FY91 INDICATOR EVALUATION FIELD STUDY FOR
    ENVIRONMENTAL MONITORING AND
 ASSESSMENT PROGRAM - FORESTS (EMAP-F)
                June 1991

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FY91 INDICATOR EVALUATION FIELD STUDY FOR
      ENVIRONMENTAL MONITORING AND
  ASSESSMENT PROGRAM - FORESTS (EMAP-F)
   Atmospheric Research and Exposure Assessment Laboratory
            Office of Research and Development
        United States Environmental Protection Agency
         Research Triangle Park, North Carolina 27711
        Environmental Monitoring Systems Laboratory
            Office of Research and Development
        United States Environmental Protection Agency
                 Las Vegas, Nevada 89153
             Environmental Research Laboratory
            Office of Research and Development
        United States Environmental Protection Agency
                 Corvallis, Oregon 97333
                      June 1991

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             PY91 INDICATOR EVALUATION FIELD STUDY FOR
                                       i



ENVIRONMENTAL MONITORING AND ASSESSMENT PROGRAM - FORESTS (EMAP-F)
                             Edited By




                      R.C. Kucera and B.E. Martin

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                                         NOTICE
      The information in this document has been funded wholly or in part by the United States
Environmental  Protection Agency under Contract No. 68-DO-0106 to ManTech Environmental
Technology,  Inc.,  in  Research  Triangle Park,  IMC; cooperative agreement (CR81470) with  the
Environmental Research Center of the University of Nevada at Las Vegas; Contract No. 68-CO-0049 to
Lockheed Engineering & Sciences Company; and Contract No. 68-C8-0006 to ManTech Environmental
Technology, Inc., inCorvallis,'OR.

      Mention of trade names  or  commercial  products  does not constitute  endorsement  or
recommendation for use.

      Kucera, R.C. and  B.E. Martin, eds.   1991.   FY91  Indicator Evaluation Field  Study  for
Environmental  Monitoring  and Assessment Program  -  Forests (EMAP-F),  U.S. Environmental
Protection Agency, Atmospheric Research and Exposure Assessment  Laboratory, Research Triangle
Park, NC.

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                               TABLE OF CONTENTS



SECTION                                                                      PAGE

    Notice  	      iii
    Figures 	      vii
    Tables  	     viii
    Acknowledgements 	      ix

 1   INTRODUCTION (C.J. Palmer and J.E.Barnard)  	     1-1

 2   APPROACH AND RATIONALE (R.C. Kucera) 	     2-1

 3   DESIGN (D. Cassell)	     3-1

 4   QUALITY ASSURANCE (G. Byers)	     4-1

 5   LOGISTICS (M.Papp)  	     5-1

 6   INFORMATION MANAGEMENT (Cliff)  	     6-1

 7   REPORTING (R.C. Kucera) 	'.	     7-1


                        NUTRIENT CYCLING DEMONSTRATION

 8   SOIL PRODUCTIVITY (R.D. VanRemortel)  	     8-1

 9   TREE CORE ELEMENTAL ANALYSIS FIELD MEASUREMENT (T. Lewis) 	     9-1

10   FOLIAR CHEMISTRY (T.Lewis) 	    10-1

11   ROOT DISEASE EVALUATIONS (S.A. Alexander and J.Carlson)   	    11-1


                                LANDSCAPE PILOT

12   ROOT SAMPLING PROCEDURE FOR EVALUATION OF ROOT DISEASES AND
    MYCORRHIZAE (S.A. Alexander and B. Conkling)  	    12-1

13   VEGETATION AND HABITAT STRUCTURE AS INDICATORS OF
    BIOTIC DIVERSITY (S. Cline)  	    13-1

14   PHOTOSYNTHETICALLY ACTIVE RADIATION (PAR) (J. Isebrands and K. Riitters)  	    14-1

15   GLOBAL POSITIONING SYSTEM (K.Hermann) 	    15-1

16   HIGH-RESOLUTION AERIAL PHOTOGRAPHY (K.Hermann and R. Czaplewski)  	    16-1
                                                                       (continued)

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                           TABLE OF CONTENTS Continued







 SECTION                                                                     PAGE





17  LANDSCAPE CHARACTERIZATION (K.Hermann and R. Czaplewski)  	    17-1




18  AIR AND DEPOSITION (D.Shadwick.R.Baumgardner, and L. Smith) 	    18-1




19  CLIMATE (E. Cooter, P. Finkelstein, and S. LeDuc)  	    19-1




20  INDICATOR DEVELOPMENT (T. Strickland) 	    20-1




21  INTEGRATION AND ASSESSMENT (K.Riitters)  	    21-1




22  REFERENCES  	    22-1
                                       VI

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                                      LIST OF FIGURES


FIGURE                                         *                                       PAGE

 3.1   Forest Health Monitoring Plot Layout  	      3-2

 5.1   Timeline for the South/Southeast Surveys   	      5-2

 5.2   Sampling Sequence for Field Samplers  	      5-7

 5.3   Example of a Communications Network 	     5-11

 5.4   Flow of Information to and from Regional Project Leads  	     5-13

13.1   Relationship of Stressor, Response Indicator, and Biotic Integrity Endpoint   	     13-2

13.2   Relationship of Response Indicators for Different Organizational Levels of
      Biotic Integrity	     13-3

13.3   On-Plot Sampling for (a) Plant Quadrats and (b) Area Quadrats in
      Relation to the Subplot  	     13-9

14.1   Photosynthetically Active Radiation Measurement Plot Layout  	     14-5

14.2   PAR Training/Certification Form  	     14-9

16.1   High-Resolution Aerial Photography Plot Design  	     16-4

19.1   Digitized Location of Severe Weather Events,  1961-1990  	     19-3

19.2   Intersections of Digitized Severe Weather Events with NEFHM Program Sampling
      Hexagons, 1961-1990   	     19-4

19.3   Percent of New England Region Impacted  by Climate Stress, 1981-1990  	     19-5

19.4   Location of Hexagons Reporting Five or more Intersections with
      Climate Disturbances  	     19-6

19.5   Climate Information for a Selected Hexagon, 1981-1990  	     19-7

20.1   Societal Value Placed on Forested  Ecosystems  	     20-4

20.2   An Exampleof the Relationships in the Assessment Framework   	     20-5

20.3   The Framework of Specific Decision Criteria Driving Indicator Progression  	    20-10

20.4   Indicator Utility	    20-13
                                             VII

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                                      LIST OF TABLES



TABLE                            .                                                    PAGE

  1.1   Agency responsibilities in 1991 FHM indicator evaluation studies 	      1-5


  2.1   Core, Demonstration, and Pilot Measurements Schedule  	      2-2


  5.1   EMAP Logistics Elements for Implementation of Forest Monitoring Programs   	      5-1

  5.2   Estimated Time Requirements for Landscape Pilot with Core and Nutrient Cycling
       Demonstration Measurements 	      5-5


  5.4   List of Supply Needs   	     5-16


  8.1   Field Soil Characterization Parameters  	      8-5


  8.2   Soil Preparation Parameters	      8-5


  8.3   Soil Analytical Parameters  	c.	      8-7

 13.1   Response Indicators of Biotic Integrity  	     13-4

                                                                                       *
 21.1   Policy, Program, and Technical Integration Issues in EMAP   	     21-2
                                            VIII

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                                  ACKNOWLEDGEMENTS
      The authors thank the peer-reviewers, Dr. Robin L. Graham, Dr. Mike Kelly, Dr. Ram Oren, and




Dr. Tim Sherbatskoy for their substantial contributions of time in reading the document and offering




many detailed and constructive comments.






      Appreciation goes to Pam Denton and Janice Braswell Parker for their many contributions as




technical editors, to Jo Anne Barker for her high quality word processing, and to Lorraine Blake for




her assistance in communications and distribution of documents.
                                            IX

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                                     1. INTRODUCTION


                                 C.J. Palmer3 and J.E. Barnard*3



      For the past two years, several government agencies have been working together to develop a

multiagency program to monitor the condition of  the nation's forested  ecosystems.    The U.S.

Department of Agriculture (USDA)  Forest Service (F5) has contributed to  this initiative under the

auspices of their Forest Health Monitoring program (F5-FHM). The Environmental Protection Agency

(EPA) has  participated  through  the forest  component of  the  Environmental  Monitoring and

Assessment Program (EMAP-Forests).  Other contributing agencies include state forestry agencies, the

National  Park Service (NPS), the Soil Conservation Service (SCS), the Fish and Wildlife Service (FWS),

the Tennessee Valley Authority (TVA), and the Bureau of Land Management (BLM). In this document;

this multiagency program will  be referred to as the Forest Health Monitoring (FHM) Program.


      A  major impetus behind  the development of this program has been the concern  about

documented and potential effects of air pollutants in combination with other multiple, interacting

stresses on forested ecosystems.  In 1988, Congress directed the  FS, through the Forest Ecosystems and

Atmospheric Pollution Research Act (Public Law 100-521), to  undertake monitoring of "long-term

trends in the health and productivity of domestic forest ecosystems." In  1990, Title IX of the Clean Air

Act charged the Administrator of the EPA in cooperation with other agencies to "evaluate the effects

of air pollution on forests, material, crops, biological diversity, soils, and other terrestrial and aquatic

systems exposed to air pollutants."


      An important component in the development of the FHM program has been the identification

and selection of indicators of forest condition.  An indicator has been defined as "a characteristic of

the environment that, when measured, quantifies the magnitude of stress, habitat characteristics,
  a Acting EMAP-Forests Technical Director, University of Nevada-Las Vegas, Environmental Research Center
  b FHM National Program Manager, USDA FS, Forestry Sciences Laboratory, RTP, NC
                                             1-1

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degree of exposure to the stressor, or degree of ecological response to the exposure" (Hunsaker and




Carpenter, 1990).   Indicators  need to be evaluated  to see if they are  appro'priate and effective




representations of  status and trends  in.  forest  ecosystem  condition  prior  to  their regional




implementation.  This document presents a plan to evaluate several indicators during the 1991 field




season.






      This introductory section provides an overview of the scope and purpose of this document.  A




short historical background of the development of  an indicator evaluation strategy in the FHM




program  is given  to help the reader  put the present planning process  in  perspective.   The




organization of the study with the anticipated  roles and responsibilities of participating agencies is




delineated. The importance of indicator evaluation studies to the overall success of FHM is discussed.






1.1 PURPOSE OF THE PLAN






      The first objective of this study plan is to provide a mechanism for the coordination of indicator




evaluation efforts  by scientists from  the participating  agencies  in the FHM  program.   This  is




particularly important as our long-term objective is to evaluate indicators as  a set rather than each




one individually.  As a result, numerous technical and coordination issues have been identified and




subsequently resolved in the preparation of this plan.






      A second objective of this study plan  is  to provide a mechanism  for input by the scientific




community into indicator evaluation activities in the FHM  program.  This objective will be achieved




through the peer review of this plan and the sharing of  this plan with interested scientists.






      The  third important objective  is  to provide guidelines and direction to those individuals




charged with implementing this plan.  A study plan must provide enough detail to allow field




scientists to carry out the study effectively and efficiently.  It should be recognized that this plan does




not contain all the details that will be required.  Thus, a quality assurance project plan and a methods




manual are currently being prepared as supplements to this plan.
                                             1-2

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      The final objective of this plan is to meet agency requirements for field data collection efforts.




The EMAP program, for example, requires that a plan be prepared before funds can be spent in the




field.  The review and approval  of study plans  by management within each participating agency




ensures management input, support, and cooperation.






1.2 CONTENT AND ORGANIZATION OF STUDY PLAN






      This plan is organized into twenty-three sections.  Sections 2 through 7 highlight the planning




elements of design, quality assurance, logistics, information management, and reporting required to




conduct the Field Study. Sections 8 through 17 describe the specific indicators that will be tested in




the field  studies.   Sections  18  through  22  describe  overall  planning topics such as indicator




development,  integration and assessment, and reporting, and Section 23 combines all references.




The incorporation of off-plot information from air pollution and deposition,  climate, and landscape




characterization data collection efforts is discussed also.






      It should be noted that the preparation of this  plan has been a team effort requiring the




contributions of numerous individuals. In an effort to recognize the contributions of these scientists,




the authors will be identified at the beginning of each section.






1.3 HISTORY






      A series  of pilot studies were undertaken in 1988 and I989 under the auspices of the  National




Vegetation Survey in the Forest Response Program, an interagency acid rain research program.  The




objectives of these studies were  to  develop  techniques  to  inventory and  monitor symptoms of




atmospheric pollution-induced stress, damage, and/or death of forest stands and trees. An indicator




known as the visual damage indicator was developed and evaluated as a result of this program at 128




plots in mixed hardwood forests, 31 plots in  high-elevation spruce-fir forests, 157 plots in natural




loblolly pine stands in the piedmont, and 222 plots in loblolly pine stands of the coastal plain region.




This indicator includes a number of different measurements of tree crown condition, evaluates trees
                                            1-3

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for symptoms of abnormal growth or pests, and identifies whether or not sensitive plant species have




been exposed to air pollutants.






      Based on the success of these and  other pilot studies, the  implementation of forest health




monitoring in New England (NE-FHM) was initiated in the summer of 1990.  In the NE-FHM project,




over 200 plots were established on a grid across New England. This project was a combined effort of




the FS and state  forestry agencies with assistance provided  by the EMAP-Forests staff  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 had been identified during




interagency  FHM  workshops  and peer-reviews  of  the EMAP  ecological indicators  document




(Hunsaker and Carpenter, I990). Twenty plots were established in northern hardwood forests of New




England, and 20 plots were situated in loblolly pine stands of Virginia on sites that would not become




FHM plots. This second project was named 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 and  are  being




evaluated.






      As a result of the 20/20 pilot study, considerable information was collected about  these




indicators. For example, the PAR measurement was found to be sensitive to light variations on cloudy




days and frequent visits within and outside the canopy did hot adjust for this effect.  A new approach




of taking simultaneous PAR measurements within and outside the canopy needs to be tested. The




1991 indicator evaluation study presented in this document provides for such a test.






      An important development during  the fall of 1990 was a document  on  the EMAP indicator




evaluation strategy (Knapp et al., I990). The value of this document was that it outlined an approach




for selecting  and evaluating indicators of ecological condition  regardless of the  ecosystem  or
                                           .1-4

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indicator type.  This framework has been expanded and proposed for interagency FHM program




consideration in the Monitoring and Research Strategy for Forests - EMAP (Palmer et al., 1991).  A




summary of this approach is given  in Section 21  of this plan.  This approach has been used as a




guideline for developing 1991 indicator evaluation strategies.






1.4 AGENCY RESPONSIBILITIES IN 1991 INDICATOR EVALUATION STUDIES






      The success of  this study will depend on  the  willingness of all participating  agencies to




participate as full  partners in this activity.  It is important that the roles and responsibilities be clearly




identified  to encourage cooperation and successful  implementation. These duties are  outlined in




Table 1.1.  In general terms, EPA is responsible for preparing planning documents. Field activities will




be coordinated  by the FS. Evaluation of results will be a shared activity. The key individuals who are




most responsible for the success of this study are the indicator leads, regardless of the agency from




which they come.
Table 1.1   Agency responsibilities in 1991 FHM indicator evaluation studies.
AGENCY RESPONSIBILITIESa
TASK
Planning:
Preparation of Plan
Review of Plan
Quality Assurance Plan
Methods Manual Prep.
Methods Manual Review
Programming Data Loggers
FS

Cb
C
C
C
C
C
EPA

Lb
L
L
L
L
L
States FWS SCS

C
C C C
c
c
c c c
c
NPS TVA BLM


C C C


C C C

                                                                                (continued)
                                             1-5

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Table 1.1   Continued
AGENCY RESPONSIBILITIES*
TASK FS
Implementation:
Plot Reconnaissance L
Pretraining Workshop L
Training Workshop L
Measurement Crew Staff L
Crew Logistics L
QA Audits L
Plot Remeasurements L
Evaluation:
Debriefing Workshop L
Method Manual Revisions C
Quality Assurance Report C
Indicator Evaluations L
Synthesis Report C
Review of Report C
a FS = Forest Service (USDA)
EPA = Environmental Protection Agency
FWS = Fish and Wildlife Service (USDI)
SCS = Soil Conservation Service (USDA)
NPS = National Park Service (USDI)
TVA = Tennessee Valley Authority
BLM = Bureau of Land Management (USDI)
States = AL, GA, VA, MD, D£, NJ, ME.NH, VT, IV
b L = Lead Agency
C = Contributing Agency
EPA

C
C
C

C
L
C

C
L
L
L
L
L
IA, RI.CT
States

C

C
C
C
C


C
C



C

FWS SCS NPS TVA BLM


C C
C C C
C C C
C C
C


C C C
C C C

L C
C
C C C C C

1.5 IMPORTANCE OF INDICATOR EVALUATION STUDIES TO SUCCESS OF FHM






      The overall goal of FHM is to provide unbiased, regional estimates with known precision of the




status and trends of ecological resources in forests on an annual basis for all of the United States. This




can only be accomplished if indicators can be found that accurately reflect ecosystem condition.






      The purpose of the indicator evaluation studies presented in this plan is to begin to address the




issue of whether or not the right indicators have been chosen and whether or not they will work. This
                                            1-6

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is a complex issue and depends on whether or not answers can be found to a number of questions



asked of the indicators. For example, what is the expected variability for indicators if youweturn to



the same location at some other time in the measurement season? What is the feasibility and cost



associated with the data collection of this indicator? Which data collection method gives the most



accurate and  reproducible results?  What additional information does this indicator  provide



regarding the health of the forest ecosystem that is not already addressed by other indicators? Can



the indicator information be interpreted given natural variation and changes due to normal stand



development?






      In summary, the FHM program has made a significant start with the implementation of visual



symptoms and forest mensuration  indicators in several states.  As this  program  is expanded  to



additional states, there is a need to evaluate these indicators as well as additional indicators of forest



condition to provide a complete picture of the status and trends in our nation's ecological resources



in forests.
                                            1-7

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                               2. APPROACH AND RATIONALE





                                        R.C. Kuceraa








      The FHM program is expanding gradually due to the diverse nature of forest ecosystems, the



state of the  sciences, the organizational complexity, and the cost of program development.  The



strategy  for expansion  includes  developing  national  and   regional  support  organizations,



communicating program goals to agencies in new geographic areas, advancing the research of forest



monitoring,  and implementing advanced forest monitoring methods. In 1991 twelve states in three



Forest Service Experiment Station regions have committed to operationally monitor forest ecosystem



health. The approach of the FY91 Indicator Evaluation Field Study for EMAP-Forests (Field Study) is to



economically conduct  field research  for  advancing  forest monitoring science  by  combining



developmental research with the operational monitoring research conducted in selected  areas.  This



approach introduces the  FHM program in  new areas and  necessitates  development  of support



organizations.





      The Field Study is generally composed of two types of studies with additional measurements



incorporated according  to the opportunities provided by implementation. One type of study is the



Nutrient Cycling Demonstration which consists of a core of measurements which are believed to  be



informative concerning  regional ecological nutrient cycling status and which will be measured  over



the broad region of Georgia and Alabama.  The second  type of study is the Landscape  Pilot which



consists of measurements intended to develop procedures for measurement and correlations among



different areal scales of measurement in the locality of specific plots.  The number and  geographic



distribution of the Landscape Pilot plots are more limited than in the Nutrient Cycling Demonstration



and are selected to achieve more specific objectives.





      Each measurement proposed in the Field Study has discrete objectives to develop  or evaluate



it's usefulness for monitoring.  The EMAP-Forests national  staff  and cooperators provide planning,



implementation, and analytical support for this research  as described  in subsequent  chapters  on
  ' ManTech Environmental Technology, Inc., AREAL-RTP
                                            2-1

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design, quality assurance, logistics, information management, reporting, landscape characterization,



air pollution and deposition, climate, indicator development, and assessments.





      The FHM program is conducting operational forest monitoring in 1991 at different levels'of



implementation in New England (Maine, New Hampshire, Vermont, Connecticut, Massachusetts, and



Rhode Island), the Mid-Atlantic (New Jersey, Delaware,  Maryland), the South (Alabama),  and the



Southeast (Georgia and Virginia).  Complete  implementation consists of  plot establishment and



measurement or characterization  of tree species, radial increment, regeneration, and certain visual



symptoms of  forest condition.  Some or  all of these measures have  been  selected for immediate



operational monitoring in  the  different regions to provide data for reports of forest conditions.



Table 2.1  shows the  levels  of operational monitoring  by region  and  the additional research



measurements planned for the Field  Study in  1991.  The  Nutrient Cycling Demonstration  and



Landscape Pilot measurements listed in Table 2.1 are described in their separate chapters.




Table 2.1 Core, Demonstration,  and Pilot Measurements Schedule
Measurement
Plot Establishment
Operational Measurements
Diameter
Species
Visual Symptoms
Regeneration
Nutrient Cycling Demonstration
Soils
Tree Core Elemental
Foliar Chemistry
Root Disease Evaluations
Landscape Pi lot
Needle Age
Mycorrhizal Soil/Root
Veg. Habitat Structure
PAR
Aerial Photo Interpretation
GPS
New Mid South
England Atlantic (Alabama)
XXX

X X
X X
X X
X X

X
X
X
X

X
X



X
South
East
(Georgia)
X

X
X
X
X

X
X
X
X



X
X
X
X
South
East
(Virginia)
X

X
X
X
X












                                           2-2

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      The Field Study is being conducted in eight of the twelve states that have begun monitoring




operationally.  The twelve states are in the jurisdiction of three Forest Experiment Stations of the




Research Division of the USDA  Forest Service.   Maine, New  Hampshire,  Vermont, Connecticut,




Massachusetts, and Rhode Island are in the Northeastern Forest Experiment Station area and are




referred to as New England (NE).  New Jersey, Delaware, and Maryland are also in the geographic




territory of the Northeastern Forest Experiment Station, and these states are referred to as the mid-




Atlantic states. Alabama is in the Southern Forest Experiment Station area. Virginia and Georgia are




in the area of the Southeastern Forest Experiment Station.






      The Field  Study will  be conducted only in Georgia, Alabama, and  selected areas of  New




England. The States  of Georgia and Alabama contain contiguous areas of similar forest types and




represent two Experiment Station regions, making this combination the most attractive for the Field




Study. Researchers in the mid-Atlantic states are limiting their first year's operational work to plot




establishment and therefore the mid-Atlantic states were not considered as prime candidates for this




additional research.  The New England Forest Health Monitoring project determined that only soils




and Global Positioning Systems should be measured in New England.






2.1  NUTRIENT CYCLING DEMONSTRATION






      Operational monitoring will  be conducted on  approximately 206  forested plots in  New




England, 148 plots in Georgia, and 137 plots in Alabama. The Nutrient Cycling Demonstration will be




superimposed on a  systematic selection of one-fourth of these plots in Georgia (37 plots) and




Alabama (35 plots). The measurements to be made on these plots are soil chemistry, foliar chemistry,




tree core elemental analysis, and selected root fungi presence and taxonomy (see Table 2.1).  These




measurements were  recommended  as candidates for  further evaluation in  the Monitoring and




Research Strategy for Forests - Environmental Monitoring and Assessment Program (EMAP) (Palmer et




al.,  1991).  The Field Study is taking further advantage of the opportunity to utilize the field crews to




test methods of height  measurement on  the Nutrient Cycling Demonstration plots in Georgia, and
                                            2-3

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Global  Positioning Systems  measurements  in  Georgia  and  the New  England  states.   The




measurements are being made for specific reasons which are described in detail in Sections 8 through




11.






      There are  several reasons for making these measurements as  a group.   The chemical




measurements, when made at the same time and place, are expected to reveal significant nutrient




cycling information relevant to the status and trends of the forest ecosystem which may not be




apparent if the measurements are made at different times.  Analysis of these data in combinations




may also indicate forest condition or identify relationships that suggest causes of existing conditions.






      The systematic selection of one-fourth of the operational monitoring  plots, over the entire




area of Georgia and Alabama, as opposed to limiting sampling to a preselected forest type, provides




more opportunity for poststratification based on other classification criteria such as climate divisions,




soil classifications, or regional land use classifications. There will be certain classification types that do




not have enough  samples for thorough analysis, but these will provide preliminary information to




anticipate conditions that will be encountered when these types are more completely sampled.






2.2 LANDSCAPE PILOT






      The Landscape Pilot is a coordinated set of additional measurements that will be conducted on




20 of the Nutrient Cycling Demonstration plots in western Georgia.  These additional indicators can




benefit from the employment of remote  sensing techniques.  The additional measurements to be




made on these plots are vegetation and habitat structure,  intercepted photosynthetically active




radiation,  aerial  photography  interpretations  for  landscape  characterization  and   landscape




processes, and  finally Global  Positioning Systems (GPS) coordinate identification for the purpose of




accurately digitizing aerial photo information in a Geographical Information System (CIS) data base.




The pilot  is designed  to focus  on  an examination of  the relationships of  some  of  the field
                                            2-4

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measurements and  remotely sensed interpretations from  high-resolution aerial photography in



addition to the individual indicator development.






      The Landscape Pilot has some general objectives that make the Pilot a  coordinated effort



among the measurements that will be taken.  These objectives provide the opportunity to obtain



more information from all of the measurements taken than from  the  independent measurements



alone. The  primary objective is to  investigate linkages of field measurements  and the  remotely



sensed interpretations.  This  objective includes testing indicator  associations at each scale on  a



selection of  plots with  diverse physical and vegetative features.  This  objective and the  resulting



simplified logistical structure has lead to the decision to sample twenty consecutive Nutrient Cycling



Demonstration plots in western Georgia, thus increasing the probability  of sampling different forest



types and physiographic regions from the upper Piedmont to the Coastal Plain.






      Two further measurements in the Pilot category are soil/mycorrhizal fungi sampling technique



testing, and foliar chemistry sampling techniques to determine the effect of needle age. These two



measurements are being taken on the Nutrient Cycling Demonstration plots  in Alabama.  This plot



selection decision is based on the need to obtain samples from a more homogeneous population of



loblolly pine and the advantageous logistical opportunity to decrease the work load in Georgia and



more fully utilize the available personnel in Alabama.






      The final  pilot  measurement  is the logistical  test  of establishing plot center with GPS



technology.   This test is  incorporated within  the Georgia Pilot GPS measurement plan.  Global



Positioning Systems  measurement methods  will be tested on a selection of plots in New England



because it can be added  to the operational monitoring  project without employing additional



personnel, equipment is available, and because the additional range of  conditions encountered will



improve the test.






      Geographic Information System technology will be used in the Landscape Pilot as  a tool to



investigate the general objectives of the pilot and to provide a  record for future use in change
                                            2-5

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detection.  CIS coverages will  be developed  from the aerial photography interpretations, the



sampling locations of measurements taken, and from auxiliary data such as elevation and land cover.






      These measurements are listed in Table 2.1 and are described in their separate chapters.  The



review of  the  Monitoring and Research Strategy for Forests  -  EMAP (Palmer et al., 1991)



recommended that further research of methodologies and analytical techniques is needed for these




measurements.
                                           2-6

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                                          3. DESIGN

                                          D. Cassell3

      The network and plot design for this summer's regional demonstration and pilots are discussed
in the Monitoring and Research Strategy for Forests-EMAP (Palmer et al., 1991).  This document has
details of the development of the monitoring network design and field plot design for EMAP-Forests.
It has been through the peer review process, and the  Forest Service has held conferences to answer
specific questions of statistical design. A brief discussion follows, with specific comments applicable
to this summer's field season.

