mutual consent of the U.S.
Environmental Protection Agency (EPA) and
the U.S.D.A. Forest Service (FS) (July 1991),
regional reports of forest health will be
prepared by regional FS staffs. Details of
these reports may be obtained from the FHM
national program managers or from the
regional FS assessment leaders. It is expected
that the regional reports will be consistent with
each other in terms of tabular formats and units
of measurements. Annual releases of regional
reports are scheduled for January for data
collected during the preceding field season.
At present, however, there is no
mechanism to guarantee that the regional
assessments will be consistent in format or
units with any multiregional or national reports.
As a practical matter, individuals from each
region participate as part of the National
assessment group (see Section 8.4), and it is
expected that some elements of regional and
national reports will ultimately be standardized
for the program as a whole. Also, the regional
groups can utilize all of the data bases and
analyses that have been developed for the
national assessment machine, thus increasing
overall program efficiency. Both the regional
and national groups will improve assessments
at a faster rate by working together. Current
8-1
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administrative difficulties related to reconciling
national and regional reports, responsibilities,
and timelines has been brought to the attention
of the Technical Director and national program
manager. A new interagency agreement is the
mechanism that will resolve these potential
problems.
8.3 National Forest Health
Monitoring Assessments
To ensure that the assessment process that
is developed is workable, real data from the
1992 field season (12 states in 2 regions) are
being analyzed, and a prototype statistical
summary report is in preparation. The
scheduled release is September 1,1992. The
report will consider measures of forest
condition that are currently made on FHM plots
(visual symptoms of tree health and tree
diversity), and selected auxiliary data from
other sources (forest pest incidence, climate
and weather, and air pollution). In addition,
each plot in the existing network will be
characterized according to key attributes to give
an indication of the types of forests that have
been sampled.
Analyses of FHM plot data will utilize any
statistical technique which has been
peer-reviewed for the FHM sampling design
(Overton et al. 1990). This basic toolbox of
techniques enables the creation of a statistical
summary document. Depending on what is
revealed by the preliminary analyses, further
studies of associations among measurements
may be made. Depending on the power of the
basic techniques, other statistical techniques
may be developed and demonstrated by this
report.
The 1992 report will contain one section for
each major type of data that is analyzed. It will
also contain an overall summary section,
written for the policymaker, that attempts to
show the importance of the findings for a
"typical" policymaker in either the EPA or the
FS. This format, like the assessment process,
is being tested in the 1992 report and may be
modified later depending on reader feedback.
In summary, the regional and national 1992
reports are test-beds for assessment
procedures, communications, and reporting
formats, given the current status of the
operational monitoring data collection system.
Assessments will become increasingly
important as the program moves from a
data-collection mode into a mode of data
analysis, assessment, and discussion of
findings.
8.4 The Forest Health
Monitoring National
Assessment Group
The first meeting of the national FHM
assessment group took place in December
1991. A "manifesto", prepared during the
meeting of about 20 individuals, was approved
by the FHM Technical Director and national
program manager. The document is
reproduced below because it is an assessment
plan that has the consent of both the group
and the program managers. Note that the plan
is not complete; part of our plan is to be
flexible, to try what has been suggested, and to
later modify the process to make it more
workable over time.
The second meeting of the national group
took place at the end of March 1992.
Preliminary indications are that preparing a
useful statistical summary will require the
majority of the authors' efforts, and, therefore,
the report will not contain very much biological
interpretation of results. The majority of
information will in fact come from the non-plot
data sources such as forest pest management
and climate. In most instances, the major
constraint appears to be a lack of time
available to participants to complete their
assessment responsibilities. The group will
recommend a solution during its self-evaluation
that is planned for later in the year.
From "Summary of the 12/10-11 forest
assessment meeting (Section 1: Introduction
and statement of purpose)":
8-2
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"Forest assessments are important
to the program. There is not enough
time or resources to do the job we'd
like to do, so the scope and content of
the 1992 report will be commensurate
with what is available. We believe that
the 1992 report will demonstrate at
least some of what can be done at the
national scale with forest monitoring
and ancillary data. The process of
producing the report will help identify
bugs in the assessment system and
may force the resolution of key issues
that are beyond our control.
"The forest assessment group is
open to participation by anyone who
brings to the group: scientific rigor, a
willingness to cooperate and
contribute, and an orientation towards
producing reports for the monitoring
program (rigor, responsibility,
relevance). The group is limited by
available data; participants must have
data to make a contribution. The
group will grow over time as more
measurements are implemented, as
more ancillary data bases are made
available, as the scope of inquiry
increases, and/or as more agencies
and organizations begin active
participation in the monitoring program.
"This group fills the niche of national
scope and national scale. We are not
responsible as a group for sub-national
reports, even though some of us
participate in other groups to produce
those reports. We use the data that
are presented to us by the monitoring
program, and we add value by
constructing a view of those data with
a national scope and scale, by adding
ancillary data, by exploring
associations among data. We serve
any clients that require such value
added.
"The reports made by this group are
a subset of all program reports. This
group does not produce plans,
manuals, database reports, QA reports,
or specific policy analyses. We
provide statistical summaries and
biological interpretations of the
monitoring and ancillary data.
"In 1992, we intend to produce a
statistical summary of the available
data, and to push as far as possible in
the direction of interpretation of those
data.
"Within our group, we distinguish
subgroups for "the system", "on-frame"
data, and "off-frame" data. The -
systems group will make an engine
(software, hardware, infrastructure) that
will enable a semi-automated
production of an annual statistical
summary report. The engine will be
improved over time according to the
program's ability to agree upon the
minimal standard and peer-reviewed
set of analysis procedures. The
on-frame group will assess the data
that are flowing from the program's
field efforts, using the engine to
facilitate analyses and ensure
comparability and consistency. The
on-frame group size is in direct
proportion to the number of assessable
measurements that are presented.to
the group by the program. The
off-frame group will assess selected
ancillary data that are flowing from
other programs' efforts, matching their
outputs to the input needs of the
engine. The size of this group will be
in proportion to the number of
off-frame data bases that are available
for assessments.
"There is or will be a separate
group of reviewers and commentators
who may attend group meetings, but
who will not be responsible for
producing any parts of the reports.
"Although the current path to
statistical assessments will be readily
apparent (by examining the engine),
the path to interpretive assessments
depends on where the assessors steer
8-3
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the engine. The interpretive paths
cannot always be predicted, and the
assessors cannot always say they will
arrive somewhere, but it is the group's
intention to request the managers put
gas in the engine (funding) and the
assessors behind the wheel
(assignment). At a minimum, each
assessor will always provide a minimal
interpretation of any statistical
summary, where "minimal" implies
peer-acceptable.
"At this time, the group has no
control over who participates in the
group. Participation is by appointment
by Sam Alexander or Joe Barnard [the
national FHM managers]. Each
member of the group negotiates
separately with Sam or Joe to ensure
that resources are available to meet
individual commitments to the group.
The group raises this issue with Sam
and Joe: There is a need and desire to
broaden the participation in our group
with at least state representation and
also other agencies and organizations.
The criteria of rigor, relevance, and
responsibility should apply to the
appointments."
8.5 Relationship to Field Pilot
and Demonstration Projects
The regional and national assessment
groups are not responsible for reporting the
results of field pilot and demonstration projects,
per se. This is so because pilot and
demonstrations projects need not have
"assessments" as a primary goal. However, if
any field pilot or demonstration project offers a
data set that is useful either for an assessment
or for improving the assessment process, then
the assessment groups will consider how to
bring the project data into the ongoing
assessment process. The point is that, from
the groups' points of view, the ongoing
assessment process has a higher priority than
the report from any single pilot or
demonstration project.
8-4
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9. Reporting Overview
B.L. Conkling
Reports are an important vehicle for sharing
the data collected and the information learned,
both within and outside of the Forest Health
Monitoring (FHM) team. A variety of
documents can be produced in association with
a demonstration project, beginning at the
planning stages and continuing through
assessment. Reporting, along with
assessment, is a growing area of emphasis
within FHM. A general outline of reporting can
be found in Chapter 11 of The Monitoring and
Research Strategy for Forests (Palmer et al.
1991).
Reporting includes the planning,
preparation, review, and final production of
documents. Because reports and other
documentation are needed or required in most
phases of pilot, demonstration, and
implementation projects, many of the FHM
team members are actively participating in one
or more report-writing activities. A brief
discussion of anticipated documents associated
with FHM project, is given below.
9.1 Activities Plan
The Activities Plan contains the objectives,
rationale, and logistical plan for conducting the
projects. The chapters, written by FHM team
members for their respective areas of
responsibility, include objectives, design,
logistics, quality assurance, information
management, indicator rationale, assessment,
and reporting.
9.2 Quality Assurance Project
Plan
The Quality Assurance Project Plan
(QAPjP) (Byers and Palmer 1992), contains a
description of how each indicator will address
QA/QC concerns. A further description of the
QAPjP can be found in Chapter 10 of this
document.
9.3 Methods
Manuals
Guides and
All methods guides and manuals are
actually part of the QAPjP. Because of their
importance, however, and for convenience, the
methods documents will be discussed
separately.
Two methods documents will be used in
1992 activities, the field methods guide and the
preparation and analytical laboratory methods
handbook. The Forest Health Monitoring Field
Methods Guide (Conkling and Byers [eds.]
1992) documents the field methods used for all
indicators to be used in 1992 activities. Each
indicator lead contributes the complete field
methods for his or her indicator. The Field
Methods Guide will be available to FHM
participants in several versions. A National
Guide documents all procedures to be used in
all regions of the country. In addition, regional
versions will be prepared for detection
monitoring crews in the Eastern United States,
for the Southeast/SAMAB Demonstration
projects, and for the Western Pilot project.
The EMAP-Forests: Handbook of
Laboratory Methods for Forest Health
Monitoring (Byers and Van Remortel [eds.]
1991) documents the complete methods for
sample preparation and any analytical
procedures performed on samples collected in
the field as part of any FHM field work. Each
indicator lead contributes the methods to be
used by the contracted preparation and
analytical laboratory(s), including quality
assurance/quality control requirements.
9.4 Statistical Summaries
Statistical Summaries are annual reports
which present the data for the previous field
season in a way which is useful to the readers.
9-1
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These are intended to be systematic data
presentations which communicate the current
status of the forests which were measured
during the previous field season. Indicators
which are part of either implementation or
demonstration projects in the 1992 field season
will probably be included in Statistical
Summaries in 1993. Currently; the Statistical
Summary including 1991 data is being
prepared using available data from previous
field seasons. Assessment leads have access
to various off-frame data such as climate, wet
and dry deposition, landscape characterization
and remote sensing, geographic information
system data, and data from the Forest Pest
Management Program (see Chapter 8).
Regional reports will also be prepared as
necessary.
9.5 Demonstration and Pilot
Reports
Additional reports for demonstration and
pilot projects will be prepared which will focus
on indicator development. These reports will
be prepared by the indicator leads in
conjunction with the Indicator Development
Coordinator for FHM.
9.6 Miscellaneous Possible
Reports
Various other reports are expected to be
prepared after the end of each field season.
These reports include the Quality Assurance
Project Annual Report and Workplan and the
Logistics Report. The Quality Assurance
Project Report and Workplan, prepared by the
FHM QA coordinator, is the end-of-year QA
report. It reports how well the various parts of
the project met the QA/QC objectives. This
report not only presents the results of the work
just completed, but also is an important
planning document for the next year's work.
The Logistics Report is the end-of-year report
of what was successful and what needs
modification in the areas of logistics and
general project operations. An important part
of this report is the notes from the field crew
debriefings which are held at the end of field
season. Any notes or suggestions from crew
members, crew leaders, coordinators and other
FHM team members can be included in this
report. This is an important documentation of
the field season just completed and a vital
planning document for the next year.
Other reports are possible which are not
well-defined at this time. An example is quality
assurance reports written by assessment leads
and sent to the participating preparation or
analytical laboratory(s). Various reports or
documentation may also be requested by the
Technical Director, Program Manager, or other
management personnel within FHM or EMAP.
9-2
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10. Quality Assurance
Overview
G.E. Byers and C.J. Palmer
As discussed in the Introduction (Chapter 1)
of this document, both the USDA Forest
Service (FS), via the forest health monitoring
program, and the U.S. Environmental
Protection Agency (EPA), via the Environmental
Monitoring and Assessment Program (EMAP),
are key participants in the Forest Health
Monitoring (FHM) program. As part of the EPA
Office of Research and Development, EMAP
will participate in the Agency's mandatory
quality assurance program (Stanley and Verner
1985). Accordingly, FHM will develop an
appropriate quality assurance (QA) program to
ensure that resulting data and information are
of known, acceptable, and documented quality.
The FHM program is designed as a major
environmental data collection effort and, as
such, will operate within the guidelines of the
EPA Quality Assurance Management Staff
(QAMS). Utilizing a statistically robust design,
the FHM program will collect data across large
geographic areas over long periods of time for
multiple ecological resources. The purpose of
the QA program is to ensure that the resulting
FHM data bases will yield scientifically valid
and unbiased information related to the
principal hypotheses being addressed in the
project.
10.1 Quality
Documents
Assurance
The Quality Assurance Program Plan, being
prepared by the EMAP QA staff is the major
reference document for quality assurance for all
resource groups within EMAP. It contains
descriptions of quality assurance objectives,
policies, procedures, planning, assessment
techniques, and communications. The
"Monitoring and Research Strategy for Forests-
Environmental Monitoring and Assessment
Program" (Palmer et al. 1991) describes the
FHM QA program and organizational structure.
The FHM policies, objectives, and functional
responsibilities designed to achieve data quality
goals are described in detail. The QA section
of that report includes discussions on quality
assurance related to policy, total quality
management, organizational responsibilities,
data quality objectives, documentation, and
reporting. Other QA-related FHM docurhents
used as reference material are Burkman and
Mickler (1990), Dwire et al. (1990) and, Knapp
et al. (1991). Approval of several QA
documents is also required by QAMS prior to
the field season. These include the QA Project
Plan (QAPjP) and two methods manuals
describing standard operating procedures for
field and laboratory activities, (see Section
10.6.1 of this chapter).
10.1.1 Quality Assurance Project
Plan for FHM
As part of the FHM QA program, a quality
assurance project plan (QAPjP) was prepared
for 1991 and is being modified to reflect
continuing and expanded activities for 1992.
The purpose of the 1992 QAPjP is to describe
the FHM program, its organization and
objectives, and the QA activities needed to
satisfy the 1992 data quality requirements.
The QAPjP will also describe the QA activities
and assessment criteria that will be
implemented to ensure that data bases will
meet or exceed the FHM data quality
objectives (DQOs). The QAPjP will identify all
environmental measurements for each indicator
and address specific processes within each
measurement that could introduce error or
uncertainty in the resulting data. Data
uncertainty could arise during the initial field
measurement phase, sampling, preparation, or
analysis. Methods, materials, and schedules
for assessing the error contributed by each
process will also be addressed. In addition,
the QAPjP will define the criteria and
procedures for assessing statistical control for
each measurement parameter.
10-1
-------
Each of the 14 indicators for 1992 is, in
effect, a separate data acquisition activity or
"project" and, technically, requires a separate
and complete QA project plan. Therefore, each
indicator is discussed in its separate section
within in the QAPjP using a standard format.
One reason for this approach is that the choice
of indicators is an evolutionary process. Some
of the indicators are in the dynamic stage of a
pitot or demonstration mode, while others are
more refined and tested. Also, constraints due
to logistics and budgetary considerations
impinge on the indicator planning and testing
process. As indicators are added, modified, or
withdrawn from the FHM program,
corresponding sections can be added,
modified, or deleted without affecting other
sections of the document. Another important
effect of the structure of the QAPjP is that 13 of
the required QA items (Simes 1989; Simes
1991a; Simes 1991b)foreachof the indicators,
are contained in each indicator section rather
than distributed within a general discussion for
all indicators. Thus, the QAPjP for FHM will
continue to be a "living" document.
The QAPjP will be revised as necessary to
reflect changes in procedures that result from
continuous improvement and will undergo a
complete revision annually. All project
personnel, especially indicator leads, should be
familiar with the policies and objectives outlined
in pertinent sections of the QAPjP to ensure
proper interactions among the various data
acquisition and management components.
10.1.2 Methods Manuals for
Standard Operating Procedures
Standard operating procedures (SOPs)
have been written for all FHM field and
laboratory activities for 1992. These SOPs are
included in a Field Methods Guide (Conkling
and Byers 1992) and a Handbook of
Laboratory Methods (Byers and Van Remortel
1991). The Guide includes standardized SOPs
with formats approved by the EMAP Methods
Coordinator for all indicators included in the
1992 fieM activities and will be used by all field
crew personnel. The Handbook contains
standardized SOPs for those indicators that
have field sampling, sample preparation, and
analytical components. These SOPs are used
for contract preaward assessment of analytical
laboratories and during the laboratory analysis
phase. The use of written SOPs helps to
ensure consistency in planning,
implementation, and analysis.
10.2 Quality Assurance
Responsibilities
For the organizational structure of EMAP,
lines of communication, and responsibilities,
see the Quality Assurance Program Plan.
10.2.1 Agencies
The success of the FHM program depends
on the willingness of all participating agencies
to cooperate as full partners. It is important
that roles and responsibilities be clearly
identified to encourage cooperation and
successful implementation. These duties have
been outlined in Table 10-1. In general terms,
EPA is responsible for preparing planning
documents. Field activities will be coordinated
by the Forest Service. Evaluation of results will
be a shared activity. The key individuals who
are most responsible for the success of this
project are the indicator leads, regardless of
the agency from which they come.
10.2.2 FHM Personnel
There are many key individuals involved in
or in charge of the various activities in the FHM
project related to quality assurance.
Subcontractors whose services are important in
coordination and sample analysis are also
included. In addition, there are several
documentation activities important to the
success of the project, such as the preparation
of guidance documents, See Table 10-2 below
for a list of persons responsible for these
various activities.
10-2
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TABLE 10-1.
CONCERNS
AGENCY RESPONSIBILITY IN 1992 FHM ACTIVITIES HAVING QUALITY ASSURANCE
Responsibility
Planning
Strategy Document
Activities Plan
QA Project Plan
Field Methods Guide
Laboratory Methods Handbook
PDR Programmer's Guide
Implementation
Pretraining/Training Workshops
Crew Staffing
Field plot Activities
Audits
Evaluation
Debriefing Workshop
Methods Manuals Revisions
QA Annual Report & Work Plan
Integration and Assessment Report
FS
C
C
C
C
C
C
L
L
L
C
L
C
C
C
EPA States
L
L
L
L
L
L
C
L
L
L
C
C
C
C
C
C
C
C
C
C
Agencies
FWS SCS
C
C
C
C
C
C
C
C
C
C
C
C
NPS
C
C
C
C
C
TVA
C
C
C
C
C
BLM
C
C
C
C
FS= Forest Service (USDA) L = Lead Agency
EPA= Environmental Protection Agency C = Contributing Agency
States= AL,GA,NC,X,VA,MD,DE,NJ,ME,NH,VT,MA,RI,CT,CA,CO (16 total)
FWS= Fish and Wildlife Service (USDI)
SCS= Soil Conservation Service (USDA)
NPA= National Part Service (USDI)
TVA= Tennessee Valley Authority
BLM= Bureau of Land Management (USDI)
10-3
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TABLE 10-2. FHM 1992 PERSONNEL RESPONSIBILITIES RELATED TO QA CONCERNS
Preparation of Adivllles Plan
Preparation of EMAP QA Program Plan
Preparation of FHM QA Project Plan
Preparation of Methods Manuals:
Field Methods Guide
Laboratory Handbook
PDR Programmer's Guide
Plot Reconnaissance:
Southeast
South
West (California)
Training Workshops:
National Pretraining (Durham, NC)
Regional Pretraining/Training
Northeast
Southeast (Asheville, NC)
South (Starkevilte, MS)
West (Ogden, UT)
Stale Training
CaBfomia (Blodgett NF, CA)
Colorado (Durango, CO)
Indicator Development Coordinator
Indicator Leads:
Group 1 1. Site Characterization, Growth,
Regeneration*
2. Crown Classification*
3. Damage and Mortality*
Group 2 4. In-hand Branch Evaluation*
5. Soil Characterization/Pfiysiochem.*
6. Foliar Chemistry*
7. Stemwood Chemistry*
8. Dendrochronology*
9. Root Disease Evaluation*
10. Photo. Active Radiation (PAR)
11. Vegetation Structure
12. Wildlife Habitat
13. Air Poll. Bioindicator Plants
14. Lichens*
Global Poskionhg System (GPS)
Design
Information Management
Landscape Characterization
Integration and Assessment
B. Conklmg, North Carolina State
University{NCSU), Raleigh, NC
Linda Kirkland, EPA, Washington, D.C.
G. Byers, Lockheed-ESC,
Las Vegas, NV
C. Palmer, EPA, EMSL-LV
B. Conkling, NCSU, Raleigh, NC
G. Byers, Lockheed-ESC, Las Vegas, NV
G. Byers, Lockheed-ESC, Las Vegas, NV
R. Van Remortel, Lockheed-ESC, Las Vegas, NV
C. Liff, Harry Reid Center for Environ. Studies
(HRC), UNLV, Las Vegas, NV
B. Smith, TVA, Knoxville, TN
J. Vissage, USDA-FS, Starkevilte, MS
S. Phillips, Portland, OR
R. Kucera, AREAL, RTP, CC
M. Baldwin, VPI, Blacksburg, VA
W. Burkman, USDA-FS, Radnor, PA
I. Millers, USDA-FS, Durham, NH
M. Miller-Weeks, USDA-FS, Durham, NH
W. Bechtold, USDA-FS, Asheville, NC
W. Anderson, USDA-FS, Asheville, NC
W. Hoffard, USDA-FS, Asheville, NC
D. May, USDA-FS, Starkevilte, MS
J. Vissage, USDA-FS, Starkevilte, MS
J. LaBau, Univ. Alaska, Anchorage, AK
W. McLain, USDA-FS, Fort Collins, CO
M. Schomaker, USDA-FS, Ft Collins, CO
J. LaBau, Univ. Alaska, Anchorage, AK
W. McLain, USDA-FS, Fort Collins, CO
T. Droessler, ManTech, Corvailis, OR
W. Bechtold, USDA-FS, Asheville, NC
R. Anderson, USDA-FS, Ashevilte, NC
W. Hoffard, USDA-FS, Ashevilte, NC
K. Stolte. USDA-FS, RTP, NC
R. Van Remortet, Lockheed-ESC,
Las Vegas, NV
T. Lewis, Lockheed-ESC, Las Vegas, NV
T. Lewis, Lockheed-ESC, Las Vegas, NV
T. Droessler, ManTech,. Corvailis, OR
S. Alexander, EPA, RTP.NC
J. Isebrands, USDA-FS, Rhinelander, Wl
S. Cline, Mantech, Corvailis, OR
T. Martin, U. of Ark., FayettevHte, AR
K. Stolte, USDA-FS, RTP,, NC
B. McCune, OSU, Corvallis, OR
K. Hermann, ManTech, RTP, NC
J. Hazard, Slat. Cons. Serv., Bend, OR
D. Cassell, ManTech, Corvailis, OR
C. Liff, HRC-UNLV, Las Vegas, NV
K. Hermann, ManTech, RTP, NC
K. Riitlers, ManTech, Inc., RTP, NC
* Indicator also includes an analytical laboratory component (field sampling, sample preparation and analysts)
10-4
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10.2.3 Quality Assurance Personnel
and Responsibilities
Several personnel are in key positions in
the structure of the QA staff for FHM. In
addition, the indicator leads play an important
role in planning and implementing quality
assurance for their respective indicators (see
Table 10-2 above and Figure 10-1 below).
These QA personnel include:
• Quality Assurance Coordinator for EMAP
L. Kirkland, EPA Quality
Assurance Officer (QAO)
Washington, DC
• Technical Director for EMAP-Forests
S. Alexander, EPA, Research
; Triangle Park (RTP), NC
• Deputy Technical Director for EMAP-Forests
C. Palmer, EPA, Las Vegas,
NV
• Forest Service FHM National Program
Manager
J. Barnard, USDA Forest
Service, RTP, NC
*"• Quality Assurance Coordinator for FHM
C. Palmer, EPA, Las Vegas,
. NV
• Regional Quality Assurance Officer for FHM
C. Barnett, USDA Forest
Service, Radnor, PA
• Quality Assurance Officer for EPA EMSL-LV
L. Williams, EMSL-LV, Las
Vegas, NV
• Methods Coordinator for EMAP
G. Collins, EPA QAMS,
Cincinnati, OH
The national QA coordinator for FHM
(QAC-FHM) interacts closely with the Quality
Assurance Coordinator for EMAP, the Regional
FHM QA officers (see Figure 10-2 for 1992),
indicator leads, Forest Service staff, and other
FHM participants in coordinating QA activities
for 1992. The QAC-FHM is responsible for QA
in all FHM activities and reports directly to the
FHM technical director. The Eastern FHM QA
officer will coordinate specific QA tasks with
individuals on the Forest Service technical
staffs who are best qualified to perform them
successfully. For 1992, one regional QA
Officer has been appointed for the eastern U.S.
to represent the Northern, Southeastern, and
Southern regions of the Forest Service. For
specific responsibilities and roles, see the QA
Project Plan (Byers and Palmer [1992]) and the
EMAP QA Program Plan.
The FHM Technical Director is responsible
for determining which 1992 activities require
SOPsi and ensuring that they are developed,
reviewed, and implemented. The QAC-FHM
will work with the technical director, the
regional QAO, and especially the indicator
leads in the SOP process. The QAC-FHM also
has responsibilities in SOP identification,
interorganizational consistency, elevation to
method or protocol status, and the need for
training. The QAC-FHM will write the QA
Project Plan for 1992. Figure 10-1 identifies
the proposed organizational structure for the
FHM quality assurance participants now that
the FHM program is becoming national in
scope.
The QAC-FHM staff will coordinate the
technical systems audits (TSAs) and the
performance evaluation audits (PEAs) at the
project level for 1992. A TSA is an on-site
visit to a field plot or laboratory used to verify
conformance to the QA Project Plan, the use of
good laboratory and field practices to generate
the environmental data, and to ensure
adherance by all data collection participants to
protocols in a consistent manner. Indicator
leads will be responsible to develop and
conduct these TSAs. A PEA assesses
laboratory (and field in some cases) analyses
based on results achieved in the analysis of
blind samples. These audits provide the basis
for assessing the success of a QA program,
and aid in determining whether the QA Project
Plan and the field and laboratory manuals are
being fully implemented and if they are
currently adequate to satisfy the
10-5
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EMAP
Methods Coordinator
EPA-Cincinnati, OH
EMAP
QA Coordinator
EPA-Headquarters
Washington, DC
EPA
Laboratory
QA Officer
EMAP-Forests
Tech. Director
RTF, NC
FHM
QA Coordinator
EMSL-LV,NV
Pacific Northwest
Regional QA
Officer
(e.g. ERL-C,OR)
Intermountain
Regional QA
Officer
(e.g.,EMSL-LV,NV)
FHM Indicator
Development
Coordinator
ERL-C, OR
EMAP-Forests
Deputy TD
EPA, LV
Northeast
Regional QA
Officer
(e.g., Radnor,PA)
, Southeast
Regional QA
Officer
(e.. g. , AREAL-RTP , NC)
FHM Reports
Coordinator
RTP, NC
FHM Indicator
Leads
Field & Lab
Methods
Field & Lab
Auditing
Performance
Evaluation
Data
Verification
FS Program
Manager
RTP, NC
Section, QA
Project Plan
Figure 10-1. Proposed organizational structure for FHM quality assurance staff.
10-6
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objectives of the project. Audits are conducted
for all data collection measurements. Indicator
leads will be responsible for developing and
conducting these audits. The Technical
Director is responsibile for verifying corrective
action for any major discrepancies documented
in the audit reports. The QAC-FHM is
responsible for tracking the corrective actions in
these cases. A Memorandum of Intent
identifies the problems and/or discrepancies,
the corrective action(s) taken, and the remedial
effects.
10.3 Data Quality Objectives
At present, data quality objectives (DQOs)
are considered to be specific statements of the
level of uncertainty a data user is willing to
accept in environmental data with respect to
the kind of scientific or policy question that
motivated the data collection activity. The
DQOs are definitive, quantitative, or qualitative
statements developed jointly by data users
(e.g., indicator leads, other scientists,
managers, policy makers, interest groups) in
conjunction with the QA staff.
10.3.1 Measurement Quality
Objectives
Data quality, and therefore DQOs, may be
defined for several levels of FHM data
collection. The first level represents the
measurement-level DQOs, called measurement
quality objectives (MQOs), for specific
measurement parameters within each indicator.
The MQOs are estimated using existing or
initial baseline data. Generally, at present, the
MQOs have been established by the indicator
leads. However, MQOs for plot establishment,
characterization, and mensuration were
established independently of FHM based on
historical data. In FHM, these MQOs are being
established for several levels of data collection,
e.g., sample measurement system,
measurement parameter, or indicator level for
various indicators. The MQOs define
acceptance criteria in field and laboratory
measurement data (Byers et al. 1990) for four
quantitative attributes: detectability, precision,
accuracy, and completeness. Also included
are two qualitative attributes;
representativeness, and comparability.
Representativeness criteria have been
established a priori by the plot design and plot
sample selection procedure established for
1991. Completeness and detectivity criteria
have been established for some of the
components of the implementation indicators
and for some indicators requiring laboratory
analysis, e.g., soil analysis. Other DQOs are
recognized at a higher ecosystem level, but
they are yet to be addressed seriously (e.g.,
indicator-level DQOs (IQOs), resource group-
level DQOs (RQOs), and ecosystem-level
DQOs (EQOs).