      The EMAP-Forests statistical design produces a probability sample of field plots in each region.
The pilots and regional demonstration will use subsets of the selected plots.  The Landscape Pilot will
be done on 20 plots in Georgia that are  a subset of the plots used in the regional demonstration.  In
this way, links between demonstration and pilot indicators may be examined.

      The plots are selected by laying Forest Inventory and  Analysis (FIA) photo point grids over the
EMAP  hexagon grid and selecting  the photo  point  closest  to  the  center of the  landscape
characterization hex.  When FIA plots already exist  at the selected  photo point, the study plot will be
deliberately offset to avoid disturbing the FIA plot.  The EMAP interpenetrating grid will  be used  to
select the plots for this summer's demonstration and pilots. In other words, one fourth of the possible
plots will be  selected  in a systematic grid, as discussed in the Monitoring and Research Strategy for
Forests-EMAP (Palmer etal., 1991).

      The strategy plan also outlines selection of plot centers and the plot design. At each location, a
one hectare circle represents the experimental unit of interest. Within each such unit, a cluster  of
four fixed-area subplots (24 ft radius,  1/24 acre) will be designated (Figure 3.1).  The subplot centers
will be 120 ft apart, and destructive and extractive sampling will be limited to a 36-ft circular band
surrounding each subplot. This will ensure that as  far as possible, all studies will be considering the
same experimental units.
  3 ManTech Environmental Technology, Inc., Corvallis, OR
                                             3-1

-------
      Plot Stem
Radius           ATM
  ft
 8.8
 24.0
 58.9
 •o
1/300
1/24
S/24
                                                    	cr
                                        Subplot
                                                                   Azlmutli from
                                                                    tubplot #1
                                                                         12.0tt
                                                                         270
            Soil characterization
                                                             to 1/300 ae plot cantar
                                                              from subplot eantar
                  Figure 3.1. Forest Health Monitoring Plot Layout.
                                            3-2

-------
      A standard method has been developed  for selection of trees to be destructively sampled.

Within the destructive sampling band of Subplot 1,  two trees  will be chosen.  The portable data

recorder will give the field crew a randomly selected azimuth from the subplot center.  The crew will

take this azimuth to the middle of the destructive sampling band and move in a clockwise direction

until the first tree with a dominant or co-dominant  crown and diameter of at  least five inches  is

encountered. The crew will also select a tree using the same protocol but using an azimuth that  is

180° from the selected azimuth. If the subplot has no  sample trees or only one tree then the data for

the missing tree(s) are recorded as "missing."  The procedure is repeated in Subplot 2, or if this is in a

different forest type, in the next subplot of the same forest type. Only two subplots will be used for

selection of sample trees.  That is, further subplots will not be examined if no trees or only one are

found on either of the first two subplots examined in the forest type.

                 *
      It is expected that some  plots will  have multiple forest  resource types.  Forest type will "be

classified by subplot.  Subplots that fall in forest types distinct from that of the center subplot will not

be rotated into the original forest type, but will be left in place and measured as is.  This will ensure

that unbiased estimates can be generated from the subplot data.


      Each of the studies presented defines the  mensuration and sampling methodology unique to

its respective objectives.   However,  all methods are designed  within the  context of the  overall

sampling design and  sampling unit design  methods described here  and  in the  Monitoring and

Research Strategy for Forests-EMAP (Palmer et al., 1991).


      The Technical Coordinator for statistics will ensure that all  indicator studies will be statistically

analyzed using the guidelines discussed in Sections 5 and 6 of Palmer et al., as well as in the Design

Report forEMAP, Part 1 (Overton et al., 1990).


      The statistical analyses discussed in Palmer et al. cover a variety of areas. Statistical procedures

for  regionalization of the data  are in general based  on the theory of systematic  samples (such as

Horvitz-Thompson  estimators for means and  totals) and the utility of the cumulative distribution
                                            3-3

-------
function.   Statistical analyses to separate subgroups of the  data  include cluster analysis  and



hierarchical   regression.    Statistical  analyses  for  examining  spatial  variability  will  include




subpopulation analyses, semivariograms, and robust forms of kriging.






      The methods for estimating components of variability include the one outlined in Pamer et al.



This is a modification of Cochran's method for estimating components of variance in nested models



(Cochran, 1977) which incorporates measurement error estimates. Bootstrapping data within a  plot is




an alternative method being used to assess sampling variability and to evaluate within-plot sample



size (see Section 8).






      The statistical methods for indicator development  and  indicator linkages include a variety of



standard methods. Linear and approximately linear relationships can be evaluated using correlations,




multivariate regression, analysis of variance, analysis of covariance, principal components and  factor



analysis,  or  canonical correlation analysis.   Monotonic  relationships can  be evaluated  using



nonparametric correlations, nonparametric regression, and nonparametric analogues to analysis of



variance such as Kruskal-Wallis tests.   Nonlinear relationships can be  investigated using recent



methodologies such as projection pursuit analysis and sliced  inverse regression. Of course, any results



from these statistical analyses must be scientifically interpreted by the respective indicator leads.
                                             3-4

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                                  4.  QUALITY ASSURANCE





                                         G.E. Byersa
      The FHM program is designed as a major environmental data collection effort and, as such, will




operate within the guidelines of the EPA's Quality Assurance Management Staff (QAMS).  Utilizing a




statistically robust  design, the monitoring program will collect data  across large geographic areas




over long periods of time for multiple ecological resources. The program will employ comprehensive




QA techniques to ensure the quality and usefulness of the data.  A Quality Assurance Project Plan




(QAPjP) is being  prepared  which is  separate from this study plan and  which will  consist of a




comprehensive quality assurance plan.






4.1  QUALITY ASSURANCE PROGRAM






      The purpose of the QA program is to ensure that the resulting data bases will yield scientifically




valid and unbiased information  related to the principal  hypotheses being addressed in the project.




The fundamental basis for an intensive QA program is that policy makers and the public must have a




high degree of confidence in the environmental data and  statistics generated by the participants.




Hence, the mission of QA in the FHM program is to ensure that all data and statistical products are of




documented and sufficient quality to satisfy the needs of data users, policy makers, and the public.






      The QA program for the FHM program provides guidance to  and is responsible for oversight of




the forest ecosystem QA activities. Much of the guidance for the  various EMAP Resource Groups is




being provided through the EMAP QA Program Plan (Einhaus et al., in preparation;  EPA, 1987).  The




Monitoring and Research Strategy for Forests -EMAP (Palmer et al., 1991) delineates in greater detail




many of these aspects, including organizational  structure.  The national QA Coordinator (QAC) for
  ' Lockheed Engineering & Sciences Company, Las Vegas, NV
                                            4-1

-------
the FHM program (QAC-Forests) interacts closely with the QAC for EMAP, regional QA officers (QAO),




indicator  leads, and  other  FHM participants, including the FS.   This interaction disseminates




information and relays specific requirements of the project. The QAC-Forests is responsible for QA in




all FHM  program activities and  reports directly to the  FHM program manager and  EMAP-Forests




technical director. Within the FHM program regions, regional QA officers interact with the QAC-




Forests to identify and resolve intra- or inter-regional QA issues within the guidelines  of the QAPjP.




The regional QAOs coordinate specific QA tasks with individuals on the technical staffs that are best




qualified to perform them successfully, such as, the indicator leads.






4.2 QUALITY ASSURANCE DOCUMENTS






      The  overall policies,  organization objectives, and functional  responsibilities designed  to




achieve data quality goals for the FHM program activities are described in detail in the Monitoring




and Research Strategy for Forests - EMAP  (Palmer et al., 1991). Included are discussions on QA




related  to policy, total quality  management, organizational structure arid  responsibilities,  data




quality objectives (DQOs), documentation and reporting (e.g., QA project plan, standard operating




procedures [SOPs], documentation, and reports), and operations (audit program, data verification).






4.2.1  Companion Documents and Other Sources of Information






      Information on QA-related activities for the FHM program are presented  in  several other




documents that are in various stages of completion  prior to 1991 field  and laboratory activities.




Current versions  of the  following  documents and  information must be  distributed  among all




appropriate FHM program participants and cooperating organizations.
                                            4-2

-------
      Included are the following.

      •   EMAP Quality Assurance Program Plan (QAPP) (Einhaus etal., In Preparation)
      •   FHM Program Quality Assurance Project Plan (Byers, In Preparation)
      •   FHM Program Eastern Forest Health Monitoring Methods Manual for Field Measurements
         (Chojnacky, In Preparation)
      •   FHM Program Field Methods Manual (Conkling and Byers, In Preparation)
      •   FHM Program Laboratory Methods Manual (Byers and Van Remortel, In Preparation)

4.2.2 Quality Assurance Project Plan

      The QA policy of the EPA (Stanley and Verner, 1985;  EPA, 1987)  requires that every monitoring
and measurement project have a written and  approved Quality Assurance Project  Plan (QAPJP). This
requirement applies to all  environmental  monitoring and measurement  efforts authorized  or
supported by EPA through regulations, grants, contracts,  or other formal means. The purpose of this
QAPjP is to specify the policies, organization, objectives, and QA activities needed to achieve the data
quality requirements of the joint monitoring program.  These specifications are used to assess and
control measurement errors  that may enter  the system at various phases of the project, such  as,
during the initial field measurement stage or during sampling, preparation, and analysis.  The QAPjP
will also describe the QA activities and assessment criteria that will be implemented to ensure that the
data bases will meet or exceed all data quality objectives (DQOs) established for the FHM program.
The QAPjP must identify all environmental measurements within the  scope of the project goals and
objectives and identify specific processes within each measurement that could introduce possible
sources of error or uncertainty in the resulting data. Methods, materials, and schedules for assessing
the error contributed by each process must also be addressed. The QAPjP must also define the criteria
and procedures for assessing statistical control  for each measurement parameter.

      The QAPjP will be revised as  necessary to reflect  changes in procedures that result  from
continuous improvement.  All project personnel, especially indicator  leads, should be familiar with
                                            4-3

-------
the policies and objectives outlined in pertinent sections of the QAPjP to ensure proper interactions



among the various data acquisition and management components.






4.2.2.1 Content of the QAPjP






      The EPA QAMS guidelines suggest that the QAPjP. should specifically address, in detail or by



reference, each of the items listed  below.  These items  describe  the QA approach that will be



established for each of the data acquisition projects (e.g.,  implementation, pilot, demonstration)



within the FY91 Indicator Evaluation Field Study.





      •   Quality assurance objectives for measurement data



      •   Sampling procedure and sample handling



      •   Sampling custody, transportation, and storage



      •   Calibration procedures and frequency



      •   Analytical/measurement procedures and experimental design



      •   Data reduction, validation, and reporting




      •   Internal quality control checks and frequency



      •   Performance and systems audits and frequency



      •   Preventative maintenance procedures and schedules



      •   Specific routine procedures to be used to assess data quality



      •   Corrective action



      •   Quality assurance reports to project directors




      Data collection activities must institute sufficient control procedures, materials, and techniques



to minimize measurement errors.  Each process that could affect the quality of the data, such as,



sample collection, preservation, transportation,  storage, preparation, analysis, and data reporting,



must be evaluated and documented.  In this way, the measurement process can be controlled, the



effectiveness of the process can be documented, and the quality of the sample data being produced



can be inferred from the QA data.
                                            4-4

-------
      By using appropriate measurement quality techniques or samples, it is possible to isolate the




error  contribution and set control criteria based upon specific   measurement quality objectives




(MQOs).  This approach is essential for providing diagnostic information so that real-time corrective




action can be taken to ensure control in satisfying these MQOs.






4.2.3  Standard Operating Procedures






      Good management  of any operation that uses  protocols in a  routine or repetitive  manner




includes  the use  of SOPs,  also called  "methods"  or  "protocols"  in field and laboratory circles.




Environmental monitoring  SOPs are  devised  for 'Sampling  and  analysis,  data management, QA,




reporting activities,  accounting, project finance and contracts, and in analysis and integration phases




of the project.  The use of written SOPs helps to ensure consistency in planning, implementation, and




analysis activities over time and among personnel for routine activities within an organizational unit.




To ensure  consistency in data  among the FHM program indicators SOPs must be cooperatively




developed.






      The  EMAP-Forests technical director is  responsible for determining which activities require




SOPs and ensuring that they are developed, reviewed, and implemented.  The  personnel closest to




the actual  implementation of an activity (e.g., indicator leads) are the appropriate individuals to




develop specific SOPs. The QAC-Forests should identify in the periodic audits the status of all new




SOPs  in the project.  The  QAC-Forests works with the technical director, the regional QAO, and




indicator leads in the SOP process. The QAC-Forests also has responsibilities in SOP identification,




interorganizational consistency, elevation to method or protocol status, and the need for training.
                                            4-5

-------
                                        5. LOGISTICS


                                         M. Pappa



5.1 OVERVIEW


      The study includes field sampling, sample preparation, and analysis phases and therefore,  a

large  logistics component.  The objective of logistics is to provide the necessary assistance to all

operational  phases of the data collection program  to ensure that the program acquires  data of

sufficient quality for its intended use in an efficient, cost-effective, and timely manner. Logistics will

assist in the following operational phases.


      •   Field sampling

      •   Sample and data handling/transfer

      •   Sample preparation

      •   Sample analysis

      •   Sample archive

      Table 5.1 identifies a number of logistics elements within these five general categories.

Table 5.1 EMAP Logistics Elements for Implementation of Forest Monitoring Programs
 1.     Review of Logistical Activities
 2.     Staffing
 3.     Communication.
 4.     Scheduling
 5.     Reconnaissance
 6.     Procurement and Inventory
 7.     Training
8.     Safety
9.     Information Management
10.    QA/QC
11.    Review/Recommendations
12.    Inventory/Storage
13.    Planning
14.    Contracting
      Responsibilities for each of these elements have been determined by project  managers and

logistics leads. The logistics operation can be developed component by component.  Each component

is not necessarily the sole responsibility of the logistics team. However, the logistics team will identify

who is responsible for completing the activity.  Figure 5.1 provides a time line of the activities for the

study.
  a Lockheed Engineering & Sciences Company, Las Vegas, NV
                                            5-1

-------



PERSONNEL/STAFFING - FUNDING (

SELECTION OF INDICATORS

PROJECT DESIGN

STUDY PLAN
QA PROJECT PLAN/SOP

1 AGs/CONTRACT 1 NG

RECONN/ACCESS/PLOT SET-UP

PROCUREMENT

SCHEDULING

TRAINING
SAMPLING

PREPARAT ION/ANALYS 1 S

DATA HANDLING





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        Figure 5.1. Timeline for the South/Southeast Surveys.

-------
5.1.1 Logistics Status






      The Logistics Status is a dynamic internal document that tracks the progress and development




of each element. This document is not intended to be distributed but provides localized  information




to allow the technical director, the EMAP logistics coordinator, and EMAP management to determine




the progress of the project. It is developed and continually updated by the EMAP-F logistics lead.






5.1.2 Organization of Logistics Section






      The logistical elements (Table 5.1) of the study will be discussed in this section. Two studies will




be referred  to and, where needed, separated under specific headings.  The studies, introduced in




previous sections, are called the Nutrient Cycling Demonstration and the Landscape Pilot.  In addition,




the indicators of growth and visual symptoms will also be sampled on all sites.






5.2 STAFFING AND PERSONNEL REQUIREMENTS






      The following groups comprise staffing and personnel requirements.





      •   Field crews



      •   Logistics personnel



      •   Preparation laboratory personnel



      •   Management support



      •   Training crews



      •   QA crews




      This section will describe the personnel  responsible for each assignment.  The organization




through which each position will be hired (i.e., F5, EPA, cooperators, or contractors) will be discussed.




Work schedules will also be discussed.
                                            5-3

-------
5.2.1 South/Southeast Nutrient Cycling and Landscape Pilot Field Crews

      One field  crew  in  Georgia  will  be  developed to  sample  both the  Nutrient Cycling
Demonstration and the Landscape Pilot indicators.  One field crew in Alabama will be developed to
sample the Nutrient Cycling Demonstration indicators. However, due to the time required to sample
the Pilot indicators, two indicators, mycorrhizal root sampling, and needle age separation for foliar
samples, will be accomplished by the Alabama field crew.  The Georgia field  crew will not  be
responsible for these two indicators.

5.2.1.1 Field Personnel

      The information obtained to date (Table 5.2) indicates a six-person crew is needed to sample a
site for the Nutrient Cycling Demonstration, Landscape Pilot, and core measurements.  The six-person
field crew comprises the following.

      •   Two foresters (visual symptoms, growth) with  work-related experience  in mensurational-
          type measurements. These foresters will either be State or FS employees.
      •   One soil scientist (soil sampling) with emphasis  on soil classification. Soil scientists from the
          SCS are preferred.
      •   One foliage sampler experienced in tree climbing and foliar sampling techniques.
      •   One botanist (vegetation and  habitat,  PAR)  capable  of taxonomically  identifying
          understory vegetation.
      •   One aide (GPS, root sampling) capable of recording data, root sampling, soil excavation,
          and maintaining and shipping samples.

      The two foresters are part of the operational program for the collection of measurements  of
visual  symptoms and growth. They are mentioned herein order to represent a complete field crew.
                                            5-4

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           Table 5.2.  Estimated Time Requirements for Landscape Pilot with Core and
                     Nutrient Cycling Demonstration Measurements.
Indicator
Soil Sampling
Foliar Sampling
Growth
Visual symptoms
Vegetation/Habitat
PAR
GPS
Mycorrhizae Root Sampling
Tree Cores

# People
1
1
2
2
2
1
1
1
2

Hours
6
5
2.5
4
4
2
6
2
.5
Total
Total Hours
6
5
5
8
8
2
6
2
1
43
      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 following responsibilies.



      •   Maintaining and revising sampling schedules and itineraries


      •   Assigning duties according to sampling priorities


      •   Ensuring that all sampling protocols are followed


      •   Ensuring proper use and maintenance of field equipment


      •   Maintaining the integrity of the site and samples collected


      •   Reporting to proper management staff any problems or difficulties encountered


      •   Returning all field equipment and supplies


5.2.1.2 Field Crew Division of Labor



      In order to collect data for all indicators certain field crew members will be responsible for


more than one  indicator.  A  sampling  sequence for  the efficient use of field crew members to


complete sampling in one day will be developed based on the following.



      •   A full day is 8 h: 2 h estimated for driving and plot location, and 6 h for data collection.


      •   The crew consists  of six individuals.
                                             5-5

-------
      •   The two foresters are needed for the full day to measure growth and visual symptoms with
          the exception of branch and root sampling.
      •   The botanist will be responsible for data collection of the vertical vegetation indicator. The
          aide will assist the botanist on one of the measurements (vertical vegetation).
      •   The soil scientist is needed for a full day for the description and sampling of soils.  The aide
          will assist with the excavation of sample holes.
      •   The foliage sampler will be responsible for branch sampling, height measurements, and
          root sampling (two-root method).
      •   In Georgia, the aide will assist in the excavation of soil holes, measure PAR, and assist on
          the vertical vegetation measurement.  In Alabama the aide will assist in the excavation of
          soil holes,  evaluate in-hand  branch samples, extract tree cores, sample mycorrhizal  roots,
          and separate needle ages for foliar  nutrient samples.  After 2:00 p.m., this person will leave
          the site to transport and ship samples.
      •   Crew members will provide  assistance to other data collection  activities when completed
          with their primary responsibilities.
      •   PAR measurements must be collected between the hours of 11:00 a.m. and 1:00 p.m.

5.2.1.3 Field Crew Task Sequence

      Figure 5.2 represents the proposed task sequence for the field crew.  During  the pretraining
and training exercises this sequence will be reviewed and modified to the most efficient schedule.

5.2.1.4 Work Schedules

      Because personnel  from different organizations  will be  working on a field crew, a  work
schedule for the crew should be developed.  Within  the FS and EPA there are a number of work
schedules that can be adapted (four 10-hour days: 8 days straight, 4  days off, etc.).  Due to the
expenditures relating to this program the most efficient schedule should be determined.

      The FS regional logistics leads will determine work schedules.
                                            5-6

-------

FOREST SERVICE PERSONNEL 1 & 2
Travel to site
Plot center location
Mark soil hole 1 & sample trees at point 1
Witness trees/Veg profile/Par grid point 1
Measure regeneration point 1
Locate point 2,3,4 mark soil hole 2,3
Tally regeneration/PAR grid point 2
Select remaining sample trees/PAR grid 3,4
Collect growth/visual symptoms alt points
Indicator plant condition
BOTANIST
Vertical vegetation measurements
Plant Identification
FOLIAGE SAMPLER
Foliage sampling
Root sampling (two- root method)
SOIL SAMPLER
Hole Excavation
Pedon Description
Soil/Core sampling
AIDE
Hole Excavation (GA and AL)
Vertical vegetation (GA ONLY)
PAR (GA ONLY)
Mycorrhizal root sampling (AL ONLY)
Tree Coring (AL ONLY)
Needle age separation (AL ONLY)
In hand branch evaluation (AL ONLY)
Sample transport and Shipping


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                                           5-7

-------
5.2.2 Aides






      Two aides will be available one for each demonstration/Pilot crew in Alabama, and Georgia.




Their duties will include sample collection, storage, tracking and shipping, equipment procurement,




dispersal, and maintenance. The aides will also act as a liaison between field crews and the FHM




program management.  Because of the  time they will have available, the aides will also assist the




field crew by taking part in sampling activities.






5.2.3 Field Crew Funding






      An interagency agreement between the EPA and the F5 will be developed.  Funding will be




provided to the FS to acquire personnel for the Nutrient Cycling Study, the Landscape Pilot and the




aides.






5.2.4 Logistics Personnel






      Field crews will need logistical support in the following areas.





          • Equipment and consumable storage, maintenance, and repair




          • Vehicle maintenance and repair




          • Sample storage, tracking, packing, transfer




          • Lodging, timekeeping, and such




      The FS regional logistics leads will be responsible to support these activities for FHM program




field personnel.  Personnel  participating in the Nutrient Cycling Demonstration or  Landscape Pilots




will be the responsibility of EPA logistics leads.






5.2.4.1 Work schedules






      The work schedule for logistics personnel should be based on field crew work schedules.
                                            5-8

-------
5.2.5 Preparation Laboratory Personnel






      The preparation laboratory is designed to be the link between the sampling crews and  the




analytical  laboratories.   The primary  functions of the  preparation laboratory are to prepare




homogeneous, anonymous subsamples from processed bulk samples and to transfer batches of those




subsamples to the analytical  laboratories.  For these  tasks to be successfully accomplished,  the




preparation laboratory must accurately track, process, and store all samples.






      The preparation laboratory manager assumes the responsibility for maintaining the integrity




of all samples  upon their arrival  at the laboratory  facility.   The  manager is required  to be




knowledgeable in laboratory methods and procedures, and have demonstrated ability to track large




numbers of samples and supervise laboratory personnel.






      Ultimately, the laboratory manager is responsible for assigning duties according to the specific




project needs. The following divisioaof responsibilities is tentative and  may be adjusted.





      •   Coordinates laboratory operations and time management




      •   Communicates with QA manager and QA representative




      •   Communicates with sampling task leaders and indicator leads




      •   Oversees sample receipt and storage




      •   Oversees all computer data entry and evaluation procedures




      •   Oversees sample preparation and analysis activities




      •   Organizes analytical samples into batches




      •   Tracks all samples during processing




      •   Assists other analysts after other duties are complete




5.2.5.1 Soil Sample Preparation






      For the study, approximately 1800 soil samples will be prepared.  Adequate staffing will be




provided to  ensure  a fast and  efficient turnaround of  samples from the  field to the analytical
                                            5-9

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laboratories. All personnel must be thoroughly trained in the protocols and safety procedures by the




laboratory manager before the processing of the samples begin.






      A three-person preparation laboratory staff is needed to complete the following activities.





      •  Sample receipt/tracking




      •  Sample storage




      •  Sample drying




      •  Organic biomass determination




      •  Bulk density determination




      •  Sample disaggregation/sieving




      •  Sample homogenization and subsampling




     . •  Sample batching




      •  Sample archiving




      •  Data entry, verification, reporting




5.2.5.2 Foliar Sample Preparation






      A two-person preparation laboratory staff is needed to complete the following activities.





      •  Sample receipt/tracking




      •  Sample drying




      •  Sample maceration




      •  Sample homogenization and subsampling




      •  Sample batching




      •  Sample archiving




      •  Data verification/reporting
                                           5-10

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 5.2.5.3 Work Schedules



       The work schedules of the preparation laboratory staff will  conform to the field sampling


 schedule in order for the staff to be  available to receive all sample shipments.   Therefore, the


 preparation facility may be operational 6 days a week during the field season.



 5.3 COMMUNICATIONS



       Communications are critical for the project to proceed efficiently. There should be a method


 for project management to disseminate directions and  information (such as approved protocol


 changes) to  all  project  participants. Conversely, management  needs to obtain current progress


 information to facilitate decision making. The communications network is described in Figure 5.3.
                                         PROJECT MANAGEMENT
                             Directions
                  News
                  releases
                   Emergency
                   calls
                       Updates
                                      REGIONAL PROJECT LEADERS
1) Supply requests
2) Sample/data
    tracking info.
3) Daily activity,
    and plans
4) Administrative
    requests
5) Emergency needs
                                                                      Access
                                                                      requests
                                         FIELD CREW LEADERS
1) Supply status
2) Problems found
 with samples/data
3) Activities of
 other base sites
4) Administrative
   requests

    Visit
    notification
                                                 I Emergency calls
                                       POLICE, AMBULANCE, FIRE
Figure 5.3. Example of a Communications Network.
                                              5-11

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5.3.1  Line of Communication

      The basic line of communication is as follows.

      •   Project Manager
      •   Regional Project Leader
      •   Field Crew Leader
      •   Field Crew Personnel
5.3.1.1 Project Managers

      Project managers are  responsible for the dissemination of information vital to the project
(i.e., protocol changes, sampling schedule changes, etc.) and will also require progress reports on all
aspects of the project.

5.3.1.2 Regional Project Leads

      Regional project leads will be individuals who will be available for phone communication or
emergency communication during the hours of sampling and for electronic communication at other
times. These people are responsible for relaying information to the project managers, other technical
support leads (Figure 5.4) and from field crew leaders, as well  as disseminating information back to
these groups. The regional leads may also need to contact land owners or emergency services.

5.3.1.3 Field Crew Leaders

      The  field  crew leaders will be  responsible for informing regional  project leaders about
sampling progress as well as communicating any problems (e.g., equipment damage or supplies
needed)  or emergencies occurring  in  the  field.    They are  also  responsible  for the direct
communication of emergencies to the appropriate authorities, unless personally injured, in which
case all field crew members should be properly trained. The field crew leader is also responsible for
                                            5-12

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disseminating information  to field  crew  individuals  (e.g.,  status  of sample shipments,  data



discrepancies, supply disposition etc.).
                                       PROJECT MANAGERS
                                     REGIONAL PROJECT LEAD
                         CONTRACTORS



FIELD CREt



) LEADERS

G


                                                        GIS
Figure 5.4.  Flow of Information to and from Regional Project Leads.






5.3.1.4 Field Crew Personnel






Field crew personnel are responsible for their sampling assignments and all aspects pertaining to this.



In order for an efficient relay of information on progress, problems or emergencies occurring in the



field, they are requested to report this information to the field crew leader.






5.3.2 Mode of Communication






For this study, communication  will take place electronically through laptop computers or phone



system.  Field crew leaders will be required to log in to the portable laptop computers each day. An
                                            5-13

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"update" screen will appear, which the field crew leader is requested to fill in.  It will include the
following information.

      •  Field crew ID
      •  Field crew location (hotel name, address, phone number)
      •  Additional personnel with field crew (auditor, EPA personnel, etc.)  •
      •  Expected location of next day
      •  Hexagon sampled that day
      •  Hexagon expected to be sampled the following day
      •  Comments/problems
      The field crew leader is expected to fill in the update and send it out electronically, whether or
not data is being transmitted. This update will be electronically sent to a dedicated PC at Lockheed
Engineering and Sciences Company, Las Vegas, NV (LE5C) or the EPA VAX, and will then be used  to
update DG and E-MAIL accounts of appropriate individuals in the program. This year the information
manager will attempt to acquire an 800 dial-up number for electronic transmission.

      Some hotels  "hardwire"  phone lines, prohibiting the connection of the laptop to the phone
system.  In this instance, an 800 number will be available for updates.  Either an individual will record
the information or a recorder will store this information. An LESC individual will enter this update
information and electronically send it to the appropriate individuals.

5.3.2.1 Conference Calls

      As illustrated  in Figure 5.4, the regional project leaders are the important links with the field
crew leader, project managers, other technical leads and various groups. As problems occur in the
field or  as protocol  change, it is important that decisions are made that are consistent for all field
crews and regions. Therefore, a weekly conference call should be established where technical leads,
regional leaders and project managers are on  hand.   Discussions should  include progress on all
operational  phases,  problems  occurring, and protocol  changes.   Issues  can  be  resolved   and
disseminated consistently to all field crew  leaders.  Further, issues do arise where scientific and
administrative decisions must be made in  a more timely manner.  Each scientific  and  administrative
                                            5-14

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functional position should have a primary and a backup person identified and authorized to make
decisions relevant to that function within 24 hours of notification.

5.3.3  Geographical Information Systems (CIS)

      The CIS group can assist the project by locating facilities and services that may be necessary for
field crews. A list of facilities nearest each hexagon such as the following will be provided by the GIS
group.
      •   Hardware stores
      •   Express mail
      •   Automotive repair shops
      •   Hospitals
      •   Fire stations
5.4 SAMPLING SCHEDULE

      Based oh statistical design or  other program  requirements, an efficient schedule for field
activities will be developed. Geographical locations and other factors such as climate and site access
constraints will be considered.

      The FS logistics leads will be responsible for the development of the sampling schedule with
input from EPA logistics lead and indicator leads.

5.5 RECONNAISSANCE PLAN

      The FS regional logistics leads will be responsible for all reconnaissance activities for the study.

5.5.2 Sampling Site Reconnaissance

      Sampling sites in the NE were located in 1990; therefore, reconnaissance will not be required.
In theSE, hexagon centers will be field-checked prior to the field season. Landowner information will
be obtained and contact will be made to seek permission for field crews to enter the tract.  If
permission is denied, the site will not be sampled that year.
                                            5-15

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      Local public agencies (e.g., state forester, SCS) will be contacted for tract information to speed
reconnaissance. Also, contact will be made to gain clearance to sensitive areas, such as, military bases
and wilderness areas.

5.6 PROCUREMENT AND INVENTORY CONTROL

      The EPA will identify what specific equipment and support will be needed to satisfy each of the
categories of Table 5.4.  The  F5 will determine where back-up equipment will be stored, how crews
will be resupplied and provide contingencies for onsite emergency purchases. Shipping regulations,
especially for chemical and biolog'ical materials should be considered.
Table 5-4. List of Supply Needs
 1.  SCIENTIFIC INSTRUMENTATION
    a.  Measurement devices
    b.  Recording devices/data forms/logbooks
    c.  Power sources
    d.  Calibration gear
    e.  Maintenance/repair gear

 2.  SAMPLING EQUIPMENT
    a.  Containers
    b.  Labels and markers
    c.  Data forms/logbooks
    d.  Collection devices
    e.  Preservatives
    f.  Shipping containers and accessories
 3.  SAFETY EQUIPMENT
    a.  Clothing
    b.  Communication
    c.  Flotation
    d.  First aid
4.  TRANSPORTATION
   a.  Vehicles
   b.  Canoes
   c.  Maintenance gear

5.  COMMUNICATION
   a.  Radio
   b.  Telephone
   c.  Computer
   d.  Facsimile

   6.  ADMINISTRATION
   a.  Photocopier
   b.  Forms (e.g., time cards)
      The logistics lead will send out an inventory form to each indicator lead to identify the types
and amount of equipment and  consumables needed  to collect  data  for each  specific indicator.
Information must be provided on or before April 15, 1991, in order to acquire all items. From this list
                                           5-16

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the logistics team will purchase the supplies not in EPA inventory or that are not available through
the FS.

      Supplies will be divided by each indicator and  an appropriate quantity of supplies will be
distributed to each crew at the training sessions. An inventory list will be created for each crew that
will be checked off by the crew person responsible for the sampling of a  specific indicator.  Capital
items (e.g., portable data recorder, cameras, etc.) will be tagged.  These items will be associated with
specific field personnel who will be responsible for their return.

5.7 LABORATORY OPERATIONS

      For the study,  EPA will be responsible  for the  procurement of  sample  preparation and
analytical services.

5.7.1 Soil and Foliar Sample Preparation

      Soil and foliar samples must be prepared prior to chemical analysis.  The fact that "blind" QA
samples need  to  be  inserted into batches as part of the sample  batching process precludes  the
laboratory  responsible for chemical analysis from preparing the samples. When  thinking about  a
national  program the important concept of data comparability exists, both within and between
regions.  Data comparability can be facilitated by the use of one EMAP preparation laboratory facility
for all regions.

      The consolidation of sample preparation activities allows the following.

      •   Rapid and consistent soil drying and preparation
      •   Establishment of, and consistent adherence to, defined sample preparation protocols
      •  The ability to track and control progress at the laboratory on a real-time basis
      •   Elimination of confounded multilaboratory measurement  uncertainties at the preparation
         phase
      •  Advanced controls against sample contamination
      •  Minimization of staffing requirements
                                            5-17

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      •   Minimal time and expense for conducting technical systems audits




5.7.2 Soil and Foliar Analysis






      The following criteria should be applied when procuring analytical services.





      • The laboratory's ability to analyze using the stated methods



      • The laboratory's ability to meet the MQOs



      • The laboratory's ability to provide data in the specified time requirement



      • The laboratory's ability to provide data at a competitive cost



      Soil and foliar analysis will be accomplished in fiscal year 1992.




      For  analytical  analysis,  EPA will procure laboratory  services  through  the  government



contracting mechanism, which  will  include developing an Invitation For  Bid (IFB), advertising in




Commerce Business  Daily,  analyzing  preaward  samples,  and  awarding  contracts, to  compliant



laboratories, the IFB contains the Statement of Work (SOW), which includes the methods, as well as




the laboratory qualification requirements, and bidders' responsibilities.






      Procurement  of analytical services  may  also  be  accomplished through an  Interagency



Agreement (IAG) with the F5 laboratories. If FS laboratories meet the criteria listed above, quantities



of samples can be sent to them for analysis.






5.8  TRAINING PROGRAM






      Training for NE field sampling has been proposed for the week of June 17, 1991, in Vermont;



training for the SE has been  proposed for the week of June 10, 1991 in Asheville, North Carolina. The



indicator leads are  responsible for  training requirements specific  to their indicator.  The forest



training sites contain both coniferous and deciduous cover types.






      Training for field crews includes practice performing each of the SOPs.  Training time will be



reduced because specific data collection activities will  be assigned to each crew member and each



crew member trained in that activity.  For example, a person who is assigned to sample soils, which is
                                            5-18

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a full day activity, will not need be trained in growth measurements, therefore, training sessions can




be accomplished simultaneously.






5.9 SAFETY PLAN






      In any field operation, emphasis must be placed on safety.  Field personnel must be aware of




the potential  safety hazards to which they may be subjected, follow all project safety protocol and




equipment guidelines, and be prepared for emergency situations. The plan is intended to address the




potential safety hazards of field sampling and identify required safety protocol. The safety plan has




been developed from EPA and FS safety information. All participants in the study (i.e., SC5, private




contractors, FWS) as well as the EPA and FS are required to abide by specific agency safety regulations




where applicable.






      The safety plan will be included in the Methods Manual (Conkling et al., In Preparation).  All




personnel involved in the study must read and fully understand all safety procedures contained in this




plan. The following are some potential hazards that will be discussed in the safety plan.





      •  Travel




      •  Weather extremes




      •  Terrain




      •  Insect pests, poisonous organisms




      •  Sampling and sampling equipment




      •  Chemical hazards




      •  Tree hazards




      Personnel protection requirements and required safety equipment will also be discussed.






5.10 DATA MANAGEMENT ACTIVITIES






      The information management national and regional technical committee will  determine how




standardized  data  recording  forms or programs  will  be developed.  Within the methods manuals
                                           5-19

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(field and laboratories) there will be a section discussing data entry activities occurring in the field,




and the laboratories and data transfer between various phases of the program.  The section will also




discuss data security procedures (such as archiving forms, back-up of data files) which should be used




and explain how data recording and data management activities will be quality-assured.






5.11  QUALITY ASSURANCE INSPECTION






      The national and regional QA technical committee will provide a schedule of site  audits, which




will be performed to ensure that field personnel are following field sampling protocols. Information




will be provided in the QAPjP (Byers, 1991) and will describe who will conduct the audits and explain




how and when corrective actions will be implemented.






5.12 PROJECT FOLLOW UP/RECOMMENDATIONS






5.12.1 Debriefing/Reporting






      After completion of the  study all operational phases of logistics should be summarized in an




operations report.  Logistics personnel should  hold a meeting to discuss all activities in  order to




determine the correct procedures for next year's implementation.






5.12.2 Inventory






      All equipment and consumables will be inventoried  by  EPA.  Any EPA equipment will be




checked cleaned, and properly stored at the EMSL- Las Vegas (LV) facility.






5.12.3 Planning






      During the  logistics debriefing,  time should be allotted to planning  activities  for the next



survey.
                                           5-20

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                              6.  INFORMATION MANAGEMENT





                                          C. Liffa








6.1  INTRODUCTION TO INFORMATION MANAGEMENT






      Information  Management (IM) supports  and  facilitates  many  aspects  of  environmental




monitoring.  IM personnel work with the technical directors, project managers, logistics staff, quality




assurance/quality control (QA/QC) personnel and  scientists throughout the FHM project.  This starts




with planning and coordination  to ensure an  IM  system that is responsive to overall project needs.




During implementation and the operational phases of data collection and transfer, software systems




will be in place to support the timely acquisition of data into the IM system. After data collection, IM




supports the scientists working on integration and analysis of data and presentation and reporting of




results.  Information Management will also support the disstmination of data and information to




users outside of the FHM program.






      A key element in the FHM  IM  system is the Forest Information Center (FIC).  The FIC, located at




EMSL-LV, is the  nexus for software development, data collection (both FHM-generated and historic




data), data cataloging, data  processing, and data  dissemination.  The FIC staff will work with




appropriate personnel in the F5  and the  EPA to  ensure that the  automated data processing (ADP)




requirements of FHM are met.






6.2  GOALS AND OBJECTIVES






      The design and development of the IM program is guided by the following goals.





      •   Ensure that the data in the system are of the highest quality possible
  ' Environmental Research Center, University of Nevada-Las Vegas, NV
                                            6-1

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      •   Ensure that FHM scientists have access to the data as quickly as possible
      •   Make the data available to users both within the project and outside the FHM group

      To achieve the above goals, an IM program will be developed to meet the following objectives.

      •   Design an IM program to be responsive to user requirements from within and outside the
          FHM program.
      •   Commit to achieving complete data collection and transfer electronically.
      •   Ensure access to FHM-generated, auxiliary, and historical data.
      •   Provide an IM program  that effectively collects, processes, documents, stores, catalogs, and
          distributes the FHM data within accepted time frames.
      •   Develop a flexible IM program that can adapt to the program's future needs.
      •   Develop an integrated IM program that provides access to CIS systems, other EMAP and FS
          monitoring components, and other programs.
      •   Develop a system that is responsive to the needs of the national FHM program,  but is
          flexible enough to accommodate regional differences.
      •   Provide training and support to the field crews and users of the FHM IM system.

6.3 DESIGN OF THE FHM IM SYSTEM

      The IM  system  for the FHM program  will have two major  components: (1) a field and
laboratory data collection system, and  (2)  a data management system.  The field and laboratory
system handles data coming into the FIC. The data management system handles data in the FIC and
distributes data to the users.

6.3.1 Field and Laboratory Systems

      The field and laboratory systems provide input to the FHM FIC. These systems have close ties to
the cross-cutting activities of QA/QC and logistics. The primary objective of the field and laboratory
systems is to develop a  system to  ensure that measurement quality objectives are satisfied, and that
                                            6-2

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data are sent to the FIC in a timely manner. This mandates electronic data collection in the field and



the laboratory, and electronic data transfer to and from the FIC






      Verification checks are placed as close to the point of data entry as possible in  both the field



and the laboratory.  Close cooperation with the QA staff will be essential in the development of the



computerized verification checks.






      Electronic sample, shipment, and crew tracking will be used to give project managers daily



updates of field  and laboratory activities.  These tracking systems will be developed in conjunction



with the logistics staff.






6.3.1.1 Field Crew Hardware and Software






      Field crew equipment will include portable  data recorders (PDRs), laptop computers,  and



portable printers. If funding is available, selected crew will additionally have GPS hardware and bar



code readers.  Except for the laptop and the printer, which remain in the motel room, all of this



equipment will be used in the field.






6.3.1.2 Field Logistics Data Base






      Information describing sample site locations and logistics information will be entered into a



CIS data base. The  GIS system will produce maps showing the locations of sample sites and support



services.  With these data, the crew will easily be able to locate sample sites, express mail, motels,



airports, hospitals, repair centers, and such.  Sampling site information will include location of the



site, location of the starting point, field measurements to be taken, and samples to be collected.
                                             6-3

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6.3.1.3 POR Programs






      The EMAP-Forests indicators are dependent on field measurements such as forest mensuration




data, pedon descriptions, and visual damage data. To ensure that field data are of the highest quality




possible, EMAP-Forests will be committed to electronic data collection.






      To facilitate electronic data collection, each field crew will have one or more PDRs. The PDR is a




rugged field computer. The PDR currently used by EMAP-Forests is MS-DOS compatible, which allows




for flexibility in programming.  Custom software, written  in C and BASIC, was developed for the PDR




for use in  the 1990 field season.  The current software will be refined and new programs will be




developed for the PDR to meet the needs of the 1991 field season.  The PDR programs will include




data collection programs, sample tracking information, and communications.  A user-friendly menu




will allow the crew to choose  the  appropriate program. The next sections give details about the




programs envisioned for use on the PDR.






6.3.1.3.1 Field Data Collection Programs






      Data entry will be performed directly on the PDR in the field.  Paper forms will only be used for




back up, in case the PDR fails in  the field. A spare set of PDRs will be available that can be shipped via




express mail to a crew within 24 hours.






      The PDR will have  various  data collection programs.   Menu  choices, based on the  data




requirements  :0f the current indicators, will include soil pedon descriptions, forest mensuration




(including  visual damage data), vertical vegetation  profile,  and ceptometer data  transfer. If, for




example, the  user chooses the forest mensuration data collection program, an electronic tally sheet




will be displayed on the PDR screen.






      Using electronic  data entry allows for QA checks at the point of data entry.  These include




range checks, validity checks, and logic checks. These QA checks will be designed in close cooperation




with  the QA staff and the indicator leads. Mensuration data from 1990 will be loaded on the PDR to
                                            6-4

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help the field crews locate specific trees. The distance and direction to a tree will ensure that the




same tree is sampled in all surveys, a requirement for some indicators.






6.3.1.3.2 Sample Tracking on POR






      Many types of samples will be collected in the field.  Currently, these include soil, root, foliar,




and increment cores. The field crews must be sure that all necessary samples are collected, and that




samples are correctly .identified and tracked.  If sufficient funding is available, a  bar-coding system




will link data on the PDR to samples collected  in the field.  This will permit a relational join between




the sample ID and data in the PDR. The system will check that all samples have been collected before




the crew leaves the field. Sample tracking is described in more detail in later this section.






6.3.1.4 Field Communications System






      The communications systems will allow for two-way communications between field crews and




the FHM FIC.  Data and tracking  information will be uploaded from the crews to the FIC. Messages,




data, and program updates will be sent from the FIC to the crews.






6.3.1.5 Computerized Shipment Tracking






      The field crews will collect a plethora of samples, many of which are  perishable and require




proper handling and  quick shipment to the laboratory.  A computerized  sample and  shipment




tracking system is  necessary to ensure that samples get to the proper laboratory in a timely manner.




The field crews will have preprinted sample labels with bar codes.  When a sample is collected, data




about the sample will be entered in the PDR. The sample will be labeled, the bar  code scanned, and




the sample number recorded  on  the PDR. Before leaving the field, a program on  the PDR will check




that all samples have been collected.
                                           6-5

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      When the data from the PDR are uploaded to the laptop, the sample tracking data base on the




laptop will automatically be updated. The crew will use the bar code reader to scan the samples as




they are packing the shipment cases. The system will





      •   ensure that the correct samples are packed together,




      •   ensure that samples are shipped to the correct laboratory,




      •   check that all samples have been shipped, and




     .•   provide information about special handling required.




      After all samples are ready for shipment, the crew will  enter data about the shipment on the




laptop.  This includes shipment number, carrier  name, air bill number, destination laboratory, and




estimated time of arrival at laboratory. These data  are entered into the tracking data base which is




sent to the FIC, and then to the receiving facility.






6.3.2 Laboratory  Systems






      The FHM program will  employ a variety of laboratories for processing different sample types.




Computerized laboratory sample tracking, verification, and communications systems will be used by




the laboratories  employed  by the FHM program.  The  FHM  program will have two types of




laboratories: preparatory  and analytical.  This section describes the components common to both




laboratory types.






      Each laboratory will have an IBM-compatible computer, with a modem and bar code reader.




The FHM program laboratory system software will be installed on the computer. The tracking portion




of the system will interface with the tracking system  described above to create a complete sample




trail from field to laboratory. The verification portion of the program ensures that results from the




laboratory meet the quality standards of the FHM program.






      The communications are  similar to the field  system. The laboratory will send the following




information to the FIC via modem.
                                           6-6

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      •  Results, including QA/QC data, since last upload



      •  Samples received at laboratory



      •  Samples shipped from laboratory (for preparatory laboratories only)




      •  Messages from laboratory to central system




      •  Tracking data




      The following information will be sent from the FIC to the laboratory.





      •  The tracking data base



      •  Software updates, when required



      •  Messages from the FIC to the laboratory




      Each  laboratory will have a bar code reader.  As shipments arrive at the laboratory, the bar



code label on each sample will be scanned. Those data will be compared against the tracking data



base that was downloaded from the FIC  •






6.3.2.1 Preparatory Laboratory Systems






      Preparatory laboratories receive field samples, process the samples, then ship the samples to



analytical laboratories.   A data base  that relates batch numbers to sample  numbers  will  be



maintained based  on the information  entered in  the preparatory laboratory.   Data describing



samples that have been archived for further analysis will also be recorded.






6.3.3 Data Management System






      The core of the distributed FHM data  management system is the FHM FIC.  The FIC will support



the exchange of data with other agencies and organizations. Information  Management personnel



are responsible for maintaining a comprehensive data  inventory, data set index, code libraries, and



data dictionary. They will also maintain and disseminate FHM data and ensure that appropriate data



are incorporated into the FIC.
                                            6-7

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6.3.3.1 Data Types

      The FHM IM system will contain data generated by the FHM program and data from outside
sources. The following types of data will be maintained by the FIC.

      •  Project management and logistics data
      •  Raw data files
      •  Summarized data
      •  QA/QC data
      •  Laboratory data and associated QA/QC data
      •  Spatial data in CIS format
      •  Historic data
      •  Pointers to auxiliary data (e.g., climate data)

6.3.3.2 Data Base Structures

      The field data collected in the field study, with the exception of the continuous PAR data, will
be stored in SAS data sets on the EMSL-LV VAX cluster.  A relational schema is being employed in
designing the data sets to allow the use of the Structured Query Language (SQL) procedure of SAS
version 6.06.

6.3.3.3 Users

      Users of FHM data will include the following four groups.

      •  Group I Users  .
         -   FHM Core Group:  Responsible for the day-to-day field operations and data verification
            and validation. The group will include field crews, logistics staff, QA/QC staff, IM staff,
            indicator leads, and the technical directors of the FHM program.  Both F5 and EPA staff
            are in this group.
                                            6-8

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-  Requirements:   This group will need  to have access  to  a comprehensive  data  set,
   including project management information, sample and shipment tracking,  raw data
   files, QA/QC reports, logistics,'summary reports, and verified and validated data sets.
-  Timing of Access:  This group will  require access to the data on a real-time basis.  The
   data need not be quality-assured prior to access.  All raw data used by this group must
   be used with the understanding that the data have not  been verified or validated.  This
   group needs access to all data described in the other categories.
Group II Users
-  FHM Team: Individuals and groups who will participate in the FHM effort but will not
   be active in the day-to-day operations of the field programs or the data verification and
   validation processes.  These participants will include FHM  staff members involved in
   reporting, the FHM Integration and Analysis Team, GIS  support personnel, FHM design
   and statistical staff, and program reviewers.
-  Requirements:   This  group will  require  access to summary  information  regarding
   logistics,  project management, and QA/QC.   They will also require  access to some
.   validated and verified raw data files but will not require real-time access to the data.
-  Timing of Access: Group II users will require data one month from the time of collection.
Group III Users
-  Inter-Agency Research  Group:  Includes all researchers who will be active in the design,
   implementation, and analysis of the national EMAP program, the other FS-FHM groups,
   and scientists from other  participating agencies. These individuals will include members
   of  other EMAP resource groups, EMAP cross-cutting  groups,  the  FS  evaluation
   monitoring team, and the FS research monitoring team.
-  Requirements:   This group will require final summaries  regarding logistics, project
   management, and QA/QC.  They will require access to some validated and verified  raw
   data files. Document summaries with interpretation and graphic outputs will be most
   useful.
-  Timing of Access:  Group III users will  require data approximately  six months from the
   time of data collection.
                                   6-9

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      •   Group IV Users
          -  Other Users - Includes all potential users outside of those listed above.  This group will
            include state and federal agencies, universities, research organizations, citizen's groups,
            administrators, and legislators.
          -  Requirements:  This group will require access to validated and verified data including
            QA/QC  data  that is integrated to  the  plot  level.   They  will  need  summarized
            characterization data for each plot sampled and access to an  index of available data.
            They will also  require access to some validated  and verified  raw  data files. Document
            summaries with interpretation and graphic outputs will be most  useful.
          -  Tim ing of Access: Group IV users will require data one year from data collection.

6.3.3.4 Data Base Access

      Users on the EPA computer network will be able to access the FHM  IM system directly through
the network.  Users who are off the network will have the option to access the  system through a dial-
up line into the system. In 1991, as in  1990, there will be a heavy reliance  on mailing floppy disks for
file transfer.

      A data catalog and a  data dictionary will detail the  data available through the data  base
system.

6.3.3.5 Interagency Computer Links

      For the FHM program to function efficiently, there must be a link  between the  computer
networks of all participating agencies. These agencies include the EPA, the F5, NPS, BLM, and possibly
others.  The  link should start with an  EPA/FS connection, then progress to  other agencies.  The
interagency link will provide services such as E-Mail capability, file transfer, and data base access to all
participants across the  FHM program.  Additionally, links to other networks such as Bitnet, Internet,
and  LTERnet should  be explored.    Those  additional  links will allow  easy access  to  university
cooperators.
                                            6-10

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6.3.3.6 Data Base Security






      The tour user groups users will have different access privileges to the data bases. Until the data



have been verified and validated, very strict security measures will be employed.  Only members of



Group I will have access to raw data from the field and the laboratories and  project management



data.  Only the IM  staff will be allowed to change the data bases. If discrepancies are found during



the QA checks, those data will be communicated to the IM staff.  The IM staff will update the data



bases and record the change, the person requesting the change, and the reason for the change in a



data base. This is to ensure that there is only one official version of the data base that is maintained



by the IM staff.






      After  the data bases have passed QA/QC, the security will be changed so that members of



Group II (the FHM analysts) will have access to the data.  Members of Group III can have access to the



data at this  point with permission  of the technical director.  After the yearly statistical summaries



have been published, the data will be made available to other users. At this point the FHM data will



be made available to the EMAP-wide EMAP Information Center (EIC).






6.3.3.7 Data Confidentiality






      Certain  types of data,  both  FHM-collected and from external sources,  may have  to  remain



confidential.  Locational data are the most likely candidates for confidentiality. These data include



FHM plot location, location  of plots in other data bases used  by the FHM  program  (e.g. FIA plot



locations), and locations of rare and  endangered species.






      The CIS representations of point data will be "fuzzed" to hide the exact locations of plots, or



the data will be represented on a regional basis to hide the exact plot locations. The locational data



in the public data base will be reported at the Tier 1 hexagon center  level. Analysts outside of Group



III who need exact  locational data will need written permission from the senior administrators of the



FHM program and will be required to sign a nondisclosure document.
                                            6-11

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6.3.3.8 Data Base Management System






      The FHM data base management system will include a data set index (DSI) also known as a data




catalog, a data dictionary, code look-up tables, and a user-friendly interface.






      The DSI index will provide users with important information about the contents of each data




set.  It will also describe how to access a particular data set.  Forest health monitoring-generated,




historic, and auxiliary data will be catalogued in the OSI.






      The on-line data dictionary will provide users with information about parameters stored in the




data bases.






6.3.3.9 Yearly Statistical Summaries






      Standardized, yearly, data statistical summaries will be one  product of the FHM program.




Standard software will be developed to produce automatically the tables, graphs, and maps that go




into the yearly statistical summaries.






6.3.3.10 GIS Interface






      A major requirement of the FHM FIC will be to create maps and perform geographically based




analyses.  Therefore, the data generated for FHM will be referenced to a spatial entity such as a




latitude and  longitude.  Spatial analyses will be accomplished using ARC/INFO, a GIS that  is used



throughout the EPA and the FS.






6.3.3.11 EMAP Information Center






      The EMAP Information Center (EIC) will be  the entry point to EMAP data bases. The EIC will




allow users to access data from the seven EMAP resource groups and cross-cutting activities.
                                            6-12

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      For the overall EMAP goals to be met,  scientists must  have access to all  data  collected  in
connection with EMAP data, including FHM data. The design of the FHM Information Management
System must be compatible with the EIC design to allow other EIC users access to the data.

6.3.3.12 Standards
                                                                                       IM
      Standards are necessary for FHM to be a truly national program. The FHM program and its
system must be flexible enough to accommodate regional differences, but at  the same time be
comparable at some level throughout the country.  Standards that are used throughout the program
are necessary to meet that objective.   An interagency  workgroup should be  formed to resolve
standards issues such as the following.