Common types of- data generated by
different methods across regions within a
resource group and by different resource
groups must be compatible, especially over
time. Emphasis must be placed on data base
flexibility because of issues, unknown now,
which may arise in future years of this multi-
year program. Acceptance criteria established
during the DQO-development process serve as
benchmarks for satisfying data user
requirements. For a discussion on the
hierarchy of DQOs for EMAP, and the DQO
setting process, see Palmer et al. (1991). For
MQOs for each of the 14 indicators, see the
QA Project Plan, Part II, Sections 2 through 15
(Byers and Palmer 1992).
10.3.2 QA Activities in the Field for
1992
Several issues are of concern for the field
activities for 1992 that are related to QA for all
indicators. Their satisfactory resolution for
1992 is based partly on logistical and
budgetary constraints. The issues are:
• How to assess and satisfy MQOs for
detectivity, precision, accuracy, and
completeness within and among regions for
field crews and trainers.
• What are the logistical constraints using
different crews on the application of QA
techniques within and among the relatively
large regions or on a national scale.
10-7
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EMAP
QA Coordinator
EPA-Headqua rte rs
Washington, D.C.
EMAP-Forests
Technical Director
RTF, NC
FHM
QA Coordinator
EMSL-LV,NV
FHM
Eastern QA Coor.
Radnor, PA
FS Program
Manager
RTP, NC
EMAP-Forests
Deputy TD
EPA, LV
FHM Indicator
Leads
Field & Lab
Methods
Field & Lab
Auditing
Performance
Evaluation
Data
Verification
Section, QA
Project Plan
Figure 10-2. The organizational structure for FHM quality assurance staff for 1992.
• How beneficial and cost-effective will be the
different possible QA techniques.
10.3.2.1 Field Crews
For the eastern U.S. (Northeast, Southeast,
and South regions), approximately 30 field
crews of two persons each will be used for
collecting field data for all of the Group 1
indicators (see Table 10-2) for the Forest
Service's Forest Inventory Analysis Program.
This measuring of indicators 1, 2, and 3 (see
Table 10-2) is called Type 1 measurement.
For plots that were visited in 1991 only
measurements performed for the indicators 2
and 3 (as given in Table 10-2), called Type 2
measurements, will be made in 1992. Three
field crews of five to six persons each will be
used for collecting field data (and samples, in
some cases) for all the pilot and demonstration
indicators (Group 2 indicators), and for the
Group 1 indicators for the loblolly/shortleaf pine
Demonstration Project in the southeastern U.S.
in 1992. For the Western U.S. (CA and CO)
10-8
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four field crews of five persons each will be
used for collecting field data (and sampling in
some cases) from all plots for all indicators in
the West. The list of indicators for East and
West does not completely coincide. For
additional information on these indicators, see
Section 6. For additional information on the
crews, see Section 11. An adequate number
of field and pilot/demonstration crews will be
established in the East and West to ensure that
the planned 1992 FHM program can be
successfully implemented.
10.3.2.2 Trainers
Expert trainers will be used for crew training
purposes (Group 1 indicators) for each of the
FS regions for 1992. Accuracy standards will
be. established for all measurements for the
Group 1 indicators by a small team of
experienced USDA Forest Service personnel.
For the Group 2 indicators, the indicator leads
will serve as the expert trainers in establishing
data standards for training purposes. The
trainers will be present at pretraining sessions
to establish and characterize the reference
plots and test trees prior to training the field
and pilot/demonstration crews. To ensure a
consensus in the data standards and
agreement on training techniques, the trainers
for the Group 1 indicators will "cross over" into
other regions (e.g., Northeast and South to
Southeast; East to West; California and
Colorado to Utah) for training together at the
pretraining workshops. All data for each trainer
will be documented as well as the consensus
value for all measurements. Agreement among
expert trainers on all measurements is a QA
issue in itself, because of their impact on the
field crews.
10.3.2.3 Check Crews for Auditing and
Accuracy
A check crew is a selected expert crew
composed of two well-trained experienced
Forest Service personnel (not generally
including trainers), that will check field and
pilot/demonstration crews during the field
season. Check crews attend pretraining and
training workshops for certification. The check
crews for 1992 serve two purposes: (1) to
"audit" each field and pilot/demonstration crew
within 2 weeks after commencement of the
field season and (2) to measure independently
one plot already measured by a field or
pilot/demonstration crew for accuracy purposes
after the auditing phase. In the first case, the
check crew separates into two individual
"auditors." No field data are taken by the
auditors during this phase. Each auditor visits
preselected field plots during measurement
activities by field and pilot/demonstration crews,'
assesses the crew's performance, discusses
deviations from protocol where and when they
occurred, and provides input to the field crew
to reduce inaccuracy and imprecision in the
measurements. This audit visit is actually a
post-training assessment of all field and
pilot/demonstration crews to ensure that they
are performing as trained and that all data and
sample collection protocols are being followed
as specified in the Field Methods Guide. A
report by the audit crew will be made for all
sites visited and filed with the QAC-FHM.
In the second case, for field accuracy, the
reassembled two-person check crew will
perform the same Group 1 measurements on
a selected field plot which has been previously
measured by a field or pilot/demonstration
crew. Check crews serve, in effect, as mobile
reference standards in this case. The plot data
from the check crew are compared with the
data generated independently by the field crew.
The field crews are unaware of which of their
plots the check crew will remeasure. In the
East, approximately 20 of the 30 two-person
field crews will have one of their plots checked
in this manner; all three pilot/demonstation
crews will be checked on one of their plots. In
the West, all four pilot/demonstration crews will
have one of their plots checked. All data taken
by the check crew are documented for later
assessment.
10.3.2.4 Audits by Indicator Leads
For the Group 2 indicators, each indicator
lead will perform one technical systems audit
(TSA) of each of the three pilot/demonstration
crews in the East and the four
pilot/demonstration crews in the West during
10-9
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the field season. An audit report for all visits to
crews will be filed with the QAG-FHM.
10.3.2.5 Reference Plots
Reference plots (also called standard plots
or accuracy plots) will be selected,
characterized, measured, and documented at
each of the training sites in Durham, ,NH (for
the Northeastern Region), Asheville, NC (for
the Southeastern Region), Starkeville, MS (for
the Southern Region), and Ogden, UT (for the
Western Region). All FHM field and PD crews
will perform their suite of measurements on
these reference plots as part of the 1992
training program. The trainers together will
carefully measure and document these specific
reference sites; consensus among trainers in
the standard data sets is essential. All crews
will be judged on their post-training
performance on the reference plots using these
standard data sets. Establishing the reference
plots at the training workshops assures
availability of all crews for training and
assessment of accuracy and thus accreditation.
Already established in 1991, the reference plots
serve as unique discrete temporal standards at
each regional location for the FHM program.
10.3.2.6 Training and Test Trees
Test and training trees will be used for
training and testing the field crews, the two
foresters on the pilot/demonstration crews (in
the East), and the two foresters on each of the
four crews in the West for two of the Group 1
indicators (crown classification and damage
and mortality). The expert trainer team will
select approximately 40 trees at each training
site that possess a broad range of visual
damage conditions found in the forests. These
trees are not contained within the reference
plots. The field and pilot/demonstration crews
will be trained using the training trees with
supplemental classroom instruction. Then the
field crews will be tested as a crew on another
set of 20 test trees, and as individual crew
members on an additional set of 10 trees. All
testing data will be documented and used for
crew certification and later for QA assessment
statistically.
10.3.2.7 Field Plot Remeasurement
Precision presents itself in the field asa
remeasurement Issue for the field and
pilot/demonstration crews. Asking a field crew
to remeasure one of its own previously
measured field plots does not supply definitive
precision because of possible memory recall by
the field crew, i.e., a field plot is not a blind
sample. However, no other approach has
been brought forward for estimating within-crew
variability in the field. In 1992, as in 1991,
each field and pilot/demonstration crew will
remeasure a previously measured plot within 2
weeks after commencement of the field
season. This will result in two sets of data for
estimation of within-crew variability in the field.
Assuming that the index window is not a factor
in changing forest conditions, a second set of
within-crew variability data will result from the
two visits made to the reference plots by each
field crew. The plot to be measured will not be
Identified to :the crew until just prior to
remeasurement. This approach will ensure
that the field crews will not know what plot to
"do better at."
10.3.2.8 Training and Debrief ing Workshops
Several levels of training workshops will be
used to ensure that crew training, assessment
documentation, and certification by experts is
acceptable to address accuracy, precision, and
comparability. These are:
•A national pretraining workshop for indicator
leads and trainers in early April.
• Regional pretraining workshops for trainers,
'indicator leads, and crew leaders in mid-May.
• Regional training workshops for field and
pilot/demonstration crew in late May/early
June.
• In a few cases (e.g., California and Colorado)
brief state training sessions for specific
measurements (e.g., insect damage) in early
June just prior to going to the field.
Information management personnel will
also conduct several programmer training
workshops during this March through May
period. In addition to these prefield season
workshops, there will ibe postfield season
10-10
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(debriefing) workshops held in several of the
regions. The latter workshops serve several
purposes. For example:
• Documentation of field and pilot /
demonstration crew remeasurements of a
reference plot and the test trees for 1992
statistical analysis and reports.
• Post-season assessment of success and
short comings of the 1992 program overall.
• Feedback from crew members, crew leaders,
indicator leads and management in
improvement for 1993.
• Review of logistical, QA, and information
management performance and
documentation.
Interregional comparability is also of great
concern from a quality assurance viewpoint.
The debriefing workshops will also be used in
1992 for assessing comparability between
trainers and crews for accuracy and precision.
Each field crew will remeasure the test trees
and reference plots again at the debriefing in
September. Several crews from comparable
regions, e.g., the southern northeastern region
and southeastern region, will also remeasure
the test trees and reference plots at the
debriefing in Asheville. This process will
establish a basis for comparability among
crews among regions.
The above approaches address the QA
concerns for accuracy, precision, and
comparability for field crews, pilot and
demonstration crews, trainers, and inter-
regional comparability issues.
For further discussion on field accuracy,
precision, and comparability, see the 1992 QA
Project Plan (Byers and Palmer 1992). The
1991 data for the field remeasurement and
training and debriefing workshops for the
northeastern, southeastern, and southern
regions are currently being assessed
statistically. A report on the conclusions and
trends in the data will be incorporated in the
1991 Statistical Summary.
10.3.3 QA Activities in the
Laboratory
The same issues discussed in Section
10.3.2 can be raised for laboratory analysis.
Field sampling, sample preparation, and
laboratory analysis are required for six of the
Group 2 indicators in Table 10-2. (soil
characterization and physiochemistry, root
evaluation, foliar chemistry, stemwood
chemistry, dendrochronology, and lichens). A
sample preparation laboratory has been
established in Las Vegas for preparation of the
1992 soil and foliar samples and some
preliminary analyses with appropriate QA.
Indicator leads have established analytical
MQOs for the remaining indicators. See
Conkling and Byers (1992) for field sampling
protocols for these indicators. For sample
preparation, analysis, and MQOs for these
indicators, see Byers and Van Remortel (1991)
and Byers and Palmer (1992). Sample
analyses for most of the above indicators will
be contracted to laboratories after a
performance evaluation and contract award
process. Stringent criteria using QA and QC
samples and procedures for verification and
validation will be followed.
10.4 Documentation of Data
Collection
Effective documentation will ensure that
data quality information are available for
scrutiny for all measurement activities of the
FHM program. The scope and duration of
EMAP will certainly require re-examination of
data, methods, and conclusions as the program
progresses. Thorough documentation is
essential in establishing the flow of information,
possible alternatives, and a basis for
conclusions and decisions which form the basis
for new activities or require the revision of
previous conclusions based on new methods
and information.
Verification determines and controls the
quality of data. Verification can be
accomplished manually, electronically, or
through remeasurements. A systematic
10-11
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approach to data verification will ensure that all
data are subjected to basic standards of
accuracy that verify the authenticity, but not
necessarily the validity, of the data. Verification
is accomplished by comparing data at each
level of processing to established data quality
criteria. Verification is discussed within each
indicator section in the QA Project Plan. See
also the section on information management in
the QA Project Plan. Verification is also
discussed in detail in the logistics plan, e.g.,
related to the use of PDRs.
A systematic approach to data validation
will confirm that environmental measurements,
processes, and products are within acceptable
bounds of accuracy and validity with respect to
the population of interest. Validation is also
discussed within each indicator section.
10.5 Quality Assurance
Reports to Management
Reports to managment about quality
assurance fall into several categories.
10.5.1 Major Documents forl 992
Current versions of the following quality
assurance-related documents will be produced
during 1992 for the FHM program. These
documents, in addition to the Quality Assurance
Program Plan and the FHM Activities Plan for
1992 will be disseminated among all
appropriate FHM participants and cooperating
organizations:
• Quality Assurance Project Plan for EMAP-
Forest s for 1992
(Byers and Palmer 1992)
• FHM Field Methods Guide
(Conkling and Byers, editors, 1992)
• Handbook of Laboratory Methods for FHM
(Byers and Van Remortel, editors, 1991)
10.5.2 Audit Reports
Technical systems audits for field and
laboratory activities are stand-alone reports and
will be delivered through specified channels to
the QAC-FHM on an ongoing basis during
1992. Performance evaluation audits will be
summarized and submitted to the QAC-FHM.
The QAC-FHM is required to report the audit
results to the QAO for EMSL-LV, and Technical
Director for FHM.
10.5.3 Quality Assurance Annual
Report and Workplan
The FHM Quality Assurance Annual
Report and Workplan will include a discussion
of project activities (e.g., DQOs, QA Project
Plan and SOP implementation, total quality
management training and implementation,
corrective actions, audits and reviews, and
resources used during the prior fiscal year).
Recommendations for changes in QA policy for
1993 (with appropriate justifications) will also
be addressed. The work plan component of
this report will describe all major QA activities
for 1993 including DQO developments and
refinements, deliverables, audit schedules,
changes in the QA program, resource
requirements, active projects and tasks
involved in data generation, and approved
changes in the QA project plans and SOPs.
The QAC-FHM is responsible for this report
and its submission to the QAC for EMAP in
early February.
10.6.4 Statistical Summary
A statistical summary report of all 1991 field
and laboratory activities will be made in August
1992. A description of the QA activities and
accompanying statistical summaries for the
year for all indicators will be included as a
chapter in this report.
10-12
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11. Field Logistics Overview
R. L. Tidwell and R. Kucera
are discussed in the following subsections
under the heading of the person or persons
responsible for this activity.
Two main types of field crews will be
used for Forest Health Monitoring (FHM) 1992
activities: (1) implementation crews, and (2)
pilot/demonstration crews. The implementation
field crews will consist of two foresters. The
pilot/demonstration crews will be comprised of
several foresters, one soil scientist, one
botanist, and other members such as a
logistical aide, a tree climber, a soil
microbiologist, or a forester from Forest Pest
Management (FPM) taking off-plot
measurements for FPM. More detailed plans
for 1992 are presented in Appendices C, E,
and F of this document.
During a day of sampling, there are a
number of activities or operations that occur
which are the responsibility of certain
individuals. The following table presents an
example list of field operations and the persons
responsible for the activities. These operations
11.1 Field Crew Leader
The field crew will be supervised by a
designated crew leader. The crew leader will
supervise all field operations and, if necessary,
resolve all discrepancies or issues at the site.
The field crew leader has the responsibility of:
• Maintaining sampling schedule.
• Assembly of field crew.
• Transportation to the sampling site.
• Ensuring adherence to sampling protocols.
• Ensuring proper use of field equipment.
• Maintaining site and sample integrity.
• Data transfer.
• Daily communication.
• Safety.
TABLE 11-1. EXAMPLE OF OPERATIONS AND RESPONSIBILITIES
Operation
Sampling schedule
Crew assembly
Transport to site
Site location
Plot establishment
Sampling
Sample maintenance
Sample transfer and shipping
Equipment maintenance
Data transfer
Daily communication
Responsibility
Field crew leader
Field crew leader
Field crew leader
Foresters
Foresters
Field crew
Logistical aide
Logistical aide
Field crew and Logistical aide
Logistical aide
Field crew leader and Logistical aide
11-1
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11.1.1 Sampling Schedule
The field crew leader will be
responsible for sampling a certain number of
plots within an index period. The crew leader
will have the responsibility to meet this
sampling quota while maintaining the quality of
the measurements and samples and following
standard protocol. The field crew leader must
be attentive in assessing field crew morale and
creating a sampling schedule to maintain .an
adequate level of morale.
11.1.2 Assembly of Field Crew
The field crew leader is responsible for
assembling the field crew each morning at an
appropriate time. The field crew leader will
also determine rendezvous points to assemble
crews after scheduled time-off (e.g., after
weekends, holidays).
11.1.3 Transportation to the
Sampling Site
The field crew leader will be
responsible for providing and maintaining
adequate transportation to the site. Crews
must make efficient use of vehicles.
11.1.4 Ensuring Adherence to
Sampling Protocol
The field crew leader is expected to
have a basic knowledge of all sampling
procedures and be able to determine whether
field crew members are adhering to sampling
protocols. If protocols appear to be
inconsistent or are being misinterpreted, the
field crew leader should address the
inconsistency during the daily communications
update. Problems should be rectified by the
next day.
11.1.5 Ensuring Proper Use of Field
Equipment
Some field equipment such as PDRs,
ceptometers, laptop computers, and bar code
readers will require special care and proper
use. The field crew leader should observe the
use of equipment and if necessary instruct
crew members in the proper use. Field crew
members will inform the crew leader of
equipment that is damaged or in need of
repair. The field crew leader is responsible for
addressing the equipment problems in the note
section of the daily communication update.
11.1.6 Maintaining Site and Sample
Integrity
During sampling, flagging will be
placed around the site to mark various
sampling points (soil holes, vertical vegetation,
and PAR measurement points). Soil holes will
be dug and branches will be sampled. The
field crew leader will be responsible for
maintaining plot integrity and anonymity when
sampling is completed.
A number of samples will be collected
at the sampling site. These samples will be
prepared and analyzed and incur significant
cost related to collection, analysis and
interpretation. It is imperative that the integrity
of these samples is maintained. Some
samples require cooling and transportation to
laboratory facilities as soon as possible. The
field crew leader is responsible for ensuring
adherence to sample maintenance protocol.
11.1.7 Data Transfer
Data from PDRs and ceptometers must
be uploaded to laptop computers and
transferred to the EPA VAX. The field crew
:leader and logistical aide will be responsible for
this data transfer.
11.1.8 Daily Communication
Communication will take place
electronically through the laptop computers or
phone system. The field crew leader fills in an
"update" screen that requests the following
information:
» Field crew ID.
• Hexagon sampled that day.
11-2
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•Hexagon expected to be sampled following
day.
•Additional personnel with field crew (e.g.,
auditor, EPA personnel).
• Field crew location (hotel name, address,
Phone number).
• Expected location of the crew during the next
day.
• Comments or Problems.
The field crew leader or the logistical
aide is expected to fill in the update and send
it out electronically, each day, whether or not
data are being transmitted. This update will be
electronically sent to the EPA VAX which will
then be used to update Data General and E-
MAIL accounts of appropriate individuals in the
program.
11.2 Foresters
The foresters will be responsible for
locating the sampling site, establishing the plot,
and the data collection activities involved with
the site classification, growth, and regeneration;
visual crown assessment; and damage and
mortality indicators.
11.2.1 Sampling Site Location
In most cases, FIA photopoints will be
used for the sampling plots. Information
pertaining to the sites will be available at the
training session. Foresters will follow standard
FIA protocol in locating sampling sites.
11.2.2 Plot Establishment
. The foresters will be responsible for
establishing the plot. It is very important that
the plot establishment be accomplished in a
sequence that (1) maintains plot integrity for
other measurements (i.e., regeneration
measurements before vertical vegetation / PAR
measurements), and (2) allows all field crew
members to start data collection activities as
soon as possible in order to finish sampling
within a. reasonable timeframe. To accomplish
this, a plot layout procedure should be
developed. Figure 11 -1 is an example of a plot
layout procedure. This figure includes
sampling by foresters 1 and 2 when required to
maintain plot integrity. This procedure should
be used as an example. As the field crew
becomes familiar with the sampling methods,
they may adjust the plot layout procedure as
necessary.
11.3 Field Crew
The field crew, as a whole, will be
responsible for sampling activities. The field
crew and the logistical aide will share
responsibility for equipment maintenance and
equipment inventory. The logistical aide will aid
the crew with resupply requests, sample
shipment, and other administrative duties.
These administrative duties may include calling
in and reporting labor hours, sending or
receiving messages, and moving personnel
gear to the next hotel location.
11.3.1 Sampling
The foresters will be working together
for the majority of the day. The tasks of plot
layout, mensuration, and regeneration and
visual crown rating measurements are
expected to take a full day. The soil scientist
will be working independently, with the
exception of assistance in excavating soil holes
during the morning. The botanist will be
responsible for measurements for bioindicator
plants, vegetation structure, and PAR, which
must be sampled within a specific time period.
Therefore, an efficient sampling schedule
should be developed.
11.3.2 Equipment Maintenance
During training each field crew member
will be provided equipment and an inventory list
of the equipment they receive. If equipment is
damaged during field activities, the item should
be identified on the inventory list and the
service or repair that may be required should
be specified. The field crew member will
inform the field crew leader and the logistical
aide about the damaged equipment. The
logistical aide is responsible for communicating
this information through the daily crew update.
11-3
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Locate Plot Center
and Mark
Head Due North for
60 ft and Flag Soil Hole 1
Head Due North for
60 ft and Mark Plot
Center of Subplot 2
Return from Subplot 1
Head 120° for
60 ft and Flag Soil Hole 2
Head 120° for 60 Ft.
and Mark Center of
Subplot 3
Return to Subplot 1
Head 240° for 60 ft
and Mark Soil Hole
Head 240° for 60 ft and
Mark Soil Hole 4
Botanist Establishes
Ambient fAR Station
Soil Scientist Excavates
Soil Holes 1 and 3
Botanist Excavates
Soil Hole 2
Select Sample Trees
on Shbplot 4
Select Sample Trees
on Subplot 2
Measure Mensuration and
Regen on Subplot 1
Measure Mensuration and
Regen on Subplot 2
Measure Mensuration and
Regen on Subplot 3
Measure Mensuration and
Regen on Subplot 4
Collect Stemwood
from Sample Trees
Collect Air Pollution Plants
Figure 11-1. Example of a plot layout procedure.
11-4
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At the end of the field season, the field
crew will return all equipment to the logistics
lead. The logistics lead, logistical aide, and
field crew member will check off each item on
the inventory list that is provided at the
beginning of the field season to each field crew
member. All items must be returned. Items
will be inspected for damage and their
condition recorded.
11.4 Logistical Aide
The logistical aide will be responsible
for the following activities:
• Sample maintenance.
• Sample transfer and tracking.
• Sample shipping.
• Equipment maintenance.
• Equipment inventory.
• Data transfer and communication.
• Administrative duties.
• Assisting with field measurements.
11.4.1
Tracking
Sample Transfer and
As samples are transferred from the
field crew to the logistical aide, the number and
type of samples must be recorded in a manner
that will allow for the tracking of these samples.
This process will assure that all the samples
collected in the field have been transferred to
the logistical aide and it will allow tracking of
samples throughout the preparation, sample
analysis, and archive phases of the program.
Two types of techniques will be used for
recording and tracking samples: (1) barcode
scanning and (2) hard copy recording.
11.4.2 Sample Shipping
Shipments can occur daily or when
required by the logistical aide. The EPA will
provide prelabeled overnight shipping forms for
use in shipping samples. The express mailing
forms will be used in order and all unused
forms returned.
11-5
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12. Information Management
Overview
C.I. Lift and J.E. Teberg
The mission of the Forest Health Monitoring
(FHM) information management system is to
provide data of known quality to analysts in a
timely manner. Information management is a
crosscutting activity that interacts closely with
logistics and quality assurance and quality
control (QA/QC) in fulfilling its mission. Data
are collected in the field on portable data
recorders (PDRs), then transferred to a laptop
computer. The data are then sent via modem
to the VAX computer system at the
Environmental Monitoring Systems Laboratory-
Las Vegas, Nevada (EMSL-LV), for processing
and distribution. Once the data have been
through the QA/QC procedures, data bases are
created for use by the FHM assessment and
reporting groups. Figure 12.1 shows the FHM
data flow.
12.1 PDR System
The PDRs contain several data collection
programs. In 1992 data for the following
indicators will be collected on the PDR:
• Growth (mensuration).
• Visual crown rating suite of measurements.
• Soils data.
• Vegetative profile.
• Wildlife habitat.
• In-hand branch evaluation (sample tree).
• Root evaluation (sample tree).
Programs to record bioindicator plant data and
the 1-hectare mortality data have not yet been
developed and these data will not be collected
on the PDR.
Various QA/QC checks will be performed
on the data as they are entered on the PDR.
Checks include valid-value checks of a variable
and logic checks between a number of
variables. Performing these checks in the field
greatly improves the data quality.
For those crews performing annual crown
update measurements, certain data are
downloaded on the PDR. These data include
variables necessary to relocate the trees tallied
in the previous survey (distance, azimuth,
diameter at breast height [DBH], and species)
as well as items that need to be reconciled in
this survey (e.g., previous tree history). The
downloaded data are for display purposes only.
The crews are not to change those data except
for gross errors
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12.2.3 Sample and Shipment
Tracking
Bar coded samples that are packed for
shipping are scanned with a laser bar code
reader. Those oata are checked against the
data that have been recorded on the PDR to
increase the likelihood that all samples
collected in the field are packed for shipment.
12.2.4 Printing Data
The crew has the option to print all data
files and any electronic mail messages that
have been received.
12.2.5 Soil Discrepancy Resolution
The field soil scientist will have an
opportunity to resolve problems with the soils
data. This system is described in detail in
section 12.4.
12.2.6 Communications with the
EMSL-LV VAX Computer Cluster
The laptop is equipped with a modem and
a program that allows access to the EMSL-LV
VAX computer. This communications link
allows:
• Data Transfer - Field data are sent to the
VAX computer. The data are then
catalogued and processed. The crew can be
notified electronically of problems, with the
data.
• Electronic mail Communications - The crew
can send and receive electronic messages to
and from FHM staff members. The crew will
also send information about their present and
future locations.
12.3 VAX System
Once the data are received on the VAX,
they are parsed into the appropriate directories
(see Figure 12.1). The data are available to
indicator leads either through the EPA network
or via internet mail for users off the EPA
network. The data are reviewed for verification
errors by the indicator leads and problems are
flagged.
Tracking data bases are maintained on the
VAX and updated each time a crew
communicates with the VAX. The following
types of information are tracked on the VAX:
• Crew tracking - including crew location, plots
sampled by the crew, and messages from
the crew.
• File tracking - by crew, hex, data type, and
date.
• Sample and shipment tracking data from the
field and the laboratories -- samples are
computer tracked from the field and through
the preparation and analytical laboratories.
The tracking data bases will be used to
generate daily and weekly reports on progress
throughout the field season. Problems with
data collection can be identified with the
tracking data bases and appropriate action
taken.
12.4 Soil Discrepancy System
This system allows for quick identification
and resolution of errors in the soils data that
are collected in the field. The soils data are
entered in the PDR. These data are uploaded
to the laptop computer and sent to the VAX.
Once on the VAX, a series of programs are run
to produce a file containing inconsistencies in
the data. This file is reviewed by the soils
indicator lead and then a copy is sent
electronically back to the field crew. The crew
reviews the discrepancies, makes corrections
to the discrepancy file, and sends the file back
to the VAX. The corrections are reviewed by
the indicator lead and the soils data base is
updated with the approved corrections. This
particular system epitomizes the functionality of
the FHM information management system.
12.5 Preparation Laboratory
System
The preparation laboratory system tracks
and manages samples received from the
12-3
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field. Three types of samples are expected:
soil, foliar leaf, and tree core samples.
The preparation laboratory system is part of
the overall sample tracking system and also
maintains a data base for data generated at the
preparation laboratory.
12.5.1 Sample Receipt
When the preparation laboratory receives a
shipment of coolers, the shipment and sample
information are recorded in the preparation
laboratory data base. The data are transferred
to the EMSL-LV VAX, then parsed into a
"receive" data base. Finally, the samples that
were received by the preparation laboratory are
compared to the list of shipped samples"
prepared by the field crew and any
discrepancies are resolved.
12.5.2 Preparation Laboratory Data
Procedures
The samples are prepared in the
preparation laboratory for shipment to analytical
laboratories. Any soil sample measurements
such as moisture, bulk density, and loss on
ignition are entered into a data base.
Verification programs used with the data base,
adds flags to incomplete, invalid, and
improbable data. The preparation laboratory
manager receives a print out and is responsible
for resolving problems.
In preparation for analytical analyses, the
samples are stored until they can be separated
into batches. The preparation laboratory
system keeps track of the samples in storage.
As part of the QA/QC process, audit
samples are prepared in the preparation
laboratory. These data are also entered in the
preparation laboratory data base. Batch
numbers are assigned before shipment to the
analytical labs. The batch numbers are related
to the field sample numbers and are tracked
throughout the procedure.
Measurement results and tracking data are
uploaded to the VAX computer and appropriate
data bases are updated at periodic intervals.
12.6 Analytical Laboratory
System
For samples sent to an analytical laboratory
for chemical analysis, the tracking and
analytical data are stored and managed on the
Environmental Laboratory Verification and
Entry System (ELVES) at EMSL-LV . This
system allows one to enter new data, runs QC
and QA checks, checks chemical relationships,
checks expected ranges of the data, and
allows one to correct errors or update a set of
data if a batch is reanalyzed.
The laboratory sends an ASCII file of their
analysis results to EMSL-LV. These data are
loaded into the ELVES data base. Verification
programs check the data and a report is
generated for the indicator leads who decide
whether or not to accept the batch of data. If
the batch is not accepted, the problems with
the data are corrected. After the batch is
accepted, a final version of the data with the
sample IDs and final calculated chemical result
is stored in the system as well as uploaded to
the EMSL-LV VAX.
12.7 Field Software Testing
All software to be used in the field will be
thoroughly tested. The initial versions of the
software were sent to a group of testers the
first week of March 1992. Based on review
comments, the computer code is being
updated. The updated software will be sent
out for further review the last week in March.
Additionally, a "dress rehearsal" of field
operations will be held at Duke Forest, North
Carolina, during the first week in April. This
will allow software testing under actual field
conditions. A last round of software changes
will be made after the field test.
12.8 Training and Support
A field information management support
team will be trained to provide assistance to
the field crews. This team will consist of Forest
Service and state personnel. A support team
training session will be held at the FHM office
in RTF the second week of May.
12-4
-------
The support team will assist in the training
sessions. A self-guided PDR manual is
planned for completion in early May. This
manual and PDRs will be distributed to crew
members before training to familiarize them
with the PDR program in advance of the
training session.
12-5
-------
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13. Global Positioning System
Overview
K. Hermann
Global positioning system (GPS) technology
will be used to determine accurately the
location of Forest Health Monitoring (FHM)
plots. The geodetic coordinates (latitude,
longitude, and elevation) will be determined for
the field plot center or, if the plot center is
inaccessible, for a reference point related to the
plot center by survey measurements.
The geodetic coordinates of plot centers will
be useful for several reasons. First, accurate
coordinates help field crews relocate the plot in
future years and are an important aid in
acquiring aerial photography. The GPS-
determined coordinates will also satisfy the
requirements of the EPA Locational Data
Accuracy Policy for the information
management purpose of accurate sample
location. Finally, GPS coordinates will provide
a reliable measure of where measurements
were taken versus where the design
determined location was.
Accurate GPS coordinate determination
requires simultaneous operation of both a base
station GPS receiver and a remote, or field,
GPS receiver. A base station will operate on a
known set of geodetic coordinates at the same
time that a remote receiver is utilized by a field
crew member to record GPS measurements at
the plot center and reference point locations.
The data from each of the receivers will be run
through a set of programs in a postprocessing
mode for differential correction at the EPA
Atmospheric Research and Exposure
Assessment Laboratory. This dual operation
and postprocessing technique yields more
accurate results than simply operating a single
unit. The base station operation will be
coordinated through already established base
stations.
In addition to determining the location of the
FHM plot centers, GPS technology can assist
field crews in determining where plots will be
established. The field GPS unit can locate the
vicinity of the specified coordinates.
Finally, GPS can be employed to determine
coordinates for other important FHM locations,
such as starting points and photo/ground
control points for high-resolution aerial
photography. Starting points will be
determined using GPS in situations where
good visual references are lacking.
The photo/ground control coordinates will
be used to rectify high resolution aerial
photography to obtain a planimetrically correct
interpretation which will be accurately defined
to a datum. Photo/ground control locations will
be referenced to visible features or reference
points. The photography rectification
procedure, done with an analytical stereo
plotter, will enable the accurate capture of the
characterization delineations and their
subsequent entry into a geographic information
system (GIS).
In the spring and summer of 1992, GPS
measurements will be taken in three separate
FHM projects-the Southeast Demonstration,
the SAMAB Demonstration, and the Western
Pilot (Colorado). As a component of the
Southeast Demonstration project, GPS
measurements of the FHM plot center locations
will be taken in conjunction with the plot
establishment activities during the spring in
North Carolina. It is anticipated that plot center
GPS measurements will continue in the
Southeast Demonstration region during the fall
and winter.
In the SAMAB Demonstration project, plot
center locations will be determined with GPS
measurements taken during the summer field
sampling efforts.
In the Western Pilot project, plot center
locations and ground/photo control locations in
Colorado will be determined during the summer
field sampling efforts. The USFS Methods
Applications Group will acquire 1:6000 scale
aerial color infrared stereo photography for the
Colorado FHM plots in August. The
photo/ground control coordinates will be used
13-1
-------
in registering and rectifying interpretations from
the photography.
13.1 Objectives
Objectives for using GPS technology for the
FHM project include:
1. To accurately determine and record the
geodetic coordinates (latitude, longitude, and
elevation) of FHM field plot centers,
2. To satisfy the EPA Data Locational
Accuracy Policy, and
3. To accurately determine and record the
geodetic coordinates of photo/ground
control locations to be used in the
rectification of aerial photography
interpretations.
13.2 Design
The GPS coordinate determinations of the
FHM ptot centers will accurately determine the
geodetic position for each plot. If a dense
canopy prohibits reception of satellite signals,
coordinates will be determined for the nearest
available opening. The starting point may
provide a suitable alternative location.
Surveying measurement techniques will be
employed to link this alternate location and the
plot center.
In the Colorado GPS effort, coordinate
determination is made for photo/ground control
locations. A minimum of eight ground control
locations will be established for each FHM
photographic plot. These ground control
locations will be distributed about a 100-hectare
circular area around the plot center. This 100
hectare area will be photointerpreted from the
1:6000 scale photography. At least four ground
control locations will be placed towards the
perimeter of the 100-hectare circle in an effort
to achieve adequate point distribution over the
area. The locations of the ground control
points should be near existing roads or trails for
easy accessibility.
13.3 Logistics
13.3.1 Field Personnel
Requirements
One field crew member will take GPS
measurements with the remote GPS unit at the
plot center. In the Colorado portion of the
Western Pilot project, GPS measurements will
also be taken at each of the photo/ground
control point locations. If surveying is required,
an additional crew member may need to assist.
The GPS operator should be familiar with the
basic surveying techniques of determining
azimuth, distance, and inclination.
The base stations to be utilized in each of
the three GPS projects are already established
sites. Coordination with those base station
operations will ensure that the base station is
operational at the same time as the GPS field
activity.
13.3.2 Training
All GPS operators should receive special
training in the proper use of GPS equipment.
Training requires a minimum of 11/2 days
including field time.
13.3.3 Estimated Time on Plot
The operation of the field GPS receiver will
require about 15 minutes to determine plot
center coordinates. An additional half hour
may be required if some surveying is
necessary from a reference location to the plot
center due to canopy interference with GPS
reception at the plot center site. The estimated
time required to record photo/ground control
locations is 4 hours per day. The base station
operator will need to operate the base station
GPS receiver continuously for 6 hours to
coincide with the field operations.
13-2
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13.4 Information Management
All GPS recordings will be captured on the
GPS Polycorder device for each day's
reception. Each point location will be stored in
a separate file on the GPS Polycorder.
The GPS field and base reception of
coordinates in 3-D (latitude, longitude, and
elevation) are recorded on the GPS Polycorder.
Field notes are made on the time, location, and
conditions of GPS measurements. At the end
of each day, the collected data are transferred
to the laptop computer, using specific naming
conventions, and then backed-up on 3.5 inch
floppy diskettes. Nightly recharging of the GPS
Polycorder and receiver is required.
Plot center coordinates are confidential
information. Therefore, public access should
not be given to the coordinates.
A personal computer (model 80286 or
better) with appropriate software is required for
determining satellite availability, for post
processing the data, and for datum
conversions.
13-3
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Appendix A. National Plan Forest Pest Management and
Associated State Component National Forest Health
Monitoring Program, January, 1992
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NATIONAL PLAN
FOREST PEST MANAGEMENT AND ASSOCIATED STATE COMPONENT
NATIONAL FOREST HEALTH MONITORING PROGRAM
JANUARY, 1992
Forest Pest Management
State and Private Forestry
USDA Forest Service
P.O. Box 96090
Washington^ DC 20090-6090
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National Plan
Forest Pest Management and Associated State Component
National Forest Health Monitoring Program
January 1992
Problem Statement:
The health of the Nation's forests is increasingly in the news. Current issues include concern about
management activities, deforestation, habitat loss, air pollution, global climate change, and damage
from a variety of forest insect and disease problems. The USDA Forest Service (FS), U.S.
Environmental Protection Agency (EPA), and other Federal and State agencies must evaluate and
respond to these issues. Timely response requires credible forest conditions information based on data
which can be compiled, analyzed, and interpreted to evaluate forest health. The National Forest Health
Monitoring Program (NFHM) integrates Federaf and State monitoring activities to provide this forest
health information.
Congressional Direction:
Congress has provided the FS, through the Secretary of Agriculture, authority, direction, and funding
*- to establish a national forest health monitoring program. Within the FS, Forest Pest Management
(FPM) in State and Private Forestry (S&PF), Research, and the National Forest System (NFS) are
involved in developing and implementing the initial phases of forest health monitoring.
From the FPM and State perspective, amendments tothe Cooperative Forestry Assistance Act of 1978
(P.L. 95-313) by the Food, Agriculture, Conservation, and Trade Act of 1990 (P.L. 101-624) are
important because forest health monitoring is explicitly authorized. Section 5 of P.L. 95-313, Insect
and Disease Control, is redesignated Section 8, Forest Health Protection. Section 8(b)(1) directs the
following: "Conduct surveys to detect and appraise insect infestations and disease conditions and
man-made stresses affecting trees and establish a monitoring system throughout the forests of the
United States to determine detrimental changes or improvements that occur over time, and report
annually concerning such surveys and monitoring."
From the Research perspective, the Forest Ecosystem and Atmospheric Pollution Research Act of
1988 (P.L. 100-521) is particularly important because it authorizes a 10-year program of research and
monitoring to better understand the relationships between forest health and air pollutants and
recognizes the need for long-term monitoring.
Forest Health:
Within NFHM, forest health is considered with respect to effects of both naturally occurring factors such
as fire, forest pests, forest succession, site, drought, and weather extremes, as well as unnatural biotic
and abiotic factors such as introduced pests, air pollution, and global warming changes. For FPM, the
term forest health denotes forest ecosystem resilience and productivity relative to a specified set of
values, needs, and expectations. Thus, forest health can be defined by different standards which relate
to differing management objectives for particular forested areas. There is an expectation that forests
are healthy when biotic and abiotic influences do not threaten the attainment of management objectives
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now or in the future. Although forest condition can be specified and measured objectively, forest health
carries an element of subjectivity, as it is a value judgement.
Program Cooperation and National Coordination:
Forest Service, EPA, and States are responding to forest health concerns by initiating a National Forest
Health Monitoring Program designed to describe regional and national forest conditions and to detect
changes, determine causal relationships, and predict consequences. The NFHM success depends on
participation by FS, EPA, and States. The NFHM conceptual design incorporates recommendations
from the FS Forest Response Research Program (part of the National Acid Precipitation Assessment
Program), the FS Forest Health Strategic Plan, and the EPA Environmental Monitoring and Assessment
Program. The NFHM design builds on existing FS programs. The FS Staff units most involved have
been Forest Fire and Atmospheric Sciences Research (FFASR); Forest Inventory, Economics, and
Recreation Research; Forest Pest Management (FPM); Timber Management; and Watershed and Air
Management. A number of other FS Staffs will likely become more involved both in NFHM and in
related aspects of EPA's Environmental Monitoring and Assessment Program.
Forest Service Washington Office (WO) coordination includes (1) discussions among the NFHM
Program Manager and representatives from participating staffs
in Research, S&PF, and NFS; (2) the NFS Environmental Health Technical Group; (3) coordination
discussions among WO Staff Directors and their staff specialists; (4) weekly teleconference calls
originated by NFHM; (5) the September 1990 memorandum defining FS Research and State and
Private Forestry roles and responsibilities; and (6) FPM's National Forest Health Monitoring Plan.
NFHM Program Design:
Although FS Research, through the NFHM Program Manager, has the overall lead for NFHM design,
implementation, and reporting, FS FPM and participating States have direct responsibility for designing
and implementing the FPM/State components of NFHM. These responsibilities are discussed in the
following pages.
The National Forest Health Monitoring Program is a three-tiered, long-term process to provide regional
(multi-state) and national information on forest health status and trends. Each successive tier requires
progressively more detailed information. The tiers are:
Detection Monitoring - to detect deviation of key monitoring elements from established base
line conditions or trends. This is the first and most extensive level. Within the FS, FPM and
Research share responsibility for initial design and implementation of this tier which consists
of (1) a plot component (Research) - a geographically-based network of permanent plots
distributed throughout the Nation's forested areas; and (2) a survey component (FPM) - forest
pest and other stressor effects surveys and reports of forest damage. Detection Monitoring
will discover and describe changes in forest health conditions. The FPM Responsibility section
which follows outlines FPM's contribution to Detection Monitoring.
Evaluation Monitoring - to determine or hypothesize likely causal relationships and to
recommend management responses. This is the second level, it is FPM's responsibility to
initiate, and is activated by Detection Monitoring results. Evaluation Monitoring is the process
for determining causes for changes in forest health status beyond that initially obtained in
Detection Monitoring. The FPM Responsibility section outlines FPM's contribution to
Evaluation Monitoring.
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intensive-Site Ecosystem Monitoring - to define basic relationships sufficient to predict
consequences. This is the third level and provides the most detailed, long-term data for
ecosystem research to determine causal relationships, predict rates of change in forest
conditions, and identify management responses. Forest Pest Management has little or no
responsibility in Intensive-Site Ecosystem Monitoring.
FPM Goal:
Discover if there are large-scale episodes of forest pest and other stressor effects that are not related
to historical or normal cycles of forest growth, decline, and stressor activity. This includes effects from
(1) known agents; (2) changed activity of known agents; (3) new agents; and (4) unknown agents.
FPM Program Objectives:
1. Document status and change in large-scale effects on forests of forest pests and other
stress agents. This includes temporal and spatial change in extent, severity, duration, frequency, host,
and cause.
2. Conduct forest stressor effects surveys, provide training, and provide technical assistance
in support of the NFHM plot network.
FPM/State Responsibility:
Forest Pest Management and States are responsible for providing Regional and State forest
pest/stressor effects information and for addressing how forest health may be affected. Data must be
adequate to evaluate and interpret the relationship between forest pest and stressor effects and forest
health changes.
Information from both the survey component of Detection Monitoring and from Evaluation Monitoring
is essential for describing overall forest health conditions. Data from the plot component of Detection
Monitoring will describe important aspects of forest health based on a number of forest health indicators
including damage by some insects, diseases, and other stressors. The survey component will more
accurately describe forest pest and other stressor effects in the general forest area than can be done
from plot data alone. This information is needed to draw proper conclusions about these effects on
overall forest condition.
Forest Pest Management and States have four specific responsibilities in NFHM:
1. Conduct surveys to discover, confirm, quantify, and map significant forest pest and other
stressor effects over the general forest area so that:
(a) change in overall annual pest and other stressor effects on forests can be incorporated
systematically and quantitatively in NFHM reporting, and (b) NFHM ground plot data can be interpreted
with knowledge of broader forest stressor activity and history. Although forest pest detection surveys
are a traditional FPM and State responsibility, a change in focus is needed to meet NFHM analysis and
reporting requirements. A description of the traditional FPM program emphasis as compared to forest
health monitoring emphasis is outlined in Appendix 1.
2. Provide NFHM plot support. This includes (1) technical support such as assisting with plot
survey design and planning, pest and other stressor identification, tree vigor classification, pest-related
data management, and training for NFHM plot survey crew; and (2) supplemental aerial and ground
surveys as needed to determine forest pest/stressor effects on NFHM plot measurements. Surveys
on and in the vicinity of NFHM plots provide data for effects which would not be noticed or recorded
A-5
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during regularly scheduled plot visits by Research or State personnel.
3. Evaluate specific reports of forest damage to determine cause, extent, severity, trend, and
likely result of forest pests and other stressor effects on forest health. The need for evaluations may
be determined from Detection Monitoring results from plot data, survey information, and reports from
other sources.
4. Meet FPM, State, and NFHM reporting needs.
FPM Component Design and Implementation:
Regional and Area FPM staffs, in conjunction with Research Stations and States, are responsible for
Regional and Area FPM component design and implementation within the context of NFHM Program
and national FPM direction. To improve design and implementation coordination among NFHM,
Regions, Area, and States, FPM-WO has done the following:
1. Funded a NFHM Applications Deputy Program Manager position to be the national focal
point within NFHM for (1) FPM-related design, coordination, implementation, and reporting; and (2)
integrating both the FPM/State component of Detection Monitoring and Evaluation Monitoring in NFHM.
2. Established a national FPM Forest Health Monitoring Work Group to identify and address
specific FPM design and implementation questions. Regional and Area work group representatives
are listed in Appendix 2.
This national work group identified the following sequence of FPM/State activities in NFHM.
1. Find forest pest and other stressor effects (use mix of sensing techniques).
2. Document and quantify pest and other stressor effects (use mix of sensing techniques). Establish
quality assurance and quality control standards and guidelines. Verify performance.
3. Document and quantify change in effects.
4. Report change in effects.
5. Explain change.
6. Interpret and report forest health consequences.
7. Consequences that influence management and regulatory decisions.
The draft Eastern Forest Health Monitoring Plan, prepared jointly by R-8 and NA, describes regional
application of the national plan. This regional plan outlines a process which employs local reporting
networks to detect and report occurrence of effects. These networks include surveillance by field
personnel, incidental reporting from all sources, and selected aerial surveys. This may be followed or
supplemented by one or more of the following: (1) planned surveys to assess scope and severity of
effects noted in local reports; (2) planned surveys of known problems, hazard areas, or areas of
particular interest; (3) systematic use of Forest Inventory Analysis (FIA) data to assess scope and
severity of effects from selected forest pests/stressors; and (4) planned surveys coupled with ground
plot data to quantify scope and severity of stressor effects not readily measured by remote sensing
techniques. Ground plot data could come from FIA, FHM, or other plots.
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Unresolved issues include: (1) regional differences within the context of a nationally consistent design
- the western plan(s) have yet to be designed and written; (2) statistical soundness of approach for
measuring effects and change in effects; (3) agreement on data elements and data collection standards
and guidelines; and (4) process for data management, analysis, and reporting.
FPM Reporting and Data Management:
The NFHM Program guidelines will need to be developed on (1) annual forest health reporting
requirements; and (2) procedures for integrating information from the survey and plot components of
Detection Monitoring. It is anticipated that Regions, Area, and Stations will collaborate on regional
reports which will be aggregated by the NFHM Program Manager for the national forest health report.
The Regional, Area, Stations, and State contributions to the forest health report are to report status and
changes in forest stress agent effects and to explain relationships between stress agent effects and
forest health. Initial NFHM forest health monitoring indicators include forest growth, mortality,
distribution and structure, canopy condition, and soil condition. Forest health may eventually be
described in terms of Assessment Concerns such as forest productivity, forest resiliency, forest
biodiversity, forest distribution and structure, and forest pest status.
Forest Pest Management, WO will continue both the annual and five-year "Forest Insect and Disease
Conditions in the United States" reports in accordance with Chapter 40 of FSH 3409.11. The objective
of these reports is to report on pest outbreak status and trends. Some of the data used for the
Conditions Reports will be used in NFHM reports.
Regional, FPM-related forest health monitoring reporting will be coordinated by Region/Area FPM staff.
Specific direction will need to be developed on how data are to be managed, analyzed, interpreted, and
reported by each Region/Area in cooperation with Stations, States, and the NFHM Program Manager.
National forest health reporting will be coordinated by the NFHM Program Manager.
Forest Pest Management will focus on reporting changes in forest stressor effects both in the general
forest area and on or in the vicinity of NFHM plots. Forest Pest Management and State reporting
requires (1) a nationally consistent design; (2) core data elements with standards and guidelines for
each; (3) standards and guidelines for data collection; and (4) analysis and reporting guidelines. The
FPM's national Forest Health Work Group will be the forum to further develop program design as well
as data element, data collection, and reporting standards and guidelines.
Although reporting guidelines are under development, there is consensus that FPM should document
temporal and spatial change in large-scale effects on forests due to forest pests and other stress
agents. This information needs to be of sufficient quality and proper format so that it can be integrated
with forest resource and other data for analysis, interpretation, and NFHM reporting. Initial NFHM
reporting will probably describe (1) known forest health risks; (2) trends in forest pest/stressor effects;
(3) current year status of forest pest/stressor effects; and (4) significant changes in status and trends
of resource risks and effects.
In addition to documenting large-scale effects in the general forest area, stress agent effects on NFHM
plots will need to be documented in order to determine possible effects on plot measurements.
National core data elements and related measurements which are being considered to provide the
basis for reporting include:
1. EFFECT - Forest Pest/Stressor Effect Type (defoliation, dieback, mortality, discoloration, growth
loss, and other symptom).
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2. LOCATION - State, County, NFHM Plot, Coordinates, and other.
3. HOST(S) - Tree Species and Forest Type.
4. STRESS AGENT - Forest Pest/Stress Agent Name.
5. SCOPE - Acres Affected.
6. SEVERITY - Volume of Mortality/Acre, Trees/Acre, Intensity (high, medium, and low).
7. DATE - Year and Month.
8. OWNERSHIP - Federal and Other.
Evaluation Monitoring:
When Detection Monitoring reports identify areas or problems of concern for which more specific
information is needed, FS Forest Pest Management, WO and the NFHM Program Manager will assess
the situation and determine specific evaluation needs. These could include additional surveys, site-
or area-specific evaluations, and more detailed monitoring. In some cases specific research studies
may be required. These would be a FS Research responsibility. Criteria for Evaluation Monitoring
project design and selection are to be developed by FPM in cooperation with States and the NFHM
Program Manager.
Coordination wHh Research:
Forest Service Research, through the NFHM Program Manager, has the overaH lead for NFHM design,
implementation, and reporting. Forest Pest Management has direct responsibility for providing forest
pest and other stressor effects information in Detection Monitoring and for initiating Evaluation
Monitoring activities. Both Detection and Evaluation Monitoring activities conducted by FPM and States
will be coordinated with the NFHM Program. Forest Health Monitoring program and budget planning
are coordinated among participating FS staff units.
Deputy Chief, S&PF and National FPM Staff Director, will provide policy direction and oversight to the
FPM component of the NFHM program. The NFHM Applications Deputy Program Manager
responsibilities include NFHM program implementation and coordination with S&PF, NFS, other Federal
agencies, and States. This position will be the national focal point for FPM-related activities in NFHM.
Duties are similar to those outlined for Subject Area Managers in the May 1991 Memorandum on Fiscal
Year 1992 Forest Pest Management Technology Development. These duties include:
1. Provide management oversight to FPM component of NFHM.
2. Lead and serve on work group/steering committees to address national NFHM design,
implementation, and reporting questions.
3. Assist Regions/Area in preparing annual accomplishment and monitoring reports.
4. Review Evaluation Monitoring project proposals.
Regions, Area, and Stations, in concert with the National FHM Program Manager, will need to specify
coordination procedures applicable to their particular situations and to the States involved.
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Coordination with States:
Forest Service Forest Pest Management and States will jointly design, plan, and implement the
FPM/State component of Detection Monitoring and Evaluation Monitoring in each State. To avoid
duplication of effort and confusion, FPM staff will work with Research Station and State staff and the
NFHM Program Manager to establish the protocols and coordination mechanisms needed to enlist
State involvement in NFHM. Specific State responsibilities will be developed and documented on a
State-by-State basis.
Coordination with NFS and Other Federal Agencies:
As technical staff to National Forest System and other Federal resource managers, FPM staff will have
an important role in helping plan and execute the NFHM Program on National Forest and other Federal
lands. Regional and Area FPM staff have direct responsibility for implementing the FPM component
of NFHM on National Forest and other Federal lands.
Implementation Schedule for State and Private Lands:
Forest Health Monitoring began in Fiscal Year 1990 in the 6 New England
States (CT, ME, MA, NH, Rl, and VT). Six additional eastern States are included for Fiscal Year 1991
- DE, NJ, MD, AL, GA, and VA. The FS plans to fully implement NFHM over the next 5 to 10 years.
For purposes of fiscal
year 1992 planning, FPM expects to (1) continue FPM's part of NFHM in the 12 above-noted States;
and (2) initiate FPM's component in CA and CO. Depending on budgets and NFHM program direction,
other priority States to add in Fiscal Years 1992 and 1993 include NC, SC, MN, and WV.
Continued progress in gradually expanding NFHM will depend on when States are able to enter the
program and on continued Federal budget support. The NFHM Program Manager, FS, EPA, and State
cooperators will review and update State implementation priorities annually. Implementation priorities
are based on the following considerations: (1) develop regional bjocks of States and forest ecosystems
for sampling and analysis; (2) select States able and willing to participate in NFHM; (3) spread
workload among different FS Regions and Experiment Stations; and (4) involve new Regions to gain
monitoring experience under different conditions. The Forest Health and Research Committees of the
National Association of State Foresters (NASF) are currently surveying States to gain information on
State readiness to participate in NFHM. This information will help determine implementation priorities.
Initial priority for expanding NFHM follows. This will be updated in response to technical, budget, and
NASF information on State priorities.
1. For Fiscal Year 1994, eleven more States - MS, LA, TX, TN, AR, FL, OK, PA, NY, OH, and KY.
2. For Fiscal Year 1995, eight more States - previously considered but not implemented States plus
OR, UT, WY, Ml, and Wl.
3. For Fiscal Year 1996, eight more States - previously considered but not implemented States plus
WA, ID, MT, MO, IN, IL, and IA.
4. For Fiscal Year 1997, eight more States - previously considered but not implemented States plus
ND, SD, KA, NB, NV, NM, and AZ.
5. For Fiscal Year 1998, seven more States -- previously considered but not implemented States.
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Implementation Schedule for Federal Lands:
Forest health monitoring on Federal lands will generally follow the State implementation schedule as
determined by the NFHM Program Manager. Specific implementation schedules and procedures will
be coordinated among the NFHM Program manager, Regions, Stations, Area, and other Federal
agencies. Other Federal agencies likely involved are those in the U.S. Department of the Interior,
Tennessee Valley Authority, and the Department-of Defense.
FPM Implementation Plans and Field Methods Manuals:
Forest Pest Management Regional and Area implementation plans and field guides will be developed
by Regions/Area and will be consistent with (1) NFHM Program direction; (2) Forest Pest Management
national program direction; and
(3) Regions/Stations/States NFHM implementation plans. Coordination among Regions/Area FPM
staffs to develop plans and manuals is essential and is encouraged by the National FPM Staff Director.
This coordination will be facilitated by the National FPM Program Director and the NFHM Program
Manager.
FPM Quality Assurance Plans and Procedures Manuals:
Quality assurance plans and procedures guides will be developed by each Region and Area FPM staff
in accordance with NFHM Program requirements. Coordination among Regions/Area FPM staffs to
develop plans and manuals is essential and will be facilitated by the National FPM Staff Director and
the NFHM Program Manager.
FPM Budget Development:
Forest Pest Management will need to supplement this plan with a long-term budget plan. The FPM
budget plan will be developed in coordination with the NFHM Program Manager and States, and will
reflect NFHM program and budget planning. The FPM budget plan will show estimated annual funding
increments required to reach the fully implemented NFHM Program. Actual annual budgets will be
developed at the National level based on Regional and Area input, and cost records where monitoring
has been implemented.
Forest Pest Management's funding for NFHM is provided in the appropriation to State and Private
Forestry. Research funding is in a separate appropriation. Budget requests for FPM's component of
NFHM are incremental additions to the S&PF/FPM Federal Forest Health Management (Fed FHMgt
- formerly Fed S&TA) and Cooperative Forest Health Management (Coop FHMgt - formerly Coop
S&TA) line items. These line items include funding for monitoring as well as other FPM program
activities. Fiscal Years 1991 and 1992 budget requests provide for (1) FPM Detection Monitoring and
Evaluation Monitoring under the Fed FHMgt line item; and (2) FPM Detection Monitoring under the
Coop FHMgt line item.
FPM Funding Process:
Funds for Detection Monitoring will be allocated on a program basis toy FPM-WO, Funds for Evaluation
Monitoring will be allocated on a project basis by FPM-WO. Detection Monitoring funds will be
allocated to Regions, Area, and States based on annual proposals submitted to the National FPM Staff
Director. Once the program is fully implemented, annual proposals may no longer be needed and
Detection Monitoring allocations would be provided in the Initial Advice (Program Budgeting and
Management Information (PBMI)). Both Federal and State Evaluation Monitoring will be funded
annually on an individual project basis.
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Regions and Area will annually submit to the National FPM Staff Director
(1) progress reports of Detection and Evaluation Monitoring work already underway; and (2) specific
Detection and Evaluation Monitoring budget requests for continuing and new work in the NFHM
Program. Evaluation Monitoring proposals must be (1) an FPM responsibility; (2) distinguished from
other FPM Fed FHMgt and Coop FHMgt program activities; and (3) submitted by FS Regions/Area who
are responsible for project management, implementation, and oversight.
The NFHM Program Staff will assist the National FPM Staff Director in reviewing Evaluation Monitoring
proposals for conformance to NFHM and FPM Program guidelines and for technical merit. The
National FPM Staff Director will make the final decision on which proposals will be funded. Funding
will be provided to Regions, Area, and States on a project basis. Some funds may be held in reserve
to meet unanticipated Evaluation Monitoring needs. FHM funding levels depend on annual
appropriations and program and budget direction.
Limitations on Use of FPM Funds:
Region and Area FPM staffs will use FPM funds to carry out only those Detection and Evaluation
Monitoring activities for which they are responsible, as described in this plan. The NFHM plot
establishment and annual plot visits are a Research responsibility, and will be funded by Research.
FPM Implementation and Funding Guidelines:
General forest health monitoring implementation and funding guidelines follow:
I. Forest Pest Management's part of NFHM will expand to additional States in concert with the NFHM
Program.
2. Detection Monitoring continuation in participating States has funding priority over Detection
Monitoring initiation in additional States. Initially, Detection Monitoring has priority over Evaluation
Monitoring.
3. Regions/Afda will not assess Cooperative Forest Health Program (CFHP) funds for overhead costs.
Previously CFHP was called the Cooperative Forest Pest Action Program (CFPAP). All allocated funds
will be provided to participating States.
4. Federal cost-share with each State for Detection Monitoring supported by Coop FHMgt funds will
be 100 percent for first year participation, 80 percent the second year, 75 percent the third year, and
50 percent thereafter. This will allow States time to adjust budgets to meet priority NFHM needs.