      •  Codes - Standards for codes that are used across the country, such as species, must be
         adopted. The FIA has a standard set of some codes. It is recommended that those codes be
         adopted.
      •  Computational Algorithms - A standard set  of FHM  computational  algorithms  that
         correspond to ecological, not political, boundaries must be established. Poststratification
         along political boundaries will always be possible, if required.
      •  Portable Data Recorders - Must be standardized to the extent that all  PDRs used by FHM
         will run the same programs without modifications.
      •  PDR Software - The same software should be  used on all the  PDRs used  by FHM.  The
         software should be flexible to allow for regional  differences.
      •  Measurement Units - The FHM should use the same measurement units,  preferably  le
         Systeme International d'Unites(SI), in all regions of the country.
      •  Word  processing software - A standard word processing program should be adopted for
         producing  reports and documents.  If institutional constraints prohibit this, a  standard
         interchange format should be adopted.
                                           6-13

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6.3.3.13 Data Sharing and Access






      All agencies concerned must come to an agreement on data access.  One proposal for data



access is given in Section 6.3.3.3 of this document.






      If this model of data sharing is not acceptable to all participants, an interagency committee



should be formed to draft an  alternative policy.  A clearly stated policy on data access should  be



adopted for the entire FHM program.
                                            6-14

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                                        7. REPORTING





                                         R.C. Kuceraa








      The product of the 1991 field study will  be the Synthesis Report referenced in Table 1.1.  The




data, analysis, results, and conclusions for each indicator will be incorporated. The indicator leads are




responsible for reporting their analyses, results, and conclusions within the report. Summary sections




will be provided for activities such as QA, logistics, and information management which apply across




all measurements.  The document will  be  an EMAP-Forests multilaboratory,  multiagency  report




produced in cooperation  with the  USDA-FS  and FWS.   The Las Vegas EPA  Laboratory will be




responsible  for coordination of the  Section authors, editing, and producing  a  peer-reviewed  and




approved report. The Synthesis Report will be supported by a Quality Assurance Report.






      An important dimension of  the  analysis of results will  be the  evaluation  of correlations




between indicators.  These correlations will focus primarily on the indicators of nutrient status and




the indicators of landscape processes.






      Further reports will be suggested,  if necessary, by the supporting EMAP-Forests and FHM team




members to document the activities and  results of their contribution to the field study. For example,




.the IM, Logistics, Indicator Development, or other groups may propose and make separate reports of




their activities and results.
   1 ManTech Environmental Technology, inc., AREAL-RTP, NC
                                             7-1

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NUTRIENT CYCLING DEMONSTRATION MEASUREMENTS

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                                    8. SOIL PRODUCTIVITY





                                     R.D. Van Remortel3








      This section describes the soil characterization, sampling,  preparation, and  analysis that  is




being undertaken as part of the FY91 Indicator Evaluation Field Study for EMAP-Forests in the eastern




United States.






8.1 INTRODUCTION






      Soil productivity has generally been defined as the capacity of a given volume of soil to elicit a




vegetative response under a specified system of management (SEA-AR, 1981).  Initial measurements




of key soil  productivity parameters are used to establish baseline status in terms of levels and ratios




among certain physical,  chemical, and biological  soil constituents.  Periodic remeasurement  of these




parameters is used to assess trends that might show improvement or degradation in forest condition




over time.  Short-term changes in the balance of critical soil  fertility components may provide an early




indication  of changes in ecosystem status  or function (Johnson et al., 1988a).   The component




parameters of interest can  vary widely across  different forested regions of the U.S., but generally




include specific  soil nutrient elements, exchange capacities, toxic substances, erodibility factors,




parent materials, and ancillary data such as estimated soil moisture supply. The soil productivity data




can be used to perform statistical analyses with the response indicators, such as visual symptoms, and




other exposure indicators (e.g., foliar chemistry).






      Soil productivity data can contribute diagnostic information by indicating possible mechanisms




to explain responses in forest condition. These data also provide diagnostic information not available




through foliar chemical analysis because plants often are able to compensate for potentially limiting
  a Lockheed Engineering & Sciences Company, Las Vegas, NV
                                             8-1

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concentrations of soil nutrients and moisture (Barber, 1984).  The most reliable interpretations of
ecosystem nutrient status would likely include concurrent measurements of both soil and vegetative
productivity.

8.1.1 Overviewof Soil Monitoring Objectives

      The overall mission for the FHM program soil monitoring activities is to "monitor and evaluate
the long-term status  and trend of the nation's forest soil ecological  resources to identify and
understand environmental changes through an integrated, interagency process."

      The status, changes, and trends of the nation's forested soils and their relation to ecological
endpoints should be evaluated and  reported on a regional basis and at a known level  of confidence.
Both natural and human-induced changes should be monitored. The resulting data can have great
utility for many ecological resource groups and other interagency programs.  General objectives of
soil characterization include the following.

      •   Perform  retrospective analyses  of existing  soil  information  as  part  of   indicator
          development and implementation.
      •   Incorporate analytical results  into subsequent environmental evaluations and preliminary
          conceptual models.
      •   Develop  and implement  strategies and designs for integrated regional scale monitoring
          and evaluations of forest ecological resources.  .
      •   Provide several measurements of data uncertainty components.
      •   Establish linkages between soil measurements and other indicator measurements within
          and among terrestrial ecological resource groups.
      •   Provide a basis for the  initiation of  special studies to diagnose apparent soil-related
          problems as determined by ecological   assessment  endpoints  during  regional  scale
          monitoring.
      •   Conduct  applied research to enhance  knowledge  of soil processes, monitoring methods,
          and data interpretation techniques.
      •  Assess consequences of current practices and future managerial decisions.
                                            8-2

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      Soil is a fundamental  source of social wealth and  well-being that is essential  to sustaining
forest ecological resources and is closely integrated with water and air resources.  Soil measurements
can be an accurate indicator of change and may provide an early warning of ecological perturbations.
Specific reasons for characterizing soils are to:
      •  provide soil information to decision/policy makers and various management, regulatory,
         and  research groups for use in comprehensive planning to maintain and enhance forest
         ecological resources;
      •  identify changes and  trends in soil resources, and provide early warning of cumulative
         effects and thresholds of irreversibility;
      •  provide data to assess effects of forest management practices;
      •  distinguish adverse from beneficial changes and natural from man-made changes;
      •  provide comparable soil baseline data  among this and other terrestrial-based ecological
         resource groups;
      •  contribute to the understanding of global consequences stemming from human actions;
      •  identify present and  potential  uses of  the soil ecological resource  within terrestrial
         ecosystems;
      •  provide a mechanism for integration among terrestrial resource groups; and
      •  provide an important link within conceptual and quantitative models.
      Soils  should undergo  comprehensive baseline  characterizations  at  a statistically relevant
sampling intensity across the  nation.  Ongoing regional monitoring allows scientists to track changes
in soil resources.  This should be done:
      •  concurrently with  other ecosystem monitoring  and  measurements when  feasible  or
         appropriate;
      •  by intensifying sampling during other ecosystem indicator measurements; and
      •  by long-term monitoring on a regular basis.
      Monitoring of  forest  soil resources should  be implemented regionally  across the  nation,
including areas adjacent to aquatic systems and other ecotones. Specifically:
                                            8-3

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      •   the geographical  extent should  encompass  all forested  portions of the  United  States
          including Alaska, Hawaii, Puerto Rico, trust territories, and the District of Columbia;
      •   implementation should  be  sufficient to  provide a  statistically  valid  sample and to
          characterize uncertainty in the resulting data.
      The specific 1991 FHM  program soils monitoring objectives are the following.

      •   Demonstrate that field soil characterization and sampling, optimized for available funding
          and personnel, can be successfully implemented in two large,  subregional forested areas
          of the eastern United States utilizing a cooperative effort among multiple agencies.
      •   Continue to develop  key components of the soil productivity indicator and evaluate its
          utility in synthesis and integration with other ecological  indicators.
      •   Begin to construct regional  baseline characterizations  of the ranges of concentration for
          critical soil  parameters used in the interpretation of  soil  condition with respect  to the
          overall assessment endpoints.
      •   Develop draft versions of DQOs for the various phases of soil data collection.
8.1.2 Overview of the Soil Measurement System

      The soil field measurement and sampling protocols are based on National  Cooperative Soil
Survey (NCSS) standard methods with some specific amendments. The procedural steps  have been
defined through continuous interactions with soil scientists at EPA laboratories in Las Vegas, NV, and
Corvallis, OR; at the USDA-FS Forestry Sciences Laboratory  in Grand Rapids, MN; and at the USDA SCS
NCSS in Lincoln, NE. The procedures were amended where necessary as a result of experience gained
in the 1990 "20/20 Study" conducted in the eastern U.S.  Soil scientists from the SCS in Massachusetts
and Virginia provided expert guidance in the adjustment of specific field protocols.

      Soil taxonomic data for the field plots can be obtained from existing soil survey information or
by on-site soil excavation and characterization. Where  possible, soils on  unmapped plots should be
classified to the soil series level according to accepted NCSS standards.  Each plot must be thoroughly
characterized  for descriptive soil parameters and landform features while in the  field. Detailed
protocols for the soil characterization and sampling are contained  in a separate field methods manual
(Van Remortel, 1991a). The soil field parameters to be measured are outlined in Table 8-1.
                                            8-4

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 Table 8-1.  Field Soil Characterization 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 andJocal
 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 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
      Soil samples are to be prepared according to the protocols contained in the laboratory

methods manual (Byers and Van Remortel, 1991).  The parameters listed in Table 8-2 are measured in

conjunction with processing steps at the preparation laboratory.

Table 8-2. Soil Preparation Parameters
 Fine and medium gravel: rock fragments (particle diameter 2-mm to  4.75 mm  and 4.75 mm to
 20 mm) measured gravimetrically.

 Forest floor biomass: total mass of organic constituents in a given area of forest floor, measured
 gravimetrically and by loss-on- ignition.

 Core bulk density:  the oven-dry density of the < 2-mm soil  fraction (minus rock fragments) from
 replicate core samples, measured gravimetrically.
                                            8-5

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      Soil samples are to be analyzed according to the protocols contained in the laboratory methods




manual (Byers and Van Remortel, 1991).  The soil physical and Chemical parameters of interest to be



measured in the samples are described in Table 8-3.  It should be noted that a portion of each sample



is archived to  allow the possibility of initiating further analyses that might be identified at a later



date. It has been demonstrated that long-term cold storage of air-dried soil samples does not signifi-



cantly alter their chemical status for a wide variety of parameters (Fenstermaker et al., 1991).






      The analytical parameters have been identified as a result of an intensive review of laboratory



methods in collaboration with over 50 soil researchers and laboratory chemists across  the United



States and Canada. 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).






8.1.3 Overview of Expected Variability






      Variability is generally contingent on the form, mobility, and concentration of the parameters




of interest. Estimates of the coefficient of variation (CV) for many soil analytical parameters may be



derived by accessing existing soil survey data that have satisfied especially stringent QA criteria



(Van Remortel et al.,  1988; Byers et al., 1989; Papp and Van Remortel, 1990; Byers et al. 1990a). For



the analytical laboratory measurements, an average CV of 10% or less is typical for replicate samples.



The expected laboratory bias is ±5% 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% or less is typical.






      Soil nutrient concentrations are likely to vary on a within-season, among-season, and among-



year basis.  Mobile soil nutrients, such as nitrogen, are among the most variable (Armson,  1977). The



Logistics staff will attempt to minimize the potential effect of temporal  variability by designing the



plot sampling sequence in such a way as to ensure that each plot is subsequently remeasured at about



the same time within the index period.
                                            8-6

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Table 8-3.  Soil Analytical Parameters
Air-dry moisture: determined gravimetrically by oven-drying at 105 °C (organic soils at 65 °C); used
to report all final data values on an oven-dry soil basis.

Total sand: particle diameter between 0.05 mm and 2.0 mm, determined by wet sieving.

Total silt: particle diameter between 0.002 mm and 0.05 mm, determined by pipetting.

Total clay: particle diameter less than 0.002 mm, determined by pipetting.

Electrical conductivity: determined in  deionized water using 1:1 mineral soil to solution  ratio
(1:4 organic), measured with an electrical conductivity meter.

pH: determined in deionized water and  in a 0.01M calcium chloride solution using a 1:1 mineral soil
to solution ratio (1:4 organic), measured with a pH meter and combination electrode.

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 spectrometry.

Cation exchange capacity: determined in a buffered (pH 7.0)  1M  ammonium acetate  solution
usinga 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 titration; 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 using a back titration procedure.

Effective exchangeable acidity and exchangeable aluminum: determined in an unbuffered 1M
potassium chloride solution using a  1:20  soil to solution ratio using a direct titration procedure;

Mineralizable 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.

Extractable phosphorus: determined  in a Bray and Kurtz No. 1 extractant (acid  soils only) using a
1:13 mineral soil to solution ratio (1:52 organic) using acolorimetric procedure and autoanalyzer.

Extractable sulfate:  determined in a  deionized water extractant and  in a sodium  phosphate
extractant using a 1:20 soil to solution ratio by ion chromatography,

Total carbon and nitrogen: determined by rapid  oxidation followed  by infrared detection or
thermal conductivity detection using an automated CHN analyzer.

Total sulfur:  determined  by  automated sample combustion followed  by infrared detection of
evolved sulfur dioxide.

Total phosphorus, calcium, magnesium, potassium, sodium,  iron, manganese, copper, zinc, boron,
aluminum, lead, chromium, nickel, aluminum, lead, cadmium, nickel, chromium, vanadium, arsenic,
and mercury: determined  by initial microwave digestion followed by dilution and multielemental
readout by direct current argon plasma atomic emission spectrometry. (organic soil horizons only).
                                           8-7

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      It is recognized that a significant amount of soil spatial variability can be present within a given



ptot.  Uncertainty in soil parameter values at a plot can be greatly reduced, however, by the use of a



"composite" sample design that recognizes and  accommodates the within-plot differences in soil



characteristics. It is anticipated that a design will be adopted whereby the samples that are collected



can effectively control  the within-plot uncertainty to a level that is (1) less than the measurement



system uncertainty, and (2) negligible with respect to the regional soil aggregation variability (Taylor,



1987). The resulting data quality would allow the data users to focus on discerning "real" temporal



changes in soil productivity within a highly variable regional population.






8.2 RATIONALE






      There have been  numerous studies of the relationship between tree growth response measures



and specific chemical, physical, topographic, and climatological parameters.  The soil measurements



include those parameters which have been agreed upon as important  for the determination and



monitoring of soil productivity, and which are also economically and logistically feasible.  The forestry



literature identifies certain physical parameters (e.g., drainage  class) that have been used repeatedly



in growth response  studies.  Many of these same parameters  have been incorporated into  the soil



indicator analyses.






      Information that is known to be important to the development of a productivity index will be



collected during all phases of the project,  beginning with the field measurement and sampling.



Topographic 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



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.  The soil



drainage classification,  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).   Topographic parameters are important in  estimating the
                                            8-8

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hydrologic contributions  of runoff and  lateral water flow  (Hewlett,  1961),  as  well as  such




characteristics as soil texture (sand, silt, and clay), coarse fragments (fine and medium  gravel), and




bulk density.  These parameters are also important for their effects on nutrient availability (Mader,




1976), aeration (Mader, 1976; Steinbrenner, 1963), and root distribution (Hillel, 1980; Blanchar et al.,




1978), all of which directly affect vegetative response.






      Soil  productivity in forests is affected by a  presence or deficiency of essential  nutrients




affecting plant growth (Edmonds et al., 1989).  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. In the Douglas-fir forests of the Pacific Northwest, for example, 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. Whole tree harvesting in commercial forests




can affect changes in macronutrient cycling  (McColl  and Powers,  1984; Johnson et al., 1988b).




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




sites (Entry et al., 1987; Schulze, 1989).  Forest floor disturbances can  interfere with nitrogen cycling




(Peterson et al., 1984), and the effects of burning (Debano and Klopatek, 1988) and disruption of the




soil mycorrhizal fungi on tree roots (Vogt  and Persson, 1990) are other known stresses.  Changes in




carbon sequestration may also occur in some forests as a result of heavy disturbance (Harmon et al.,




1990).






      Soil productivity can also be affected by the presence of toxic substances and contaminants in




the soil.  This presence can  indicate exposure to potentially  detrimental chemical compounds and




elements  possibly  resulting from  land  use  practices  (e.g.,  application of  pesticides,  mineral




extraction), atmospheric  deposition (e.g.  sulfur in   acidic  precipitation), or  naturally occurring




phenomena (e.g., overabundance of magnesium in serpentinitic parent materials).  Exchangeable
                                             8-9

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iron and aluminum, as well as metals such 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 (Mclaughlin, 1985), 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 (VDIKRL, 1987) and




effectively lowering the site productivity. This 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.  Initial discovery of such substances in




the soil could warrant close monitoring of areas exhibiting exposure.






      Parameters such as exchangeable cations, cation exchange capacity, extractable phosphorus,




pH, and exchangeable acidity have all been incorporated into response studies  with species such  as




Jack Pine (Hamilton and Krause, 1985; Pawluk and Arneman, 1961) and  Douglas-fir (Green et al.,




1989). Total carbon, nitrogen, and sulfur can be used to characterize the soil organic matter, which is




an  important part of the forest ecosystem (Mader, 1976; Wilde,  1964).  Total  iron, manganese,




copper, zinc, and boron are essential elements to tree growth and are measured.  Exchangeable




sulfate, phosphorus, chloride, and nitrate are important constituents of the soil  solution  and  can be




measured easily on the same extract using ion chromatography.  These measurements,  along  with




electrical conductivity, can  be used to estimate the ionic strength of  the  soil solution (Griffen and




Jurinak, 1973). Ionic strength is used to calculate the activity of ions in solution, thus allowing study
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of chemical equilibria in soil samples and modeling of long-term chemical weathering of soil minerals




(Lindsay, 1979).






      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 plant nutrition.  However,




factors that influence soil moisture imports and exports must be evaluated because of their effects on




the availability of nutrients and toxic substances. This evaluation is a developmental aspect of the




FHM program soil  monitoring and may require the use of ancillary data (e.g.,  climate data) from




other sources.






      The FHM  program  soil  monitoring  effort presently  includes those parameters which are



generally agreed by forest soil scientists to be important for a baseline characterization of soil



productivity,  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 work has been done on



identifying the soil processes that are important in vegetative response (Bouma, 1989).  The necessary



components, however,  have not yet been linked together in  an index or  model that  is suitable for



application on a regional or national scale of monitoring.  Hence, some facets of the soil  productivity



indicator are  considered to be developmental.   It is believed that key soil productivity parameters



could be combined into an index that identifies, on a plot-by-plot or regional basis, the effects of soil



exposure on vegetative response and other indicators of forest condition. The index could be used to



track changes  in productivity over time (Gersmehl and Brown,  1990).  Detailed  information on the



indexing strategy is provided later in Section 8.10.1.1.





8.3 DESIGN






      There are a number of possible ways in which to design  a soil sampling program for a large



scale effort such as the FHM program (Borgman  and Quimby, 1988).  The soil sampling design



described in  the following subsections has been  developed  in conjunction  with  critiques  and



suggestions from forest soil scientists  across the United States and Canada.  It is believed that this
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approach provides the best possible  data to address the  FHM program objectives  within  the
guidelines and constraints provided by the p.roject coordinator. Ultimately, the regional interpretive
goals for evaluation of status, trends, and associations are the determining factors in the sampling
design.  Other considerations, such as specific within-plot parameter relationships, are better served
by research that could be undertaken at Tier 3 or Tier 4 levels.

8.3.1  Sampling Constraints

      It is recognized that a significant amount of soil spatial variability can be present within and

among different  locations in  a given  region (Conyers and  Davey,  1990;  Mausbach et al.,  1980;

Van Meirvenne et al., 1990).   The variability is often dependent on analyte concentration and  is

contingent on the plot sampling strategy, such as multi-site composite  sampling vs.  single-site

sampling (Carter and Lowe, 1986).

      The  overall objectives  of the  1990 FHM program soil  productivity pilot  study were  to

(1) estimate the within-plot and within-subplot spatial  variability in soil characteristics, and (2) test

the overall  feasibility of implementing the soil productivity sampling design on a regional or national
scale.   Using the  results of the  1990 data  analysis and  reinforced  by  data  from  the 1991

demonstrations, it is likely that an optimal sampling design can be identified that allows control of
within-plot data uncertainty to some level that is acceptable to the FHM  program data users. Specific

constraints to the sampling design include the following.

      •  The final sampling design used in the FHM program monitoring should "capture" enough
         within-plot spatial variability (through composite sampling from multiple soil sample holes
         at each plot) to state with "X" confidence that within-plot soil variability is negligible with
         respect to regional variability within the soil strata used to report the results of the project.
         Alternatively, it  may  be expedient to use a criterion which expresses measurement
         uncertainty with respect to  the amount of change we wish  to detect (Cohen, 1969).
         Although the present design has been based on a sample size of three holes per plot,
         further investigation of available within-plot information from existing regional survey
         data bases might yield a different sample size requirement.
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      •   The sampling design should ideally allow data users to make both regional (primary) and
          plot-by-plot  (secondary) evaluations  of the changes that have  occurred since  the  last
          sampling cycle.  Therefore, interpretable and defensible classification criteria  should  be
          defined. It is possible that plot-level evaluations may not be suitable for the FHM program.
      •   Destructive sampling, that is, soil excavation and sample collection, is undesirable within
          the confines  of the subplots because vegetative measurements are being conducted in the
          same general vicinity of the plot from  which soil samples are collected. For this reason and
          because of the "four-point subplot cluster" plot design, a single soil sample hole at the
          center of each field plot is not a viable  option at this time.
      •   Any such destructive sampling  must be highly selective,  be conducted  outside of  the
          vegetative measurement zones to minimize trampling by the sampling crews, and have a
          negligible long-term impact on the  integrity of the  plots.   Grid-type  or  transect-type
          sampling across the plots are not viable options under this constraint.
      •   Logistical constraints limit the actual available time to accomplish soil characterization and
          sampling to one experienced soil scientist in a 6-hour period on each plot.
      •   Equipment constraints limit the equipment used  in soil characterization and sampling to
          that which can be reasonably hand-carried by the crews to the field plots.

8.3.2 Proposed Sampling Design

      The plot design  for soil  sampling is as shown  in Figure 3-1.  The entire area represented is

approximately one hectare (2.5 acres). Each of the four fixed-radius subplots on which vegetative

measurements will be made occupies an area of about 1/60th hectare (1/24th  acre), resulting in a total

of about  1/15th  hectare (1/6th  acre) actually  measured  in  each  plot for vegetative data.  The
excavation of soil  holes for characterization  of  soil horizons  and collection of  soil  samples is
considered to be destructive sampling with respect  to long-term ecological  monitoring on  forest

plots.  Therefore, soil sampling is restricted to  sites  outside of the established fixed-area subplots
while representing the soil characteristics of the plot as a whole.

      Detailed field measurement and sampling  protocols for soils are contained in the field methods
manual (Van Remortel,  1991a).  The intention is to prepare detailed soil profile descriptions of the soil

horizons occurring  in three holes equidistant  from  the  centers of the fixed-radius subplots (see

Figure 3.1).  The holes  are excavated  to a depth of 1 meter (or to a restrictive layer, whichever is
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shallower) and a diameter of 0.5 meter.  The soil scientist is instructed to collect 0, A, E,  8, and C



master horizon samples, where present, from each of these holes. If the crew leader has identified




more than one forest cover-type group within the plot boundaries, samples will not be composited



and will  be  kept separate across the groups. Each of the mineral horizon samples should contain



approximately 2kg (about 1  L volume) of soil material. The organic horizon sample size could vary



widely based on the thickness of forest floor material on the plot.  A portion of each composite




sample is archived at the  preparation laboratory  to enable additional analyses to be identified at




some point in the future of the project.






8.3.3  Sampling Design Issues






      It is preferable that a statistically relevant  number of plots are sampled within each major



forest cover-type group to  ensure a large enough sample size to establish significance for a particular



data evaluation stratum.  Implicit in this criterion is the assumption that all forest cover-type groups



generally respond in a manner similar  to those being evaluated, and that estimates  of data



uncertainty derived from the demonstration plots should be representative of the actual  regional



data uncertainty (Palmer et al., 1990).  During any such evaluation,  it is preferable to encompass a



population  of plots that display a  wide range of  vegetative response, otherwise the true  regional



population variability could be underestimated.






      Initially it may be important to make general characterizations of indicator status on a plot-by-



plot  basis to use  in developing a regional interpretive framework, although a broad  regional



characterization without regard to plot-specific considerations  may be sufficient.  This decision has



important ramifications for the soil sampling strategy, development of indices, and  estimation of



data uncertainty. The plot-by-plot approach ensures that the regional interpretations can be derived,



but the "broad regional" approach precludes the possibility of making plot-specific or subregional



characterizations that would allow for interpretive research or mitigation programs (Riitters et al.,



1990). Also,  some of the individual state forestry cooperators have expressed a strong desire for plot-
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specific data.  From a soil productivity standpoint, a basic plot-by-plot level of characterization is
desirable but can be considered to  be of secondary importance to the collection of regional-level
information.

      The appropriate number of soil samples to be collected from each plot in order to  detect
regional changes in soil productivity must also be determined.  Retrospective analysis using existing
soils data  bases  (Church et al., 1989;  Van Remortel et al., In Preparation) are providing statistical
estimates of the optimum or average number of samples that must be collected from a particular plot
in order to limit within-plot variability to a minor  or negligible component  of the overall data
uncertainty (Dane et al., 1986; Miah  et al., In Preparation).  To address the issue of sampling intensity,
simulations are conducted using "bootstrapping" and "relative difference" statistics; the techniques
evaluate within-plot variability by selectively varying the number of sites sampled from each plot
(Van Remortel et al.. In Preparation). Also, the effects of destructive sampling, logistical constraints,
composite vs.  single-hole  sampling, and horizon  vs. depth  sampling are examples of  issues that
continue to be deliberated prior to  full implementation of the FHM  program  monitoring.  An
acceptable protocol for refilling the holes from which the samples are collected should be adopted, as
this issue has long-term implications for plot utility and integrity.

      In summary, the primary sampling design issues to be resolved as a result of the 1990 pilot and
1991 demonstration projects include:

      •   identifying the  logistical  and  financial  resources required for soil  characterization and
          sampling;
      •   estimating the  uncertainty  from single-hole  vs. multiple-hole sampling on the plots (to
          identify the optimum number of sites per plot that must be sampled);
      •   determining  whether samples should be composited  and at what  stage (i.e., field  or
          laboratory;
      •   determining whether provision of a destructive-sampling zone in an annulus encircling the
          fixed-radius subplots allows  collection of soils data  that are representative of the plot as a
          whole;
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      •   determining the required sampling depths and types of horizons that should be sampled;
      •   examining the utility of characterization, sampling, preparation,  and analysis methods
          selected;
      •   identifying specific types of ancillary data (e.g., regional climatic data) that may be needed
          to link the component parameters of the soil productivity indicator.
      •   defining the appropriate reporting units for the different soil parameters;
      •   determining the utility of various classification scenarios  in the post-stratification  and
          aggregation of data for interpretive reporting; and
      •   identifying possible regional differences in within-plot variability across forested regions of
          the United States.

8.4 QUALITY ASSURANCE

      Specific QA information related to this indicator has been consolidated into the overall 1991
FHM program Quality Assurance Project Plan (Byers, 1991).

8.5 LOGISTICS

      The primary  soil  logistical  issue  to  be resolved  during the demonstration project  is  the
determination of the resources  (e.g.,  time,  personnel, funding, equipment, etc.)  required  to
adequately characterize the soils within  the plots and to collect, prepare, and analyze selected soil
samples from the plots.

      The logistics staff is planning to implement a plot sampling sequence for each region that
enables each designated plot to-be resampled at about the same interval of the index period over the
course of the project. In the New England states, for instance, the optimum index period for sampling
the test plots is late June through  early September. If the field crews always  begin sampling  in the
north part of the region in  June and work towards the south part of the region during the remaining
index period, some of the temporal variability may be reduced  for subsequent sampling cycles.  For
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interpretive purposes, soil sampling should be performed concurrently with the vegetative measure-



ment/sampling at a given plot.






8.5.1 Field Personnel






      Each field measurement/sampling crew, hereafter termed "field crew," consists of (1) a crew



leader, (2) one or more other crew members (depending on the  types of plots being  measured)



performing vegetative  measurements  and sampling,  and (3)  a soil scientist experienced in  NCS5



procedures performing soil  measurements  and sampling.   The  crew leader supervises  all  field



operations and resolves any issues that arise at each plot.






      The soil  scientist  assigned to each  field crew has the responsibility of  making decisions



concerning soil description and  sampling including  horizon  delineation, horizon thickness,  and



material excluded from the sample's. Profile descriptions, logbooks, and sample labels must be legible



and accurate, and  photographs must have the proper exposure and settings. The field equipment



must be properly used and maintained, and  all sampling equipment must be cleaned following the



collection of each sample.  Caution should be exercised to prevent sample cross-contamination that



could possibly be the result of soil peds dislodged from adjacent horizons or of free water above or



below the horizon being sampled.






      The integrity of all samples collected must be ensured by the field crews until the samples are



shipped to the preparation laboratory.  The appropriate project coordinators are to be notified at the



earliest possible opportunity of any problems or difficulties encountered while sampling or during



the transport of soil  samples.  All unused field equipment and supplies should be returned to the



preparation laboratory at the end of each sampling period.





8.5.2 Training






      All personnel involved in field soil measurement and sampling activities must be trained by an



independent  regional  correlator  (IRC) and a  QA  representative or other designated persons
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knowledgeable of the procedures and protocols described in the field manual (Van Remortel, 1991 a).
A week-long training session is conducted immediately preceding the field season, during which all
field crew members are to be trained in their specific facets of field measurement and sampling.  All
field crew members should also be trained in the basics of first aid.

      The total time requirement for the soils training session  is five days including travel time to and
from the training site. Actual training would begin on Monday at noon and end on Friday at noon.
The allocation of training time by activity could be as follows.

      •   Monday afternoon (classroom  overview): General orientation of crew members, overview
          of the field measurement and  sampling manual, crew interactions, communications, QA
          procedures, etc.
      •   Tuesday morning (plot establishment in the field): All crew members observe protocols for
          locating plot center, setting up subplot  and sampling boundaries, interacting  with  and
          supporting other crew members.
      •   Tuesday afternoon (group soils training  in the field):  Distribution of equipment to soil
          scientists, hands-on sequential  walk-through  of procedures on a practice plot, use of PDRs,
          question-answer session.
      •   Wednesday morning (continue group soils training in the field).
      •   Wednesday afternoon (continue group soils training in the field).
      •   Thursday morning (individual  practice by crews in the  field):  Soil scientists join their
          respective crews to practice plot location and establishment, soil excavation, description,
          sampling, etc.
      •   Thursday afternoon (continue individual practice by crews in the field).
      •   Friday morning (summary session): Additional training as  necessary, summary discussions,
          training questionnaire.

8.5.3 Communications Structure

      The soil scientist on each field crew is responsible for all soil sampling assignments and support
activities as required.  All sampling issues  should be relayed to  the field  crew leader so that
information on sampling progress, difficulties, or emergencies occurring in the field can be relayed to
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the appropriate individuals.   The field crew leader  is responsible  for  informing  the regional



implementation leaders about sampling progress as well as for communicating any difficulties, such



as equipment damage or supplies needed, or emergencies occurring in the field unless  personally



injured. The field crew leader is also responsible for disseminating information to field crew members



(e.g., status of sample shipments, data discrepancies, remaining supplies, etc.).






      The  regional implementation  leaders  (or  their representatives) should  be  available for




telephone communications or emergency response on a 24-hour basis during the field  measurement



and sampling activities. The regional  implementation leader is responsible  for relaying information



from the field crew leaders to the project coordinators as well as disseminating information from the



project  coordinators to  the  field crews.   The  project coordinators are  responsible for  the



dissemination (through the regional implementation leaders) of information vital to the project, such



as changes in protocol or sampling schedules, and also should  solicit and receive progress  reports on



all aspects of the monitoring work.






      The likelihood that field measurement and sampling issues will be raised and that changes to



the protocols will occur requires that resolutions be disseminated in a consistent manner for all field



crews and that the resolutions are compatible for both regions. Therefore, a weekly conference call



should be established  with the  project coordinators and  regional  implementation leaders as



participants.   Discussion  should include  field measurement and sampling  progress, difficulties



encountered, and suggested  amendments to the protocols.






8.5.4 Equipment and Supplies






      Detailed lists of the equipment and consumable  supplies used to perform  soil  measurement



and sampling are provided in the chapter on soils in the field methods manual (Van  Remortel, 199la).
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8.6 INFORMATION MANAGEMENT






      The IM coordinator is presently working with the FHM program soils staff to plan and develop




interactive, relational data bases that are compatible with other data bases being created as part of




the field activities.  For soils,  it is expected that a  VAX-based data management system  will  be




developed to service the entire spectrum of soil data collection and  management activities.  Such a




system would greatly  enhance the FHM sample tracking ability.  Ideally,  the system  would bring




together data entry/verification  computer programs that  are,  at present, discrete units used  on




individual personal computers to enter and verify data from field measurements, sample collection,




sample  preparation, and sample analysis.  Planning for a possible VAX-based system is already




underway.






      The soils software programs and data files typically occupy a large amount of disk space.  For




example, it is expected that the total 1991 FHM program soils data base storage space requirements




will be as much as 26 megabytes; specifically, 2 megabytes for the soil field measurement and sample




collection data base, 4 megabytes for the  soil preparation data base, and 20 megabytes for the soil




analysis  data base.






      Specific IM features for this indicator include computer-automated data entry and verification




programs for the field, computer manipulations of soil preparation laboratory data, and  data analysis




in conjunction with detailed lists of acceptable codes and logic checks. These features are described,




where appropriate,  in supporting documents such as  the  QAPjP (Byers, 1991), the field  methods




manual (Van Remortel, 1991 a), and the laboratory methods manual (Byers and Van Remortel, 1991).






8.7  LANDSCAPE CHARACTERIZATION






      Due to  funding and time limitations, there  are presently no plans to  perform landscape




characterization (LC) with  regard to soil classification in the 1991 pilot study.  As more time and




funding  become available, the  LC coordinator is expected to prepare soil classification overlays that
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will tie into other overlays (e.g., forest cover-type group), as part of the FHM program CIS.  Upon

completion  at  some  future  date,  these overlays  are  expected to provide  much interpretive
                                                                            «
information for broad-scale soil/vegetation relationships and regional soils representation.



8.8 INDICATOR DEVELOPMENT



      The following subsections briefly describe the work that has been performed on developing

the soils indicator components and what is expected to be accomplished in 1991.



8.8.1 Strategy



      The  FHM program staff  has  been developing an appropriate way of  presenting  forest

monitoring data in  a  format  that is consistent with the overall program goals.  Initially, the soils

indicator documentation  consisted of  general  fact  sheets  that provided a  rationale  for  the

monitoring of "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 been broadened to facilitate the integration of all essential soil-related parameters influencing

forest condition, or "health."  As a result,  the concept of "soil productivity" has been an appropriate

and useful strategy for addressing the monitoring objectives set forth in the FHM program.



      Ultimately, it should  be determined  whether the soil  productivity parameters  can  be

incorporated with  confidence into some type of index for future  application  in  across-indicator

associations and assessment endpoints.  The utility of individual soil productivity parameters can be

tested  with respect to  their  association with  response  parameters  or  indicators.  An indexing

framework that is suitable for application  in a comparable  manner across all regions must be defined.

The possible use of indices is contingent upon further development and testing in forest systems.

Also, exploratory multivariate techniques that would address associations of indicators should be
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investigated. Ancillary data, such as annual precipitation and temperature, should be gathered from

outside sources and used in evaluating and fine-tuning the soil productivity estimates.
                                                               ^

      Variability is generally contingent on the form and mobility of the productivity parameter of

interest.   Soil nutrient concentrations  can vary according to several  different conditions: within-

season, among-season, among-year, within-plot, and among-plot variability are prevalent. Within

the sample measurement system, there is variability due to within-crew, among-crew, within-run,

within-batch, among-batch, and among-laboratory differences.  Each of these possible sources of

uncertainty must be evaluated and controlled within acceptable standards during the project.


8.8.2  Retrospective Analysis


      A  repository of  particle size  and organic carbon data (i.e.,  the USDA's  Soil  Interpretations

Record [Soils-5] data base) exists for about  22,000 soil series across the United States.  Using this data

base and others such as EPA's Direct/Delayed Response Project data bases (Church et al., 1989), it is

possible to identify strata of forest soils aggregated by average  percent clay class or, alternatively,

organic carbon content or particle size class (discussed later in this section). In either case, these strata

could be aggregated by forest cover-type group to provide a basis for modeling and simulation.  For

example, an evaluation of the  Diagnosis and Recommendation Integrated System (DRIS) techniques

(Beaufils, 1973; Walworth and Sumner,  1987) using soil chemistry and dendrochronology data from a

Southern Appalachian  spruce-fir data base (Kelly and  Mays,  1989; Van Deusen, 1988) is presently

being performed by the FHM program staff at Las Vegas in conjunction with scientists from the TVA

and Oak Ridge  National Laboratory.  A  previous evaluation in 1990 using an acidic deposition

gradient  data base from  the north-central U.S. (Ohmann et al.,  1989) was performed by the FHM

program  staff at  Las Vegas and Research  Triangle Park in conjunction with FS cooperators  in

Minnesota.   These  evaluations are  expected  to   increase   our   level   of  understanding  of

exposure/response  phenomena and interactions among indicator components.
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8.9 Air and Deposition/Climate






      The FHM program soils staff has a vital interest in obtaining regional climatic and deposition




data to use as part of its indicator development and assessment framework. Climate data, specifically




regional  isothermoplethic  and isohydroplethic maps, will help  to define generalized soil  moisture




relations across the regional plot network.  Some interpolation of existing data may have to be done




to enhance the usefulness of these maps.  In addition, a time series display of the Palmer Drought




Severity Index (Palmer, 1965; Alley, 1984) modified for specific FHM  program uses is highly  desirable




for evaluating drought stresses in long-term forest soils monitoring. Regional information on dry and




wet deposition of  point-source  and  non-point-source  sulfur, nitrogen,  and other elemental




compounds will be invaluable for nutrient cycling and exposure assessments.  The  FHM  program




climate group has agreed to support the data-gathering effort when funding becomes available.






8.10 DATA INTEGRATION, ASSESSMENT, AND REPORTING






      The following subsection describes some of the strategies for integrating and assessing soils



data collected during the 1991 field season, and how these data are to be reported.






8.10.1 Integration and Assessment






      There is some uncertainty as to how the plot-by-plot data are to be aggregated in order to



derive regional estimates for specific forest cover-type groups.  Simulations using existing soils data



bases are being used to test  preliminary sample  aggregation schemes for different  regions of the



eastern U.S. Preliminary results using the  "forest cover-type  group /percent clay class" and  "forest



cover-type  group/percent  organic carbon class" simulations are promising  (Byers  et  al.,  1990b;



Conkling et al., 1990). These classification schemes are being applied to the FHM  program study plot



framework to  assess their utility.  Other  possible classification schemes, such as higher  category



taxonomic groups, should also be tested for their utility.
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      The soils data integration and assessment framework for the FHM program is not yet clearly
defined nor understood. Nonetheless, possible features of this framework include the following:

      •  development of a productivity  index  that  could  distinguish nominal,  marginal, and
         subnominal  ranges of specific soil productivity indicator components (e.g., parameter
         groups), with respect to forest health societal values;
      •  correlation of soil productivity with other FHM program indicators using either a modified
         version of  DRIS  or other interpretive frameworks to evaluate  indicator components and
         assessment  endpoints; graphical presentations  of  parameter correlations or  ratios  of
         component parameters;
      •  development of new integration approaches through retrospective analysis of  historical
         soil-vegetation data bases.
      •  emphasis on integration of parameters and methods with demonstrated utility;  with few
         exceptions,   the  methodology  for field  measurements,  sample  collection, sample
         preparation,  and  sample  analysis is  presently  well  documented  and  requires little
         additional development; and
      •  testing of  measurement  parameters  and  other  ancillary  components of  the soil
         productivity indicator (e.g., plant-available moisture) for their utility in characterizing and
         indexing forest soil condition in specific forest cover-type groups.

8.10.1.1 Indexing Strategy

      The cornerstone of this ecological  indicator is the ongoing development of a soil productivity
index that  includes configurations of several soil parameters.  There have already been significant
advances in the development of indexing systems (Ott, 1978),  and efforts are underway to broaden
the range of contacts and acquiring data from the scientific literature and from resource scientists to
support this work.  Once developed  and  tested, the index is expected to  provide a reliable synoptic
"snapshot" of overall  soil  productivity status  and trends for individual forest cover-type groups in
each region, and is based on a  soil's ability to supply plant nutrients and  sustain forest productivity.
An  index might also  be identified  for  each of  several appropriate aggregations of soils in the
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different regions and then used in association with the other FHM program indicators to evaluate the




overall condition of regional forest ecosystems on a national basis.






      It is believed that the single greatest use of the soil productivity indicator is to provide regional-




level information on the "exposure" characteristics of soils as they relate to the response indicators




(such as visual symptoms and tree growth).  Secondary uses of the soil productivity indicator might




include the provision of plot-level information for these response indicators and the establishment of




linkages to foliar chemistry, soil biological processes, and other FHM program exposure indicators.






      The ongoing  development of a soil productivity index composed of several soil measurements




is expected to provide a reliable synoptic snapshot of overall soil productivity status and trends in




relation to forest response indicators.  A general  history  of  soil  productivity rating systems and




general model classes for productivity rating scales in the United States is presented in an excellent




review by Huddleston (1984).  Much of the research on productivity ratings has been  done in  an




agricultural  setting, which resulted from the  desire  to  have a  method for  using  soil survey




information to classify the quality of farmland for purposes such as tax  assessment (Fenton, 1975;




Scholtes and Riecken,  1952) and other loan activities (Berger et al., 1952). More recently, the concept




of a productivity index or rating scale has been  applied to erosion studies (Bruce et al., 1988; Scrivner




et al., 1985; Larson et al., 1983; Pierce et al.,  1983), and is generating greater interest for possible




applications in forestry and forest soils research. A primary approach is to base the productivity rating




on soil and  climatic effects on plant growth  or yield, where actual  yield data are  often used to




calibrate the model. Two main types of models presently exist: multipiicative and additive.  It is also




possible to develop a model which combines additive and multiplicative processes.






      Soil productivity indices based upon plant root distributions have been proposed,  mainly for




agronomic crops (Kiniry et al., 1983), although adaptations are being developed for forests (Gale and




Grigal, 1987; Henderson et al., 1988). This approach, however, is labor intensive and tends to be crop




specific. There have been numerous projects that have studied relationships between some measure
                                            8-25

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of growth response in various forest species and its associated soil chemical, physical, topographic,
and climatic characteristics.

      Some possible advantages to using an index to assess changes in soil  productivity include the
following.

      •   By virtue of the soil sampling design,  the  index  should be a valuable measure  of soil
          condition regardless of the soil mapping unit composition within the field plots.
      •   The index could provide a  direct composite measure of soil productivity status for  a
          particular field plot or region, and can initially be used for the establishment of baseline
          condition.
      •   The index could be a nonarbitrary measure of soil productivity trend for a given  plot or
          region  over time,  both in terms  of  total  plot  productivity and  individual horizon
          productivity.
      •   The index could allow the data users to  evaluate the association  of the index with other
          FHM program indicators.
      •   The index focuses  on "operative" soil  properties influencing  productivity, such  as clay
          content, organic carbon, horizon thickness, or soil depth.
      •   Component soil parameters  could be aggregated  or dispersed to the level necessary to
          define appropriate indices for interpreting the assessment endpoints of interest.
      •   The index initially could be used in  DRIS equations or other interpretive frameworks for
          determining appropriate ranges  or confidence  intervals for the independent  variables.
          Later applications could capture response data  for the dependent variables from other
          FHM program indicators.
      •   The index could accommodate and account for  differences  in parameters, methods, and
          procedures used to measure soil productivity across all regions of the United States.

      Some possible disadvantages to using an index to assess changes in soil productivity include the
following.

      •   The index may not be useful for making reliable estimates of productivity at the soil order
          or suborder taxonomic level  because of the  expected large variability in soil physical and
          chemical characteristics of soils aggregated within a soil genesis-based higher category
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          classification. Conversely, the soil family or series levels, while desirable from a regional
          interpretation standpoint, are  likely  to be too low of a category to  allow sufficient
          statistical degrees of freedom on which to base the data analysis and estimates of change
          (at  the  present  grid density).   Therefore,  characteristics other  than  soil  genesis
          (i.e., "operative" factors such as soil physical and chemical parameters) are probably more
          useful (Fralish etal., Incomplete Reference).
      •   The index cannot be fully applied to DRIS-type equations until appropriate concurrently-
          measured dependent variable values, such as response indicator data, can be collected.

      The indexing strategy was selected by considering  soil characteristics on an interactive system
basis. For example, if an unmanaged stand of rain forest in the Amazon Basin was evaluated using
only those response indicators such as visual leaf symptomology or tree growth efficiency, it might be
concluded that this tropical forest ecosystem was in "healthy"  condition. However, it is known that
the majority of soils in the Amazon  Basin are naturally infertile and have achieved a delicate
ecological symbiosis with the indigenous flora. In this system, annual nutrient cycling from decaying
woody and leaf litter provides the only significant buffer against acute productivity depletion. In this
sense, it could be argued  that the  soils in this ecosystem are marginal;  that is, the soils display
chronically low levels of productivity that are highly susceptible to disruption. Anything that would
disrupt this cycling  balance, such as wildfire effectively removing the  understory plants and ground
cover, could abruptly shift the forest health to a  subnominal status. Similar scenarios could occur in
the United States (e.g..scrub oak/pine forests in Northern Florida).

8.10.1.2 Classification/Aggregation Framework

      For interpretation purposes, it is possible  that soils could be classified  on the basis of one or
more specific soil characteristics  (e.g., particle size class,  organic matter content,  depth to bedrock,
taxonomic group, etc.).  As an example, it was hypothesized that certain Ultisols, Entisols, or shallow
rocky soils could possibly be categorized as nutritionally  "subnominal" or "marginal" whereas deep
Alfisols  or Mollisols may have the greatest possibility of being nutritionally  "nominal."  Although
there are a number of soil properties of importance to productivity in forested ecosystems, it  is
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generally accepted that two of the most important soil physical characteristics affecting  nutrient



status are the organic carbon content and the percentage  of clay-size particles in the soil matrix



(Soon, 1985; Barber, 1984). Both of these parameters are readily quantified during the FHM program




soils data collection activities.






      It is anticipated that soils could be aggregated in a number of ways, such as by plot (through a




weighting function applied to all master horizons on the plot) or by individual master horizon type.



A first-order reference stratum might be "forest cover-type  group," as this presently appears to be




the intended basis for regional indicator estimates.  A second-order reference stratum might be



"average percent clay content" on a field plot or in a particular master horizon, as clay is expected to




be one of the dominant soil physical factors relating to potential soil productivity. The effect of



organic carbon on productivity may be addressed adequately through the aggregation of data by



different master horizon types.  Using this mode of classification, a soil's baseline potential for specific



assessment endpoints can  be determined and then rated qualitatively in "nominal,"  "marginal," or



"subnominal" terms.






      At this time, a classification scheme is being developed which allows the data users to clearly



differentiate between index values for different strata and still encompass enough samples in each



stratum to make reliable estimates of changes in status and the uncertainty associated with those



estimates. Initial efforts along these lines have been fruitful in that there appear to be distinct ranges



of nutrient concentration for a given concentration of clay and organic carbon (Byers et al., 1990b;



Conkling et al., 1990).   As demonstrated  in previous studies, there  is  significant micro-  and



macrospatial  variability in the ranges of concentration  for different master horizons,  such as



O-horizon vs. A-horizon (Mausbach  et al., 1980;  Van  Remortel, Unpublished Data).  A  weighting



function  has been tested  which would allow the effect of the relative thickness/volume  of each



master horizon on the plot to be appropriately weighted  in the estimation of plot classification



parameters (e.g., average  percent clay) and, ultimately, of the overall plot productivity status for a
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given plot. It is anticipated that algorithms and weighting functions could be developed that have




utility for both regional and plot-by-plot evaluation.






8.10.2 Reporting






      The soils indicator participants will provide input to the appropriate reports on operations, QA,




and data analysis that will be written upon completion of the 1991 field and  laboratory work (see




Section 7).  The disposition of these specific reports and their timeframe for delivery is uncertain at




this time.
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                   9. TREE CORE ELEMENTAL ANALYSIS FIELD MEASUREMENT





                                          T. Lewis3








9.1 INTRODUCTION






      The study of current nutrient cycling in forests in  relation  to atmospheric  deposition and




climate change must be examined from the perspective of past, current, and future influences of




natural and anthropogenic processes on nutrient cycling.  To assess the current status of nutrient




cycling in forested ecosystems, it is important to evaluate evidence of their historical nutrient status.






      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  from leaded gasoline, Strontium-90  from atomic weapons




testing). Detection of a 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 chemical  changes  in tree ring chemistry reflected




changing inputs of regional pollutants in forests.  Increasing levels of iron were found in those tree




cores during  the  50 years  of open-pit 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, possibly in response to increasing  acidic




deposition.  Bowers and  Melhuish (1987)  observed  a similar pattern in tree cores collected from




loblolly and red oak growing near the Chromasco Smelter outside of Memphis, TN.






      Examining the relationship between tree  ring chemistry and changes in soil chemistry during




the life of the tree is a more recent approach (Legge et al.. 1984; McClenahen et al., 1987; Guyette




and McGinnes, 1987; Bondietti et al., 1990). Bondietti et al. (1989) observed a significant increase in
  1 Lockheed Engineering & Sciences Company, Las Vegas, NV
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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 had a
negative correlation with the radial growth of the species. Bondietti et al. (1990) also sampled red
spruce and  other species in New  England  and North  Carolina,  in addition to Tennessee.   The
researchers observed an increase in  divalent cations present in red spruce wood formed  in the mid-
19005 that was coincident with  rapid increases in sulfate and nitrate deposition in eastern North
America and with increases  in radial growth. A decrease was noted in divalent cations in the red
spruce wood formed in the late-1900s with a concomitant decrease in radial growth.

      Most determinations of elemental concentrations in tree cores have involved digestion of
tissue with subsequent  analysis  by  atomic absorption spectrometry (AAS)  (Bowers and Melhuish,
1987), inductively coupled plasma-optical emission spectroscopy (ICP-OES) (Bondietti et al., 1990), or
similar approachs. These techniques  have several disadvantages including:

      1.  several  years  of wood growth must  be  pooled in order to obtain sufficient tissue for
          analysis, and
      2.  the technique is destructive.

      Alternatively, some less destructive techniques have been successfully employed. These include
particle-induced X-ray emission (PIXE)  (Bondietti et al., 1989) and neutron activation analysis (NAA)
(Bondietti et  al., 1990).  These methods do not require digestion of the tree core sample, but suffer
from a lack of spatial resolution (i.e., the dimensions of the tree core sample which can be irradiated,
which ultimately corresponds to temporal [seasonal and  annual] resolution).  Their lack of spatial
resolution is due,  in part, to the potential loss of those components having significant volatility  and
the probability of sample damage  due  to interaction with the charged particles,  even when  the
sample has been coated with a conducting medium to avoid charging.
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      Early work on the use of X-ray fluorescence (XRF) for the determination of trace elements in




plant material depended on the use of various preconcentration methods which were destructive and




required significant amounts of  material (Reuter, 1975),  similar to AAS and ICP-OES techniques.




Attempts  to  obtain annual  resolution  in tree cores could  only  be accomplished by the use of




microtomed sections, which were treated chemically to produce the analyzed sample. More recently,




XRF spectroscopy  analysis has  been  refined  to allow  direct measurement  of the  elemental




composition of individual tree rings, with subannual resolution (Gilfrich et al., In Press). Within-year




seasonal differences can be discerned due to the ability to focus, or aperture, an X-ray  beam to sizes




approaching tens of micrometers  (Jones et al., 1988).  Coupled with the intensity of X-rays generated




by a synchrotron radiation light source, such as  the one housed at Brookhaven National Laboratory,




minimum detection limits for most elements by XRF can be as low as 20 ppb.






      Gilfrich and co-workers at the U.S. Navy Research Laboratory (NRL) in Washington, DC will




provide XRF analyses of tree cores collected during the Nutrient Cycling Demonstration.






9.2 RATIONALE






      The elemental analysis of  tree cores may  provide a critical  link between soil nutrient and




contaminant levels and foliar chemistry. Elemental analysis of tree cores will provide direct evidence




of nutrient status and historical trends in nutrient cycling.  Stemwood elemental concentrations will




serve to round out the overall suite of nutrient cycling indicators.






9.3 DESIGN






      Tree core samples will be collected from the same specimens from which visual injury and foliar




chemistry  samples are to be collected during the Nutrient Cycling Demonstration in Georgia and




Alabama.  A total of two cores will be collected  from  each tree. Tree ring growth measurements are




made in the process of elemental XRF analysis.  A 5-mm (inside diameter) Teflon®-coated increment
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borer will be used to collect the sample.  Samples will be placed in plastic tubes for shipment to the



sample preparation.laboratory in Las Vegas, NV.






      In the South and Southeast, a total of approximately 72 plots will be sampled in the Nutrient



Cycling  Demonstration.  Two trees on each of the two selected subplots will be cored for elemental




XRF analysis.  These trees will be the same trees sampled for foliar chemistry and visual injury on the




same day.






9.4 QUALITY ASSURANCE






      A  pretraining and training course will be conducted  prior to collection of samples for the



Regional Pilot. The purpose of this training is to familiarize the indicator leaders and sampling crews



with the sampling design and sample collection methods. Crews from both the South and Southeast



will be instructed similarly to ensure consistancy between regions.  More detail on field methods is



provided in a separate methods manual.






      A  rigorous QA/QC program will  be employed for  laboratory  analyses of tree  cores.  This



program consists of numerous system and performance audit samples.  A  laboratory audit will be



performed prior to sample analysis. Greater detail on this QA/QC program is provided in a separate



QA manual.






9.5 LOGISTICS






      Logistical components to be assessed during  the Regional Pilot include (1) testing  of the



feasibility of the sampling protocols, and (2) estimation of costs and time required for each step in the



process (e.g.,  sample tree selection, tree core collection, shipping, sample preparation, and sample



XRF  elemental analysis).  These logistical considerations will  be evaluated  in  light  of  costs to



determine whether the sampling design adequately compensates for temporal and spatial variability.
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9.6 INFORMATION MANAGEMENT






      Tree location on the subplot will be recorded on PDRs in the field.  The plastic tubes in which




the tree cores will be shipped will be clearly marked with the appropriate identifying information.




When samples are received at the sample preparation laboratory in Las Vegas, NV, the integrity of the




sample will be noted. Pertinent information will be entered into a SAS batch tracking data base prior




to shipment to NRL The samples will be allocated into batches for chemical analyses. A batch and




sample ID number will identify the samples sent to NRL's analytical laboratory. This unique number




will follow the sample through  the  entire analytical process.  Results  of the XRF analyses will be




obtained by hard copy and electronic format (tentatively ASCII format).  The analytical results will be




merged with the batch tracking data base by the appropriate sample-tree identifiers.