5. Evaluation Monitoring project proposals considered in NFHM must be in response to Detection
Monitoring results, submitted to the National FPM Staff Director, and be coordinated with the NFHM
Program Manager. Evaluation Monitoring is currently limited to the Fed FHMgt line item. Fed FHMgt
funds may be used on State or private lands as part of evaluations involving Federal lands or as
otherwise needed in the National interest. The Federal/State cost-share for these projects will be
determined on a case-by-case basis, as they are not subject to predetermined cost-share requirements.
6. Forest health monitoring funds will be provided to the State agencies participating in the
Cooperative Forest Health Program (CFHP). Forest Pest Management's contribution to NFHM is a
new initiative which will be conducted as an integral part of CFHP. Forest health monitoring funds are
provided to meet the additional: reporting and evaluation responsibilities FPM and States have in the
NFHM program. Traditional FPM detection and evaluation activities will continue as in the past.
National Forest Health Monitoring is not a substitute for CFHP. Information from CFHP will supplement
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Detection and Evaluation Monitoring in NFHM. Table 1 compares traditional FPM program emphasis
with the additional requirements of NFHM.
7. Although NFHM reporting is designed to provide regional-level information, the forest pest
component of Detection Monitoring and Evaluation Monitoring may have sufficient detail to allow
State-level reporting.
Forest Plan Monitoring Linkage:
Although Forest Plan monitoring and NFHM have distinctly different objectives, NFHM provides an
opportunity to improve forest pest data collection, management, analysis, and reporting to better
respond to both. Regions and Area are encouraged to adapt, to the extent possible, NFHM forest
health reporting to meet other Regional needs.
Chapter 6 of the FS Land and Resource Management Planning Handbook - FSH 1909.12 ~ describes
three levels of Forest Plan monitoring: Implementation Monitoring, Effectiveness Monitoring, and
Validation Monitoring. As technical support staff to National Forest resource planners and managers,
FPM is directly involved with the Forest Planning process. Now that the National Forest Planning
Regulations, FSM, and FSH are being revised, it is anticipated that the monitoring phase of Forest
Planning will receive more emphasis. Forest Pest Management will continue to be directly involved
in the process of identifying monitoring needs and for collecting the forest pest data required at each
monitoring level.
For FPM, the link between Forest Health Monitoring and Forest Plan Monitoring is the damage/stressor
agent conditions, trends, and effects information provided to each. It is expected that the basic
Regional and Area pest data bases will support both monitoring activities. Initially, NFHM summary
information will likely be most applicable to Validation Monitoring. However, Regional and Area pest
data bases may have site-specific information useful in Implementation Monitoring and Effectiveness
Monitoring.
Additional Follow-Up Needed
Additional follow-up is divided into two parts - technical and management. Target accomplishment
dates and responsible person, organization, or staff are noted.
Technical Follow-Up
1. Specify annual forest health reporting required from FPM to NFHM. (Robert Loomis, FPM Forest
Health Monitoring Work Group [FHMWG]) June 1992.
2. Design a nationally consistent program for measuring forest stressor effects and change in effects.
This includes:
a. Nationally consistent design which accommodates regional differences (Robert Loomis,
FHMWG) September 1992.
b. Core data elements with standards and guidelines (Robert Loomis, FHMWG, Regions, and
Area) September 1992.
c. Data collection standards and guidelines draft (Robert Loomis, FHMWG, Regions, and
Area) March 1993.
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d. Data management, analysis, and reporting guidelines draft (Robert Loomis, FHMWG,
Regions, and Area) September 1992.
e. Regional implementation plan development and coordination(Robert Loomis, FHMWG,
Regions, and Area) East - May 1992, West - draft February 1993.
f. Quality assurance and quality control plan development and coordination (Robert Loomis,
FHMWG, Regions and Area) National - draft March 1993, Regional - draft supplements
December, 1993.
3. Design the process by which off-plot data and NFHM plot data can be integrated for analysis and
reporting. This includes off-plot data such as forest pest and other stressor effects, weather, and air
pollution.
(Robert Loomis, NFHM, and FHMWG) September 1992.
Management Follow-Up
1, Specify Evaluation Monitoring project selection process and criteria. (Robert Loomis, FPM, and
NFHM) September 1992.
2. Document State responsibilities and program expectations in NFHM (Regions and Area) annual
update and as States enter program.
3. Implement the October 1991 and March 1992 NFHM Program Review action plans (NFHM FPM
FFASR, and others). ' '
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APPENDIX 1
Traditional FPM Program Compared to NFHM
(Detection and Evaluation)
TRADITIONAL PROGRAM EMPHASIS
FOCUS
TRADITIONAL PROGRAM PLUS NFHM
Pest Conditions
Major Pests
Localized Data
Timber
Short-Term Use (supression)
Short-Term Trends
Data Management - Reports/Maps
Forest Conditions (damage/stress agent
trends for major episodes of effects
such as mortality, dieback, defoliation,
discoloration, and growth loss)
All Major and Potentially Important
Damage Agents and Abiotic Stressors
(insects, biotic and abiotic diseases,
and other natural and anthropogenic
stressors)
National, Regional, State Data
All Resources - recognize intrinsic
value of forests independently of
commodity production
Long-Term Use (analysis/interpretation
of stress agent effects on forest health
for strategic planning and prevention)
Long-Term Trends
Data Management - GIS
Survey Design/Timing Variable
Within and Among Regions
Survey Design/Timing Planned and
Consistent Among Regions/Area
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APPENDIX 2
Forest Pest Management
Forest Health Monitoring Work Group
Regional and Area Representatives
REGION 1
REGION 2
REGION 3
REGION 4
REGION 5
REGION 6
REGION 8
REGION 10
NA
NFHM/WO
JIM BYLER
DAVE JOHNSON
BORYS TKAGZ
RALPH WILLIAMS
JOHN DALE
JERRY BEATTY
BOB ANDERSON
KEITH REYNOLDS
BILL BURKMAN
DANTWARDUS
MARGARET MILLER-WEEKS
MANFRED MlELKE
BOB LOOMIS
J.BYLER:R01A
D.JOHNSON:R02A
B.TKACZ:R03F04A
R.WILLIAMS:R04F02A
J.DALE:R05A
J.BEATTY:R6/PNW
R.ANDERSON:S29A
K.REYNOLDS:R1 OF04A
W.BURKMAN:S24A
D.TWARDUS:S24L08A
M.MILLERWEEKS:S24L06A
M.MIELKE:S23A
R.LOOMIS:S29L03A
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Appendix B. Working Plan, Integration of Forest Pest
Management and Forest Health Protection Activiteis
With Forest Health Monitoring for the Eastern
United States, June 25, 1992
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WORKING PLAN
INTEGRATION OF FOREST PEST MANAGEMENT AND
FOREST HEALTH PROTECTION ACTIVITIES
WITH FOREST HEALTH MONITORING
FOR THE EASTERN UNITED STATES'
Prepared by Northeastern Area Forest Health
Protection and Region 8 Forest Pest Management
June 25, 1992
This eastwide work plan is designed to build upon the Washington Office National Plan for
the Forest Pest Management Component of the National Forest Health Monitoring Program.
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TABLE OF CONTENTS
INTRODUCTION . - • • 3
FHM PROGRAM STRUCTURE - 5
COOPERATING GROUPS AND RESPONSIBILITIES - . - 7
BUDGET PROCESS 8
FPM/FHP RESPONSIBILITIES 9
Detection Monitoring, On-Plot Activities 9
Detection Monitoring, Off-Plot Activities • • • • 9
Evaluation Monitoring 9
Reporting and Interpretation of Results 1°
OBJECTIVES • • • - 11
FPM/FHP PERMANENT FHM PLOT SUPPORT 12
Crown Evaluations, tree 1.0" in dbh and larger 12
Damage Assessment, tree 5.0" in dbh and larger 12
Crown Evaluation (Seedlings/Saplings) • - • 13
Air Pollution Bio-indicator Plots - 13
Sample Tree (Located in the annular plot of the
detection plots) 13
Spring Visits to .the detection Plots (Region 8 only) 13
Other Plot Measurements • 14
Training . - . 14
QA/QC 14
FPM/FHP OFF-DETECTION PLOT EFFECTS/PESTS SURVEYS 15
Monitoring of Specific Effects/Pests > • • • • 15
Minimum Reporting Standards for Effects/Pests Surveys 19
Survey Field Guide , 19
Standard Pest Codes 19
DATA MANAGEMENT AND REPORTING 20
Regional FHM Reporting of Effects/Pests , • 20
State Forest Health Reports 20
EVALUATION MONITORING - - 22.
Past Activities 22
Components of Evaluation Projects • 22
APPENDIX 24
Example of a Cooperative Forest Health Agreement Narrative 24
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INTRODUCTION
The public's concern for the 'health and productivity of forests in the United States* resulted in
federal legislation authorizing 'such surveys as are necessary to monitor long-term trends in the
health and productivity of domestic forest ecosystems*. Monitoring forest health is an integral
part of the USDA Forest Service (FS) Research Forest/Atmosphere Interaction Program pro-
posed under the Forest Ecosystems and Atmospheric Pollution Research Act of 1988 (PL
100-521). This legislation authorizes a 10-year program of research and monitoring to better
understand the relationships between forest health and air pollutants, and recognizes the need
for long-term monitoring. Subsequent legislation ('Farm Bill" of 1990, PL 101-624) encouraged
the USDA FS State and Private Forestry to work in partnership with the state foresters or state
agencies to 'monitor forest health'. The result is the implementation of a National Forest Health
Monitoring (FHM) Program to determine and annually report on the condition of the nation's
forests.
Congress has provided the USDA FS, through the Secretary of Agriculture, authority, direction,
and funding to establish a national forest health monitoring program. From the Forest Pest
Management/Forest Health Protection (FPM/FHP) view, amendments to the Cooperative
Forestry Assistance Act of 1978 (P.L 95-313) by the Food, Agriculture, Conservation and Trade
Act of 1990 (P.L 101-624) are important because forest health monitoring is explicitly author-
ized. Section 5 of P.L 95-313, Insect and Disease Control, is re-designated Section 8, Forest
Health Protection. Section 8(b) (1) directs the following: 'Conduct surveys to detect and ap-
praise insect infestations and disease conditions and man-made stresses affecting trees and
establish a monitoring system throughout the forests of the United States to determine detri-
mental changes or improvements that occur over time, and report annually concerning such
surveys and monitoring'.
Currently, the USDA FS gathers data on the forest resource and forest pests in various ways.
The two most notable activities are the periodic FIA surveys conducted on most forest lands by
the FIA Projects; and insect and disease surveys conducted by FPM/FHP on federal forest lands
and together with state agencies on state and private forest lands.
The National FHM Program is a cooperative venture with the U.S. Environmental Protection
Agency (USEPA) Environmental Monitoring and Assessment Program (EMAP) Forest Program;
State Forestry and Agriculture Agencies; USDA FS Forest Inventory and Analysis (FIA), FPM/
FHP, Experiment Stations, and National Forests; and a broad spectrum of government, re-
search, and private interests. Monitoring can be defined as the repeated measurement or
sampling of pertinent data for comparison to a reference system or identified base line. It always
involves the determination of changes over time, usually interpreted against a base line. Its
ultimate value comes when the information that is collected, analyzed, and interpreted is used
in making management decisions. The FHM program utilizes the expertise of these groups in
a coordinated fashion, to collect, archive, analyze, interpret, and report on forest conditions. The
initial objective is to develop a base line on the health and condition of forests with permanent
plots and forest effect surveys in every State. This base line will be used yearly to monitor forest
health, detect deviations, evaluate deviations, and direct research to explain deviations.
The FPM/FHP program objective is to detect, evaluate, and report changes in forest damage-
the effects of forest insects, biotic and abiotic diseases, and other natural and anthropogenic
stressors on forests; and explain the relationships between forest health changes and these
effects.
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These objectives will provide needed information to guide management activities which can
improve forest health.
Forest conditions will be described by the measurement and reporting of data from several
•health" indicators from a permanent plot network and from off-plot information. For the plots,
five current indicator groups are currently being measured or are planned to be measured in
the future:.(1) growth and mortality; (2) tree condition and symptoms; (3) soil physical character-
istics, nutrients, and toxins chemistry; (4) foliar nutrients and toxin chemistry; and (5) landscape
characterization, vegetative structure, and diversity. Individual measurements may support one
or more indicators. Measurements will be made and indicators characterized on a periodic
basis; annually for those that change frequently (e.g., foliage transparency) and on a 4-year or
greater cycle for those that change less frequently (e.g., soil chemistry). Other indicators will be
added as necessary. The measurements will be monitored to establish a base line and to detect
any changes in forest condition. In addition, analyses of spatial variability can be used to
determine regional forest health conditions.
For the effect surveys, specific data will be collected and compiled on the effect types identified
in the section on FPM/FHP survey support of FHM. Data will also be collected on other pests
judged to be of significance for a particular region or state. These data will be used to show the
current condition and change over time.
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FHM PROGRAM STRUCTURE
Although USDA FS Research, through the National FHM Program Manager, has the overall lead
for FHM design, implementation, and reporting, USDA FS FPM/FHP and participating States
have direct responsibility for designing and implementing the FPM/FHP/State components of
FHM. These responsibilities are discussed in the following pages.
The National FHM Program is a three-tiered, long-term process to provide regional (multi-state)
and national information on forest health status and trends. Each successive tier requires
progressively more detailed information. The tiers are:
Detection Monitoring - to detect deviation of key monitoring elements from
established base line conditions or trends. This is the first and most extensive
level. Within the USDA FS, FPM/FHP and Research share responsibility for initial
design and implementation of this tier which consists of <1) a plot component
(Research) - a geographically-based network of permanent plots distributed
throughout the nation's forested areas, and (2) a survey component (FPM/FHP)
- forest pest and other stressor effects surveys and reports of forest damage.
Detection Monitoring will discover and describe changes in forest health condi-
tions. The FPM/FHP Responsibility section which follows, outlines FPM/FHP con-
tribution to Detection Monitoring.
Evaluation Monitoring - to determine or hypothesize likely causal relationships
and to recommend management responses. This is the second level and is the
responsibility of FPM/FHP to initiate and is activated by Detection Monitoring
results. Evaluation Monitoring is the process for determining causes for changes
in forest health status beyond that initially obtained in Detection Monitoring. The
FPM/FHP Responsibility section outlines FPM/FHP contribution to Evaluation
Monitoring.
Intensive-Site, Ecosystem Monitoring - to define basic relationships sufficient to
predict consequences. This is the third level and provides the most detailed,
long-term data for ecosystem research to determine causal relationships, predict
rates of change in forest conditions, and identify management responses. FPM/
FHP has little or no responsibility in Intensive-Site, Ecosystem Monitoring.
The detection plot network will be visited annually and managed by FIA and state cooperators.
The extensive network of permanent locations is selected to correspond to a systematiasam-
pling grid (40.6 sq. km; 10,030 acres; or 15.7 sq. mi. hexagons located on a triangular grid)
developed by the USEPA for EMAP. Periodic measurements relating to the functioning of a
forest, will be made on a series of plots, which are linked to the FIA inventory system.
Each location consists of a cluster of four subplots. All trees, including seedlings and saplings,
are located, marked (not seedlings), and measured. On, or adjacent to the location, openings
in the forest are searched for bio-indicator plant species known to be sensitive to ozone. At each
location, data are collected on the geographic and topographic position and physiographic
description of the location; tree species, diameter, crown position, crown condition, and dam-
age; other vegetation; and foliar symptoms on bio-indicator plants.
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Collection of off-detection plot information can be used to attempt to explain deviations of
detection plot information. The off-detection plot information includes insect and disease effect
and distribution surveys, weather and climate data, pollutant deposition information, and fire
incidence and severity data. The forest pest condition reporting system will provide state and
region-wide information on effects from major insects, pathogens, forest damages, and fire
incidence and severity to supplement data collection on the detection plot network. The weath-
er, climate and pollutant deposition information will be assembled and reported by the EMAP.
Evaluation monitoring will focus on specific forest types, tree species, and/or regions that have
deviations from established base lines as identified by detection monitoring activities. This
activity will be coordinated by the National FHM Deputy Program Manager. The investigations
will attempt to explain the specific cause-effect relationships between the deviations and forest
stressors such as insects, pathogens, weather, and climate data or combinations of the stres-
sors. If specific cause-effect relationships can not be determined then identification of a re-
search hypotheses will be developed for ecosystem monitoring activities.
Ecosystem monitoring will focus on intensive forest ecosystem measurements to improve the
understanding of ecosystem functions and cause-effect relationships. This level of FHM will
occur on a small number of sites that represent major forest types.
Another area which will support all levels of monitoring will be research on monitoring tech-
niques. This area will focus on the development and refinement of measurement procedures for
alt levels of FHM.
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COOPERATING GROUPS AND RESPONSIBILITIES
1. The USDA FS Research (primarily the FIA projects) has the lead responsibility for plot
measurement, data management, national and regional reporting, indicator development
(through research on monitoring techniques), and ecosystem monitoring.
2. State Forestry and other State Agencies will have the overall responsibility for field crews
and field measurements on the plots, plus statewide effect/pest surveys. These groups will
also be involved in the planning and coordination of FHM. (A field guide is being developed
to establish comparable/standardized survey procedures and reporting codes for effect/
pest surveys.) The states will also have the responsibility to provide the information in time
for an annual report, as well as prepare an annual state forest health report. The states
will also be involved in the planning and organization of FHM through the National Associa-
tion of State Foresters.
3. The USEPA is working with FHM as part of its broader EMAP program aimed to monitor,
evaluate and report on long-term status and trends of the nation's ecological resources.
Forests are one of six ecological resource categories of interest to USEPA. USEPA-EMAP's
contribution to FHM includes the overall sampling grid for locating and establishing
permanent monitoring plots; indicator variables for measurement that have been agreed
to as valid by the scientific community; technical assistance with quality assurance (QA);
weather, climate, and air pollution information; data management support; and other
activities. The program also has the capacity to utilize satellite imagery where appropriate.
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BUDGET PROCESS
FHM is one program with the budgets being distributed from two sources, USDA FS Research
and FPM/FHP. The USDA Forest Service Research funding is coordinated and distributed to
support the major activities associated with installation and collection of samples and data on
the detection plots. This negotiation is between the appropriate state agency and the respective
USDA FS Experiment Station.
The planning and funding for the off-detection plot effect/pest surveys is through the USDA FS
FPM/FHP unit to the appropriate state agency. This funding is above the current funding level
underthe Cooperative Forest Health Program (formerly CFPAP) and is designed to supplement
additional activities required through FHM. (See Appendix for an example of a Cooperative
Forest Health Agreement Narrative). Currently, the submission process for the CFHP and the
additional activities for FHM are separate, but in the future the funding may be consolidated into
one program. The additional FHM funding through FPM/FHP is structured on a 'sliding' scale
depending on the number of years participating in FHM and is outlined below:
Period
YeaM
Year 2
YearS
Year 4+
FPM/FHP %
100
80
75
50
State %
0
20
25
50
Funding for evaluation monitoring is the primary responsibility of USDA FS through the appropri-
ate FPM/FHP unrt(s). The funding structure will vary depending on the focus of the various
evaluation monitoring projects.
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9
FPM/FHP RESPONSIBILITIES
Detection Monitoring, On-Plot Activities
The role of FPM/FHP involves the application of tree crown and vigorprocedures conducted on
the detection plot network. This includes the development of the procedures for the field guide,
the training of field crews, and the follow-up on site visit with field crews while they are collecting
information (relating to the above procedures). This also includes the area of bio-indicators
plants for general air pollution characterization. These activities should account for 15% to 30%
of FPM/FHP's effort in the FHM Program. In Region 8, FPM will coordinate and train the state
crews on how to conduct spring plot visits.
Detection Monitoring. Off-Plot Activities
The need still exists to determine standards for data collection. In many cases, annual or
periodic surveys are already conducted for many effects/pests and data is collected on fire
incidence and severity. Thus, the principal obstacle for a effect/pest/fire conditions database is
survey method standardization, coordination, and development of GIS management. This
should be our primary function in detection monitoring, encompassing 50% to 60% of our effort
in FHM; This will require the establishement of a base line and to determine changes in forest
conditions/health requires the maintenance of information on forest pests, fire, and other events
that occur yearly which affect the forest resource.
To better assess forest effect/pest/fire conditions and establish data on trends, spatial mapping
or GIS information is necessary. Thus, the intent of an effect condition database is to collect,
store, assess, and manage information on damage needed for FHM. Much of the data will be
acquired from annual, routine surveys combined with periodic special pest surveys conducted
by FPM/FHP and/or state cooperators. Examples include insect and disease aerial detection
surveys; hazard rating for various insect and disease pests, surveys to delineate the occurrence
or range of a pests such as hemlock wooly adelgid and dogwood anthracnose; or data collected
during specific pest evaluations such as for gypsy moth, eastern spruce budworm, and south-
ern pine beetle. In some cases, this will require looking at the same area periodically. This same
data base could also be utilized in other pest management and fire management activities.
Information on forest pests would be collected, stored, and managed by FPM/FHP and state
cooperators. Information compiled through the FHM program will be particularly useful in
providing technical support for developing management recommendations. As a minimum, all
states have agreed to try and meet the level of detection noted in Table 1 (page 16).
Evaluation Monitoring
This second level of FHM involves designing and implementing intensive surveys to evaluate
a detected or unexplained problem. FPM/FHP has been active in evaluation monitoring and will
continue to provide leadership and technical assistance in this area. This area should involve
10% to 35% of our effort in FHM. When reports from detection monitoring identify areas or
problems of concern, FPM/FHP and the National FHM Program Managers will assess the
situation and, as appropriate, convene multi-disciplinary teams to determine specific evaluation
needs. These could include additional surveys, site- or area-specific evaluations, more detailed
monitoring, and specific research studies. Criteria for evaluation monitoring project design and
selection are to be developed by FPM/FHP in cooperation with the National FHM Deputy
Program Manager.
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10
Reporting and Interpretation of Results
The goal of monitoring is to detect and report problems requiring management response. The
success of repotting depends on clear monitoring objectives and organized plans to produce
timely information. Various levels of reports/summaries will be developed for the public, scien-
tists, and decision makers. The information reported will also have relevance and meet the
needs for various data users groups such as state cooperators.
FPM/FHP responsibilities for reporting include the interpretation of specific information on the
detection plot network (i.e., crown ratings, damage signs and symptoms, and spring visit
information in Region 8 only). The off-detection plot information will be the major aspect of
FPM/FHP activity. This includes the development of regional effect/pest information which will
be collected and organized into a data base for GIS applications. Both of these information sets
arid activities will be combined into annual regional and national reports on FHM which are
being organized by USDA Forest Service Research and US-EPA EMAP.
In addition, the off-detection plot information along with other sources of data on forest condi-
tions and stressors will be used in the development of annual state forest health reports. These
reports will be developed by the cooperating state agencies with assistance from FPM/FHP.
Evaluation monitoring reports will be developed as needed and will vary depending on the focus
of the evaluation monitoring project. Some of this information may be used to support observa-
tions and conclusions contained in the annual detection monitoring reports discussed earlier.
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11
OBJECTIVES
The emphasis of the eastern FHM Program for FPM/FHP is on detecting unexpected deviations
from established forest base line conditions or trends, conduct evaluations of these observed
deviations, coordinate the activities with research, and report changes in forest damage. The
FPM/FHP objectives discussed in this plan emphasize the adaptation of FPM/FHP assessment
activities to the National FHM Program.
The objectives of the FHP/FPM activities are as follows:
1. To develop an eastwide approach (including standardized data collection and reporting)
to detection of major types of forest effects, including adverse weather and air pollution.
a. Annually produce data bases, reports,-and maps of areas where forest effects are
exerting stress that might cause forest decline and tree mortality. Special emphasis
will be placed on new problems.
b. Provide impact estimates for forest trees severely damaged by insects, diseases, and
fire.
2. To determine probable causal agents for reduced tree vigor and mortality on the detection
monitoring plots and to assist in the assessment of cause-effect relationships of forest
declines and tree mortality in FHM.
3. To assist in the implementation of tree vigor rating methods and air pollution injury as a
way to measure annual trends of forest health. To provide training to the field crews in
proper use of the methods.
4. To assist the appropriate groups in the development of annual State and National forest
health reports.
5. To help formulate Integrated Pest Management (IPM) guidelines applicable to a variety of
problems or potential problems.
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12
FPM/FHP PERMANENT FHM PLOT SUPPORT
The permanent forest health network of plots is designed to detect possible changes in forest
conditions from man caused events on a regional level. FIA has the primary responsibility for
the permanent plot network. FPM/FHP has the responsibility to provide support for the plots,
as needed. To date, this support has been in the area of developing and implementing proce-
dures which assess the vigor of the trees and detect changes in forest health. Procedures have
been developed and training provided for crown evaluations and tree damage. Procedures for
assessing sample (on annular plot) trees will not be used in 1992 but will be implemented in
future years. The details of the measurements are contained in the Eastern FHM Measurements
Reid Guide. Each of these procedures is designed to assess a specific part of the plant or
ecosystem as it relates to plant or forest health. The measures are also designed to be sensitive
to changes over time so that change can be detected.
Crown Evaluations, trees 1.0' in dbh and larger
1. Crown Class - This measure describes the tree vigor in relation to sunlight received
and a trees' position in relation to the neighboring trees.
2. Crown Diameter - This procedure measures the diameter of tree crown, ft helps
assess the growing space of a tree and can be used with other measures to estimate
crown bibmass. [Note: only on trees 5.0* in dbh and larger}.
3. Crown Ratio - This is a measure of the ratio of tree crown length to total tree length.
It can be used to estimate crown and foliar biomass.
4. Crown Density - This measurement estimates the amount of foliage, branches, and
fruiting structures that block light transmission through the crown. This measure has
been shown to be correlated with basal area of increment growth, [Note: only on
trees 5.0" in dbh and larger}.
Damage Assessment trees 5.0* in dbh and larger
1. Crown Diefaack -This procedure measures the amount of recent dieback in the
crown. The system estimates the percent of dieback in the crown as a percent of the
total crown. Dfeback is often related f:o tree vigor,
2. Foliage Transparency - This is art estimate of defoliation. The crown transparency is
measured as the percent of light visible through the foliated portion of the crown. This
measure may reflect short term defoliation or longer term effects that are expressed
as reduced foliage in the crown,
3. Tree Damage -This procedure identifies damage to the tree that may result in growth
loss or mortality. Up to three damages can be recorded per tree. For each damage,
the location on the tree and possible cause are recorded.
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13
Crown Evaluation (Seedlings/Saplings)
The crown evaluation procedure for seedlings and saplings is designed as a rapid method to
place the seedlings/saplings into good, medium, or poor vigor classes. This is done by assess-
ing the amount of normal foliage and dieback in the crown.
Air Pollution Bio-indicator Plots
This procedure is designed to assess visible damage to bio-indicators by ozone. The methods
are being tested and have been implemented for 1992.
Sample Tree (Located in the annular plot of the detection plots)
There are a number of measurements planned for sample trees such as root sampling and
in-hand branch examination. There are no plans to implement any of these measurements in
1992.
Spring Visits to the Detection Plots (Region 8 only)
Since the forest health plots will be visited in the summer by FHM field crews, it is desirable to
monitor pest activity on the plots in the spring for defoliators. These spring tree damages can
result in growth losses but cannot be detected later in the summer. To monitor this potential
damage, the state cooperators in Region 8 will visit each detection plot in the spring using the
following method:
1- Scope and purpose: Spring plot visits will help to insure that certain spring foliar
conditions (e.g., frost, selected insects and diseases, air pollution incidence confined
to spring episodes) are not overlooked. Such conditions might be significant as FHM
variables, but might have disappeared by the time crews visit the plots later in the
year. All plots in the cooperating states will be visitecTin the spring.
2. Procedures:
a Sample Collection and Preparation: In cases where field diagnosis is not possi-
ble or is suspect, crews will supply specimens to the state FPM specialist for
analysis. Specimens will be collected so as to preserve the condition for diagnosis,
and crews will be trained/equipped for specimen collection and forwarding. FPM will
assist state agencies in diagnostic work upon request.
b. Examination: Trees from the detection plot tree species list will be examined
within the plot area. Trees with 15 percent or more leaves affected will be reported
for each species on the detection plot list. Crews will use crown rating cards to
calibrate the 15 percent threshold. Trees will not be evaluated on an individual
basis, but by species.
c. Training:
Indoor: Crews will be trained using 35 mm projected slides on foliar conditions they.
will likely encounter. Special emphasis will be placed on conditions specific to their
geographic area Trainees will be tested with an examination slide series.
Outdoor: Trees showing various foliar conditions will be flagged and the conditions
discussed. Field crews will be tested on: 1) their ability to discriminate between
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14
various conditions listed on the spring plot form, and 2) their ability to calibrate their
perception of the 15 percent and above foliage affected threshold.
3. Quality Assurance/Quality Control (QA/QC): Crews will be tested immediately after
initial training to insure that they perform satisfactorily. Additionally, audits will be
made throughout the field season by qualified individuals to insure that perfor-
mance is up to standards. Crews will be observed in action and plots will be
" re-evaluated after field crew analysis. In the event of unsatisfactory performance,
individual crew members will receive additional instruction until performance is up
to standards. Records of performance will be maintained and used as a basis for
attaining improvement goals within tolerances yet to be determined.
Other Plot Measurements
All other plot measurements are under the control of FIA. In future years there will be procedure
modifications and additions where FPM/FHP may play a major role.
Training
Effective and repeatable training is vital to the success of the program. To accomplish this task
FPM/FHP will hold a pre-training session of all trainers to discuss procedures and standardize
training procedures. The pre-training will include a QA/QC testing at the end to insure that all
trainers are providing the same estimates for a given tree.