9.7 LANDSCAPE CHARACTERIZATION






      Remote sensing information will be obtained from a subset  of the  plots  in the southeast.




Remote sensing will provide information on crown cover, crown condition, land-use patterns, and




harvesting. All of the aforementioned landscape characterisitics have  a marked influence on nutrient




cycling in forested ecosystems. An evaluation of these landscape characteristics in conjunction with




the nutrient cycling indicator suite may provide estimates of regional trends in nutrient cycling.






9.8 INDICATOR DEVELOPMENT






      Development of this indicator may prove a better alternative to a  complicated suite of nutrient




cycling indicators. The minute spatial resolution afforded by XRF analysis may be capitalized upon by




examining  other  components   of  the  specimen   to  obtain  better  estimates  of  nutrient




compartmentalization.
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9.9 AIR AND DEPOSITION/CLIMATE






      The opportunity exists for relating historical trends in nutrients and contaminants in tree cores




to historical trends in atmospheric deposition and  climatological patterns.  Linkages are possible




between nutrient uptake, radial growth, soil nutrient availability, and  environmental atmospheric




and climatic data, using tree core elemental XRF analysis as an integrator of past, present, and future




condition.






9.10 DATA INTEGRATION, ASSESSMENT, AND REPORTING






      Elemental analysis of tree cores will serve as an integral link between below-ground and




above-ground  processes in forest nutrient cycling. The historical record revealed by elemental tree




core analysis will provide valuable information for the interpretation of current levels of nutrients




and contaminants in soils and foliar tissue.






      The same strategies for the development of  the integration and assessment framework for




foliar chemistry would be applicable to elemental tree core analysis.






9.10.1 DRIS






      One  of the essentials in the use  of  the  Diagnosis and Recommendation  Integrated System




(DRIS) is the establishment of a data base.  The use  of historical tree core elemental nutrient ratios




may be able to provide such a data base.






9.10.2 CERES






      CERES is a submodel which can be used to predict short-term and  long-term accumulations of




solutes when coupled with other submodels of the Unified Transport Model (UTM)  (Dixon et al.,




1978).  The CERES model  is separated into various compartments, one representing heartwood.  By




adjusting the levels of nutrients in the heartwood, as determined by XRF  analysis of tree cores, short-




and long-term flucuations in elemental concentrations in the other compartments can be modeled.
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The model-generated values could be compared to the current levels in the various compartments to



detect possible departures from normal nutrient and heavy metal uptake and translocation.
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                                   10. FOLIAR CHEMISTRY





                                          T. Lewis3








10.1 INTRODUCTION






      Previously, this indicator has been termed "Foliar Nutrients." This is considered a misnomer,




inasmuch as elemental contaminants are also included  in the measurements. Therefore, the "Foliar




Nutrients" indicator will henceforth be termed the "Foliar Chemistry" indicator.






      Foliar chemistry is an example  of an exposure-habitat indicator.   This class of indicator is




designed to quantify factors which may  be associated with changes in forest condition (e.g. visible




injury, growth, soil productivity). The foliar chemistry indicator is also a key component in the suite of




indicators that contribute to the nutrient cycling assessment endpoint.






      The elements to be determined in foliar samples include macro- and micronutrients (e.g., total




N, P, K, Ca, Mg, S. Fe, Mn, Zn, Cu, B, Mo,  and Cl) and potential contaminants (Na, Al, F, Cd, Pb, As, V,




Cr, Ni, and  Hg).  Some  essential  nutrients  may also enter  the system in  excessive amounts from




anthropogenic sources (total N, Fe, Mn, Zn, Cu, B, and Cl). For example, chromium smelters emit Mn,




Cr, Fe, Al, Ca, Mg,  Na, Zn, K, Pb, Ba, Ti, Hg,  Cd, Be, V, and As. These were measured  in particulates




emanating from the stacks at the Chromasco smelter in Memphis, TN (Bowers and Melhuish, 1987).






      Foliar chemistry as a "stand-alone" indicator may not in itself be sufficient for establishing




status and discerning trends in forested ecosystems. However, it is believed that in conjunction with




other indicators it is a vital component in  the nutrient cycling and contaminants assessment endpoint.




Further, the combined use  of a number of existing procedures which assess foliar nutrient status




(e.g., critical levels, DRIS, correlation with various growth variables) will be tested.  The foliar  nutrient
   Lockheed Engineering & Sciences Company, Las Vegas, NV
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screening method  developed by Timmer and co-workers (Timmer and Stone,  1978; Timmer and



Morrow, 1984) may also aid in the detection of nutrient limitations.






      Nutrient deficiencies or excesses and metal toxicity can often be detected  as visual symptoms



on foliage.  When nutrient  deficiencies are severe, visible  symptoms such as leaf yellowing and



scorching become  apparent.  Other symptoms  may include stem deformities and loss of  leaves.




Although some visual symptoms may relate to a specific nutrient limitation,  in many cases, foliar



chemical analysis is needed to accurately diagnose the cause.  For example, twisted, deformed leaders



in Douglas fir have been related to copper deficiency (Will, 1972), boron deficiency (Carter et al.,



1983), and arsenic  toxicity (Spiers et al., 1983).  Foliar nutrient chemistry may also correlate with



visible injury caused  by  gaseous pollutants such as ozone, sulfur  dioxide, oxides of nitrogen, and



peroxyacetyl nitrates.






      Foliar nutrient concentrations are  known to vary in response to a number of biological,



structural, geographical, and environmental  factors.  The effect  of some of  these factors can be



partially controlled by selective sampling (e.g., sampling the upper-third of the crown, sampling at a



certain time of year).  These selective sampling procedures will be discussed below.






10.1.1 Sampling Considerations






     The problems of sampling position involve several considerations:





      1.  Which trees in a forest stand to sample



     2.  Where on the trees to sample



     3.  Number of trees to sample



     4.  When to  sample




     Generally,  the  dominant and  codominant trees  are  sampled because  they  are  more



representative of the  plot and they are usually of greater economic importance. Also, dominant and
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codominant trees in a forest stand tend to show less variation among trees in nutrient element levels




than over-topped trees growing in various degrees of shading by the larger trees (Lavender, 1970).






      The position on  the tree for collecting foliar samples has been of considerable controversy in




the early stages of indicator development. The nutrient element composition of foliage varies both




vertically and horizontally in the tree crown. For conifers, there are several position considerations:





      1.  Age of needles




      2.  Vertical position in crown




      3.  Position of needle in growth flushes




      4.  Branch order in relation to physiological activity




      The age of the needles has been shown to influence elemental concentrations in the tissue. For




many years, coniferous foliage sampling for diagnostic purposes has been restricted to foliage of




current-year age at the terminal portions of the uppermost lateral branches (Leaf,  1973). However,




recent evidence indicates that at least for some species and elements, foliage from  other portions of




the tree crown  and, possibly, from other than the current-year's growth  may be more diagnostic




(Kabata-Pendias and  Pendias, 1984).   For example,  when  the supply  of  Mg  is adequate,  its




concentration in older needles, such as 4th-year  needles, will be  similar to that in  current-year




needles.  However, as  deficiency develops, Mg moves from older to current-year needles, with the




concentration in the older needles dropping to very low levels (Tomlinson,  1990). The nutrient ratio




between old and current-year needles may serve as a diagnostic index of nutrient deficiency. This




ratio will be examined in the Nutrient Cycling Demonstration in Georgia and Alabama.






      Vertical position in the crown is also an  important consideration.  The outer-crown foliage or




"sun-leaves" have  anatomical  and morphological differences from the internal-crown foliage or




"shade leaves"  and there are differences in nutrient element status between these two groups of




foliage.  Generally the  upper-third of the crown is sampled. Foliage located in the  upper crown also




acts as an interceptor of atmospheric pollutants. For that  reason, and to stay consistant with other
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national and international foliar surveys, the upper-third  of the crown will be sampled in the



Regional Demonstration.  However, the logistics and cost  of collecting from the upper-third are



substantial.  Wallihan  (1944) reported no significant differences  in sugar maple foliage  nutrient



content in upper and lower crown positions, but this warrants further investigation.






      Temporal  variability in foliar  nutrient concentrations exists  between years, within year, and



within season.  Large variability can exist (e.g., coefficients of variation from 8 to 60%) within and



between years (Bickelhaupt et al, 1979; Smith et al., 1970; Wells and Metz, 1963; Mead and Pritchett,



1974). Mobile elements (N, P, and K) tend to increase during the first half of the growing season and



decrease during the latter portion. Generally, elemental concentrations in deciduous foliage tend to




level  off approximately 1  month  prior to senescence (Leaf, 1973). Samples  will be collected 2 to



3 months  prior  to this  time  period  during  the  Nutrient Cycling  Demonstration.   Nutrient




concentration in current-year coniferous foliage has been found to  be more stable during the winter



months than during the growing season.  The concentration in previous-year  needles is more stable




over the entire growing season than in current-year needles (Wells and Metz, 1963).






      Unfortunately, due to financial and logistical constraints, winter sampling is prohibitive.  Both



current- and previous-year foliage on conifers will be sampled in June and July during the Regional



Demonstration. By examining the ratios between these two groups as a diagnostic tool, the seasonal



variance component will  hopefully be offset. The time  scale for which trends are expected to be



detected in foliar chemistry are in the order of decades. The intraseasonal , interseasonal, and annual



variation in foliar chemistry will probably necessitate monitoring for longer-term  changes (i.e., 10 to



50 years). It is anticipated that regional  long-term trends will be  detected notwithstanding short-



term temporal variability.  The short-term variability, however, may be useful in  understanding the



coincident measurements of other indicators.






      At a later date, when funding  becomes available, we will, propose an off-frame pilot to assess



temporal variability and fine tune the sampling window. In the off-frame pilot, time of sampling will
                                            10-4

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be used as a covariate.  However, using time as a covariate will not solve the problem of interactions
between time and climate as one moves from one region to another.

10.2 RATIONALE

      The rationale for making foliar chemistry measurements are as follows.

      1. Foliar chemistry is an important component in other long-term monitoring programs in
         Europe and  North America.  The data generated in the FHM  program  will  be directly
         comparable to these other programs.
      2. The  foliar  chemistry indicator  is an important component  in the set of indicators for
         assessing nutrient cycling in  forested ecosystems.  It will  provide information for the
         interpretation of other indicators such as soil productivity and visible injury.
      3. The foliar chemistry indicator, with the other monitoring data, will be a valuable addition
         to and basis for evaluatfon and ecosystem research monitoring-.

10.3 DESIGN

      Based on the previous discussions on the variability in  foliar chemistry the following sampling
design strategy is proposed for the measurement of foliar chemistry.

      The primary objective is to determine  the within tree and within plot variability of foliar
elements on a regional scale.  At the time of  writing of this Study Plan, data from the 20/20 Study
have  not been  evaluated.  Therefore,  the variance  estimates from that study are not available.
Additional knowledge will  be gained that can be added to the 20/20 Study.

      One pilot study has been  structured into  the study, the  Needle Age Evaluation in Alabama.  The
objective of the Needle Age Evaluation is to evaluate the use of the current-year vs. previous-year
coniferous foliar nutrient ratio as a diagnositic for detecting nutrient deficiency.  One year of data
will be evaluated for detecting such deficiencies. The logistics and data interpretation methods for
this measurement will be evaluated.
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      In Alabama and Georgia, approximately 72 plots will be sampled for foliar chemistry analysis.




These plots coincide with those of the Nutrient Cycling Demonstration. Sample tree selection will be




based on crown class dominance and codominance. Species are not selection criteria, although this is




a factor in the Needle-Age  Evaluation, which will be conducted on a  subset of the plots in the




Demonstration.






      In the Nutrient Cycling Demonstration and Needle-Age Evaluation two trees will be climbed on




two subplots and branch samples will be collected from the upper-third portion of the crown.  These




branches will be evaluated for visual damage and the visual damage evaluation will be performed on




the whole tree. For all coniferous species encountered, the previous year's (1-year-old) needles will




be obtained. Additionally, for the Needle-Age Evaluation, from the first 20 subplots that  have two




loblolly pine selected, the current year's growth will also be collected.






      Foliage collection is considered  destructive, therefore, these samples will be obtained  from




trees off the subplot. Climber's spikes will not be used by tree climbers.






10.4 QUALITY ASSURANCE






      A pretraining and training course will be conducted prior to collection of samples.  The purpose




of this training  is to familiarize the indicator leaders and sampling crews with the sampling design




and sample collection methods. Crews from both Alabama and Georgia will be instructed similarly to




ensure consistency between regions.  More detail on field methods is provided in a separate methods




manual.






      A rigorous QA program will be employed for laboratory analyses of  foliar  samples.   This




program consists of numerous system and performance audit samples.   A laboratory audit will be




performed  prior to sample analysis. Greater detail on this QA program is provided  in a separate



QAPJP.
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      Foliar chemistry data collected in the 20/20 and Regional Demonstration Studies will be used in




the development and testing of a data verification and validation program similar to the Soil Quality




Assurance Template (SQAT) program used for soil chemistry data evaluation.






10.5 LOGISTICS






      Logistical components to be assessed  during the  Regional  Pilot  include (1)  testing of the




feasability of the sampling protocols, and (2) estimating costs and time required for each step in the




process (e.g., sample  tree selection,  branch  collection,  separation of current-year and  1-year-old




needles, shipping, sample preparation, and sample chemical analysis). These logistical considerations




will be evaluated  to determine whether the sampling design adequately compensates for temporal




and spatial variability in a cost-effective manner.






10.6 INFORMATION MANAGEMENT






      Information management is critical for the implementation of indicators and interpretation of




data.  Field data describing the location of sampled trees on the plot and other pertinent information




will be recorded in the field using PDRs. The PDRs will be preprogramed  prior to deployment in the




field.  Data will be downloaded from the PDRs to PCs at the end of each day's field activities.  The




hexagon, subplot, tree number, branch number, species code, state, crew identification (ID), needle




age (for Alabama Needle-Age Evaluation plots), azimuth, and distance are essential indentifiers for



sample tracking.






      At the preparation laboratory in Las Vegas, NV, samples will be matched  with the field data to




verify receipt of all samples. The samples will be allocated into batches for chemical analyses. A batch




and sample ID number will identify the samples sent to the analytical laboratory. This unique number




will follow the sample  through the entire analytical process. Data will be received from the analytical




laboratory in ASCII format.  The data will be converted to SAS format and merged with the existing




SAS data base which contains all the descriptive information recorded in the field.
                                            10-7

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10.7 LANDSCAPE CHARACTERIZATION






      Landscape characterization can be useful in the interpretation of  nutrient cycling  suite of




indicators and vice versa. Large areas of tree mortality or defoliation detected by aerial photography




may be linked to nutrient deficiencies or pollutant toxicities. Estimates of leaf area index obtained by




high-resolution remote sensing methods may relate to one or more of the nutrient cycling indicators




(e.g., visual injury, foliar chemistry, soil productivity). Evidence of extensive drought conditions in the




forest stand as determined by high-resolution aerial photography may aid in the interpretation of soil




and  foliar chemistry data, particularly  for highly mobile nutrients.  Nutrient deficiencies in crown




foliage  may  be detectable  on a broad scale by remote sensing in various wavelengths.   These




possibilities are actively being investigated.






10.8 INDICATOR DEVELOPMENT






      The foliar chemistry indicator is in the developmental stages at the present time. Refining of




the sampling window and location in the crown are two issues that must be addressed in order to




understand the variability in foliar chemistry.  Both these  issues have been topics  of discussion and




active research for several decades.   The historical data  have not been adequately  examined to




warrant proposing demonstration research.  Furthermore, the variability in the data collected during




the  20/20 Study have not been  evaluated.   Historical data  bases  are being  sought to assist in




addressing these important issues.






      The  foliar chemistry  indicator is  composed  of  several  measurements  of  macro- and




micronutrients in addition to potentially toxic elements.  Deficiencies, excesses, and imbalances in




essential nutrients may act as a stressor on the plant, and high levels of toxic elements may also stress




the plant.  This stress may act directly on a particular tissue of the plant or  interfer with the plant




indirectly by altering soil chemical and biological activities.
                                            10-8

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      Recent evidence demonstrates the ubiquitous nature of organic contaminants in terrestrial



ecosystems, which may impose an additional stress to the system. These compounds include polycyclic



aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), pesticides, dioxins, and many more.



These compounds undergo long-range transport and are deposited in forested  ecosystems by wet



and dry deposition (Levsen et al., 1990).  While concentrations of organics in the atmosphere have



increased, our knowledge of their movement through terrestrial food chains has remained  static.



Aromatic hydrocarbons and chlorinated hydrocarbons are solvents and may dissolve in the wax layer



of needles and leaves.  The leaf surfaces having been perforated  by these organics, acids and  heavy



metals may attack the metabolism of the plants (Muller, 1989). However,  to date it is not clear if the



concentrations of aromatic  and chlorinated hydrocarbons  are sufficiently high to be  inducers of



forest decline. Nevertheless, knowledge about the concentrations of these contaminants in forested



ecosystems in remote and polluted areas is indispensible.






      Toxaphene, a once widely used pesticide that replaced DDT, has been shown to be  highly toxic



to soil microoganisms (Saleh, 1991).  Such toxicity may influence nutrient cycling in forest soils. PAHs



have been shown to accumulate in plants (Edwards, 1989).  While little  is known about the direct



effects of organic contaminants on tree species, the regional distribution of these compounds in



forested areas would be of considerable value for anticipatory purposes. A soil screening  method for



detecting total  organochlorine contaminants coupled with  a bioassay, such as ATPase activity or



Ames test, is being researched as a possible measurement in the soil chemistry suite of measurements.






10.9 AIR AND DEPOSITION/CLIMATE






Evaluating  regional  concentration and  deposition  patterns of atmospheric  pollutants  will  be



instrumental in the interpretation of the nutrient cycling suite of indicators. Climatological data must



also be linked with nutrient cycling and contaminant data to knowledgably make statements about



variability in the data and relationships between indicators.
                                           10-9

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10.10 DATA INTEGRATION, ASSESSMENT, AND REPORTING






      Interpretation of foliar analysis data and subsequent extrapolation to a regional scale is crucial




to the success of this indicator. The total physical environmental and biological characteristics of the




site, together with spatial and temporal variations,  must be considered, along with the foliar analysis




data  to  make  adequate  interpretations.   For  example, atmospheric composition, temperature,




moisture, light quantity and quality characteristics,  soil productivity, and the metabolic activity of the




tree all can affect the level of a particular nutrient or toxic element in the foliar tissue.






      The data integration  and  assessment  framework in the  FHM  program  is still in the




developmental  stages.  However, some potential strategies for the development of the integration




and assessment framework are proposed.






10.10.1 DRIS






      Problems with the integration and assessment of foliar chemistry analysis have been overcome




in agroecosystems by use of the DRIS. The foundation of DRIS is the concept of nutrient balance, the




interrelationships  between all nutrients being considered simultaneously. The application of DRIS




requires four steps: creation of a data base, establishment of DRIS norms, establishment of DRIS




indices, and testing of the norms (Schutz and deVilliers, 1987).  In forestry, DRIS has been tested on a




small-scale, exploratory basis only. Given the increasing evidence that DRIS is a useful diagnostic for




agricultural crops, the opportunity exists to evaluate its usefulness during the FHM program activities.






10.10.2 CERES






      The CERES  model  was developed for  the  purpose of predicting solute transport within




vegetation and  litter components of a forest ecosystem (Dixon et al., 1978).  CERES can be used to




predict short-term and long-term accumulations of solutes when coupled with  other submodels of




the Unified Transport Model (UTM).  This model, or a modification of such a model, may assist in
                                           10-10

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linking the foliar chemistry indicator together with other indicators, such as soil  productivity, soil




biological processes, and PAR.






10.11  QUALITY ASSURANCE






      Specific QA information related to this indicator has been consolidated into the overall 1991




FHM program Quality Assurance Project Plan (Byers,  1991).
                                           10-11

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                              11. ROOT DISEASE EVALUATIONS





                                 S. Alexander and J. Carlson3








      Root diseases are significant contributors to the decline and  mortality of our forests.  The




pathogens  that cause root disease  may act alone or in combination with  other  factors such  as




drought, insects, and air pollution. Unlike above-ground pests, root pathogens are difficult to detect




and therefore may be overlooked as contributors to the forest condition. The following method of




determining the presence and severity of root diseases is the best available for use in a survey mode




(Alexander and Skelly, 1973; Wargo and Bergdahl, 1986; Alexander and Carlson,  1989). The single




tree evaluation procedure will be applied to each plot in the Nutrient Cycling Demonstration in the




South and Southeast.






11.1 OBJECTIVES






      To determine the presence and severity of root diseases.






11.2 DESIGN






11.2.1 Plot selection






      The plots of the interpenetrating design selected for the Nutrient Cycling Demonstration will




be used. The design will obtain regional representation.






11.2.2 On-plot sampling scheme






      Root samples will be collected on one pair of sample trees at each of two subplots per  plot.




These will always be the same trees from which branch data have been collected. All specified  trees




will be sampled using the following procedure. Two root samples from each of two roots per tree will
  1 Virginia Polytechnic Institute and State University, Blacksburg, VA
                                            11-1

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be collected. The  following symptoms, and signs will be recorded on the portable data recorder.
Because more than  one is likely to be found on diseased  roots, there are three fields available on the
                                                                                      *
sheet.

Code Description

0     None
1     Resin-soaked: Bark and outer wood have a brownish, wet appearance.
2     Stain:  Streaks of black or brown discoloration within the wood of the root.
3     White rot: The decayed wood is white in appearance.
4     Brown rot: The decayed wood is brown in appearance.
5     Rhizomorphs: Black strings of fungal hyphae attached to root.
6     Mycelial fan: A white sheet of fungal  mycelium in a fan shape on the surface of the root under
      the bark.
7     Mushrooms
8     Conks: Large, leathery fruiting bodies of a fungus protruding from a colonized area.
9     Insects
10    Other: A symptom  or sign not falling  into one of the above categories.  This should be
      described in the comments section.

Sampling Procedure:

1.     On each selected sample tree, starting at due North, locate a buttress root (a lateral root at the
      root collar).  Locate a second buttress root on the opposite side of the tree or as  close to the
      opposite side as possible.
2.     Excavate the two roots to a distance of approximately 3 feet. Remove the soil from the top and
      both sides of the roots.
     A.  Hardwoods:  Examine root surfaces for dead or sunken bark.  Dead bark will appear moist
         and darker brown to black in color compared  to healthy.  Examine surface for presence of
         black to brown shoestring-like rhizomorphs. Rhizomorphs are structures produced by the
         root pathogen Armillaria mellea  and are  1 to 3 mm wide and can be oval, round, or
                                           11-2

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         flattened in appearance.  They  are usually attached fairly  tightly to the bark surface.
         Symptoms and signs will be recorded on the data sheet.
         Where a dead, or apparently dead, patch of bark is encountered, remove a plug of bark
         down to the wood with a 1.25-inch arch punch.  If a hatchet is used, a wedge of wood
         approximately 1 inch long  by 1 inch wide by 1 inch deep should be taken. Look for mycelial
         material or rhizomorphs in the bark or on the wood. Mycelium will be creamy to white and
         fairly leathery in consistency.  Rhizomorphs may be mahagony to black in color.   If no
         mycelium or rhizomorphs  are encountered proceed to take a  sample 6 inches distal  to the
         necrosis.  Note that all four samples from a tree may be taken in the arch punch together,
         then pushed out into the labeled bag for that tree; the samples do not need to be labeled
         or packed separately.
      8.  Conifers:  Examine the root surface, especially on pine, for dried resin  or the adherence of
         soil to the root.  Using a knife, remove bark from the root down to the wood. Examine for
         symptoms of resin soaking, stringy white decay, and black to blue-black coloration.  The
         wood of a healthy root will be white. The root collar zone should be examined in the same
         manner. Symptoms and signs will be recorded on the portable  data recorder.
         Where symptomatic (resinous, decayed, or black-stained)  roots are found,  remove root
         samples with the punch or  hatchet as described in hardwood section above.
3.     On roots where no apparent  symptoms occur take a sample  6  inches from root collar and
      another 6 inches further down  root.
4.     Replace soil about roots.
5.     The four root samples from each tree (i.e., two arch punch disks or hatchet wedges from each
      of two roots) will be placed in a ziplock bag or similar container.  Bags will be marked with all
      pertinent plot  and  tree information and  date.   Samples  must be maintained  at cool
      temperatures, <65 °F, to prevent death of any fungi which are present.

11.3  QUALITY ASSURANCE

      Specific QA information related to this indicator has been consolidated into the overall 1991
FHM program Quality Assurance Project Plan (Byers, 1991).
                                          11-3

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11.4 LOGISTICS

11.4.1 Field Personnel Requirements

      One field crew member will be required for sampling.

11.4.2 Training

      Training will consist of classroom instruction on sampling method and root disease symptom

recognition and a field demonstration of sampling techniques.

11.4.3 Estimated Time on Plot

      Two hours.
                                                  4
11.4.4 Transportation Requirements

      Transportation will be required for training, field work, and debriefing.

11.4.5 Equipment and Consumable Supply Procurement Needs

      Shovel - one per crew

      Mattock - one per crew

      Axe or hatchet - one per crew

      Knife - one per crew

      Arc punch (1 1/4 in) - one per crew

      Plastic spray bottle - one per crew

      Bleach (to be mixed  1 part bleach to 5 parts water)

      Labels

      Water-resistant markers

      Portable coolers for field samples - one per crew

      "Blue ice" for field use and for shipping
                                           11-4

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      Boxed coolers for shipping samples




11.4.6 Communication






      Protocol to adjust procedures: Crew chiefs will contact the regional coordinator.  The regional




coordinator will contact S.A. Alexander.






11.4.7 Prep Lab and Analytical Lab Requirements






      The laboratory wfll have the facilities for storage of samples and culture and identification of




fungi.






11.4.8 Safety Considerations






      All safety policy and procedure requirements of the Logistics Section of this Field Study, and all




safety considerations suggested  by the field crew leader,  and all  safety procedures of Virginia




Polytechnic Institute and State University will be observed.






11.4.9 Debriefing Requirements






      Time required:   The field personnel  responsible for taking the samples and for sample




shipment will be interviewed at a debriefing session at the end of the field season. The time required




will be 1 h.






11.4.10 Inventory and Storage Requirements






      Adequate refrigeration (<65 °) and storage for samples until shipped to lab is required.






11.5 INFORMATION MANAGEMENT






      Systems for sample data recording were developed in the 20/20 study in  1990. The authors are




working directly with IM to develop improvements.
                                            11-5

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11.6 REPORTS






      Reports will be provided on training activities, field audits, and data evaluation.  The authors




will contribute their analyses in the Synthesis Report referenced in Table 1.1.
                                             11-6

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    12. ROOT SAMPLING PROCEDURE FOR EVALUATION OF ROOT DISEASES AND MYCORRHIZAE

                              S.A. Alexander* and B.L. Conklingb


      Soil organisms, important in the retention and release of nutrients and energy transfer in
forest soils, are sensitive to process changes in the forest floor. When the key linkages formed by soil
organisms are disrupted, ecosystems  become fragile and subject to threshold  changes (DeAngelis
etal., 1986). Among the important soil biological processes are nitrogen fixation, antibiotic activity
and metal chelation, nutrient cycling, material transfer between plants through mycorrhizal hyphae,
and creation and maintenance of soil structure through the production of  humic compounds and
polysaccharide glues (Perry et al., 1989).  Some measure of species composition is important to help
discern  and  interpret  the categorical  quantitative  changes  reflected  in   microbial biomass
measurements.   Initially, measurements of key soil biological variables will be  used to establish a
baseline.  Measurements of variables relating to  mycorrhizal  fungi, soil microbial biomass, and soil
respiration are among the initial components of interest.