Training of the crews will be done using the methods agreed upon at the pre-training meeting,
and in accordance with the FHM field guide. The training will include classroom and field
segments. Crews will receive the proper field aids, and be trained on a representative number
of trees and species. The crews will then be checked on a number of trees and will then given
a final test. Crew members with an unacceptable error rate will be retrained and retested.
QA/QC
QA/QC must be considered in all aspects of FPM/FHP FHM support. The two major activities
are training and crew evaluation.
Training - All measurements will have a specified level of precision. This precision will
be used to determine if the crews have been properly trained. Each trainee will receive
instruction and hands-on experience with all methods. The trainees will then participate
in an open-discussion type of instruction where actual values on numerous trees are
discussed with the trainers and trainees. The purpose of this activity is to calibrate field
crew personnel with the various methods. This will then be followed with a QA/QC test.
If the error rate does not meet the specified precision, the person will be retrained and
retested.
Crew Evaluation - FPM/FHP trainers will meet with each of the crews during the course
of plot establishment and/or measurement. The purpose of the visit will be to answer
questions by the crews and to check on how close the crews are in relation to the trainer.
If problems are found, on site training will be done and documented. Also, the field crews
will be requested to provide feedback at the end of the field season to identify problems
associated with data collection, including training. This information will be used to modify
and improve next years program.
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15
FPM/FHP OFF-DETECTION PLOT EFFECTS/PESTS SURVEYS
Monitoring of Specific Effects/Pests
There are two processes that are important in the integration of FPM/FHP activities into FHM.
The first process is the standardization of surveys and survey methods for known pests and
various types of effects. FHM requires that certain major effect types (Table 1) and certain pests
(Table 2) be surveyed for in a standardized manner and reported in a standardized manner so
that the information from these surveys is quantifiable and comparable. With this process we
are determining that all groups will attempt to monitor certain pests or effects. (However, it is
recognized that funding, staffing, and priorities may make it impossible to complete all of the
major pest surveys. If this does occur, FPM/FHP and the states will work together to establish
priorities.) A list of these major pests follows in Tables 1 and 2. The items in Table 1 are required
when a state participates in FHM. The items in Table 2 are optional and will be negotiated
between the state agency and the appropriate USDA FS FPM/FHP unit.
Two aspects are reported with each major pest effect type, total amount of acres or other unit
affected for each category of effect or major pests and the total amount unaffected. This
information is important to determine the severity of a problem within a state or region. It is
important to note in this list that minimum thresholds for survey and reporting are given. That
is, the agencies agree that they will not miss the effect above a certain threshold. The reason
for these minimum thresholds is to set a filter for detection level reporting below which data is
not required. Cooperators can set lower thresholds and reporting standards to meet their own
requirements, but the levels in Tables 1 and 2 are required for FHM. For all of these specified
effects/major pests, a map is required for use in the development of regional GIS data layers.
A-survey method will be provided for each of these effects/major pests.
Table 1. General Effects
Effect
Defoliation
Discoloration
Dieback/Decline
Stem Damage (Defect)
Mortality
Breakage
Action Threshold
>30% foliage loss over 5000 concentrated acres
>30% of foliage affected over 5000 concentrated
acres
>30% of trees affected with 30% of crown affected
over 5000 acres
>30% of trees affected per acre over 5000 acres
>30% of trees/acre affected over 5000 acres
>30% of trees affected over 5000 concentrated
acres
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Table 2. Specified Effects/Pests
H.6
Effects/Pests
Gypsy Moth
Pine Sawflies
Budworm
Thrips
Forest Type
Hardwoods
Pines
Spruce and
Jack Pine
N. Hdwds.
Frequency
Annual
Annual
Annual
Annual
Threshold for Survey
Defoliation V
Defoliation
Defoliation
>30% of foliage
of individual
trees and > 1.000
affected on any
definable area
Map Resolution
Not specified
Not specified
Not specified
Not specified
Hemlock Wooly E. Hemlock Annual
Adelgid
Forest Tent Hardwoods Annual
Caterpillar
Southern Pine S. Pines Annual
Beetle
Other conifer Conifers Annual
bark beetles
Dogwood Dogwood Annual
Anthracnose
{Discufa)
Fusiform S. 'Pines Annual
Rust
Annosus Root Conifers .5. years
Disease
Armillaria All types Annual
Root Disease
Distribution map
Defoliation
Average of 1 spot
per:2000 acres of
type per county
.Mortality
Distribution map
'(Lab confirmed)
>10% of living :trees
with galls on or
within 12" of
main stem
Mortality
Mortality
Not specified
Not specified
Not specified
Not specified
Not specified
County
Not -specified
'Not specified
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Table 2. Cont.
17
Effects/Pests
Littleleaf
Disease
Butternut
Canker
Beech-Bark
Disease
Diplodia
Spruce-Fir
Mortality
Dutch Elm
Disease
Oak Decline/
Mortality
Oak Wilt
Dieback
Defoliators
Heartrot
Drought
Storm Damage
Ozone
Frost/Cold
Injury
Forest Tvoe
S. Pines
Butternut
American
Beech
Pines
High elev.
spruce-fir
Amer. Elm
Oak
Oak
AH types
All types
All types
All types
All types
Sensitive
species
All types
Frequency
Annual
Annual
Annual
Annual
Annual
Annual
5 years
Annual
5 years
Annual
1 0 years
Annual
Annual
Annual
Annual
Threshold for Survey
Mortality
Distribution map
Stem damage
Dieback
Mortality
Distribution map
(Lab. confirmed)
Dieback/decline
Any occurrence
Dieback/decline
Defoliation
FIA Data
Proper effect,
Table 1
Breakage
>30% sensitive
species with >30%
of foliage/tree
affected on 1 000 acres
Discoloration
Map Resolution
Not specified
County
Not specified
Not specified
Not specified
County
Not specified
County
Not specified
Not specified
Not specified
Not specified,
Not specified
Not specified
Not specified
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Table 2. Cent.
18
Effects/Pests
Animal-caused
damage
Ash decline
Walnut canker
PFne tussock
moth
Saddled
prominent
Walnut shoot
borer
Cankerworms
White pine
weevil
White pine
blister rust
Red pine
shoot moth
Hardwood
borers
Needle cast
Forest Type
All types
Ash
Black
walnut
Pine
Hardwoods
Walnut
Hardwoods
White pine
White pine
Red pine
Hardwoods
Conifers
Frequency'
Annual
5 years
5 years
Annual
Annual
5 years
Annual
5 years
1 0 years
5 years
10 years:
Annual
Threshold for Survey
Proper effect,
Table 1
Dieback/decline
>30% of trees over
100 acres
Defoliation
Defoliation
>30% of trees on
100 acres
Defoliation
Dieback/decline
Stem damage;
Stem damage
Stem damage
Discoloration
Map Resolution
Not specified
Not specified
Not specified
Not specified
Not specified
Not specified
Not specified
Not specified
Not specified
Not specified
Not specified
Not specified,
11 Refers to standard criteria in Table 1.
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19
2.
3.
4.
5.
6.
7.
8.
Minimum Reporting Standards for Effects/Pests Surveys
These values are set as the minimum reporting standard. Cooperators are encouraged to
collect more detailed data, but as a minimum to collect these data This could also serve as a
threshold to decide when a particular effect/pest should be surveyed in more detail. New pests
can be added as the need arises where practical and all of these data should be organized and
reported in a GIS-compatible format (e.g., longitude and latitude). The state and federal cooper-
ators will coordinate these activities to insure compatibility between surveys and agree upon
what is practical in lieu of funding and personal limitations.
The minimum to be reported, regardless of effect/pest, is:
1. Latitude and longitude to the nearest degree, map, county name, or quad coordi-
nates (depending on pest/effect)
Host species - using FHM codes
Forest type - using FHM codes
Number of acres by county
Symptoms observed
Survey method used (aerial sketch map, video, windshield survey etc)
Effect/Pest
Intensity levels - when appropriate
This process will also provide for the detection and reporting of unspecified pests.or effects. The
inherent value of FHM is in the detection of factors that may play a role in forest health BEFORE
they become a problem. Therefore, the monitoring and reporting of unspecified effects/pests
is also important in evaluation of trends or patterns. For this process a FPM/FHP Monitoring
Database will be created in which all effects/pests data is entered. A standard data entry format
is being developed. The value of this database is that it will allow effects/pests data to be stored
in a standard format, retrievable for summary and analysis within and among states, and makes
effects/pests data readily available for reporting purposes. Distribution maps are not required
now, but location information is required.
Survey Field Guide
Considerable work has been done to develop survey methods for certain effects/pests. FPM/
FHP in cooperation with the state will build upon the existing methods and produce a effect/pest
FHM survey field guide. The field guide will be developed by selecting specialists to author the
methods used for each of the effects/pests. Prior to selecting the specialists, a standard format
will be developed. This booklet will be maintained as a separate guide and updated as needed.
For 1992, procedures will be developed for those general effect types in Table 1. Specific pest
methods will be developed later.
Standard Pest Codes
A list of standard pest codes for forest pests is available. All FHM activities can use their own
coding system. All codes will be referenced to the national coding system for reporting.
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20
DATA MANAGEMENT AND REPORTING
Regional FHM Reporting of Effects/Pests
Major specified effects/pests will be organized through the use of GIS. Maps provided for each
effect/pest will be entered as a data layer to be used in conjunction with other layers such as
FHM plot locations, forest type, weather patterns, etc by FPM/FHP. Tabular data for major
specified effects/pests as well as unspecified effects/pests will be entered into FPM/FHP Moni-
toring databases. The purpose of these databases is to provide a capability for entering, storing,
processing, summarizing, and retrieving forest effect/pest data for use in monitoring trends.
Each year, effect/pest data will be summarized for use in the development of an annual regional
FHM report. This report is intended to summarize FHM plot data and make use of off-detection
plot data The maps and tabular effect/pest data represent one example of off-detection plot
data that contributes to the annual FHM report. Weather and air quality data represent examples
of other off-detection plot data to be used in reporting. FIA/USDA FS Research has the lead
responsibility in summarizing plot data and producing the annual FHM report. FPM/FHP special-
ists have responsibility in providing off-detection plot effect/pest data so that it can be incorpo-
rated into the report. This is one reason why so much emphasis is being placed upon standard-
ization of methods and reporting.
State Forest Health Reports
These reports will focus on the forest types and the factors that influence tree condition as
opposed to traditional forest pest reports that focused on the individual pest. A matrix table may
be helpful to relate forest pests to the various forest types. It is important to include a definition
of "forest health" and "significant change in forest condition" as ft relates to a particular state's
needs.
The following is a suggested outline for the annual state forest health report:
1.
2.
3.
4.
5.
6.
7.
8.
Purpose - What is reported and what is included
Description of the forest resource
Description of the factors affecting the condition of the resource - including historical
information
Description of changes/trends in forest condition and stressors
Description of survey methods
Discussion of protection/control activities
Description of monitoring activities and projects
Issues and recommendations
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21
• Wi" "?° *" '"nC'Uded concemi"9 climate/weather and urban community
as available and/or appropriate.
At a minimum, the states are asked to report annually the following:
1. Summary
2. Agreed upon Effects/Pests*
Defoliation (Table 1)
Discoloration (Table 1)
Dieback/Decline (Table 1)
Stem Damage/Defect (Table 1)
Mortality (Table 1)
Breakage (Table 1)
Gypsy Moth (Table 2)
Major Storm Damage (Table 2)
Southern Pine Beetle (Table 2)
Fusiform Rust (Table 2)
Region 8
x
x
x
Northeastern Area
X
X
X
X
X
X
X
X
See minimum reporting standards for list of data needs (Tables 1 and 2)
3. Other Major Effects/Pests - to be determined individually by each state
4. Describe unusual events
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EVALUATION MONITORING
Detection monitoring may identify growth declines or tree mortality for which obvious causes
can not be established. In addition, situations arise where the forest managers, researchers, or
the general public suspect a significant forest decline. Multi-year measurements are needed to
confirm the degree and/or the extent of decline and to establish probable causes for the decline
before plans may be developed to mitigate the losses or to develop in-depth research studies.
This level of FHM involves designing and implementing intensive surveys to evaluate a detected
or unexplained problem. FPM/FHP has been active in evaluation monitoring and will continue
to provide leadership and technical assistance in this area When reports from detection
monitoring identify areas or problems of concern, FPM/FHP and the National FHM Program
Managers will assess the situation and, as appropriate, convene mufti-disciplinary teams to
determine specific evaluation needs. These could include additional surveys, site- or area-
specific evaluations, more detailed monitoring, and specific research studies.
Past Activities
North American Sugar Maple Decline Project (NAMP) was initiated as a result of a joint assess-
ment by about 40 scientists and pest management specialists from Canada and the United
States. The Canada-US project was planned jointly with federal, state, and provincial participa-
tion. Eventually, it was formalized with a Memorandum of Understanding. More than 170
plot-cluster are established and about 15,000 trees are monitored annually with standard
assessment methods to determine the rate of tree decline and possible .association with air
pollution. Data are reported annually to the cooperators and to the respective federal.govern-
ments. NAMP is acclaimed to be the first international project that was jointly planned, used the
exact the same methods, and was jointly administered, and provided annual results with uniform
analyses.
Cooperative survey to determine decline and mortality of red spruce and balsam fir was initiated
in 1984. First phase included photographic color infrared sampling survey to establish that
mortality was occurring. Consequently, as phase 2, plot-clusters were established in New
England, New York and in West Virginia, to monitor the rate of tree decline. The field measure-
ments continued from 1985 through 1989. The photo survey was repeated in 1990 to determine
if the extent of mortality areas have changed.
A cooperative dogwood anthracnose project was initiated in 1988. These funds were used to
conduct pilot tests on control techniques, determine the disease distribution and assess dis-
ease severity. The pilot testing has produced a set of control procedures for urban trees and
the impact assessment, using 210 permanent plots, has documented the spread of the disease
in the south from 0.5 million acres in 1988 to 9.5 million acres in 1992.
Components of Evaluation Projects
The following components need to be included in an evaluation monitoring project work plan.
Also all participants in the project should be included in the organization and planning aspects
if the project is expected to succeed.
1. Problem statement - Clearly identify the problem(s) that need to be answered as
identified by FHM detection monitoring.
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23
2. Objectives - The objectives should clearly state the products and the user(s) of the
product. A statement concerning the procedures to modify and/or terminate the project
should also be included.
3. Methods -The methods should be clearly stated and if possible be similar to methods
for detection monitoring, when applicable. Data management procedures should also be
developed.
4. QA/QC - The precision and if possible the accuracy of the methodology should be
stated and meet the objectives of the project and products. Data precision estimation
procedures should be clearly stated. Other training and standardization techniques
should be addressed and developed.
5. Reports - Establishment, annual progress, and final reports should be planned for
in the work plan in as much detail as possible. Consideration should be given to technical
and general public reports.
6. Budgets - The entire budget for the project and divided into annual increments. If
cooperative funding is involved, shares should be clearly stated. All of the funding groups
should make long-term commitments with the understanding that funds are available
annually and amounts can change.
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24
APPENDIX
Example of a Cooperative Forest Health Agreement Narrative
Cooperative FHP (or FPM) Program for FY 199x
Forest Health Monitoring
(State)
The (Agency) will undertake the following activities for FY 199x:
1) The (Agency) will adopt the minimum standards for effect/pest surveys and use the
standard FHM methodology (if different, procedure must be justified and documented)
as described in Eastwide FPM/FHP Working Plan for the following effects/pests (from
Table 1 or 2 in the Eastwide Working Plan):
List appropriate effects/pests
2) The (Agency) will conduct spring visits (Region 8 only) to all detection plots or visit the
detection plots throughout the'year as needed and/or install supplemental effect/pest
plots to document any potentially active pests. The (Agency) will agree to participate in
the necessary training session to accomplish the additional visits to the detection plots.
3) The (Agency) agrees to conduct the following special surveys in support of the FHM
program:
List applicable surveys
4) The (Agency) will report the information collected under 1) and 2) to the USDA Forest
Service FHP (or FPM) by January 1, 199x+1.
5) The (Agency) will develop and submit a forest health report by April 1, 199x+1 to the
USDA Forest Service FHP (or FPM).
6) The (Agency) will (purchase, contract, conduct, other) (equipment, services, information
management, training sessions, other) in support of FHM activities in the state.
List all
The USDA Forest Service FHP (or FPM) will provide the following support to the (Agency) in FY
199x:
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25
7) The USDA Forest Service FHP (or FPM) will provide training, including supporting docu-
mentation, to the (Agency) to support activities identified in items 1), 2), and 3).
8) The USDA Forest Service FHP (or FPM) will provide the necessary (technical support, raw
or summarized data, summarized maps or tables, other), in an appropriate format, to
accomplish items 4) and 5) by February 1, 199x+1 to (Agency).
The above items were developed in conjunction with the appropriate USDA Forest Service FHP
(or FPM) personnel and agreed to by both groups. Items 4), 5), and 8) will be completed in the
FY after the the field activities [items 1), 2), 3), and 7)] have been completed. Both the USDA
Forest Service FHP (or FPM) and the (Agency) agree to implement the principles of quality
assurance to accomplish the above activities. This agreement does not cover the main FHM
detection plot effort (except specific items under 2)). Those activities will be covered in a
separate agreement with USDA Forest Service Research.
The costs for each of the above activities is itemized below:
Activity
1) Effect/Pest Surveys
Effect x
Effect y
Effect z
Pest x
Pesty
2) Additional Plot Visits
xxx plots will be done, average cost per plot - $xxxx
3) Forest Health Report
4) Effect/Pest Reporting from item 1)
5) Special Surveys
Survey x
Survey y
6) Other
Total
Less xx% State share
Federal share
Cost
xxxx
yyyy
7777
XXXX
yyyy
xxxx
xxxx
xxxx
xxxx
yyyy
xxxx
XXXX
YYYY
7777
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Appendix C. Field Logistics
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16. Field Logistics
R. Kucera and R. L. Tidwell
16.1 Eastern Field Logistics Plan
16.1.1 Introduction
A field crew will be comprised-of a core of 5 individuals; two foresters, one soil scientist, one
botanist, and one tree climber
During a day of sampling, there are a number of activities or operations that occur which are
the responsibility of certain individuals. The following table lists the field operation and the person
responsible for the activity.
Operation
Sampling schedule
Crew assembly
Transport to site
Site location
Plot establishment
Sampling
Sample maintenance
Sample transfer/shipping
Equipment maintenance
Data transfer
Daily communication
Responsibility
Field crew leader
Field crew leader
Field crew leader
Forester 1 & Forester 2
Forester 1 & Forester 2
Field crew
Tree Climber
Tree Climber
Field Crew
Field crew leader
Field crew leader
These operations will be discussed under the heading of the person or persons responsible
for this activity. The following table lists the crew members and the agency that is responsible for
providing the crew person.
Position
Forester 1
Forester 2
Botanist
Soil Scientist
Tree Climber
Agency
Forest Health Monitoring
Forest Health Monitoring
Forest Health Monitoring
Forest Health Monitoring
Forest Health Monitoring
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16.1.2 Field Crew Leader
The field crew will be supervised by a designated crew leader. The crew leader will supervise
all field operations and, if necessary, resolve all discrepancies or issues at the site. The fieW crew
leader has the responsibility of:
• Maintaining sampling schedule.
• Assembly of field crew.
• Transportation to the sampling site.
• Ensuring adherence to sampling protocol.
• Ensuring proper use of field equipment.
• Maintaining site and sample integrity.
• Data transfer.
• Daily communication.
16.1.2.1 Sampling Schedule
Tharrl? T^ '^.f^" bfu responsible for sampling a certain number of plots within an index
Jth leader w,ll have the responsibility to meet this sampling quota while maintaining the
qualrty of the measurements and samples, and following standard protocol The field crew leader muS
" Sarnp""ng SChedule to maintain ™ adequate
16.1.2.2 Assembly of Field Crew
The field crew leader is responsible for assembling the field crew each mornina at an
appropriate time. The field crew leader will also determine rendezvous points to assembled ew after
scheduled time-off (after weekends, holidays, etc.).
16.1.2.3 Transportation to the Sampling Site
to thp «Johere'd °reW 2adeLWi"J ?e resP°nsible for Provi'ding and maintaining adequate transportation
to the site. Crews must make efficient use of vehicles.
16.1.2.4 Ensuring Adherence to Sampling Protocol
ho ah. Th!ififld CreW '!a
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16.1.2.6 Maintaining Site and Sample Integrity
During sampling, flagging will be placed around the site to mark various sampling points (soil
holes, vertical vegetation and PAR measurement points). Soil holes will be dug and branches will be
sampled. The field crew leader will be responsible for .maintaining plot integrity and anonymity when
sampling is completed. The field crew leader will direct all of the crew members to assist in these
activities.
A number of samples will be collected at the sampling site. These samples will be prepared
and analyzed and incur quite a cost from collection through analysis and interpretation. It is imperative
that the integrity of these samples are maintained. Some samples require cooling and transportation
to laboratory facilities as soon as possible. The field crew leader is responsible to ensure adherence
to sample maintenance protocol.
16.1.2.7 Data Transfer
Data from PDRs and ceptometers must be uploaded to laptop computers and transferred to
the EPA VAX. The field crew leader will be responsible for this data transfer. Instructions for this
operation are presented in section 17.12 of the PDR instructions
16.1.2.8 Daily Communication
For this study, communication will take place electronically through the laptop computers, or
phone system. An "update" screen will appear which the field crew leader is requested to fill in. It will
include the following information:
• Field Crew ID.
• Hexagon sampled that day.
• Hexagon expected to be sampled following day.
• Additional personnel with field crew (auditor, EPA personnel etc.).
• Field crew location (hotel name, address, Phone #).
• Expected location of next day.
• Comments/Problems.
The field crew leader or the logistical aide is expected to fill in the update and send it out
electronically, each day, whether or not data are being transmitted. This update will be
electronically sent to the EPA VAX which will then be used to update DG and E-MAIL accounts of
appropriate individuals in the program. Instructions for this operation are presented in section 17.12
of the PDR instructions.
Some hotels "hardwire" phonelines, prohibiting the connection of the laptop to the phone
system. In this instance, an 800 number will be available for updates (800-288-3171). Either an
individual will record the information or a recorder will store this information. A LESC individual will
enter this update information and electronically send it to the appropriate individuals.
16.1.3 Forester 1 & Forester 2
Forester 1 and forester 2 will be responsible for locating the sampling site, establishing the plot,
and the data collection activities involved with the growth, visual crown rating, branch evaluation,
bioindicator plants, increment cores, and root samples.
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16.1.3.1 Sampling Site Location
In most cases, FIA photopoints will be used for the sampling plots. Information pertaining to
the sites will be available at the training session. Foresters will follow standard FIA protocol in locating
sampling sites. Procedures for this activity are detailed in Section 2.
16.1.3.2 Plot Establishment
Forester 1 and forester 2 will be responsible for establishing the plot. Details of this procedure
are provided in Section 2. However, Section 2 only details establishment of the plot for growth and
visual crown rating measurements. Soil sample holes and vegetation structure plots must also be
established. It is very important that the plot establishment be accomplished in a sequence that 1)
maintains plot integrity for other measurements (i.e., regeneration measurements before vegetation
structure measurements), and 2) allows all field crew members to start data collection activities as soon
as possible in order to finish sampling within a reasonable timeframe. To accomplish this, a plot layout
procedure should be developed.
16.1.4 Field Crew
The field crew, as a whole, will be responsible for sampling activities. The field crew will share
responsibility for equipment maintenance and equipment inventory. The tree climber will aide the crew
with resupply requests, sample shipment and other administrative duties. These administrative duties
may include calling in and reporting labor hours, sending or receiving messages and moving gear to
the next hotel location.
16.1.4.1 Sampling
Individuals on the 5-person crew will be responsible for the following sampling activities:
• 2 Foresters
Forester 1 & Forester 2 - Plot set-up, Mensuration, Regeneration, Visual Crown Rating,
Damage and Mortality, Increment Cores, Root Samples, Bioindicator Plants, In-hand
Branch Evaluation
• Botanist - Vegetation Structure
• Soil scientist - Soils - Physical (characterization/sampling).
• Tree climber - Branch Sample Collection, Assist with excavation of soil holes, Sample
Shipments
Forester 1 and forester 2 will be working together for the majority of the day. The tasks of plot
layout, mensuration and regeneration, visual crown rating, damage and mortality measurements, root
samples, and branch evaluation are expected to take a full day. The soil scientist will be working
independently, with the exception of assistance in excavating soil holes during the morning. The
botanist will be responsible for vegetation structure. Therefore, an efficient sampling schedule should
be developed. The tree climber will obtain branches and provide them to the forester for in-hand
evaluation. The tree climber will package the foliar samples and assist with sample handling for root,
soil, lichen, and increment core samples. The tree climber will be responsible for sample shipments.
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Sampling Schedule-
In order for the field crew to collect data in an efficient manner, a list of sample activities has
been developed (Figure 16.1). Figure 16.1 attempts to identify which sampling activities will be
accomplished. Field crew members must work on measurements together and certain measurements
or operations must take place in sequence. This figure should be used as an example and should be
modified during sampling if a more efficient schedule is developed.
Measurement
Uchens,
Wildlife,
Veg. habitat
Dteback
Crown ratio
Density
Diameter
Transparency
Tret damage
Soil
Productivity
Foliar chemistry
Branch evaluation
Root samples
Increment
cores
Plot establish
Mensuration
Pollution
Indicator
plants
Sub-
plots
4
4
4
4
4
4
4
Spits
2
2
2
2
all
4
toff
plot
Samples
7 points
each
all
trees
>5"
dbh
yes
2lrees/ subplot
x2 subplots
4trees
1/tree
4trees
1/tree
4trees
2/tree
all/sub
plot
one plot off plot
FOR
2
.5
.5
1
1
1
FOR
2
.5
1
1
1
1
SOIL
SCI
6.5
BOT
6.5
CLIMB
2*
3.5*
.5*
.5*
.5*
Figure 16.1 List of measurements in Eastern Demonstration. * denotes sample handling activity.
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16.1.4.2 Equipment Maintenance
During training each field crew member will be provided equipment and an inventory list of the
equipment they will receive. If equipment is damaged during field activities, the item should be
identified on the inventory list and the service or repair that may be required should be specified. The
field crew member will inform the field crew leader of the damaged equipment. The field crew leader
is responsible for communicating this information through the daily crew update. If available, the field
crew member will use replacement equipment. If supplies run low or an item of equipment is needed,
contact the following personnel for resupply of equipment and consumables:
Bob Kucera
ManTech Environmental Technology, Inc.
2 Triangle Drive
Research Triangle Park, NC 27709
Work Phone: (919) 541-7589
Home Phone: (919) 490-5289
inventory Procedure -
At the end of the field season, the field crew will return all equipment to the logistics lead. The
logistics lead and each field crew member will check off each item on the inventory list that is provided
at the beginning of the field season to each field crew member. All items must be accounted. Items
will be inspected for damage and their condition recorded.
16.1.5 Tree Climber
The tree climber will be responsible for the following activities:
• Obtaining branch samples.
• Sample maintenance.
• Sample transfer and tracking.
• Sample shipping.
Six types of samples will be collected from the sample plots; 1) soil samples, 2) bulk density
samples, 3) lichen samples, 4) increment cores, 5) foliar chemistry samples, and 6) root samples. The
number of samples expected from each plot are identified in the appropriate methods section.
16.1.5.1 Sample Maintenance
This SOP is intended to describe the procedures necessary to maintain samples collected in
the field.
All samples and film will be packaged by the field crew in a manner that will maintain integrity.
Of the samples mentioned in the overview, only the soil samples must be maintained at approximately
4° C. Root samples must be stored at a temperature between 13° C and 0° C. Lichen samples must
not be placed in coolers due to the possibility of fungal growth. Photographs of the soil pedon
and plot will be taken. This film should also be maintained in a cool atmosphere. Cooling of samples
and film can be accomplished in the field with the use of the frozen gel-packs and coolers. The tree
climber is expected to provide and maintain coolers and frozen gel-packs for the field crews. The
field crew is expected to maintain samples until received by the tree climber. The tree climber is
expected to maintain sample integrity from time of receipt until sample shipment.
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Equipment and Supplies -
• Cooler (hard plastic/polyfoam).
• Gel-packs.
Calibration and Standardization -
There are no calibration and standardization steps in this procedure.
Quality Control -
Coolers and gel-packs should be inspected for damage on a daily basis. Polyfoam shipping
coolers should be contained in their cardboard packing cases at all times to avoid breakage. Avoid
wetting the packing case around the shipping coolers because it will reduce the containers strength,
which is intended to protect the cooler within. Leaking gel-packs should be discarded. Sample
material should not be left outside of coolers or in direct sunlight for considerable lengths of time (>15
minutes).
Procedure -
1. The crew will be provided hard coolers and frozen gel-packs.
2. As samples are brought out of the field, they are placed into hard coolers. Place a layer of
frozen gel-packs on the bottom of the cooler. Place a layer of plastic bags over the gel-packs
to prevent samples from coming into direct contact with gel-packs.
3. Place soil samples into coolers.
4. Coolers containing sample material should be placed in cool areas (hotel rooms, refrigerators,
etc.) until shipped.
5. Once the tree climber has received samples, this individual is expected to maintain samples
at the appropriate temperature by either the use of gel-packs or storage in available cooling
facilities until shipment to preparation or laboratory facilities.
6. Other samples (bulk density, roots, and lichens) must be stored in a manner that will protect
their integrity. Bulk density samples can be stored in coolers for their protection until they can
be shipped.
16.1.5.2 Sample Transfer and Tracking
As samples are transferred from the field crew to the tree climber, the number, and type of
samples must be recorded in a manner that will allow for the tracking of these samples. This will
assure that all the samples collected in the field have been transferred to the tree climber and it will
allow tracking of samples throughout the preparation, sample analysis and archive phases of the
program. Two types of techniques will be used for recording and tracking samples: 1) bar code
scanning for soil samples and 2) hardcopy recording for the remaining samples.