12.1  OBJECTIVES

      A.   Determine whether or not the root collection method (proposed for pathogen testing), as
          described  by Alexander   (1989),  can be  used  to  obtain  samples  appropriate for
          morphological determination of mycorrhizal fungi.   Expected  outputs are the data to
          answer the above question,  and preliminary data describing percent mycorrhizal infection.
      B.   Conduct the  required literature work and information synthesis in anticipation of a soil
          biological processes pilot study in FY92.
  a Virginia Polytechnic Institute and State University, Blacksburg, VA
  b University of Nevada, Environmental Research Center, Las Vegas, NV
                                            12-1

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12.2 DESIGN






12.2.1 Plot Selection






      The Nutrient Cycling Demonstration plots in Alabama will be used for this measurement. The




samples will be taken from any subplot with two loblolly pine trees selected as sample trees.






12.2.2 On-Plot Sampling Scheme






      Samples will be taken off-plot. One soil-root sample associated with each of the subplots with




two loblolly pine sample trees will be collected. The sample will be located near the first sample tree



in such  a  manner that it  will maximize  the number of tree root systems sampled.  A square area



30.5 centimeters (12 inches)  is chosen.  Within this square, a 76-millimeter (3-inches) diameter  by



152-millimeter (6 inches) deep core is taken through the litter layer (Marks et al., 1967).  All of the



core is then placed in a labeled plastic bag and sealed.  The remaining duff layer is then removed from




the square and a 0.3 cubic meter  (1 cubic foot) sample of soil is removed to a  1  square meter plastic



sheet for evaluation. All pine root segments 0.32 centimeters (.12 inches) in diameter or larger are



separated and placed in a plastic bag that has been labeled for identification. The remaining soil will



be returned to the excavation hole.  The root and soil samples are placed on ice and transferred to the



Forest  Pathology Laboratory at  Virginia Tech,  Blacksburg, VA, each  week for  isolation and



identification of any root pathogens (Alexander, 1989) and evaluation of ectomycorrhizae. Time on



plot to collect samples is estimated at 2 hours.






     Samples received at the lab will be logged in and the root samples will be evaluated  for root



disease symptoms, and isolates (Schenck, 1982) will be taken from symptomatic roots. The large roots



will be removed from the soil sample to be evaluated with the root sample. The soil samples will be



shaken in a 2-millimeter sieve to separate the organic matter from the soil. Ectomycorrhizal roots will



be placed in water in standard 16x 100-millimeter petri plates  and  examined under a dissecting



microscope and the active ectomycorrhizal tips counted. (Harvey et al., 1976).
                                           12-2

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12.3 QUALITY ASSURANCE

      Specific QA information related to this indicator has been consolidated into the overall 1991
FHM program Quality Assurance Project Plan (Byers, 1991).

12.4 LOGISTICS

12.4.1 Field Personnel Requirements

      One field crew member will be required for sampling.

12.4.2 Training

      Training will consist of classroom instruction on sample  location and sampling method and a
demonstration in the field of the technique. Time required is approximately 1 hour for each.

12.4.3 Estimated Time on Plot

      Two hours.

12.4.4 Transportation Requirements

      One vehicle will be required but transportation sharing will be acceptable.

12.4.5 Equipment and Consumable Supply Procurement Needs
      Measuring tape (1 meter) -One per crew.
      Soil corer (76mm) - One per crew
      Shovel - One per crew
      Canvas or plastic sheet (1 m2) - One per crew
      Plastic bags (zip-type) - Eight per plot
      Labels
      Water-resistant markers
      Portable coolers for field samples - One per crew
      "Blue ice" for field use and for shipping
                                           12-3

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      Boxed coolers for shipping samples




12.4.6 Communication






      Protocol  to  adjust  procedures:  Crew  chiefs  will  contact  the  indicator  coordinator




(S.A. Alexander).






12.4.7 Prep Lab and Analytical Lab Requirements






      The laboratory will have the facilities for storage of samples and culture and identification of




fungi.






12.4.8 Safety Considerations






      All safety procedures recommended by  the FHM program and Virginia Polytechnic Institute




and State University will be followed.






12.4.9 Debriefing Requirements






      Time required: 1 hour.






12.4.10 Inventory and Storage Requirements






      Adequate refrigeration for samples until shipped to lab is required.






12.5 INFORMATION MANAGEMENT






      The authors will prepare an adequate quantity of labels for the field crew.  The authors will




manage sample and laboratory  data on  hard copy and  personal computers.  The  data  will be




forwarded to the FHM Information Management System as it is acquired.
                                           12-4

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12.6 REPORTS






12.6.1 Reports






      A.  Reports will be provided on training activities, field audits, and data evaluation.






      B.  The anticipated result of the literature work is a pilot study  proposal  which meets the




          research indicator development criteria described in Knappetal. (1990).






      C.  The authors will participate in analysis and reporting  of results in the Synthesis  Report




          referenced in Table 1.1.
                                             12-5

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         13. VEGETATION AND HABITAT STRUCTURE AS INDICATORS OF BIOTIC DIVERSITY





                                          S.
      Maintenance of biotic diversity is an assessment endpoint within EMAP-Forests. Biotic diversity




is at risk from six major types of threats: direct population reduction, physical alteration of habitats,




chemical pollution and solid waste pollution, global atmospheric change, introduction of alien




species, and cumulative or multiplicative effects of interactions among these major  threats (EPA,




1990).  Monitoring effects due to physical alteration of habitats will be the initial focus of EMAP-




Forests  because, while the  effects of global atmospheric change are potentially more serious and




widespread, physical  habitat alteration is an immediate concern and may  exacerbate  the potential




impacts of future atmospheric change (Figure 13.1).  Furthermore, habitat alteration or destruction




was identified as the greatest threat to diversity of birds, perhaps the best studied vertebrate taxon




(EPA, 1990).






      The Landscape Pilot is part of an overall  effort to select, develop, and test indicators of the




status and extent, trends, and risks to forests of the United States.  Numerous candidate indicators of




compositional, structural, and functional  aspects of biotic diversity  might be measured  depending




upon the objectives of the monitoring program  (Noss,  1990). Given an initial emphasis upon effects




due to physical alteration of habitats, the area,  range, pattern, and structure of land use/land cover




types and animal habitats are leading candidate response indicators (Figure 13.2, Table  13.1). It may




be necessary to monitor a suite of these response indicators to make a comprehensive assessment of




biotic diversity of forests.
  1 ManTech Environmental Technology, Inc., Corvallis, OR
                                            13-1

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         Stressor(s)
        Effects
   Response Indicators        Endpoint
       Physical Alteration
       of Habitats
      Conversion
      Fragmentation
      Simplification
Decrease forest area -
Increase dominance of
  favored patch type
Decrease mean patch area
Increase patch isolation —
Decrease area-sensitive
  species
                          Change dispersal of
                            plants, animals, and
                            pathogens.
Homogenization of forest
  structure (e.g. slocking
  control)
Loss of special habitat
  features (e.g. snags)
Decrease species diversity
  (e.g. tree monoculture,
  vegetation control)
Increase dominance of
  young age classes
»- Area and extent of
""   forests    	
Compositional
  heterogeneity
  Landscape pattern (e.g.
<*•   area, shape and
    arrangement of
    patches)

^ Land use pattern and
    road density  	
                                                               Structural
                                                                 complexity
                                  Species richness
                                                          ^ Vegetation stratification/
                                                          y   and patchiness —
  Population range and
    abundance

  Mortality rates
                                                                                         Functional
                                                                                           processes
Figure 13.1.  Relationship of Stressor, Response Indicator, and Biotic Integrity Endpoint. The stressor, physical alteration of habitats,
           is considered the most serious immediate threat to biotic diversity in the United States (EPA, 1990).

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                                                                   Organization level
Purpose
Focus
   Landscape / region

Provide extrapolation
  units for region
Coarse patch delineation
  and arrangement
   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
    Population / 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
Data source
Response
  indicators
Satellite and small-
  scale photo imagery

See Table 14.1
Large-scale photo
  imagery
Ground measurements on
  plots
  Figure 13.2. Relationship of Response Indicators for Different Organizational Levels of Biotic Integrity.

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Table 13-1. Response Indicators of Biotic Integrity
 A. Coarse patch delination - Landscape/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 connectivity of patches)
 B.  Fine patch delineation based on external features - Community/Ecosystem level (by source and
    person/organization)
    Remote-based  variables from  large-scale (1:6,000 or  12,000)  photos/Hermann  and  EPA
    Environmental Photographic Interpretation Center)
    •   Number of vertical strata
    •   Understory cover and composition in gaps
    •   Tree density and height
    •   Overstory cover, roughness, and patch!ness
    •   Forest area by class (conifer/deciduous/mixed)
    •   Forest type (Society of American Foresters System)
    •   Location and area of ecotones
    •   Land area by use type
    •   Landscape pattern (area, shape, juxtaposition, and connectivity of patches)
 C.  Fine patch characterization based on internal features - Population/species level (by source and
    person/organization)
    Ground-based variables from pole and quadrat methods/Cline
    •   Profile of understory vegetation cover
    •   Patchinessof understory vegetation cover
    •   Canopy cover
    •   Species and growth-form composition
    •   Species richness
    Ground-based variables from 24-ft radius subplots/FIA
    •   Tree species
    •   Tree diameter and basal area
    •   Tree density
                                            13-4

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13.1  OBJECTIVES
      Within the Landscape Pilot, the objectives for the vegetation structure indicator are as follows:

      1.  To test the operational feasibility of measuring vertical and horizontal vegetation structure
         with a point quadrat (pole) method.
         RATIONALE:  Within  EMAP  the need  to monitor  biotic diversity was identified.   A
         vegetation profile indicator was proposed originally to provide better assessment of non-
         tree, understory vegetation, which comprises most of the plant species diversity in forests,
         is more sensitive to environmental gradients, and  has higher turnover rates and thus a
         potentially faster reaction time to stress than trees.
         Furthermore, vegetation profile is an important aspect of wildlife habitat structure, which
         was  appealing because monitoring  animal  habitat in  EMAP  may be  a cost-effective
         alternative to directly monitoring animal populations. The pole method is an adaption of a
         proven method used by Short (1990) to ground-truth habitat layers estimated from aerial
         photos. The method measures quantitatively the vertical and horizontal arrangement of
         vegetation cover by species and growth form.  We limit our measurements to the lowest
         10 M of the understory for two reasons: (1) wildlife studies show  a need to define finer
         divisions of strata near to the ground as compared to the overstory (Karr, 1968, Willison,
         1974), and (2) data on the tree stratum is collected on the plots by other crew members.
         In  1990 the pole method was tested. Several adjustments have  been made for 1991  that
         require testing: (1) In order to better estimate spatial variability, sampling points will be
         established on all four subplots (1990 - 2 subplots), (2) in order to  save time, data will be
         recorded in 20, 0.5-meter intervals (1990 - 30, 1-foot intervals), and (3) in order to better
         estimate plant diversity, species will be recorded (1990-growth form).
      2.  To test the operational feasibility of measuring the floristic structure of forest stands using
         an area quadrat method.
         RATIONALE:   a comprehensive assessment of the biotic diversity of forests requires a
         reliable determination of plant species  composition, including cryptogams. For example,
         the reaction of plants to different environmental factors, competition,  and  disturbance
         varies  on  a species-specific basis (Daubenmire,  1959).   Furthermore,  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
                                            13-5

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    examined as potential  ecological  indicators when the species composition  of  sites  is
    adequately  represented.   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).
    Although floristics data is collected with the point quadrat (pole) method, the primary
    focus of the method is  estimation  of the vertical and horizontal structure of vegetation
    cover. Thus the pole method may not adequately sample floristic structure. For example,
    how well floristics data  from the pole method fully represents the species composition of
    the site is unknown.  Consequently, an alternative area quadrat method for measuring the
    structure  of  forest vegetation will  be evaluated  for use  in EMAP.   A difficultly in
    determining plant species  composition is  that the  number  of species  sampled (species
    density) increases with the area sampled up to some asymptote or continuously (Pielou,
    1977). Use of a series of successively larger contiguous quadrats allows such species - area
    relationships of plant communities to  be  determined (Mueller-Dombois  and  Ellenburg,
    1974). The results will be used as an empirical guide to whether most plant species have
    been  recorded  and to estimate the degree that  vegetation samples  over a certain area
    represent the total plant species at a site. In addition, results will estimate the area that can
    be routinely sampled at a site given certain time and manpower conditions.
3.   To compare plant species lists and quantities generated  from the point quadrat (pole)
    method with that generated from the nested area quadrat technique.
    RATIONALE: The  pole and  quadrat methods will provide independent estimates of the
    composition of vegetation cover.  The objective is to determine the relative efficiency of
    each method with respect to full  representation of species composition and to the rate of
    additional species accumulated per sampling or time unit.  Results will be used  to select a
    refined method for estimating floristic structure, separate from or combined  with  a
    method for estimating vertical and horizontal structure.
    If both methods fully capture the species present (i.e., an asymptote in species number is
    reached with increasing number of points or area), the minimal number of points or area
    that still captures all species can be  determined. If a species-area asymptote is reached for
    only one of the methods,  then the degree to which the other method represents the
    species composition can  be determined (e.g., 80%  of the species sampled). In both of these
    situations the relative efficiency of the methods can also be determined; that is, the rates
    of species accumulation by  area, point, and time. It is also possible,  or even likely, that
    neither method will  produce an  asymptotic  species-area curve due to self-imposed time
                                      13-6

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          limits for sampling.  For example, the point method has  been allotted 30 minutes per
          subplot or 2 hours total per plot and the area method has been allotted 60 minutes per
          subplot  or  4 hours.  In  this case,  the  relative efficiency  of the methods can  still  be
          determined.
      4.   To determine when predictable relationships exist among ground-  and  remote-based
          measurements of vegetation structure.
          RATIONALE: We will test whether certain structural and compositional elements of forests
          - for example, overstory cover and composition, tree and snag density, height,  percent
          coniferous/deciduous,  number and position  of  vertical   strata,  ground  cover  and
          composition in gaps -  can be assessed  from large-scale  aerial  photography  (or other
          remotely sensed data) and their comparability to ground-based measurements.  It is hoped
          that results will justify a reduction in the redundancy of measurement variables and the use
          of  expensive   ground-based  measurements.   It   is likely  that  some  ground-based
          measurements  will always be  needed to (1)  to 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 a ground-truth  as  relationships between remote and ground measurements are
          developed.  Furthermore, these types of studies will  be repeated regionally until a baseline
          relationship can be established.  Finally, the data will be used to determine the relative
          sensitivity  of  overstory  and understory  features  of the cover types  to natural  and
          anthropogenic stress.
      5.   To recommend  a refined and streamlined measurement system for vegetation structure for
          1992.
          RATIONALE: We  seek  sensitive indicators of forest vegetation structure that  can  be
          monitored precisely and cost effectively. Based on results from the previous objectives, we
          will recommend a subset of the current ground- and remote-based indicators most useful
          for assessing biotic diversity in a cost-effective manner.

13.2 DESIGN

13.2.1 Plot Selection

      The 20 plots selected  for the  landscape pilot will include a variety of  forest  types  and
elevations. Plots will be concentrated in Western Georgia.  These sites will present challenging and
                                            13-7

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diverse  conditions.   Experience at the pilot  level with such conditions will  indicate operational



capabilities and analytical difficulties and prepare us for pilots and regional implementation in other




regions of the US.






13.2.2 On-plot Sampling Scheme






      The  point  and area  quadrats  employ different sampling  schemes over  the same  areas



(Figure 13.3).  The point quadrats of the pole method  will be laid out as  a subset of the pattern



proposed for measuring PAR (see Chapter 14).  Vegetation profile will be  sampled on a subset of




seven of the 19 points used to sample PAR (Fig. 13.3a). This design will provide the data necessary to



analyze the relationship between vegetation profile and PAR.






      A series of area quadrats of increasing size will be used to sample the vegetation on the same



area as the point quadrats (Fig. 13.3b).  Plant species with different scale and intensities of spatial



pattern can be efficiently sampled using a series of increasing quadrat sizes.  Results will be used as a



guide to whether or not most species present are sampled.  Species lists from the point and area



quadrats will be compared.






      If all subplots of a plot are in the same land or forest cover type, then the subplots will be



measured in order 1 through 4. If subplots are split among different cover types, the subplots will be



ordered to ensure that all types are sampled once before any type is sampled twice.






13.3 QUALITY ASSURANCE






      Specific  QA information related  to this  indicator has been consolidated into the overall 1991



FHM program Quality Assurance Project Plan (Byers, 1991).
                                            13-8

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a)
b)
4
4
5
6
7
8
9
Plot No. Dimensions Area Cumulative
(m) (m3 'm3
1 0.75x0.75 0.5625 0.5625
2 0.75x0.75 0.5625 1.1250
3 0.78x1.50 1.1250 .2.2500
4 1.50x1.50 2.2500 4.5000
5 1.50x3.00 4.5000 9.0000
6 3.00x3.00 9.0000 18.0000
7 3.00x6.00 18.0000 36.0000
8 6.00x6.00 36.0000 72.0000
9 6.00x12.00 72.0000 144.0000
 Figure 13.3. On-Plot Sampling for (a) Plant Quadrats and (b) Area Quadrats in Relation to the
            Subplot.
                                            13-9

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13.4 LOGISTICS






13.4.1  Field Personnel Requirements






      One person will be mainly responsible for vegetation structure and PAR measurements on the




20-plot Landscape Pilot (see Chapter 15).  A primary qualification of this person is the ability to




identify, key out, or collect and press plant species found in northern Georgia.  This person will be




assisted by one or two crew members during plot layout, pole measurements, and possibly for data




recording.






13.4.2 Training .






      The main field botanist and any assistants will be trained. In addition, up to three more people




(one each from Rhinelander, Wl; Moscow, ID; and TV A) will be trained for a concurrent, off-frame,




research project on PAR methodology, in which vegetation structure data will be collected.






      The classroom training time will require about 30 min. An overview of the point and area




quadrat methods will be presented during this time  (10 min each), followed by a question/answer




session  (10 min).  I am  assuming that this will be presented to  a  general audience of all training




participants.






      Four hours will be needed for  field training. First, more detailed instructions for making  the




vegetation structure measurements will be presented (45 min).  Next, the field staff will practice




sample  point and quadrat lay out, pole and quadrat measurements, and  data recording (45 min).




Practice will be followed by a testing session during which the trainees will  remeasure two test plots




(1  h each).  These  plots will  represent  different structural and  compositional  conditions (e.g.




deciduous and coniferous, few to many vertical strata, tall vs. short stature,  high vs. low plant species




richness).  Field training  will  end with an evaluation and discussion  session  (30 min). Times for each




session are approximate  and will be adjusted to limit total time to 4 h.
                                            13-10

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      After individualized  training on the vegetation structure measurements, the field personnel




will work with the rest of the crew during "plot day."   Results of plot day will be evaluated  to




determine feasibility of proposed  time,  manpower,  and sampling  estimates,  and to  estimate




remeasurement errors under more realistic conditions.  The measurement schemes for vegetation




structure will be adjusted accordingly for implementation in the Landscape Pilot.






      A debriefing session  will be held at the end of the training session to discuss results of "plot




day,"  remeasurement evaluations,  and  necessary adjustments in measurement  procedures  for




vegetation structure (30 min).






13.4.3 Estimated Time On Plot






      The point quadrat method has been allotted 30 min per subplot or 2 h total per plot and the




area quadrat method has been allotted 60 min per subplot or 4 h. These estimates assume that the




FIA crew will establish the points for the pole sampling. The estimates include travel time between




subplots and time necessary to establish the boundaries of the nested area quadrats. The training




session will be used  to judge the realism of  the time estimates  and adjustments will be  made




accordingly.  If time estimates are exceeded, sampling adjustments will  first be made for the area




quadrats measurements.  Time will be saved by reducing the total area  searched on each subplot.




Reduction in the sampling of the point measurements will be made as a last resort, since sampling has




already been reduced from  1990.






      The person responsible for the vegetation structure measurements will split time with the PAR




measurements. The sequence of activities is envisioned as starting with set up of the solar radiometer




in  a nearby open area.  Pole measurements would begin at  Subplot 1  upon return to the plot,




assuming that the FIA already established the sampling points.  Area quadrat measurements would




follow.   Pole  and area quadrat measurement would  proceed on  Subplot  2  and so  on until




approximately  solar  noon,  at which  time vegetation  measurements  are  stopped  and PAR




measurements started. PAR measurements begin on Subplot 1 and continue on subsequent subplots
                                          13-11

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until they are completed.  At this point vegetation measurements are restarted  and proceed until
completion.

13.4.4 Transportation Requirements

      The field person responsible for vegetation structure measurements will be part of the regional
demonstration crew.  This person will sample  vegetation structure on the first 20 plots selected,
beginning in northern Georgia.

13.4.5 Equipment and Consumable Supply List

      The pole method requires:

      •   a telescopic pole capable  of reaching 10 meters, calibrated in decimeter increments and
          read at eye level
      •   bubble level, affixed to pole to aid vertical positioning
      •   wire pin markers with flags
      •   quiver (to hold pin markers)
      •   hand compass
      •   loggers tape
      •   binoculars (to resolve difficulties at top of pole)
      •   regional/local plant taxonomy handbook
      •   plant press, labels, indelible ink pens
      •   access to data recorder or supply of field sheets

      The area quadrat method requires:

      •   collapsable, plastic pipe, sampling frame
      •   ball or roll of cotton string
      •   double right angle prism for plot layout
      •   wire pin stakes with flags and quiver (to hold pin stakes)
                                            13-12

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      •  hand compass



      •  camera for photodocumentation




      •  loggers tape, metric



      •  regional/local plant taxonomy handbook




      •  plant press, labels, indelible ink pens



      •  access to data recorder or supply of field sheets




13.4.6 Communication






      The indicator lead will be involved with training prior to data collection. Results of the training



session will be discussed by trainers  and field personnel and changes in protocols outlined prior to



field implementation.






      Proposals to make significant  changes in vegetation structure protocols once data collection



begins will be communicated through  the regional  coordinator to the indicator  lead. Significant



changes  include  reduction in  sampling intensity (e.g.,  fewer plots  or subplots sampled,  fewer



measurements per subplot), changes  in time or labor allotments that might reduce data collection on



vegetation  structure, and  changes in prescribed equipment or  protocols that  might reduce data



comparability  among plots or reduce  data  quality (e.g., change in  frame size or quadrat area



sampled, denial of plant pressing privileges).






      Results of  program-level  QA checks will be reported to the indicator lead in a timely fashion



through the QA  regional coordinator.  Results of QA checks planned  by the indicator lead will be



communicated directly, and an audit report sent to the regional QA coordinator  and  the indicator



lead.   Ongoing  data problems, as  indicated  by measurement errors exceeding quality  control



objectives, will be reported directly to the indicator lead by the field botanist or through the regional



coordinator. Data problems will be discussed with responsible parties to assure improvement.
                                           13-13.

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13.4.7 Preparation Lab Requirements






      N/A






13.4.8 Safety Considerations






      All   personnel   working   in   the  field  for  training,  measuring,  quality  assurance




auditing/debriefing will adhere to the safety requirements of the Logistics Section of this Study Plan




and of their organization.






13.4.9 Debriefing Requirements






      The author will be responsible for participating in debriefing and  reporting appropriately to




the QA coordinator.






13.4.10 Inventory and Storage Requirements






      N/A






13.5 INFORMATION MANAGEMENT






      Pole data will be entered into the portable data loggers. The information on the data loggers




will be  verified  nightly by the field  botanist.   Data not meeting MQOs will be flagged  and/or




remeasured if possible.  Verified data will be down-loaded nightly to a  portable computer by the




botanist or crew chief.  Data.transmitted to the central data bank from the portable computers will




be verified and validated by the information manager and made available immediately to the EMAP-




Forestteam.






      Area quadrat data will be recorded onto field sheets or entered into the portable data loggers.




Area quadrat data on the data logger will be handled as described  for the pole data. Standard field




forms will be made available to field personnel. Data sheets will also be verified nightly. Field sheets




will be maintained by the field botanist or crew chief and copies will be sent weekly to the indicator
                                           13-14

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lead and to the information manager to be entered, verified, and validated. When this is completed
the data will be made available immediately to the EMAP-Forest team.

13.6 REPORTS

      Results of pretraining and training will be reported. This will be followed by a QA audit report
during the field season.  Results of QA activities and assessments of data quality will be reported as
part of the  Landscape Pilot report. The measurement associated with vegetation structure methods
will be reported as a component of total variability.

      Analytical reports will be  prepared on the following topics:

      •  the operational feasibility of measuring  vegetation structure with the  pole method and
         with the area quadrat method,
      •  the comparability of plant species lists and quantities generated from the pole and quadrat
         methods,
      •  the relationships of vegetation structure measurements with each other, with an emphasis
         upon the relationships among ground-  and  remote-based measurements of vegetation
         structure, and to site conditions and others indicators, and
      •  a measurement system for vegetation structure for 1992,  refined and streamlined based
         upon the reports in 1-3 above.
                                           13-15

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                      14.  PHOTOSYNTHETICALLY ACTIVE RADIATION (PAR)


                                 J.G. Isebrandsa and K. Riittersb



      The goal of the FHM program is to assess the health of forest ecosystems on a national scale.

Ecological indicators are needed to assess the effects of climate, pests, and anthropogenic stresses on

forest ecosystems, and to monitor the habitat condition of forests, so that assessments can be made

(Hunsaker and Carpenter,  1990). One indicator that  is  potentially applicable to all forest types and

that addresses  both stress-induced  changes in  both health  and  habitat condition is the quantity

(e.g., leaf area, leaf biomass) and production efficiency of the forest canopy (Russell et al.,  1989).

Knowledge  of  leaf  area quantity,  distribution, and phenology,  coupled with information  about

canopy efficiency in capturing and  utilizing light energy, provides insights  about forest health and

habitat condition that cannot be obtained from other indicators.


      A ratio constructed  from canopy measurements and tree growth data known  as "growth

efficiency"  (e.g.. Waring and Schlesinger, 1985) has  been shown to be an  integrative measure (or

indicator) of carbon assimilation and allocation  patterns. Environmental stresses that change either

tree growth or leaf area will often alter growth efficiency, and reduced growth efficiency has been

identified as a precursor of insect outbreaks and mortality (Mitchell et al., 1983; Larsson et al., 1983;

Waring, 1983). Moreover,  leaf  area index (LAI) can be  a  sensitive measure  of stress-induced

defoliation  of the forest canopy and changes in LAI have implications for many ecophysiological

processes in forests (Waring and Schlesinger, 1985;  Russell  et al., 1989).   LAI can also be used in

modeling efforts in  conjunction with remote sensing  to predict the effects of global climate change

on forests.
  a USDA Forest Service, North Central Forest Experiment Station, Rhinelander, Wl
  b ManTech Environmental Technology, Inc., Research Triangle Park, NC
                                             14-1

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      Many canopy/growth measures have been suggested for monitoring.  These measures range
from simple expressions of volume growth per unit of light transmitted by the canopy per unit area
per year  to more  complex  formulations that  consider phenology,  spatial  arrangement, and
environmental factors such as weather and pollution. However, many of these measures are difficult
and time  consuming to determine.  To be useful for the  FHM program, rapid and inexpensive
measurements are needed that have potential to be linked to remote sensing approaches. Thus, the
general goal of this pilot study is to develop procedures to accomplish those canopy measurements in
the context of the FHM program.  In the  FY91  pilot  study,  procedures will be tested that allow
efficient measurement of PAR.