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Equipment and Supplies -
• Bar code reader.
• Bar codes.
• Laptop.
• Interface cables.
• Coolers.
• Frozen gel-packs.
•• Large plastic bags.
• Clear adhesive tape.
• Sample tracking form.
Calibration and Standardization -
There are no calibration and standardization steps in this procedure.
Procedure -
Soil Samples
1.
2.
4.
5.
6.
7.
8.
9.
Through the electronic data information system, the samples ready for sample transfer by each
crew will be available on the field crew laptop. The hexes and bar code sample numbers of
outstanding samples will be updated on the computer.
When samples are ready to be shipped, set up the laser bar code reading device and initiate
the " Pack Samples in Cooler" program on the laptop computer (see Section I7.l2of PDR
instructions).
If samples are in shipping boxes, remove samples from each box. Place gel-packs in the
bottom of the box as stated in the sample maintenance procedure. If samples are in hard
coolers, they should be removed and placed into shipping coolers. Hard coolers should remain
with field crews and must be returned to the crews as soon as possible.
Place a bar code on the shipping cooler and tape over this barcode with clear adhesive tape.
Scan this bar code. This will set up the computer for scanning samples to be placed in this
cooler.
Enter the header information that is required for the " Pack Samples in Cooler" program.
Scan each bar coded sample and place the sample into the shipping cooler that was bar
coded in procedure 5. This will record what samples are in each box and will be used later for
sample transfer. Once the shipping cooler is full it can be taped shut if the sample material
will be shipped that day.
Repeat procedure 5 and 6 for each shipping cooler to be filled.
After all samples are packaged, press the appropriate commands on the laptop (Section 17.12
of the PDR instructions). This will cross-check the samples transferred with the samples
collected in the field. Any discrepancies will be identified. The tree climber will be expected
to attempt to rectify discrepancies (i.e., if a sample for a hex has not been recorded, the tree
climber should look through the samples to see if the sample is actually there and was not
scanned, etc.).
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10. Upon completion of sample transfer, the transfer file will be electronically sent to EPA-LV
where the file will be reviewed and discrepancies forwarded to the field crew for rectification.
Bulk Density, Root, and Lichen Samples
1. Bulk density, root, and lichen samples will not be scanned with bar code readers. Therefore,
upon transfer of samples, the hexagons, and number of samples of each type (root, tree cores,
etc.) will be recorded on a packing slip.
2. Upon completion of sample transfer, place a copy of the packing slip into a plastic bag and
place it into the shipping container.
(Note: Bulk density, root, and lichen samples may be accumulated from a number of plots before
shipping to the laboratory (one sample plot should not fill up a sample container). Keep a running tally
of samples on the packing slip until samples are ready to be shipped.)
16.1.5.3 Sample Shipping
All samples, except root and lichen samples, will be shipped to the preparation laboratory:
Rob Tidwell
Lockheed-ESC Warehouse
4675 S. Valley View Drive
Las Vegas, Nv 89103
(702) 795-8937 or 8938
Root samples will be shipped to:
Moss Baldwin
Forest Pathology Laboratory
VPI & SU
Glade Road Research Center
Blacksburg, VA 24061
(703) 231-5030
Lichen samples will be mailed to separate laboratory facilities.
Shipments can occur daily or when appropriate for the tree climber. EPA will provide pre-
labeled federal express shipping forms for use in shipping samples. Use the express mailing forms in
order and return all unused forms.
(Note: A packing slip (discussed in sample transfer section) must accompany each shipment. In the
case of soil samples, the packing slip will record the number of boxes sent; for bulk density samples
and lichens, the packing slip will contain the hexagons and the number of samples.)
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Equipment and supplies -
• Bar code reader.
• Bar codes.
• Laptop.
• Interface cables.
• Coolers.
• Frozen gel-packs.
• Large plastic bags.
• Clear adhesive tape.
Calibration and Standardization -
There are no calibration and standardization steps in this procedure.
Procedure -
Soil Samples and Film
1.
3.
If samples have been transferred from field crews and are being shipped the same day, the
gel-packs in the coolers do not have to be replaced. If transfer and shipping occur on different
days, gel-packs must be replaced and samples repacked. Care must be taken not to place
samples in different boxes during this procedure.
Fill out two packing slips identifying the number of coolers in the shipment, the actual numbers
of bulk density samples and rolls of film. Keep one copy and place the remaining copy into
one of the coolers heading to a specific destination. More than one cooler can be sent on a
single packing slip. Therefore, label the other coolers as to their destination. Tape coolers
shut.
Set up the laser bar code reading device and initiate the "SHIP COOLERS TO EMSL-LV"
program from the "SAMPLE AND SHIPMENT TRACKING SERVICES" menu on the laptop
computer. See Section 17.12 of the PDR instructions for the details of this program.
Lichen Samples
1. Fill out two packing slips identifying the samples in the shipment. Keep one copy and place
the remaining copy into one of the coolers heading to a specific destination. More than one
cooler can be sent on a single packing slip. Therefore, label the other coolers as to their
destination. Tape coolers shut.
2. Proceed to the express mail facility for shipping. Lichen samples will be sent to:
Dr. Bruce McCune
Oregon State University
Dept. of General Sciences
Weniger Hall 355
Corvallis, Oregon 97331-6505
16.2 Western Field Logistics Plan
(NOTE: This section is not included in the Southeast or SAMAB Demonstration field guides.)
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Appendix D. Oil-frame Indicator Development
C.G. Shaw, III, K.W. Stolte, and C.J. Palmer
A rigorous program of constant testing and evaluation of parameters to measure on Detection
Monitoring Plots needs to be maintained to ensure that all indicators used in the Forest Health
Monitoring (FHM) program meet the desired criteria. These criteria include:
• Early observable responses to perturbation events.
• Unambiguous interpretability (known inferences can be drawn from changes over time)
• Simple quantification.
• Regional responsiveness.
• Index period stability and capability of regular remeasurement.
• High signal-to-noise ratio and a high precision (low variation) for measurement recording.
• Low environmental impact.
• Relatively inexpensive to implement.
To be considered a "core" indicator, all of these criteria should be met and documented.
This is the reason FHM will be testing several indicators this field season off-frame , and in
some cases, in on-frame, pilot studies. Although the actual indicators to be tested this year are not
yet determined, the following are possible candidates:
Photosynthetically active radiation (PAR).
Soil and root microbes and arthropods.
Soil chemistry.
Various foliage and crown assessments and foliage sampling techniques.
Lichen communities and elemental analysis.
Expanded vertical vegetation profiles to include dead and fallen material.
Various indicators that can use remotely-sensed data.
Pest organisms as indicators of forest health.
Terry Shaw, Ken Stolte, and Craig Palmer will meet in early May to evaluate the indicator
development needs for this year. Indicator leads will be contacted to determine their priority research
needs and reviews will be made of past work to determine which indicators need further development
From this, we will select the indicators to be evaluated off-frame this summer, based on priority needs
and available funding.
In addition, we will initiate a process to guide future off-frame indicator development We
envision this process to consist of evaluation panels rating proposals for off-frame, developmental
research on high-priority indicator needs. The process will be in place for use during FY93 We
consider that having an operating system in place to direct off-frame indicator development a critical
but currently lacking component of the FHM program. When the process for off-frame indicator
development is in place, it should receive the recognition that off-frame indicator development deserves
as a critical, driving component of the FHM program.
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Appendix E. California FHM State Plan, May 1, 1992
E-1
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CALIFORNIA FHM STATE PLAN
May 1, 1992
Vernon J. LaBau
Anchorage Forestry Sciences Laboratory
Pacific Northwest Research Station
Detection Monitoring
-------
TABLE OF CONTENTS
Page
INTRODUCTION
BACKGROUND [[[ ±
Legislative Mandates and Directives ...................... . . ] . i
The Problem Statement ................... .................... ..... 2
The Mission Statement and Objectives for Research's
Detection Monitoring ................................ 2
The Goal and Objectives for Forest Pest Management .......... .... .2
THE PROGRAM DESIGN .......................................... . . o
Detection Monitoring ............................ '.'.'"'. ........... 3
Evaluation Monitoring ............................. * .............. o
Intensive-Site Ecosystem Monitoring .............................. 3
PROGRAM COOPERATION AND COORDINATION ............................. 3
Cooperation within the Forest Service ...................... .. . . . '.k
Coordination with States .................... ................ ..... 4
FPM/State responsibility ........................ ................ 4
THE DETECTION PLAN ................... . .................. 4
THE SAMPLE DESIGN ......... ................................... 5
BASIC PREPARATIONS ......... ,-
.......................................... 5
LOGISTICAL PREPARATIONS ..................................... 5
QUALITY CONTROL/QUALITY ASSURANCE ................................... g
FIELD METHODS GUIDES .......................................... j
ANALYSIS
, Forest Service Role . ........................ 7
EPA Role ............................ ................. ............ 8
INFORMATION MANAGEMENT ............. .................. g
REPORTING RESULTS ....................... . ........... q
IMPLEMENTATION SCHEDULE ................................... n
-------
TABLE OF CONTENTS (Continued)
FUNDING NEEDS 10
SELECTION OF THE INDICATOR SUITE 11
LIST OF APPENDICES 12-13
A Gant Chart of Activities 12-13
B List of Analysis Table Titles 14-15
C Summary of Anticipated Expenses 16
D Summary of Western U.S. Indicators and Data
Elements for FHM. 17-^21
11
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INTRODUCTION
In accordance with the master plan for implementing the Forest Health
Monitoring Program in the United States, the State of California is scheduled
for implementation of the Forest Health Monitoring field plot grid during the
summer of 1992. This plan is intended to serve as the basic guide for the.
Detection Monitoring phase of the Forest Service's Forest Health Monitoring
Program in California. The intended audience for this document are the
planners, implementors, users, and cooperators associated with the Forest
Health Monitoring program in California and the rest of the US. It is intended
that this Plan be consistent with and integratable with other NFHM efforts
throughout the United States. Major sections from the Forest Pest Management
(FPM) Plan have been borrowed and used in this plan to assure later
integratability of these plans into one major National FHM/FPM Plan.
BACKGROUND
Legislative Mandates and Directives
The Forest Health Monitoring (FHM) Program in California is being implemented
in response to several national legislative mandates (i.e. Public Law 100-521),
which have directed the Forest Service, USDA and the Environmental Protection
Agency to, among other things, "increase the frequency of forest inventories in
matters that relate to atmospheric pollution, and conduct such surveys as are
necessary to monitor long-term trends in the health and productivity of forest
ecosystems--." The Forest Service's Forest Inventory and Analysis (FIA)
program which has been establishing and periodically re-measuring forest
inventory and evaluation field plots throughout the United States for as much
as 50 years, has a nationwide data base that will be used as a baseline data
set in this endeavor.
Amendments to the Cooperative Forestry Assistance Act of 1978 (P.L. 95-313) by
the Food, Agriculture, Conservation and Trade Act of 1990 (P.L. 101-624)
authorize FPM involvement in forest health monitoring. Section 5 of P.L.
95-313, Insect and Disease Control, is redesignated Section 8, Forest Health
Protection. Section 8(b)(l) directs the following: "Conduct surveys to detect
and appraise insect infestations and disease conditions and man-made stresses
affecting trees and establish a monitoring system throughout the forests of the
United States to determine detrimental changes or improvements that occur ever
time,, and report annually concerning such surveys and monitoring."
The Environmental Protection Agency (EPA), also received similar legislative
directives, which resulted in their Environmental Monitoring and Assessment
Program (EMAP), which was much better funded than the Forest Service Programs.
EPA was directed to monitor all the major terrestrial and inland and coastal
water ecosystems of the United States, and the Forest Ecosystem was one of the
components EMAP began planning to monitor.
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In addition to these federal legislative mandates and directives, the
California Department of Forestry had a Forest Health Monitoring Coordinator
position approved with State Legislature funding in the 1991 legislature.
This, in effect, gave the California Department of Forestry impetus to proceed
with FHM.
The federal agency planning for the California Forest Health Monitoring effort
evolved out of the afore named legislative impetus. That national plan,
developed in EPA and the Forest Service called for the establishment of a set
of field plots to be established in California, beginning in 1992. The purpose
of these plots was basically to look for early signs of degradation in the
health of the forest ecosystems in California. These plots, therefore, were to
be called Detection Monitoring sites.
The Problem Statement
The health of the Nation's forests is increasingly in the news. The public is
concerned about management activities, deforestation and habitat loss, air
pollution, global climate change, and damage from a variety of forest insect
and disease problems. The USDA Forest Service (FS), U.S. Environmental
Protection Agency (EPA), and other Federal and State agencies are increasingly
required to provide information which responds adequately to these concerns.
Unfortunately, much of the resource data now collected by the FS, other public
agencies, and private organizations can not be compiled, integrated, analyzed,
and interpreted to evaluate forest health.
The Mission Statement and Objectives for Research's Detection Monitoring
The Mission of the Detection Monitoring phase of FHM for the Pacific Northwest
Forest Research Station is to develop and improve science and techniques for
monitoring forest health of the forest and rangeland ecosystems of the Pacific
Coast States at several levels of spatial and temporal resolution. The main
objective associated with that goal is to conduct long-term periodic
measurements or observations of selected physical and biological parameters for
the purpose of initially establishing a baseline for detecting change, with the
ultimate objective of detecting and quantifying deviation over time from that
baseline due to changes in health of the forest ecosystem. Forest Inventory
and Analysis (FIA) have the lead responsibility for NFHM and have a lead
responsibility in the Detection Monitoring Phase of this effort.
The Goal and Objectives for Forest Pest Management
The goal of FPM in this endeavor is to discover if there are large-scale
episodes of forest pest and other stressor effects that are not related to
background cycles of forest growth, decline and stressor activity. This
includes effects from (1) known agents; (2) changed activity of known agents;
(3) new agents; and (4) unknown agents.
The objectives for the FPM Program is (1) document status and change ,in
large-scale effects on forests of forest pests and other stress agents. This
includes temporal and spatial change in extent, severity, duration, frequency,
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host, and cause; and (2) conduct forest stressor effects surveys, provide
training, and provide technical assistance in support of the NFHM plot network.
THE PROGRAM DESIGN
Although Forest Service (FS) Research, through the National Forest Health
Monitoring (NFHM) Program Manager, has the overall lead for NFHM design,
implementation, and reporting. Forest Service Forest Pest Management (FS FPM)
has direct responsibility for designing and implementing the FPM components of
NFHM.
The NFHM Program is a three-tiered, long-term process to provide regional
(multi-state) and national information on forest health status and trends.
Each successive tier requires progressively more detailed information. The
tiers are:
Detection Monitoring ~ to detect deviation of key monitoring elements from
established baseline conditions or trends. This is the first and most
extensive level. FPM and Research share responsibility for this tier which
consists of (1) a plot component (Research) ~ a geographically-based
network of permanent plots distributed throughout the nation's forested
areas, and (2) a survey component (FPM) ~ forest damage surveys and
reports of forest pest effects. Detection Monitoring will discover and
describe changes in forest health conditions.
Evaluation Monitoring ~ to attempt to identify causal relationships and
management responses. This is the second level, is FPM's responsibility to
initiate, and is activated by Detection Monitoring results. Evaluation
Monitoring is the process for determining causes for changes in forest
health status.
Intensive-Site, Ecosystem Monitoring — to define basic relationships
sufficient to predict consequences. This is the third level and provides
the most detailed, long-term data for ecosystem research to determine
causal relationships, predict rates of change in forest conditions, and
identify management responses. FPM has little or no responsibility for
Intensive-Site, Ecosystem Monitoring.
PROGRAM COOPERATION AND COORDINATION
Forest Service, EPA, and States are responding to forest health concerns by
initiating a National Forest Health Monitoring Program (NFHM) designed to
describe regional and national forest conditions and to detect changes,
determine causal relationships, and predict consequences. NFHM success depends
on participation by FS, EPA, and States. It incorporates recommendations from
the Forest Response Research Program (part of the National Acid Precipitation
Assessment Program), the Forest Health Strategic Plan, and the EPA
Environmental Monitoring and Assessment Program. A number of FS Staffs will
become involved both in NFHM and in related aspects of EPA's Environmental
Monitoring and Assessment Program (EMAP).
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Coordination within the Forest Service
National Forest Health Monitoring is designed to build on existing FS
programs. The FS Staff units most involved have been Forest Fire and
Atmospheric Sciences Research; Forest Inventory, Economics, and Recreation
Research; Forest Pest Management; Timber Management; and Watershed and Air
Management.
FS Research, through the NFHM Program Manager, has the lead for NFHM design,
implementation, and reporting. FPM has direct responsibility for providing
pest and forest conditions information in Detection Monitoring and for
initiating Evaluation Monitoring activities. Both Detection and Evaluation
Monitoring activities conducted by FPM will be coordinated with the NFHM
Program. Forest Health Monitoring program and budget planning are coordinated
among participating FS staff units.
As technical staff to National Forest System (NFS) resource managers, Regional
and Area FPM staff will have an important role in planning Regional execution
of the NFHM Program. Regional and Area FPM staff also have the responsibility
for implementing the Regional FPM component of NFHM on NFS and other Federal
lands.
Coordination with States
State cooperators will be fully involved with all phases of NFHM Program
planning and execution. Regional and Area FPM staff will work with the
Stations and the NFHM Program Manager to establish appropriate mechanisms for
the State involvement essential for NFHM Program success. In California, the
Department of Forestry and Fire Protection have a clear directive to be
involved in forest health monitoring.
FHM/State Responsibility
Forest Pest Management and States are responsible for providing Regional and
State forest pest/stressor effects information and for addressing how forest
health may be affected. Data must be adequate to evaluate and interpret the
relationship between forest pest and stressor effects and forest health
changes.
THE DETECTION PLAN
A great deal of planning and general conceptualizing of aspects of the national
FHM/EMAP went into establishing a national sampling scheme that would meet the
mandated directives and intentions of the US Congress. A very comprehensive
national plan was developed for selecting and installing monitoring sites and
instilling and maintaining quality control in data acquisition through very
structured training and through imposing quite strict QA/QC criteria. These
stringent criteria were imposed on, the process all the way through the data
reduction, analysis, and reporting of results.
In addition, a very structured approach was developed to select a suite of
indicators that would efficiently and effectively detect the "flags of change,"
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with sufficient precision to make develop meaningful scientific results
regarding changes in health of the nation's ecosystems. EMAP took the lead in
this indicator development.
The planning phase in California began with an Information Needs Assessment
(INA) meeting in Sacramento in May of 19-91. The purpose of this meeting was to
define needs to be addressed, evaluate the amount and level of expertise and
technology required to speak to the need, and defining a strategy, politically,
financially and practically, as to how the need should and/or could be met. A
second part of the INA involved determining what the various federal, state,
and other agencies and groups were doing in California in terms of monitoring
ecosystem health. A third aspect of this INA was to provide information on the
national FHM/EMAP program, and what that might entail when implemented in
California.
THE SAMPLING DESIGN
The national EMAP sample design that was adopted to the FHM program was
developed by a team of statisticians with provision for considerable
flexibility in implementation of the design. It is based on a grid of 160,000
acre hexagons laid over the contiguous US, with plots systematically offset
from the hexagon centroids and are about 27 kilometers apart. It requires
about 12,500 of these hexagons to cover the contiguous US. It is estimated
that about 4000 of those will occur on forest ecosystems. California has about
680 hexagon sites within its boundaries (Figure 1), with and estimated 280
forested sites. It is planned that about 75-80 of the plots will be visited
each year for 4 years (Black dots in Figure 1).
At each of the centroid offset plots, a 40 square kilometer mini-hexagon is
evaluated, wherein landscape characterization is performed using remote sensing
media (thematic mapping classification). Within that hexagon, type maps are
compiled on vegetation type and on land use.
Finally near the centroid of all the hexagons that are classified as forest
ecosystem, a ground plot is established over a 1-hectare area. Four sample
sub-points are placed equidistantly apart over the 1 hectare area (see Figure
2), and at these sub-points, a suite of forest health indicators is measured to
look for signs of degradation of the ecosystem.
BASIC PREPARATIONS
As with any program of this magnitude, very thorough planning and very close
coordination with cooperators are key criteria in conducting a successful
Program. It was and is necessary for the U.S. Forest Service to work in close
concert with other Federal agencies, especially EPA, and with the California
Department of Forestry and Fire Protection.
Basic considerations including funding, staffing, the sampling design,
preparation of operational plans and methods manuals, quality control and
similar aspects. Much coordination and cooperation is needed to integrate
these basic considerations in a mutually compatible manner. A great deal of
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CALIFORNIA.
FOREST HEALTH KONTTORING SITES
Black = 1992 Sites
Major Forest Types
Douglas - Fir
Redwood
Ponderoso Pine
lodgepole Pine
Fir Spruce
Western Nardvoodi
Chaparral
J Pinyon - Juniper
Figure 1
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California forest Health Nonltorini
DATE PSEPAKED: 1/6/92
oan'Osuaia
STATUS AS OF: 1/6/92
10 20 ISO 10 20 3D
1* rinding committments
2.' SUfflng: Support Team Requirenenta
3. Project Sanpling Designs
4. Selection of Indicators/nethods
5. Operational Plans-
6. Quality Assurance Plans
7. Methods Manual
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strength in supporting these basic needs comes from approaching a plan of this
magnitude from a position of multiple strengths.
An overview of how these basic preparation are planned for can be seen in the
Gant Chart, Appendix A.
LOGISTICAL PREPARATIONS
The logistics consideration require careful and fairly precise planning so that
each logistical hurdle is cleared in a timely manner, and does not "bottleneck"
the process and cause missed deadlines at critical points. Again, the Gant
Chart, Appendix A gives an idea of the critical flow patterns that must be
followed in such an effort. Some of the important items include:
Establishment of the sample grid to maps and aerial photos
Acquisition of aerial photos and maps for the potential
monitoring sites (flying 1:8,000 photography over sites).
Interpretation of plot sites from aerial photos
Selection of the set of forested field sites to visit on the
ground
Equipment procurement
Contracting for transportation (jeeps, helicopters, pack animals)
Determining land ownership
Obtaining access permission on private or restricted lands.
Obtaining Archeological permits, permits to traverse critical
habitat, observe endangered plant and animal restrictions.
Hire field crews
Arrange for field crew lodging and meals.
Arrange for training sites and instructors
Coordinate with Government Resource Managers (District Rangers,
Forest Supervisors, Resource Offices, Park Rangers, etc.) to
access sites under their management.
Recon in the plot centers for as many sites as possible so the
5 person crew does not waste time waiting on a photo
interpreter— they can walk into the site via a flagged route.
Based on initial indications, cooperators will play a major role in supporting
these logistical operations. For instance, the California Department of
Forestry has indicated a desire to assist and advise in the implementation of
several of the logistical phases.
QUALITY CONTROL/QUALITY ASSURANCE
The Quality Control/Quality Assurance discipline developed by EPA for their
EMAP effort has basically been adopted for the FHM/EMAP implementation in
California. It has served the process well in the Eastern US, and will
continue to stand us in good stead in the West. In addition, excellent quality
control of data acquisition is assured through a very - through set of data
editing algorithms in the Portable Data Recorders. All data collected on all
aspects of the FHM program are entered through the Portable Data Recorders
(PDR), and have to pass through edit algorithms that screen for illogical or
otherwise erroneous entries. The PDRs also prevent the recorder from closing
out a plot without completing all data items.
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In addition, all data are transferred via electronic mail to a data center each
evening via dial-up modems. Also, all samples taken in the field are handled
in such a manner to guarantee a minimum amount of contamination to samples as
they are collected. There are stringent processed for getting the samples
(soil, foliar, lichens, tree cores, etc.) to the labs at EPA in a timely
manner. A computerized bar-coding process is" used to assure proper coding of
all samples.
Finally, there is a disciplined schedule and format for "hot-checking" and
"cold-checking" the work of the field crews. Two people spend about half the
summer in this type of Quality Control.
THE FIELD METHODS GUIDES
The field methods guides section is very extensive, and has been converted to
metric and rewritten in that mode for California. It is included as an
attachment to this document.
One important aspect of the field data collection process is the identification
of damage and pathogens, etc. that contribute 'to forest health decline. At
least one Forest Pest Management specialist will be hired as part of each field
crew to help identify these situations. Hopefully, these specialists will be
able to strengthen forest mortality estimates, which are an important component
of the tables listed in Appendix B.
ANALYSIS
Most analysis will be done by Forest Service or EPA scientists. FIA analysis
will likely be primarily restricted to presentation of tabular summaries and
graphics and the analyses thereof. Scientific analyses will likely be deferred
to more specialized analysts. If cooperators are used for special studies, or
have spec,ial expertise in certain areas of data reduction and analysis they may
be availed of for the analysis. Many analyses will be aimed at completing a
core set of report tables, .which is consistent across the whole U. S. In
addition, some State specific tables will be produced, depending on the desires
of the users. The eastern core set of table labels is shown in Appendix B,
along with some Mortality and Lichen analysis tables added specifically for
California. The mortality tables will be very general in nature, recognizing
the difficulty of making specific calls of "cause of mortality" in the field.
The presence of trained FPM people on the crews improves the possibility of
making usable general tables on mortality.
All cooperators in this endeavor will be provided with an opportunity to review
any narrative reports, conclusions and recommendations that result from the
California Forest Health Monitoring effort. Cooperators will also have access
to the data base for their own analyses, and it is expected that they will
sometimes perform independent analyses from that of their colleagues.
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The Forest Service Role in Analysis
The Forest Service has the responsibility of conducting all data reduction
associated with forest mensurational aspects of FHM. Analyses of these forest
mensurational and forest damage data will be done by Forest Service scientists
using appropriate statistical analysis and reporting methods.
The EPA's Role in Analysis
The EPA has the responsibility for analyzing all soils data, tree foliar data,
and similar chemical analyses. Likewise, analyses of these data samples will
be done by EPA scientists using appropriate statistical analysis and reporting
methods. Where collaboration of analyses are necessary, prior to report
writing, this will be done in a timely manner to minimize delay in the
reporting of results.
INFORMATION MANAGEMENT
Information Management, is herein assumed to be the following activities:
The conduct of Information Needs Assessments to obtain guidance
and suggestions from Forest Health Monitoring and related
field scientists, as well as cooperators and potential users.
The planning of data to be collected, based on desired results.
The development of data recorder programs to record and field edit
the data.
The transfer of these data from field location to a central data
repository site.
The editing and screening of data in preparation for processing.
The compilation of data into a form where it becomes information.
The distribution of the information through reports, technical
papers and other release media..
The management of the data and information central repository files
so the data can be easily accessed and evaluated by scientists
cooperators, and other potential users.
All cooperators will be given an opportunity to participate in various aspects
of Information Management. The Environmental Protection Agency will play a
major role in most of the Information Management activities, being most evident
in the Planning, Data Collection, and Data Analyses phases. State and local
cooperators will also have major roles to play in these phases.
10
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REPORTING RESULTS
Data analysis and interpretation will be performed at various centers of
expertise, depending on the discipline being evaluated for change. The goal is
to have data processed and preliminary reports of results ready within 9 months
after the data are collected. More detailed reports, and additional studies
will be forthcoming for a period of years following the initial data
evaluation. Prior to final publication of the monitoring results, reports will
be reviewed by both Forest Service and EPA scientists. Other cooperators will
be given the opportunity to review results if they are asked or request to be
included in the review process.
It is anticipated that some preliminary reports will be published within one
year of the completion of the field work, however, results from these data will
necessarily be limited since only one-quarter of the field sites will be
measured in the first year. The California Forest Pest Council and the
California Department of Forestry will have a major role in reviewing reports.
•The Pacific Northwest Station's Inventory and Economics RD&A Program will have
the lead role in preparing these reports. Analysts will endeavor to identify
any differences between Forest Health Monitoring statistics and previous Forest
Inventory and Analysis reports. These differences will be explained if
possible.
IMPLEMENTATION SCHEDULE
It is estimated that, given the indicator and measurement requirements and
pilot study work loads for the crews in the summer of 1992, up to 3 plots can
be taken in a 5 day work-week by each crew. At that rate, over the 6 pay
periods of summer training and work, about 50 plots will be completed by the
end of August. Early results indicate that about 80 plots represent about
one-quarter of all of the forested EMAP grid plots in California.
The current schedule of implementation..calls for installing one-quarter of the
total California EMAP grid each year for four years. It is the intention of
the NFHM Program to begin re-visiting at least some of the first year's
established sites in the second year, and again in the third year, while adding
sites from the second year's implementation. By the fourth year, all of the
grid of plots will be installed, and a large percentage of those that were
installed in the first three years will have been revisited, some on every
occasion.
Decision on the frequency of re-visitation will be made before beginning the
second year of implementation. This will, of course be dependent on future
budgets. It is recognized that such a revisitation. system has to have solid
standing in approved statistical methods, and such will be provided by the
EPA/FS Consulting Statisticians.
11
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PERSONNEL NEEDS
For the initial year of implementation, given the need to measure one-quarter
of the forest sites, estimated to be about 75 sites, three five-person crews
will be required. However funding levels -restrict the program to hiring only
two crews. Four of the five-person crew will be field staff. Plans are to
hire a GS-11 Soil Scientist, a GS 7 Forest Pest Management person, a GS 5
Forest Mensurationist, and a GS 5 Biologist/Botanist. Mid-level skills are
required for the GS-11 and GS-7 positions, which are quite specialized. Less
specialized skills are needed for the two GS 5 positions, except that the
Biologist must have good plant identification/classification skills, and the
Forester must have some experience in forest measurements.
In addition, there will be a GS-9 Technician assisting with Recon and Quality
Assurance. This person will, be stationed in Portland, and will have the
responsibility for getting the sampling grid put in place, and most logistics
handled each year.
There will be a GS 4 assisting the GS-9, as well as GS 4s available to handle
crew needs while they are in the field, such as mail, vehicle and equipment
repair and maintenance, mailing collected samples to Las Vegas, interfacing
with Las Vegas daily by phone or electronic mail, and doing data transfer, etc.
A GS-11 Forester will serve as Party Chief, making daily plot assignments, and
seeing to it that the schedules are adhered to as much as possible,. This
person will be stationed in Sacramento.
There are also costs associated with Headquarters overhead, such as Team Leader
and Time Card Clerks, etc. After the first field, season, it will be necessary
to add at least one Analyst to the staff.
Costs associated with these positions and their per-diem, etc can be found in
Appendix C.
FUNDING NEEDS
The funding needs for the first year of implementation are estimated at about
S517,000 to install 50 plots using three four-person field crews. About
$133,000 of that is for field crew and field supervisor salary. About $84,000
is required for per diem for the crews. About $34,000 is for jeeps,
helicopters and other transportation. About $149,000 is to cover Headquarters
salary, per diem, and clerical support. About $55,000 is needed for field
equipment purchase.
A detailed breakdown of all anticipated budget items for the 1992 field effort
in California is presented in Appendix C
Most of the funding is provided by EPA and the Forest Service. However, the
State of California is playing a significant role in providing funding and
in-kind support. Additional in-kind support is forthcoming from other Federal
agencies, such as NASA and the U. S. Park Service.
12
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SELECTION OF THE INDICATOR SUITE
A suite of indicators was selected for testing in the Eastern US FHM/EMAP
implementation and Pilot studies during the summers of 1990 and 1991. A great
deal of concern existed with the suggestion that these be tested in the western
US for applicability to FHM there. It was therefore decided that a Pilot study
should be done to evaluate the logistics of using the Eastern Indicator set.
In a meeting in Redding, California in May, 1991, a set of additional
indicators was selected for testing in the west. The suite of indicators
tested, along with a total list of variables measures or observed is summarized
in Appendix D.
The suite of indicators generally encompasses the following general criteria:
Forest Mensuration Indicators.
Tree Growth, Mortality, Age, etc.
Crown Evaluation (density, dieback, foliar transparency)
Foliar Evaluation (chemistry, needle density, needle age, etc.)
Dendrochronological Data (tree cores).
Forest Ecosystem Damage
Tree damage —Signs and Symptoms (Insect, Disease, Other)
Damage to Indicator Plants
Biodiversity Indicators
Vegetation Structure
Wildlife Habitat
Soils Characteristics
Physical
Chemical
Fertility/Productivity
There were several additional data items collected, such as the standard
plot/site descriptors (elevation, slope, aspect, terrain, etc.), as well as the
new variables being tested under the Pilot study, such as Photosynthetic Active
Radiation (PAR), root disease presence, tree height measurements, lichen
characterization, and others. Many indicators listed above will hot be fully
implemented until they have passed through a very structures Pilot Study
process.
In addition those listed above, numerous data are anticipated from ancillary
sources, such as climatology, history of the ecosystem, remote sensing data,
and etc.
LIST OF APPENDICES:
APPENDIX A GANT CHART OF ACTIVITIES
APPENDIX B LISTING OF ANALYSIS TABLE TITLES
APPENDIX C SUMMARY OF ANTICIPATED EXPENSES
APPENDIX D SUMMARY OF WESTERN U.S. INDICATORS AND DATA ITEMS
13
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APPENDIX A
GANT CHART OF ACTIVITIES
For those receiving this via electronic mail, the
transfer-rafale to the DG. Activities are plotted on a
time-lines for the following:
Start
10/1/91
11/1/91
10/1/91
10/1/92
11/1/91
4/1/92
1/15/92
2/10/92
1/25/92
3/1/92
Gant Chart is not
weekly basis, as to
End
Funding Planning Targets
Staffing: Support Team- Requirements
Project Sampling Design Planning
Selection of Indicators Time-line
Operational Plan (This Plan)
Draft
Final
Quality Assurance Plan
Draft
Final
Methods Manual (Metric)
Draft
Final
Portable Data Recording Testing
Logistics Schedules:
EMAP Grid points to NASA Ames
NASA Ames completes 7 1/2 minute
quad overlays
FIA/FHM Point Selection
Photo interpretation
Field Plot Selection—Forest Sites
Field Photo Acquisition
Map Acquisition
Equipment Procurement
Field Crew Staffing
Helicopter/vehicle contracting
Site Recon
Land Ownership Evaluation
Access Permission Letters to Owners
Archeological/Habitat Clearances
Hotel/Motel Lodging Arrangements
Preparation of Field Work Packets
Make Training Arrangements
Training
Train the Trainers
Train the Crews
Field Sampling
Plot Implementation
Quality Control
Debriefing Session
Analysis and Reporting
Data Editing/Screening
Data Reduction/Analysis
Reports/Annual Statistics
Ahead *- On-target §= Slightly behind
11/30/91
4/05/92
2/10/92
11/30/92
3/30/92
5/15/92
2/10/92
4/1/92
3/1/92
3/30/92
*
*
*
*
e
11/15/91
12/15/91
1/15/92
2/20/92
2/25/92
2/25/92
3/5/92
3/20/92
1/1/92
4/1/92
4/10/92
3/5/92
3/25/92
3/25/92
3/25/92
4/15/92
11/10/91
3/30/92
6/1/92
6/14/92
3/14/92
8/27/92
6/30/92
10/1/92
11/15/92
11/20/91
2/1/92
4/1/92
4/1/92
4/1/92
4/15/92
4/1/92
3/30/92
4/15/92
4/20/92
7/15/92
3/25/92
4/15/92
4/15/92
4/30/92
4/30/92
5/25/92
5/29/92
6/12/92
8/26/92
8/30/92
8/28/92
10/25/92
12/1/92
ongoing
*
@@
@@
@@
@@
@
#
*
@&
@@
*
*
7
7
7
7
&
3= Deadline problem.
14
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APPENDIX B
LISTING OF
ANALYSIS TABLE TITLES
California Forest Health Monitoring
Table 1 Total area sampled by land use class and state
Table 2 Area of forest land sampled by forest type group and
state.
Table 3 Area of forest land sampled by forest type group and
stand size class.
Table 4a Relative percentages of live trees sampled by detailed
species and tree size, all forest type groups.
Table 4b Relative percentages of live trees sampled by detailed
species and tree size, by forest type group.
Table 5& Percentages of live trees 10 cm and larger by major
species and crown class, all forest type groups.
Table 5b Percentages of live trees 10 cm and larger by major
species and crown class, by forest type group.
Table 6a Percentages of overstory trees 10 cm and larger by major
species and crown die-back class, all forest type
groups.
Table 6b Percentages of overstory.trees 10 cm and larger by major
species and crown die-back class, by forest type group.
Table 7 Percentages of understory trees 10 cm and larger by
major species and crown die-back class, all forest type
groups.
Table 8a Percentages of overstory trees 10 cm and larger by major
species and foliar transparency class, all forest type
groups.
Table 8b Percentages of overstory trees 10 cm and larger by major
species and foliar transparency class, by forest type
group
Table 9a Percentages of understory trees 10 cm and larger by
major species and foliar transparency class, all forest
type groups.
Table 9b Percentages of understory trees 10 cm and larger by
major species and foliar transparency class, by forest
type group.
15
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Table lOa Percentages of overstory trees 10 cm and larger by major
species and crown ratio, all forest type groups.
Table lOb Percentages of overstory trees 10 cm and larger by major
species and crown ratio, by forest type group.
Table lla Percentages of understory trees- 10 cm and larger by
major species and crown ratio, all forest type groups.
Table lib Percentages of understory trees 10 cm and larger by
major species and crown ratio, by forest type group.
Table 12a Percentages of overstory trees 10 cm and larger by major
species and crown density class, all forest type groups.
Table 12b Percentages of overstory trees 10 cm and larger by major
species and crown density class, by forest type group.
Table 13 Percentages of understory trees 10 cm and larger by
major species and crown density class, by forest type
group.
Table 14 Percentage distribution of mortality trees, 10 cm and
larger, by cause of death within major species, all
forest type groups.
Table 15 Percentage distribution of mortality trees, 9-9 cm and
smaller, by cause of death within major species, all
forest type groups.
Table 16 Percentage distribution of damage signs and symptoms
within major species, live trees, 10 cm and larger, all
forest type groups.
Table 17 Percentage distribution of species impacted by damage
signs and symptoms, live trees, 10 cm and larger, by
forest type groups.
Table 18 Percentage of seedlings and saplings by mkjor species
and crown vigor class, all forest type groups.
Table 19 Percentage distribution of pollutants by in foliage by
tree species by forest type groups.
Table 20 Percentage distribution of pollutants by lichen species
by forest type groups.
Table 21 Percentage occurrence of soil pollutants by forest type
group and soils taxonomic class.
16
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APPENDIX C
SUMMARY OF ANTICIPATED EXPENSES
The following is a listing of costs that have been prepared using
an extensive spread sheet analysis (available on request).
COSTS TO SUPPORT THREE FIELD CREWS
Salaries, associated with field work
Office salary and overhead
Per diem, associated with field work
Equipment
Vehicle/pack train/helicopter support
Travel costs
Supplies, such as photos and maps
Other miscellaneous expenses*
$167,000
149,000
104,000
69,000
34,000
24,000
10,000
55.000
Total $612,000
*Qther miscellaneous—such as training site, 5% contingency, etc.
COSTS TO SUPPORT TWO FIELD CREWS
Salaries, associated with field work
Office salary and overhead
Per diem, associated with field work
Equipment
Vehicle/pack train/helicopter support
Travel costs
Supplies, such as photos and maps
Other miscellaneous expenses*
$133,000
149,000
84,000
55,000
34,000
24,000
10,000
28,000
Total $517,000
*0ther miscellaneous—such as training site, 5% contingency, etc.
Approximate Funding Shares: FS 57%; EPA 28%; Calif.
17
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APPENDIX D
SUMMARY OF WESTERN U.S. INDICATORS
AND DATA ITEMS
FOR
FOREST HEALTH MONITORING
September, 1991
SITE DESCRIPTIONS
Plot identification
State (administration unit)
County (administration unit)
Plot number (identifier)
Map Identification Code
Universal Transverse Mercator Coordinates
Photo Identification Code
Photo year (year aerial photo was flown)
Elevation
Terrain position (i.e. floodplain, side slope,
ridge-top)
Date of visit (mm/dd/yy)
Plot description
Land condition class
Forest type (ecosystem)
California ecosystem code
Greenness Index (NDVI) for AVHRR Satellite imagery
Origin of stand/vegetation
Size of stand/vegetation
Past disturbance history
Disturbance period
Age of stand/vegetation (optional)
Forest site data-(tree species, height, age)
Point description (subplot)
History
Slope
Aspect
Micro-relief
Subplot center condition
Slope correction (used in measuring from previous
subplot)
Site and Risk Classification Indices (ie Keen/Dunning),
18
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TREE MENSURATION (list may be reduced for seedling and sapling
trees)
Tree diameter
Radial growth (from coring or remeasurement)
Age (from coring)
Core element analysis
Damage (presence of pathogens or insects, etc.)
Tree mortality
Condition class (describes veg./use condition in which tree
exists)
Regeneration
Seed/cone production and viability
History (history of tree establishment)
Height growth (?? —in pilot test)
Root disease evaluations (?? —in pilot test)
Mycorrhizae root sampling (?? -in pilot test)
Tree crown evaluation
Ratio (percent of the tree in crown)
Class (dominance of tree in stand)
Density -
Dieback
Foliar transparency
Needle retention
Shape (optional—used for vegetation profile changes)
Diameter (optional—used for vegetation profile changes)
Foliar sampling (from tree climbers or shotgun
extraction)
Chemistry (including nutrients and nutrient
cycling)
Needle age
Needle position in growth flushes
Branch order (relative to physiological activity)
Branch vertical position in crown
GENERAL MENSURATION
Micro-plot vegetation characterization and description
Air pollution bioindicators (visual symptoms on trees and
plants)
Ozone damage
Sulfur dioxide, damage
Hydrogen fluoride damage
Photosynthetic active radiation-PAR (?? under pilot test)
Vegetation habitat structure (vegetation profile)
Lichen occurrence and chemistry (?? under pilot test).
19
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SOILS
General processes:
Characterization and evaluation of:
Physical properties
Chemical properties, including toxins
Fertility/nutrient attributes
Productivity, in general
Soil analytic parameters evaluated:
Air-dry moisture
Total sand
Total silt
Total clay
Electrical conductivity
Ph
Exchangeable calcium, magnesium, potassium, and sodium
Cation exchange capacity
Total exchangeable acidity
Effective exchangeable acidity and exchangeable aluminum
Mineralizable nitrogen
Extractable phosphorus
Exchangeable sulfate
Total carbon and nitrogen
Total sulfur
Total phosphorus, calcium, magnesium, potassium, sodium,
iron, manganese, copper, zinc, boron, aluminum,
lead, chromium, nickel, cadmium, vanadium, arsenic,
and mercury.
20
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General list of soil variables observed or measured
Taxonomy
series
order
suborder
great group
subgroup
particle size class
mineralogy class
reaction class
temperature regime
moisture regime
other class
Major land resource area
Slope
percent
shape
geomorphic position
hill-slope position
aspect
Physiography
regional and local
Water table
depth
days
kind
Land use class
Surface Stoniness class
Hydraulic conductivity class
Drainage class
Elevation
Parent material
bedrock inclination
mode of deposition
origin
bedrock fracture
Hydrologic group
Water erosion class
Water run-off class
Flooding frequency
Ponding frequency
Particle size control section
Diagnostic feature
depths
kinds
Horizon
depths
discontinuity
master and suffix designations
21
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Moist color
location
percent
hue
value
chroma
Boundary
distinctness
topography
Texture
class
modifier
Structure
grade
size
shape
Mottles
quality
size
contrast
hue
value
chroma
Field property
quality
kind
Roots
quantity
size
location
Pores
quality
size
continuity
shape
Concentration
quality
size
shape
kind
Rock fragments
volume percent
roundness
kind
size
' 22
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Appendix F. Operations Plan, Colorado, Forest Health Monitoring
F-1
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OPERATIONS PLAN, COLORADO
FOREST HEALTH MONITORING
INTRODUCTION
Western Forest Health Monitoring data collection activities will be initiated in 1992 in California and
Colorado. Field crews will collect Implementation phase and Pilot phase Detection Monitoring data on
systematic grids of ground plots covering the two States.
Data files will be transmitted to the Northeast Research Station for compilation, analysis, and
reporting.
FHM Program
The Forest Service's Forest Health Monitoring (FHM) Program is a cooperative venture with the
Environmental Protection Agency (EPA); State Forestry departments; USDA Forest Service Forest
Inventory and Analysis (FIA), Forest Pest Management (FPM), Research Stations, and National
Forests; and a broad spectrum of government, research, and private interests.
The objective of FHM is to develop a National baseline on the health and condition of forests. This
baseline will be used to monitor forest health, detect deviations, and direct research to explain the
deviations.
The FHM program comprises three levels of monitoring:
Detection Monitoring - a geographically based netwbrk of permanent plots coupled with
off-plot damage/pest surveys distributed across the United States to describe the status
of all forests. This type of monitoring is designed to detect health problems with the
Nation's forests that are worthy of special evaluation.
Evaluation Monitoring - a more intensive assessment of forest health problems directed by
the detection network, insect and disease surveys, or problems already known to exist.
For specific forest health problems identified, multi-disciplinary teams will design specific
activities.
Ecosystem Monitoring - the monitoring aspects of ecosystem research on causal
relationships, rate change in forest condition, change mechanisms in forest ecosystems,
and techniques research to improve detection monitoring methods. Several permanent
sites across the country will be identified as ecological monitoring research sites.
Detection Monitoring - the level of monitoring being conducted in the West in 1992
comprises three levels of data collection activities (studies):
Pilot Study - an on-frame (data are collected on the plots of the sampling grid) or off-frame
field study, usually limited in the number of locations where activity occurs. Its scope and
purpose are to evaluate research indicators with respect to design, logistics, spatial and
temporal variability, and/or comparability and effectiveness of measurement technique.
F-2
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Demonstration Study - an evaluation of developmental indicators, Testing occurs
regionally, on-frame, and for the purpose of determining if implementation criteria are met.
Implementation Study - the initiation or continuation of data collection at specified field
locations on the EMAP grid. This is the onset of detection monitoring, and the variables
measured are those that meet the criteria of being appropriate, ready, and interpretable
for reporting.
Authority and Origins
The 1988 Forest Ecosystems and Atmospheric Pollution Research Act authorized the Forest
Service to undertake monitoring to track long-term trends in the health and productivity of United States
forest ecosystems. The amended Clean Air Act (1990) directed the Forest Service and EPA to
research the short-term and long-term effects of air pollution on forests. The Forest Service began
initial forest health monitoring work in the mid-1980's and initiated FHM as an outgrowth of its Global
Change Research Program and its Forest Response Program.
Background
Data were collected on forest health monitoring plots in the New England States in 1990 and States
in the South and Southeast in 1991.
Forest Health Monitoring moved West in the form of a pilot study conducted in California and
Colorado in July and August 1991. This effort, the Western Pilot, tested the logistics and feasibility of
establishing four 1/24th-acre subplots on the EMAP grid and collecting data and laboratory samples
on trees, soils, vegetation, and solar radiation. The procedures being piloted, demonstrated and
implemented in the East were tested on approximately seven sites in each of the two western States.
The same crew collected the data in both States. The crew comprised foresters from the Pacific
Northwest FIA unit and the Intermountain FIA unit, and soil scientists from the SCS and the Forest
Service. The crew trained at Blodgett Forest near Georgetown, California. Trainers were from the
EPA's EMAP-Forest program and various Forest Service associated with FHM. A representative
of the Colorado State Forest Service was also in attendance. The pilot plots in California were
scattered about the State; those in Colorado were concentrated near Durango, Colorado,
A debriefing session was held in Durango on August 29. Participants comprised field crew
members, indicator leads, and other interested scientists. Problems were reported, discussed, and a
written report was prepared by the EMAP-Forest field operations coordinator for FHM.
On December 5-6, 1991, a meeting was held in Ogden, Utah, to establish what Forest Health
Monitoring activities would be conducted in the West in 1992. This plan reflects the conclusions
reached at that meeting, with some minor and some major revisions.
F-3
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PROCEDURES
General
Forest Health Monitoring plots will be established and measured in California and Colorado in
1992.
Grid
The 1/4 interpenetrating EMAP grid of hexagons will be established in Colorado, and either the 1/4
or a less intense interpenetrating EMAP grid sample will be established in California. The FIA sampling
grid point nearest the center of the EMAP hexagon will be selected for the field plot. Field plots will
be established at those FIA grid points determined to occupy forest land. This determination is made
by viewing the prospective plot locations on aerial photographs.
In Colorado, the FIA sampling grid is the intersections of the 1,000 meter UTM lines oriented
north/south with those oriented east/west.
Field Plot
The FHM plot consists of four circular subplots, each 1/24th-acre in size, and each ringed by an
annular plot (collar) .208 acres in size (1/4 x 5/6 acre). The four subplots total 1/6-acre in size. The
four subplots plus the annular plots total 1 acre in size.
Tree and understory vegetation tally and measurements, as well as photosynthetically active
radiation measurements, are conducted on the 1/24th-acre subplots. The annular plots are used for
data collection on soils, indicator plants, tree branches and roots; and for collecting tree foliage, tree
core, tree root, and soil samples for laboratory analysis.
Field Plot Establishment
A plot will be established and measured in the field if forest land occupies any part of any subplot.
Plots will not be established for those grid points that fall where all four subplots are nonforest.
Forty-two plots in Colorado have been identified from aerial photo inspection as requiring field
visits. Some of these may be found to be nonforest upon field inspection.
Plot Variables
The Colorado and California data collection efforts tor 1992 encompass both implementation and
pilot studies. Pilot variables are divided into on-plot and off-plot variables; some variables are
measured both on-and off-plot.
F-4
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The following lists the variables, grouped by category:
Implementation Variables
1. Plot-level variables
a. Plot Identification Variables
1. State
2. County
3. Plot Number
4. Photo Year
5. Elevation
6. Terrain Position
7. Date
8. Crew Member
b. Condition Classification
1. Condition
2. Land Use Class
3. Forest Type
4. Stand Origin
5. Stand Size
6. Pest Disturbance
7. Disturbance Period
2.
Point-Level Variables
Point Description Variables
1. Slope Connection
2. Point History
3. Percent Slope
4. Aspect
5. Microrelief
6. Subplot Center Condition
7. Old Point History
Boundary Delineation Variables
1. Plot Type
2. Condition
3. Left Azimuth
4. Corner Azimuth
5, Corner Distance
6. Right Azimuth
F-5
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3. Point-Level Microplot Variables
a. Understory Vegetation
1. Microplot Center Condition
2. Percent Moss
3. Percent Ferns
4. Percent Herbs
5. Percent Shrubs
6. Percent Seedlings
b. Seedlings
1. Species
2. DBH/DRC
3. Condition
4. Number of Seedlings
c. Saplings
1. Tree Number
2. Species
3. DBH/DRC
4. DBH Check
5. Number of Stems
6. Diameter of Largest Stem
7. Slope Distance
8. Azimuth
9. Tree History
10. Condition
4. Point Level Subplot
a. Tree Variables
1. Tree Number
2. Species
3. DBH/DRC
4. DBH Check
5. Number of Stems
6. Diameter of Largest Stem
7. Slope Distance
8- Azimuth
9. Tree History
10. Condition
F-6
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On-Plot Pilot Variables
Soils
Tree Crown Assessment
Tree Damage - Insect, Disease, and Other
Indicator Plants
PAR
Understory Vegetation
Tree Mortality
Off-Plot Pilot Variables
Tree Root Inspection and Sampling
Understory Vegetation Sampling
Soil Microbiology
PAR
Tree Height
Tree Core
Branch Extraction
Foliage Sample
Soils
Off-Plot FPM Activities
Forest Pest Management, R-2, and the Colorado State Forest Service will conduct additional
detection phase data gathering activities in the vicinity of the FHM plots. This data will be collected
simultaneously with FHM data collection.
ORGANIZATION
National organization of Forest Health Monitoring:
Joe Barnard - National Forest Health Monitoring Program Director
Sam Alexander - EMAP-Forests Technical Director
Ken Stolte - Deputy Program Director for Research
Bob Loomis - Deputy Program Director for Applications
Craig Palmer - National Indicator Lead for pilot studies
Rob Tidwell - Field Operations Coordinator, EMAP-Forests
Jerry Byers - Quality Control, EMAP-Forests
Forest Health Monitoring (West) Organization:
Terry Shaw - Western Lead for pilot studies
Dave Johnson - Region 2 Indicator Lead for Damage
Mike Schomaker - Colorado Co-Lead for Damage
Bill McLain - Colorado Field Operations Lead
Jim LaBau - California Field Operation Lead
John Hazard - Sampling Design Lead
Dave Cassel - Sampling Design
Field Crews
F-7
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Two FHM crews will measure the 42 plots in Colorado. Each crew will be structured as follows:
Forester 1
Forester 2
Plant Identification
Specialist
Soils Scientist
Entomologist/
Pathologist
Entomologist/
Pathologist
Logistics Specialist
Crew Leader
Insect, disease, and damage specialist
Schooled and/or experienced or specialist skilled in plan identification.
Collects soils data
Collects off-plot FPM data
Collects off-plot insect, disease, and damage data
for Colorado State Forest Service
Transmits field data from PDR's to disc, packages and mails laboratory
samples, maintains trucks and field equipment; utility field crew member.
Assignments
Operations Plan (Colorado) - Bill McLain
Field Operations (Colorado) - Bill McLain
Operations Plan (California) - Jim LaBau
Field Operations (California) - Jim LaBau
Procedures Development
1. Plot Establishment and implementation tree tally variables - Bill McLain, Jim LaBau, Bill
Bechtold
2. Pilot Variables (On-Plot) - Craig Palmer
Soils - Rick Van Remortel
Crown Assessment - Ken Stolte, Terry Shaw, Mike Schomaker
Tree Damage (insects, disease, other) - Dave Johnson, Mike
Schomaker
Indicator Plants - Ken Stolte
PAR - Judd Isembrands, Sarah Steele
Understory Vegetation - Steve Cline, Renee O'Brien
3. Pilot Variables (Off-Plot)
Root Wedging or Coring
Vegetation Habitat
PAR
Soil Microbiology - Craig Palmer
Tree Heights - Ken Stolte
Tree Cores - Terry Shaw
Branch Extraction
Foliage Sample
Needle Retention
Soils
Personnel Recruitment
FIA, INT Station, United States Forest Service, Ogden, Utah, will hire:
F-8
-------
Crew Leader (Forester 1) - 2 each
FPM, State and Private Forestry, R-2, U.S. Forest Service will hire:
Forester 2 (I&D and Damage Specialist) - 2 each
Entomologist/Pathologist - 2 each
EMAP-Forest, Environmental Monitoring Systems Laboratory, U.S. Environmental Protection
Agency, Las Vegas, will hire:
Soils Scientists - 2 each
Logistics Specialist - 2 each
Through the Bureau of Land Management
Region 2, U.S. Forest Service will hire:
Plant Identification Specialist - 2 each
The Colorado State Forest Service will hire:
Entomologist/Pathologist - 2 each
Equipment
Trucks - FPM, R-2 will supply four trucks (two for each field crew) for field work.
EMAP-Forest (Craig Palmer) will supply two trucks (one per crew) for logistics functions.
F-9
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Forest Mensuration Equipment
FIA, INT Station is supplying the baste forest mensuration tools:
Angle Guage
Back Scribe
Clinometer
Compass
Cruisers Vest
D-Tape
First Aid Kit
Hand Lens
Hand Axe
Hard Hat
Increment Borer
Loggers Tape
Photo Scale Protractors
Pocket Calculator
Quart Canteen
Stereoscope
Tatum
100ft. Tape
Six Inch Rule
Flagging
Photo Bags
Witness Tree Tags
Stakes
Duffel Bag
Tents
4
4
4
4
4
4
4
4
4
14
4
4
enough for training
4
14
2
2
4
4
4
2
Pilot Indicator Equipment
EMAP-Forest, Rob Tidwell, will provide all necessary Pilot indicator equipment.
PDR
EMAP-Forest, Chuck Lift, will provide PDR's.
Field Manuals
EMAP-Forest, Barbara .Conkling, will provide field manuals.
F-10
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Training
Plot layout and mensuration
Soils
Crown Assessment
Crown Forms
Indicator Plants
PAR"
Understory Vegetation/Habitat
Other Pilot Indicators
Insect, Disease, and Damage
PDR
- Bill McLain
- Jim LaBau
- Rick Van Remortel
- Mike Schomaker
- Jim LaBau
- Ken Stolte
- Sarah Steele
- Steve Cline
- Renee O'Brien
- Craig Palmer
-Ken Stolte
- Terry Shaw
- Dave Johnson
- Mark Rubey
QUALITY CONTROL
Test plots will be measured by trainers and then remeasured by the field crews. This will be done
immediately after the completion of training and again towards the end of the field season.
Hot checks will be conducted during the season; indicator leads will accompany the field crews to
new plots and observe that portion of the data collection for which the lead has quality responsibility
and authority.
SCHEDULE
March 30-April 3
Test PDR and field procedures in Durham, North Carolina.
May 18-22
Visual crown rating procedures training of VCR trainers in Ashville, North Carolina.
May 26-29
Training of all western trainers in Ogden, Utah.
June 1-5
Training of California and Colorado field crews in Logan, Utah.
June 8-12
Training of Colorado crew in Colorado.
June 15-August21.
F-11
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Field data collection in Colorado.
REPORTING
Data will be compiled at the USDA Forest Service Northeast Research Station.
The following lists the tables of data to be produced
Table
1. Total area sampled by land use category.
2. Area of forest land sampled by forest type.
3. Area of forest land sampled by forest type group and stand-size class.
4. Relative percent of live trees tallied by species and diameter class.
5. Percent of tallied live trees 5.0-inches d.b.h. and larger by species and crown class.
5a. Percent of tallied live trees 5.0-inches d.b.h. and larger by species and crown class and
forest type group.
6. Percent of tallied overstory trees 5.0-inches d.b.h. and larger by species and
crown-dieback class.
6a. Percent of tallied overstory trees 5.0-inches d.b.h. and larger by species and
crown-dieback class and forest type group.
7. Percent of tallied understory trees 5.0-inches d.b.h. and larger by species and
crown-dieback class.
8. Percent of tallied overstory trees by species and foliage-transparency class. ...
8a. Percent of tallied overstory trees by species and foliage-transparency class and forest type
group.
9. Percent of tallied understory trees 5.0-inches d.b.h. and larger by species and
foliage-transparency class.
10. Percent of tallied overstory trees 5.0-inches d.b.h. and larger by species and crown ratio
class.
10a. Percent of tallied overstory trees 5.0-inches d.b.h. and larger by species and crown ratio
class and forest type groups.
F-12
-------
11.
12.
12a.
13.
14.
15.
16.
17.
Percent of tallied understory trees 5.0-inches d.b.h. and larger by species and crown ratio
class.
Percent of tallied overstory trees 5.0-inches d.b.h. and larger by species and crown density
class.
Percent of tallied overstory trees 5.0-inches d.b.h. and larger by species and crown density
class and forest type group.
Percent of tallied understory trees 5.0-inches d.b.h. and larger by species and crown
density class.
Percent of overstory conifers 5.0-inches d.b.h. and larger by species and needle retention
period.
Percent of understory conifers 5.0-inches d.b.h. and larger by species and needle retention
period.
Percent distribution of damage signs and symptoms by species, live tally trees 5.0-inches
d.b.h. and larger.
Percent distribution of species impacted by damage signs and symptoms, live tally trees
5.0-inches d.b.h. and larger.
18. Percent of seedlings and saplings by major species and crown vigor class.
F-13
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BUDGET
Distribution of the proposed FY 1992 FHM funding for Colorado implementation field data
collection.
2
2
2
2
2
2
15
6
6
6
6
6
16,500
5,430
3,97'8
6,600
4,386
5,544
5,544
5,544
5,544
5,544
1,920
Item No. PP Salary Per Diem
Crew Leader
Entomologist/Pathologist
Botanist
Soils Specialist
Logistics Specialist
Truck
Administration
McLain .75 GM-13
Rubey .50 GS-12
Ogden Office and Compilation Support 20,880
Supplies
Travel for Planning, Training, Quality Control 24,000
Station Overhead
Totals
21,948
EPA will supply the soils scientists (2), logistics specialists (2), and
trucks (2).
19,044
24,288
19,860
3,840
43,500
19,000
4,000
27,500
250,000
R-2 FPM
21,948
F-14
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Appendix G. Southern Appalachian Man And Biosphere (SAMAB)
Demonstration Plan
E.R. Smith
G-1
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SAMAB Plan
The Southern Appalachian Man and Biosphere (SAMAB) Cooperative consists of eight federal
agencies and a non-profit foundation whose members include a number a universities and private
organizations The eight federal agencies are: the United States Forest Service (USFS); the United
States Fish and Wildlife Service (USFWS); the Department of Energy- Oak Ridge National Laboratory
(DOE-ORNL); (EDA);the Tennessee Valley Authority (TVA); the National Park Service (NPS); the US
Environmental Protection Agency (EPA); and the United States Geological Survey (USGS). At the
February meeting of the SAMAB executive committee, it was decided that SAMAB would support a
demonstration of the Forest Health Monitoring (FHM) program in the SAMAB Zone of Cooperation, and
that a committee be formed of representatives from member organizations to: 1) serve as an interface
between SAMAB and FHM, and 2) identify resources from member organizations that could be
contributed to the demonstration effort.
The TVA will have lead responsibility for all SAMAB region activities:
• Coordinating field personnel.
• Analyzing data.
• Publishing a report.
The field crew will consist of two foresters - both provided by TVA, a botanist - provided by TVA
and the NPS one soil scientist - provided by TVA, SCS, and FHM, and one tree climber - provided by
the FS We have estimated 25 forested plots will be established using the Environmental Monitoring
and Assessment Program (EMAP) 1/4 interpenetrating design. Map identification of plot locations will
be done on a Geographic Information System (GIS) at TVA using a standard offset from the EMAP grid
center (coordinates or an algorithm will be provided by EMAP). Forest Health Monitoring has agreed
to take responsibility for training and all quality assurance/ quality control (QA/QC) work including
audits Training will be done in conjunction with training for the Southeastern Demonstration project,
June 8, at Bent Creek Experimental Forest, near Asheville, NC. Field work will then commence the
next week, June 15. We estimate it will require 12 weeks to complete the 25 plots.
Ptot location and data collection will be consistent with FHM methodologies. More detailed
information about the logistics can be found in Appendix 5. Plot field location and measurement will
take place at the same time. Plot locations will be identified using a portable Global Positioning System
(GPS) unit Plot measurements will consist of those done on a standard FHM plot by the foresters:
site condition, growth and regeneration data, visual symptoms data, plus collection of increment cores
and a sample of lichens. The PAR measurements will be collected using the methods outlined for the
Western Pilot for this indicator. Storage of samples will follow FHM protocol, as will transfer of data
collected on the PDRs.
Collected data will be turned in daily to FHM and edited using standard FHM procedures.
Once edited the "clean" data will be made available to SAMAB participants. Analysis of data will be
done in coordination with FHM and a report for the SAMAB Demonstration will be published by TVA.
G-2
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Appendix H. Table of Contents, Environmental Monitoring and
Assessment Program FHM Quality Assurance
Project Plan
H-1
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Section Appendix 7
Date: 7/92
Page 2 of 11
Table Of Contents-
Environmental Monitoring and Assessment Prog'ram
FHM Quality Assurance Project Plan
Notice « J
QAPJP Approval m °
Abstract \ °
Acronyms vi °
Contributors v" °
Acknowledgements x °
Parti Quality Assurance Program Descriptors
1 Introduction ••• tof18 °
1.1 Overview of EMAP • •• 2of18 0
1.2 Goals and Objectives of EMAP-Forests 3 of 18 0
1.2.1 Short-Term Objectives of EMAP-Forests 5 of 18 0
1.3 Assessment Endpoints 8 of 18 0
1.4 Quality Assurance Within EMAP 9 of 18 0
1.5 Scope of the Quality Assurance Project Plan • 9of18 0
1.5.1 Content of QAPjP 10 of 18 0
1.5.2 Methods Manuals for SOPs 17 of 18 0
2 Project Description 1 of 36 0
2.1 Background • 1 of 36 0
2.2FHMDesign 3of36 0
2.3 History 5 of 36 0
2.3.1 1988-1989 Activities 5 of 36 0
2.3.21990 FHM Activities 5 of 36 0
2.3.3 1991 FHM Activities 7 of 36 0
2.41992 FHM Activities • • • • 11 of 36 0
2.4.1 Detection Monitoring 11 of 36 0
2.4.2 Evaluation Monitoring 25 of 36 0
2.4.3 Intensive Site Ecosystem Monitoring 26 of 36 0
2.4.4 Indicator Development 30 of 36 0
2.4.5 Design Overview 31 of 36 0
2.4.6 Assessment Overview 32 of 36 0
2.4.7Logistics 33of36 0
2.4.8 Off-Frame Indicator Development 33 of 36 0
2.4.9 Global Positioning System 34 of 36 0
2.4.10 National Plan for Forest Pest Management 36 of 36 0
2.4.11 Safety Plan 36 of 36 0
H-2
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Section Appendix 7
Date: 7/92
Page 3 of 11
Table of Contents
Environmental Monitoring and Assessment Program
FHM Quality Assurance Project Plan
3 Project Organization 1 of 13 o
3.1 Organizational Structure for EMAP '.'.'.'.'.'.'.'.'.'.'.'. 1 of 13 0
3.2 Agencies and Responsibilities for 1992 FHM Program 1 of 13 o
3.2 Personnel Responsibilities for 1992 FHM Program 3 of 13 o
3.4 Quality Assurance Personnel and Responsibities .... 4 of 13 o
3.4.1 Quality Assurance Coordinator for EMAP ".'.'.' 3 of 13 0
3.4.2 Techical Director for EMAP-Forests 8 of 13 0
3.4.3 Program Manager for FS-FHM 8 of 13 0
3.4.4 Quality Assurance Coordinator for FHM !. 9 of 13 0
3.4.5 EPA Laboratory Quality Assurance Officers 10 of 13 0
3.4.6 Regional Quality Assurance Officers 10 of 13 0
3.4.7 Indicator Development Coordinator 10 of 13 o
3.4.8 Indicator Leads ''' 11 Of 13 0
3.4.9 Methods Coordinator for EMAP ' 11 Of 13 0
3.5 Matrix Activities 11 of 13 0
3.5.1 Within EMAP .' " H of 13 «
3.5.2 Within FHM ..." .12 of 13 0
3.6 Communications '12 of 13 o
3.6.1 Conference Calls 12 of 13 0
3.6.2 Contractors 13 of 13 0
3.6.3 Other Communications 13 of 13 0
4 Quality Assurance Objectives 1 of 17 0
4.1 Data Quality Objectives ,\\" 1 Of 17 0
4.1.1 Measurement Quality Objectives '.'.','.'. 1 of 17 o
4.1.2 Current Status 3 of 17 0
4.2 Specific Objectives '.'.'.'.'.'.'.'.'. 4 of 17 0
4.2.1 Compatibility of Data .' 4 of 17 0
4.2.2 Satisfying Data Quality Objectives 4 of 17 0
4.2.3 Documentation of Data Collection 13 of 17 0
4.2.4 Data Verification 13 of 17 0
4.2.5 Data Validation '.'' 15 Of 17 o
4.3 Control and Evaluation of Data Quality 15 of 17 0
4.3.1 Audit Program 15 of 17 0
4.3.2 Categories of Audits 15 of 17 0
4.3.3 Corrective Action 16 of 17 0
4.3.4 MQOs for Indicators 17 of 17 0
4.3.5 Calculations for Data Quality Attributes 17 of 17 0
H-3
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Section Appendix 7
Date: 7/92
Page 4 of 11
Table of Contents
Environmental Monitoring and Assessment Program
FHM Quality Assurance Project Plan
Part II Indicator Descriptors
1 Introduction 1 of 1 0
2 Site condition, Regeneration, and Growth 1 of 34 0
2.1 Index of Implementation Variables by PDR Screen 1 of 34 0
2.2 Permanent Plot Design and Establishment 7 of 34 0
2.2.1 Plot Design '. 7 of 34 0
2.2.2 Plot Establishment 7 of 34 0
2.3 Plot-Level Data 8 of 34 0
2.2.1 Plot Identification 8 of 34 0
2.'2.3 Condition Classification 10 of 34 0
2.4 Point-Level Area Descriptors 14 of 34 0
2.4.1 Point Description 14 of 34 0
2.4.2 Boundary Delineation 16 of 34 0
2.5 Mfcroplot Understory Vegetation 18 of 34 0
2.5.1 Overview 18 of 34 0
2.6 Microplot Tree data 19 of 34 0
2.6.1 Seedlings 20 of 34 0
2.6.2 Saplings 20 of 34 0
2.7 Subplot Tree Data 26 of 34 0
2.7.1 Overview 26 of 34 0
2.8 Measurement Quality Objectives 32 of 34 0
2.9 Offset Procedures 34 of 34 0
3 Crown Classification 1 of 27 0
3.1 Field Operations 2 of 27 0
3.1.1 Plot Location 2 of 27 0
3.1.2 Personnel 2 of 27 0
3.1.3 Crown Definition 3 of 27 0
3.1.4 Measurement Procedures 5 of 27 0
3.2 Quality Assurance Objectives 12 of 27 0
3.2.1 Pre-Training 13 of 27 0
3.2.2 Training 13 of 27 0
3.2.3 Measurement Quality Objectives 13 of 27 0
H-4
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Table of Contents
EnvironmentalMonitoring and Assessment Program
FHM Quality Assurance Project Plan
3.3 Quality Assurance Implementation 14 of 27 0
3.3.1 Sources of Measurement Error 14 of 27 o
3.3.2 Pre-Training 20 of 27 0
3.3.3 Training "!!'..'!.'!!! 21 of 27 0
3.3.4 Visual Aids 23 of 27 0
3.3.5 Field Procedures 25 of 27 0
3.3.6 Field Audits .' 25 of 27 0
3.3.7 Control of Data Quality 26 of 27 0
3.4 Data Quality Assessment and Reports 26 of 27 o
4 Damage and Mortality 1 of 1 0
5 Evaluation of Tree Branch Damage 1 Of 5 0
5.1 Field Operations 2 of 5 0
5.1.1 Sample Collection: Climber/Pole Pruner Technique 2 of 5 o
5.2 Quality Assurance Objectives 3 Of 5 0
5.2.1 Training and Sample Collection 3 Of 5 o
5.2.2 Re-evaluation of Collected Samples 4 of 5 o
5.3 Data Quality Assessment 5 Of 5 0
6 Soil Productivity Indicator ^ of 73 0
6.1 Field and Laboratory Operations '.'.'.'.'.'.'. 1 of 73 o
6.1.1 Field Soils Operations .." 2 of 73 0
6.1.2 Soil Preparation Laboratory Operations !. 5 of 73 o
6.1.3 Soil Analytical Laboratory Operations 9 of 73 0
6.2 Quality Assurance Objectives 16 of 73 0
6.2.1 Training ......'.'.'.'.'.'.' I7of73 0
6.2.2 Types of Quality Objectives 18 of 73 0
6.2.3 Measurement Quality Samples ' 20 of 73 0
6.2.4 Specific Measurement Quality Attributes 28 of 73 0
6.2.5 Specific Measurement Quality Objectives 36 of 73 0
6.3 Quality Assurance Implementation 49 of 73 o
6.3.1 Training .'.'_'_'_' 490773 0
6.3.2 Technical Systems Audits 49 Of 73 o
6.3.3 Data Verification Procedures 52 of 73 0
H-5
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Table of Contents
Environmental Monitoring and Assessment Program
FHM Quality Assurance Project Plan
6.4 Data Quality Assessment and Reporting — 61 of 73 0
6.4.1 Assessment of Measurement Quality Attributes 61 of 73 0
6.4.2 Estimation of Uncertainty 61 of 73 0
6.4.3 Data Verification Procedures 71 of 73 0
6.4.4 Data Quality Reporting 72 of 73 0
7 Foliar Chemistry Indicator 1 of 36 0
7.1 Field and Laboratory Operations 1 of 36 0
7.1.1 Field Operations 1 of 36 0
7.1.2 Preparation Laboratory Operations 10 of 36 0
7.1.3 Analytical Laboratory Operations 15 of 36 0
7.2 Quality Assurance Objectives 24 of 36 0
7.2.1 Training , 24 of 36 0
7.2.2 Batch Analysis 25 of 36 0
7.2.3 Nature and Character of Measurement Quality Samples .. 25 of 36 0
7.3 Quality Assurance Implementation 31 of 36 0
7.3.1 Training 31 of 36 0
7.3.2 Audits .' 32 of 36 0
7.3.3 Control of Data Quality 33 of 36 0
7.3.4 Data Verification 33 of 36 0
7.4 Data Quality Assessment and Reporting 34 of 36 0
7.4.1 Assessment of Measurement Quality Attributes 34 of 36 0
7.4.2 Validation Procedure 35 of 36 Q
7.4.3 Quality Assurance Reports to Management 35 of 36 0
8 Stemwood Chemistry 1 of 37 0
8.1 Reid and Laboratory Operations 1 of 37 0
8.1.1 Field Operations 2 of 37 0
8.1.2 Preparation Laboratory Operations 11 of 37 0
8.1.3 Analytical Laboratory Operations 15 of 37 0
8.2 Quality Assurance Objectives 22 of 37 0
8.2.1 Training 22 of 37 0
* 8.2.2 Batch Analysis 23 of 37 0
8.2.3 Nature and Character of Measurement Quality Samples .. 23 of 37 0
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Table of Contents
Environmental Monitoring and Assessment Program
FHM Quality Assurance Project Plan
8.3 Quality Assurance Implementation 32 of 37 0
8.3.1 Training ...'.'.'.'.':'.'.'.'. 32of37 0
8-3-2 Audits 33 of 37 0
8.3.3 Control of Data Quality 34 of 37 0
8.3.4 Data Verification 34 Of 37 rj
8.4 Data Assessment and Reporting '' [[ 35 of 37 o
8.4.1 Assessment of Measurement Quality Attributes 35 of 37 0
8.4.2 Validation Procedure 35 of 37 0
8.4.3 Quality Assurance Reports to Management 37 of 37 0
9 Evaluation of Root Disease 1 of 5 0
9.1 Introduction '.'.'.'.'.'.'.'.'. 1 of 5 0
9.2 Field Operations '.-'.'.'.'.'.'. 1 of 5 0
9.2.1 Plot selection 1 Of 5 g
9.2.2 On-Plot Sampling Scheme '.'.'.'.'.'.'.'.'.'.'.'. 2 of 5 0
9.3 Description of Method 2 of 5 0
9.3.1 For Hardwoods 2 of 5 0
9.3.2 For Conifers 3 Of 5 g
9.4 Quality Assurance Objectives 4 of 5 g
9.4.1 Data Quality Objectives 4 of 5 g
9.4.2 Training .'.'.'.'.'.' 4 Of 5 0
9.4.3 Audits 4 of 5 0
9.4.4 Laboratory Requirements 4 of 5 o
9.5 Data Assessment and Reporting 5 of 5 g
10 Photosynthetically Active Radiation 1 of 14 o
10.1 Field Operations 2 of 14 0
10.1.1 Plot Location 2 of 14 0
10.1.2 Personnel 2 of 14 0
10.1.3 Description of Measurement Site 3 of 14 0
10.1.4 Description of Measurement Procedure 3 of 14 0
10.1.5 List of Parameters 5 of 14 0
10.1.6 Data Entry and Reporting 5 of 14 0
10.1.7 Evaluation of Measurement Quality Data 6 of 14 0
10.1.8 Communications 5 of 14 0
10.1.9 Data Custody 6 of 14 0
H-7
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Section Appendix 7
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Table of Contents
Environmental Monitoring and Assessment Program
FHM Quality Assurance Project Plan
10.2 Quality Assurance Objectives 6 of 14 0
10.21 Training 7 of 14 0
10.2.2 Sources of Measurement Errors 7 of 14 0
10.2.3 Measurement Quality Objectives 8 of 14 0
10.3 Quality Assurance Implementation 11 of 14 0
10.3.1 Training 11 of 14 0
10.3.2 Field Audits 11 of 14 0
10.3.3 Control of Data Quality 12 of 14 0
10.4 Data Quality Assessment and Reports 13 of 14 0
10.4.1 Data Validation 13 of 14 0
10.4.2 Quality Assurance Reports 13 of 14 0
11 Vegetation Structure Indicator 1 of 7 0
11.1 Field Operations 1 of 7 0
11.1.1 Plot Selection 2 of 7 0
11.1.2 On-Plot Sampling Scheme 2 of 7 0
11.1.3 Personnel 2 of 7 0
11.1.4 Description of Measurement Site 2 of 7 0
11.1.5 Description of Measurement Procedures 2 of 7 0
11.1.6 Data Entry and Reporting 3 of 7 0
11.1.7 Data Custody 3 of 7 0
11.1.8 Intended Use of Data 4 of 7 0
11.2 Quality Assurance Objectives 4 of 7 0
11.2.1 Training 4 of 7 0
11.2.2 Sources of Measurement Error 4 of 7 0
11.2.3 Measurement Quality Objectives 5 of 7 0
11.3 Quality Assurance Implementation 6 of 7 0
11.3.1 Control of Data Quality 6 of 7 0
11.3.2 On-Site System Audits 6 of 7 0
11.3.3 Data Verification 6 of 7 0
11.4 Data Quality Assessment and Reporting 6 of 7 0
11.4.1 Precision..' 7 of 7 0
11.4.2 Accuracy 7 of 7 0
11.4.3 Validation • 7 of 7 0
11.4.4 Quality Assurance Reports , 7 of 7 0
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Section Appendix 7
Date: 7/92
Page 9 of 11
Table of Contents
Environmental Monitoring and Assessment Program
FHM Quality Assurance Project Plan
12 Wildlife Habitat and Population Measurements 1 of 9 o
12.1 Field Operations . 1 Of 9 0
12.1.1 Plot Location 1 of 9 0
12.1.2 Personnel 1 of 9 0
12.1.3 Description of Measurement Site 2 of 9 0
12.1.4 Description of Measurement Procedure 2 of 9 0
12.1.5 Data Entry and Reporting 4 Of 9 o
12.1.6 Data Custody 4 of 9 o
12.2 Quality Assurance Objectives 4 qf 9 Q
12.2.1 Training .' 4of9 Q
12.2.2 Measurement Quality Samples 4 of 9 o
12.2.3 Sources of Measurement Error 5 of 9 o
12.2.4 Audits ' " 6 of 9 0
12.3 Quality Assurance Implementation 5 of 9 0
12.3.1 Training 6ofg o
12.3.2 Control of Data Quality 6 of 9 0
12.3.3 Data Verification 7 Of 9 o
12.4 Data Quality Assessment and Reporting 7 of 9 o
12.4.1 Detectability 7 of 9 o
12.4.2 Precision 7 of 9 g
12.4.3 Accuracy 8 of.9 0
12.4.4 Representativeness 8 of 9 0
12.4.5 Completeness 9 of 9 o
12.4.5 Comparability 9 of 9 0
13 Air Pollution Bioindicator Plants 1 of 1 0
14 Lichen Community 1 of 1 0
15 Global Positipning System 1 Of 1 0
16 Information Management 1 of 10 0
16.1 FHM Information Center 1 of 10 0
16.2 Overall Principles 1 of 10 0
16.2.1 Automated Data Collection Versus Manual Entry 1 of 10 0
16.2.2 Data transfers Between Machines 2 of 10 0
16.2.3 Numeric data 2 of 10 0
16.2.4 Code Identification 3 of 10 0
16.2.5 Redundancy 3 of 10 0
H-9
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Section Appendix 7
Date: 7/92
Page 10 of 11
Table of Contents
Environmental Monitoring and Assessment Program
FHM Quality Assurance Project Plan
16.2.6 Backups 3 °no °
16.3 Access Limitation to Data 4 of 10 °
16.4 Standard Operating Procedures 4 of 10 0
16.5 Activities for FHM for 1992 4°f1° J
16.5.1 PDR System 5 of 10 0
16.5.2 Laptop PC System 6 of 10 0
16.5.3 Data TRansfer from the PDR 6 of 10 0
16.5.4 Data Transfer from PAR Equipment 6 of 10 0
16.5.5 Sample and Equipment Tracking 6 of 10 0
16.5.6 Printing Data 7 of 10 °
16.5.7 Soil Discrepency Resolution 7 of 10 0
16.5.8 Communications with EMSL-LV Vax Computer CLuster... 7 of 10 0
16.5.9 VAX System 7 of10 °
16.6 Soil Discrepancy System 8 of 10 0
16.7 Preparation Laboratory System < — 8 of 10 0
16.7.1 Sample Receipt 8 of 10 °
16.7.2 Preparation Laboratory Data Procedures 9 of 10 0
16.8 Analytical Laboratory system 9 of 10 0
16.9 Field Software Testing • - 10 of 10 0
16.10 Training and Support 10 of 10 0
17 Quality Assurance Reports to Management • • • • 1 of 5 0
17.1 Introduction 1 of 5 J
17.2 Major Documents 1 of 5 °
17.2.1 Activities Plan for 1992 1 of 5 0
17.2.2 Quality Assurance Project Plan 2 of 5 0
17.2.3 Field Methods Guide and Handbook of Lab Methods 2 of 5 0
17.2.4 Logistics/Operations Report 3 of 4 0
17.2.5 Assessment Report 3 of 4 °
17.2.6 Miscellaneous Reports 3 of 4 0
17.3 Monthly reports 3 of 4 0
174. Audits 4of 4 °
17.5 QA Annual report and Workplan 4 of 5 0
References 8 °
H-1Q
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Section Appendix 7
Date: 7/92
Page 11 of 11
Table of Contents
Environmental Monitoring and Assessment Program
FHM Quality Assurance Project Plan
Appendix A
A.1 SAMABPIan
A.2 Design Overview
A.3 Assessment Overview
A,4 Off-Frame Indicator Development
A.5 Indicator Development
A.6 Field Logistics
A.7 Global Positioning System Overview
A.8.1 FHM for Colorado
A.8.2 Operations Plan for Colorado
A. 9 Operations Plan for California
A. 10 National Plan for Forest Pest Management
Appendix B
FHM Field Methods Guide for 1992
Appendix C
Handbook of Laboratory Methods for FHM
H-11
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Appendix I. Table of Contents, FHM Field Methods Guide
1-1
-------
Table of Contents
Section Appendix 8
Date: 7/92
Page 2 of 6
Section
Revision
Title/Section
Notice i'i 1
Contributors viii 1
Acknowledgements x 1
Acronyms xi 1
1. Introduction 1 of 3 1
2. Site Condition, Growth, and Regeneration 1 of 111 1
2.1 Index of Implementation Variables by PDR Screen 1 of 111 1
2.2 Permanent Plot Design and Establishment... 8 of 111 .1
2.3 Plot-Level Data 13 of 111 1
2.4 Point-Level Area Descriptors 33 of 111 1
2.5 Microplot Understory Vegetation 42 of 111 1
2.6 Microplot Tree Data 43 of 111 1
2.7 Subplot Tree Data 57 of 111 1
2.8 Measurement Quality Objectives 67 of 111 1
2.9 U.S. Tree Species Codes 71 of 111 1
2.10 State and County FIPS Codes 81 of 111 1
2.11 Forest Type Descriptions 89 of 111 1
2.12 Offset Procedures 98 of 111 1
2.13 Sample Tree Selection 110 of 111 1
2.14 References > 111 of 111 1
3. Crown Classification 1 of 23 1
3.1 Seedlings 1 of 23 1
3.2 Saplings 2 of 23 1
3.3 Trees 5.0-inches DBH and Larger 6 of 23 1
3.4 Crown Classification Measurement Quality Objectives 23 of 23 1
4. Damage and Mortality Assessment 1 of 12 1
4.1 Damage Signs and Symptoms 1 of 12 1
4.2 Mortality Assessment 6 of 12 1
5. In-Hand Branch Branch Evaluation for Visual Damage 1 of 17 1
5.1 Overview •. 1 of 17 1
5.2 Sample Collection, Preservation, and Storage 2 of 17 1
5.3 Equipment and Supplies 3 of 17 1
5.4 Calibration and Standardization 5 of 17 1
5.5 Quality Control - 5 of 17 1
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Section Appendix 8
Date: 7/92
Page 3 of 6
Section
Revision
Title/Section
Page
5.6 Procedure
5.7 Method Performance.
5.8 Calculations
5.9 References
6. Soil Classification and Physiochemistry
6.1 Overview
6.2 Sample Collection, Preservation and Storage.
6.3 Equipment and Supplies
6.4 Calibration and Standardization
6.5 Quality Control
6.6 Procedure
6.7 Method Performance
6.8 Calculations
6.9 References
7. Foliar Chemistry Indicator
7.1 Overview
7.2 Sample Collection, Preservation and Storage.
7.3 Equipment and Supplies
7.4 Calibration and Standardization
7.5 Quality Control
7.6 Procedure
7.7 Method Performance
7.8 Calculations
7.9 References
8. Stemwood Chemistry and Dendrochronology Indicators.
8.1 Overview
8.2 Sample Collection, Preservation, and Storage
8.3 Equipment and Supplies
8.4 Calibration and Standardization
8.5 Quality Control
8.6 Procedure
8.7 Method Performance
8.8 Calculations
8.9 References
9. Evaluation of Root Diseases Indicator
9.1 Overview
9.2 Sample Collection, Preservation, and Storage.
9.3 Equipment and Supplies
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Section Appendix 8
Date: 7/92
Page 4 of 6
Section
Revision-
Title/Section
9.4
9.5
9.6
9.7
9.8
9.9
Calibration and Standardization.
Quality Control
Procedure
Method Performance
Calculations
References
10. Photosynthetically Active Radiation (PAR) Indicator..
10.1 Overview
10.2 Sample collection, Preservation, and Storage...
10.3 Equipment and Supplies
10.4 Calibration and Standardization
10.5 Quality Control
10.6 Procedure
10.7 Method Performance
10.8 Calculations
10.9 References
11. Vegetation Structure Indicator
11.1 Overview
11.2 Sample Collection, Preservation, and Storage.
11.3 Equipment and Supplies
11.4 Calibration and Standardization
Quality Control
Procedure
Method Performance
Calculations
References
11.5
11.6
11.7
11.8
11.9
12. Wildlife Habitat and Population Estimates
12.1 Overview
12.2 Sample Collection, Preservation, and Storage.
12.3 Equipment and Supplies
12.4 Calibration and Standardization
12.5 Quality Control
12.6 Procedure
12.7 Method Performance.
12.8 Calculations
12.9 References
Page
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Section Appendix 8
Date: 7/92
Page 5 of 6
Section Title/Section
Revision
13. Air pollution Bioindicator Plants...
13.1 Overview
13.2 Sample Collection, Preservation, and Storage.
.13.3 Equipment and Supplies
13.4 Calibration and Standardization
13.5 Quality Control
13.6 Procedure
13.7 Method Performance
13.8 Calculations
13.9 References
14. Lichen Biomonitoring Procedures.
14.1 Overview
14.2 Sample Collection, Preservation, and Storage.
14.3 Equipment and Supplies
14.4 Calibration and Standardization
14.5 Quality Control
14.6 Procedure
14.7 Method Performance
14.8 Calculations
14.9 References
15. Global Positioning System (GPS)...
15.1 Overview
15.2 Sample Collection, Preservation, and Storage
15.3 Equipment and Supplies
15.4 Calibration and Standardization...
15.5 Quality Control
15.6 Procedure
15.7 Method Performance....
15.8 Calculations
15.9 References
16. Field Logistics
16.1 Eastern Field Logistics Plan
16.2 Western Field Logistics Plan
17. Portable Data Recorder and Software User Information
17.1 Paravant RHC-44 Portable Data Recorder Reference Guide
17.2 TALLY Reference Guide
1 7.3 TALLY Self-Guided Guide - Introduction
1 7.4 TALLY Self-Guided Guide - Annual Crown Remeasurement
Page
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{Section Appendix 8
Date: 7/92
Page 6 of 6
Section
Revision
Title/Section
17.5 TALLY Self-Guided Guide - New Southeast Forested
Plot Option
17.6 TALLY Self-Guided Guide - Sample Tree Option
17.7 TALLY Self-Guided Guide - Nonforested, Denied Access, and
Unsafe Plots Option
17.8 TALLY Self-Guided Guide - Western Forested Plot Option
17.9 A User's Guide to the SOILS Program
17.10 Vegetation Habitat PDR Program
17.11 Self-Booting Disk (Portable Data Recorder
to Personal Computer) •
17.12 Notebook Computer Hardware and Software
18. Safety Plan 1 of 34
18.1 Overview 1 of 34
18.2 Potential Field Hazards 1 of 34
18.3 Helicopter Safety 4 of 34
18.4 Terrain 21 of 34
18.5 Insect Pests, Poisonous Organisms, and Dogs 22 of 34
18.6 Sampling and Sampling Equipment 22 of 34
18.7 Training 26 °J 34
18.8 Documentation 26 of 34
18.9 Personal Protection 27 of 34
18.10 Accident Reporting • 30 of 34
18.11 Safety Equipment 30 of 34
18.12 Additional Forms • 30 of 34
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•*U,S. GOVERNMENT PRINTING OFFICE: 1993 - 750-002/8028!?
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