14.1 OBJECTIVES

14.1.1 General Objective

      The general  objective of this and related  research is to develop and evaluate PAR as an
indicator of canopy condition that can be applied in monitoring the health and ecological condition
of U.S. forests.  This objective includes evaluating alternate  means of making the required PAR
measurements and developing  knowledge that will  enable  interpretation of data by  the  FHM
program.

      The  emphasis  of  the regional pilot  and demonstration  tests  is developing  a suite of
concurrently measured indicators including PAR in an operational setting. This rationale leads to the
following specific objectives of the FY91 field study.

14.1.2 Specific Objectives

      1.  Develop an efficient and reliable method of  using a  ceptometer and quantum sensors for
         measuring forest canopy light (PAR) environments under different stand conditions.
      2.  Develop and test procedures for linking PAR measurements to vertical vegetation structure
         (VVS) measurements.
                                           14-2

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      3.  Develop and test procedures for linking ground measurements of PAR to photointerpreted
         measures of stand and canopy attributes.

      The pilot field study will help to accomplish the first specific objective above, by (1) evaluating
and recommending new or  modified field sampling procedures, instrument modifications, and field
data  handling  procedures  for  measuring PAR with  a ceptometer  based  on  1990 20/20  pilot
experiences, and (2) recommending efficient sampling procedures to achieve specified precision for
various forest types and stand conditions.

      The second objective will be realized by measuring PAR and VVS on common sample points on
the subplots on  the  same  day  and relating  the measurements quantitatively as described in
Section 14.  For the third specific objective of linking photo- and ground-based  measures, the spatially
referenced  PAR measurements will be correlated  with forest canopy attributes derived  from the
1:12000 and 1:6000 scale photography as described in Section 17.

14.2 DESIGN

14.2.1 Plot Selection

      The PAR measurements will be made on the 20  locations selected for the "Landscape Pilot"
project in Georgia. The plot selection rules are dependent on the needs of all participating indicators
as well as on logistical constraints.  To meet the objectives of the PAR portion of the pilot project, the
20 selected stands should be a representative of available locations (to provide estimates of expected
regional variability of terrain,  forest type, and stand  conditions). PAR measurements in Georgia
should be made  during a  6-week "window" beginning  on or about June 15 after full  canopy
development and before canopy senescence.

14.2.2 On-Plot Sampling Scheme

      At each selected location, the standard FHM four-point subplot cluster will be established (see
Figure 3.1).  Under-canopy PAR  will  be  measured at  each of 19 sample points at each subplot
                                            14-3

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(Figure 14.1). The 19 sample points in each subplot are on a hexagonal grid overlying the subplot and

centered at the plot center. The six outer corners of the hexagon and the center point will be located

and marked during plot establishment. The other 12 sample points will be located (but not marked)

by pacing between corners.  The first sampling point will be at the plot center point followed by the

second sampling point at 30 ° azimuth, 24 feet from the center of the subplot. Subsequent hexagon

corner points will be at 30° intervals around the subplot outer circle for a total of six outer corner

points (i.e., #2, 4, 6, 8, 10, and 12). The remaining 12 points will be  as follows: six additional points

(i.e. #3, 5, 7, 9, 11, and 13) will be sampled one-half way between the outside corner points, and six

more  points (i.e., #14,  15, 16, 17, 18, and 19) will be sampled one-halfway between the corner points

and the subplot center.


      At each sample point, PAR will be measured using a "ceptometer" model SF-80 ( User's Manual,
                                                                                          •f
Decagon Devices, Inc.,  Pullman, WA). The ceptometer is a linear array-of 80 radiometers sensitive to

PAR (400 to 700 nanometers) coupled to a data processing and  storage  device.  The standard

operating procedures  described in  the following  section and in the methods manual  (Decagon

Devices, 1989) will result in at least 400 nearly instantaneous radiometer measurements within a circle

centered at the sample point. These measurements are taken for about  30 seconds, averaged, and

stored (one value per sample point) along with the associated  time of day (hours and minutes).  The

ceptometer also will calculate and store the average percentage of radiometers exposed to a preset

threshold PAR intensity.
                                           14-4

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                                  NORTH
     SUBPLOT     330
    BOUNDARY
       270
        PAR
      SAMPLE
       POINT
        AND
      NUMBER
210
                         30
                                                         12ft
                                                       BETWEEN
                                                        POINTS
                                                       ON LINES
150
                                               AZIMUTH FROM
                                              SUBPLOT CENTER
Figure 14.1. Photosynthetically Active Radiation Measurement Plot Layout.
                              14-5

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      Because incident, and thus transmitted, PAR is affected by ambient cloud conditions, it is



necessary  to  obtain concurrent measurements of incident PAR in .an open  area.  This will be



accomplished by establishing a sample location as near as practical to the plot, subject to the location



having a clear sky field of view of at least 45 °. At this location, two quantum sensors (Li-Cor model Ll-




190SB  or equivalent)  mounted on  1  meter PVC poles equipped with a  level  and  attached to  a



polycorder (Omega  Polyrecorder OM-160)  will  be used  to measure and  record incident PAR at



Ca1-minute intervals during the time that the ceptometer is being operated  under the canopy.



Measurements will be  synchronized  between the ceptometer and quantum sensors by synchronizing



the time clock on the polyrecorder and the ceptometer. The ceptometer and quantum sensors will be




calibrated to a Li-Cor LI-190SB.






      The timing  of PAR measurements is important because incident PAR and the percentage of



PAR transmitted by  the canopy vary with sun angle, particularly under cloud-free sky conditions.



Variation in incident PAR is accounted for by the concurrent measurements of the quantum sensors.



To obtain comparable estimates of  the  percentage of PAR transmitted by different canopies, it is



necessary to account for changing  sun angle by fixing either the time (at least) or sun angle (if



possible) at which the measurements are made. The PAR measurements will be initiated at 1100 solar



time (1200 daylight savings time) and completed by 1300 solar time (1400 daylight savings time) so as



to minimize the effects of changing sun angle. Procedures are being developed to permit field  crews



to determine the appropriate zone time to initiate measurements so as to achieve more comparable



sun angles.  Standard calculations  based  on latitude, longitude, date, slope, and  aspect will be



programmed into the PDRs or PCs (see Section 6) to permit this.






      At the end of the day, the field crew leader will transfer the data from the ceptometer and the



polyrecorder to a portable computer using software and protocols developed for this purpose.  All



data then enter the data stream  to the mainframe during the daily dump of plot data (see Section 6).
                                           14-6

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14.3 QUALITY ASSURANCE

      Specific QA information related to this indicator has been consolidated into the overall 1991
FHM program Quality Assurance Project Plan (Byers, 1991).

14.4 LOGISTICS

14.4.1 Personnel Requirements

Pretraining:
      Sarah Steele, USFS, Rhinelander, Wl
      Kurt Riitters, ManTech/EPA, Research Triangle Park, NC
      Elizabeth Smith, TVA, Norris, TN
Training:

      Sarah Steele, USFS, Rhinelander, Wl (PAR Trainer)
      Ronald Teclaw, USFS, Rhinelander, Wl
      J.G. Isebrands, USFS, Rhinelander, Wl
Crew:

      FIA crew member or shared GPS crew member - plot layout
      One person - PAR measurements (shared with VHS indicator - see Section 13).

14.4.2 Training Requirements
      Time required -4 hour
         Classroom -2 hour
         Field-2 hour

14.4.3 Time on Plot Required

      Time required - 2.5 hour
         Plot layout - 0.5 hour (done by FIA or GPS crew member, and shared with VVS, Section 13)
         PAR measurements - 2.0 hour
                                           14-7

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14.5 INFORMATION MANAGEMENT

      The PAR measurements are supported by information management in the following ways.
                                   %
      1.   Preparing and testing software and hardware connections to transfer data from the field
          equipment to the PDRs and PCs, and from those devices to the mainframe. The ceptometer
          procedures developed for the 1990 field test will be modified slightly and utilized  again.
          The data transfer from the polyrecorder procedures will have to be developed and will be
          shared with the field crew at training.
      2.   Programming the polyrecorders to automatically query the quantum sensors and store
          time-of-day and ambient PAR. (Tentative)
      3.   Managing and maintaining ASCII data files resident on the mainframe, and facilitating
          access to PAR data and other measurements for data analyses and reports.
      The IM function will be supported by the preparation of two reports at the end of the field
season:

      1.   Data editing and verification report, including edit trail
      2.   Meta-data file to permit archival  of crew comments and any other pertinent information
          about each plot that is not otherwise captured by the IM system

14.6 REPORTS

      The following reports will be produced as a result of this study.

      1.  A  training/operator  certification  report,  provided  to  the project  manager and  QA
          personnel (Figure 14.2).
      2.  A  field  audit/operator recertification  report,  provided to the project manager  and QA
          personnel.
                   !
      3.   IM reports as described in Section 14.5.
      4.  A summary QA report, provided to the project manager and QA personnel.
      5.  A project report concerning data analysis and significant findings, to be included  in the
         overall project report.
      6.  Depending on the  findings, a  research  manuscript may be prepared based on the PAR
         measurements, alone or in combination with measurements at the complimentary research
         sites.
                                           14-8

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                      FHM/EMAP                     Name
                   Certification Form                  Date
                    PAR Procedures                   Trainer
                                                    Pass:  Yes	  No
         I.  EQUIPMENT OPERATION              Pass            Notes
                     Sampling       '           	
                     Average/store              	
                     Erase mistake              	
                     Send data                 	
                     Clear memory              	
                     Time of day                	
                     Battery change             	
                     Calibration                	
                     Transport/storage             •
                     Safety                    	
                     Clean probe               	

         II.  PLOT PROCEDURES
                     Subplot locations           	
                     PAR sample locations       	
                     Order/number of points      	
                     Locating ambient station     	
                     Time of day                	
                     Rain procedures           	
                     Tall shrub technique        	
                     Rotation technique         	
         III. PDR PROCEDURES
                     Cable attachment           	
                     PDR software              	
                     Weather entry codes        	
                     Comments                	
         IV. BASIC CONCEPTS: PAR

         V.  TEST PLOT RESULTS
                                           % TPAR by
                                         Crew     Trainer   Difference
                           Subplot #1    	       '      	
                           Subplot #2    	    	    	
                           Subplot #3    	    	    	
                           Subplot #4    	    	               pass
                           Overall

Figure 14.2.  PAR Training/Certification Form.
                                       14-9

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14.7 DATA ANALYSIS

      The data will be analyzed in several ways to help address the following.

      1.   Questions about logistics and measurement procedures can utilize the training, audit, and
          other QA data to develop recommendations for modifying standard operating procedures
          and to develop DQOs for future tests.
      2.   Questions related to sampling  designs can utilize the experimental layout (nested design
          with plots, subplots, and sample points as levels) to estimate variance components and
          develop recommended numbers of samples for future studies.
      3.   Depending on the plot selection rules, the analysis of variance can also be used to contrast
          various groups of plots to test hypotheses of interest (e.g., contrasts among forest type,
          size, or density classes).
      4.   Exploratory  analyses of associations between point-  and plot-level measurements of PAR
          and other ground-based indicators can help to elucidate the inter-relationships among
          various indices of forest condition and serve to direct future studies.
      5.   Exploratory  analyses of associations between spatially referenced PAR measurements and
          the photointerpreted measurements of site and stand attributes can help  to establish the
          linkages  between ground-based and  remote sensing measurements and serve  to direct
          future studies.

      It is anticipated  that the PAR data will be analyzed using the  percentage of transmitted PAR
(%TPAR) as the response variable of interest.  %TPAR can be estimated by a time-referenced ratio of
within-canopy ceptometer  measurements  to ambient quantum sensor measurements.   Several
estimation schemes will be tested on other sites to develop the appropriate procedure (e.g., ratios of
running averages or minute-by-minute measurements, value of logarithmic transformations,  etc.).
                                           14-10

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                              15.  GLOBAL POSITIONING SYSTEM





                                        K. Hermann*








      In the Landscape Pilot study, and on a limited basis in New England, GPS technology will  be




used to accurately determine point locations. In both areas, the x, y, and z geodetic coordinates will




be determined for the field plot center. In the  Landscape Pilot, coordinates will also be determined




for ground control locations for high-resolution aerial photography.  The establishment of x, y, and z




coordinates for the plot center will be useful for the logistical purpose of relocating the same location




in subsequent visits and for the information management purpose of accurate sample location.






      The  ground control coordinates  will be used in the  rectification  process  of the  aerial




photography in order to obtain a planimetrically correct interpretation of the aerial  photography




which will be acurately defined to a datum. The North American Datum of 1983 (NAD83) will be the




datum employed, that will be visible on the photographs. The ground control locations determined




during the first few weeks of the pilot operations will be  paneled so that the locations can  be




interpreted from the aerial photography which will be obtained during the third week of operations.




Ground control locations after the third  week of operations will  be referenced to features or




reference  points that will be visible on the photograph. The photography rectification procedure,




done with an analytical  stereo plotter,  will enable the accurate capture of the characterization




delineations and subsequent entry into a CIS.






      Accurate GPS coordinate determination requires simultaneous operation of both a base station




GPS  receiver and a remote, or field, GPS receiver.  One  person will operate the  base station on a




known set of x, y, z geodetic  coordinates while another person  will accompany a field crew and
  a ManTech Environmental Technology, Inc., Research Triangle Park, NC
                                            15-1

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record at the plot center and photo control locations.  The data from each of the receivers will be run
throagh a set of programs for differential correction in a postprocessing mode at EPA.

15.1 OBJECTIVES

       1.   To use GPS technology to accurately determine  and record the x, y,  and z geodetic
          coordinates of the FHM field plot center.
      2.   To use GPS technology to accurately determine  and record the x, y,  and z geodetic
          coordinates of  ground control  locations to be  used in  the rectification  of  aerial
          photography interpretations.
      3.   To establish a procedure for incorporating detailed and landscape characterization data
          into a CIS.

15.2 DESIGN

15.2.1  Plot Selection

      The plots visited should be more remote locations where physical reference points are few so as
to test the utility of GPS  in  such areas.  The GPS field activity in the Landscape Pilot should be
accomplished shortly before (0  to 3 weeks) the photography is  done so that the panels of ground
control locations are visible.  If too much time elapses  between the paneling and the overflight,
panels may be lost.  Given the tentative flight schedule of Forest Pest Management for the week of
July 8th, the Landscape Pilot field activites should begin at least by June 17th.

15.2.2  On Plot Sampling Scheme

      In both New England and  in the Landscape Pilot, the GPS coordinate determination of the FHM
plot center will accurately determine the plot's geodetic position. In situations where a dense canopy
will prohibit reception of satellite signals, coordinates will be determined for the nearest available
opening. Surveying techniques  will be employed to link  the GPS coordinate determination location
and the plot center.  This plot center determination is the  only GPS activity on the New. England plots.
                                            15-2

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Two New England crews will be supported with GPS receivers. The base station will be continuously




operated by EPA Region I personnel.






      Global Positioning System coordinate determination will be done for the FHM plot centers and




the ground control locations on the 20 Georgia pilot plots in the Landscape Pilot.  In addition to the




plot center, there will be a minimum of 8 ground control locations established for each FHM plot




area. All of these ground control locations will be paneled or referenced to visible features. The




Landscope Pilot GPS field operator will  establish coordinates and record  data  at  each of these




locations. These ground control locations will be distributed in an approximately 400 hectare circular




area around the plot center.  At least 4 ground control locations will be placed near the perimeter of




the 400 hectare circle in a fairly uniform spacing in order to attempt to establish a good distribution




over the area. The locations of the ground control points should be near existing roads or trails for




easy accessibility. The locations should also be in the open so that the panels can be observed on the




photography.   The Landscape Pilot  base  station  GPS  operator  will operate  the base  station




continuously on a known benchmark location during the day  in order to coincide with the timing of




the field operator recordings.






15.3 QUALITY ASSURANCE






      Specific QA information related to.this indicator has been consolidated into the  overall 1991




FHM program Quality Assurance Project Plan (Byers, 1991).






15.4 LOGISTICS






15.4.1  Field Personnel Requirements






      In the Landscape Pilot, the staffing requirement is for two people capable  of working in the




woods and of being trained in the use of electronic instruments  for  a 7 week period during the




summer. One of these individuals would accompany a crew to the 20 pilot plots selected from the set




of Nutrient Cycling Demonstration plots. This person will take GPS readings at the plot center and the
                                            15-3

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ground control point locations.  These ground control points will be paneled where necessary.  This




GPS operator should be familiar with  basic surveying techniques. Ideally, this person will also  take




field notes for the aerial photography ground truth purposes. Given that additional task, this person




should be also be skilled in aerial photo interpretation. Preferably, a photo interpreter could be the




field GPS operator in order to gain first-hand knowledge of the area that he or she will interpret.






      In  New England there is no aerial photography component, therefore, field requirements are




for one person to take GPS readings for the plot center only. If surveying is required another person




can assist in that task.  This activity will take place with  two of the New England crews. The  base




station GPS receiver will be operated by an EPA Region 1 staff person for a number of summer GPS




activites.  Therefore, there is no requirement for a separate base station operator. The two field GPS




operators in New England should both be capable of working in the woods and of being trained in




the use of electronic equipment for a 9 week period in the summer. Both of these people should also




be.familiar with basic surveying techniques.  The GPS operator positions should be incorporated into




existing jobs because the time requirement is small.






15.4.2  Training






      All GPS  operators will need  to be trained  in the proper use of GPS.  Training for both  the




Georgia and New  England crews would  require several days in the  field  and would be done in




conjunction with the overall demonstration training.






15.4.3  Estimated Time on Plot






      In  both  New England and the Landscape Pilot, the operation of the field GPS receiver will




require approximately one half  hour for the plot  center coordinate determination.  An additional




half hour may be required if some surveying is needed due to canopy interference with GPS reception




at the pilot center location.
                                            15-4

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      In the Landscape Pilot there is an additional time requirement for the field GPS person to




record and panel the photo ground control locations for 5 hours per day. The Landscape Pilot base




station operator will need to operate the base station GPS receiver continuously for 6 hours at the




same site.  This base station site is at a location of known geodetic coordinates which must be located.






15.4.4 Transportation Requirements






      One vehicle would be shared among the Landscape Pilot GPS crew. The field GPS operator in




the Landscape Pilot should also have a mountain bike with a rack for the GPS equipment in order to




get around the 400 hectare area.






      The New England operations require that the field GPS operator be able to get to and from the




starting point for the plot by vehicle.






15.4.5 Equipment and Consumable Supply Procurement Need






      Equipment needs consist of the GPS equipment and  supporting materials and tools.  Three




remote GPS receiver units (two for New England and one for the Landscape Pilot) and a base GPS unit




for the Landscape Pilot are required. The New England base GPS unit is already in  place. Each field




crew requires a  laptop computers with a modem and 20 megabyte hard  disk.  These laptops will be




used to store transferred data from the GPS unit each day. The modems will be used in the transfer of




the daily GPS recording, stored on the laptop, to the EPA host computers.  Sixty 4x4 foot black plastic




sheets (6 mil) with four inch wide 'V's painted in white are needed for paneling.  Eight stakes for each




panel are  required.  Each field crew needs a  30- to 50 meter measuring tape, an inclinometer  for




surveying  requirements,  and  a 3-5  meter  height pole for attaching  the GPS field receiver, and a




compass.  Additionally, plastic protective covering  for photographs and computer plots are needed.




A mountain bike and a rack for the GPS unit and panels are needed for the Landscape Pilot.
                                           15-5

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15.4.6  Communication






      There should be some type of communications device for communication between the GPS



crew members in the Landscape Pilot. There is no communication requirement for New England.






15.4.7  Debriefing Requirements






      Debriefing would require 1 day for the base station and each field operator.






15.4.8 Inventory and Storage Requirements






      Storage of GPS units, laptops, and other field equipment is needed to prevent damage in



transport and when they are not in use during the field season.






15.5 INFORMATION MANAGEMENT






15.5.1  Electronic Data Recording Capability






      The time  and plot referenced  location need to be recorded for each GPS reading on a PDR



because the GPS polycorder does not handle this. All GPS data recordings will be captured on the GPS



polycorder device for each day's reception.






15.5.2  Description of Codes






      Unique plot-referenced GPS  site  location  codes will  be  established with  file-naming



conventions for each hexagon.






15.5.3  Explanatory Text to be Used in Help Screens






      Menus are available with GPS unit polycorders. Menus and software are also available for the



laptop.
                                           15-6

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15.5.4  Order of Data Collection Within Field Sampling Activity


      GPS field and base reception of coordinates in 3-D on the GPS polycorder, then after the day's

field work, there needs to be a transfer of the day's data to the laptop computer with specific naming

conventions, and finally transporting by modem to the VAX or temporary storage of the data on

floppy disk. Nightly recharging of the GPS polycorder is needed.


15.5.5  Data Security Requirements


      Plot center coordinates are confidential information that will not be made public access.


15.5.6  Computer Hardware and Software Needs  and Availability  for  Data  Quality Assurance,
       Summarization, and Analysis


      A PC with software is required to  determine satellite availability, for postprocessing the data,

and for datum conversions.  This PC has already been obtained and is located at the EPA in Research

Triangle Park, NC.


15.6  REPORTS


      Data will be  reported in the manner described  in the general section  on Data Reporting.

Contributions will be made to the QA report, the Methods  Manual,  and  the Field  Study summary

report.
                                           15-7

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                        16. HIGH-RESOLUTION AERIAL PHOTOGRAPHY





                                        K. Hermann^







      The purpose of the aerial photography component of the Landscape Pilot study is to evaluate



the utility of high resolution aerial photography in the Tier 2 sampling process and provide materials




for the linkage of Tier 2 and Tier 1  indicators. In the pilot study effort, the logistical implications and



the informational contributions of the photography will be examined.






      The high-resolution photography will be used to characterize the landscape of the field plot



locations and  associated surrounding area.  Specifically, the  landscape characterization  will  be



performed on a 400 hectare circle centered on a field plot.  The characterization includes interpreting



and mapping with a detailed classification of both the land cover and land use. This classification is



an enhancement of the EMAP-Landscape  Characterization classification and will be performed with



1:12000 scale color infrared aerial photography.






      The  characterization  derived  from  the   high-resolution  photography  will  provide  an



opportunity to develop linkages between field measurements, the remotely sensed interpretations of



landscape processes and Tier 1  landscape  indicators.  Such a linkage is not as apparent between the



40 square kilometer landscape characterization and the  field measurements  because of  the lower



resolution of the  remote sensing materials used in covering the broader area. The high-resolution



photography will  provide an intermediate instrument for the linkage of the field measurements and



the larger area characterization by allowing more appropriate changes in scale.






      High-resolution photography will be used to make specific interpretations in addition to the



landscape classification and mapping effort. These interpretations will  be made  with  1:6000 scale
   ManTech Environmental Technology, Inc., Research Triangle Park, NC
                                            16-1

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color infrared photography which will be obtained for a smaller area around the center.  These
specific interpretations may provide significant information about forest ecosystem condition which
is useful in connection with vegetation structure and  wildlife habitat indicators as well as stressor
indicator  measurements of  pest  populations. The  1:6000 scale  photo interpretations will  be
performed in a 100 hectare circle centered on the field plot and will focus on field measurement
locations.

      The  USFS  Region 8  Forest  Pest  Management (FPM)  group will obtain  summer  aerial
photography at the scale of 1:12000 in  the State of Georgia  for the purpose of detecting pest
conditions at the FHM plot locations. In addition to the 1:12000 scale, FPM will obtain 1:6000 scale
photography for the 20 plot locations in the Landscape Pilot.

      The combination of the high resolution  aerial photography interpretations and  the GPS
coordinate determination (Section 15) allows for the precise mapping of the field plot location and
broader 400 hectare photo plot. An analytical stereo plotter will  be used in the mapping procedure
to obtain accurate digital results of the interpretations. This precision mapping facilitates the entry
of the data into a GIS. Subsequently, this process facilitates the analytical and change detection tasks
that will be eventually performed with the data.

16.1 OBJECTIVES

      1.   To provide detailed landscape characterization  information derived  from high resolution
          aerial photography for supporting the development of some response  indicators.
      2.   To use  the  landscape -characterization  information  for  examining associations with
          response indicators and some remotely sensed indicators. The study will provide materials
          for investigating techniques to determine the linkages  of  field measurements and
          landscape characterization information.
      3.   To provide high resolution aerial photography for remotely sensed indicators such as forest
          pest data derived from photo interpretation.
                                            16-2

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16.2 DESIGN






      Aerial photography will be obtained for each of the Landscape Pilot FHM field plot locations.



At each of these field plot locations stereo triplet photo coverage will be obtained at both 1:12000



and 1:6000 scales. The coverage of each of these scales will be centered on the FHM field plot.






      Interpretations of the  1:12000 scale photography will use an enhanced  classification of the



EMAP-Landscape  Characterization classification scheme to  classify a  400  hectare  circular  area



centered on the field plot.





      Interpretations of  the 1:6000 scale photography  will focus on  specific locations that are



coincident with the PAR, vertical vegetation, and wildlife habitat indicator sampling locations.






      Interpretations of  disturbance,  defoliation,  and  mortality will  also be  made within  a



100 hectare circle around the field plot.






      The design  layout of the two photoplots is shown in Figure 16.1.  In the top diagram of this



figure, the area "A" represents a 40 square kilometer hexagon with respect to the two photoplots.  In



the middle diagram, "B" illustrates  the 400 hectare photoplots with respect to  the 100 hectare



photoplot, "C", and the FHM field plot, "D". The bottom diagram shows the FHM field plot.






16.2.1  Plot Selection






      Twenty field plot locations will be chosen in Western Georgia that represent several different



forest cover types and terrain conditions.  The  plots will  be selected  from  the  set of Nutrient



Demonstration plots. Selection will be determined from examining existing photography of the field



plot locations and from the written descriptions of the plots which will be provided by the FIA plot



establishment crews which will visit all the Georgia plots in March, April, and May.
                                             16-3

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Figure 16.1.  High-Resolution Aerial Photography Plot Design. (A) 4000 hectare EMAP hexagon.
            (B) 400 hectare 1:12,000 aerial photoplot. (C) 100 hectare 1:6,000 aerial photoplot.
            (D) 1 hectare FHM plot.
                                            16-4

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16.2.2  On Plot Sampling Scheme






      The ground truth notes taken in the field should be distributed around the entire 400 hectare




photoplot.  These locations can be identified by the GPS ground control locator.  Ground truth notes




should also be taken at the measurement locations for vegetation structure and PAR.






16.3 QUALITY ASSURANCE






      Specific QA information related to this indicator has been consolidated into the  overall 1991




FHM program Quality Assurance Project Plan (Byers, 1991).






16.4 LOGISTICS






16.4.1  Field Personnel Requirements






      One field person is required to take field notes  and to make mapping notes of the  composition




of land cover and  land use of  the 400 hectare area.  This person will be required to take more




extensive notes within the 100 hectare area and in particular at the locations of other measurements.




The field person should have knowledge of aerial photography interpretation, mapping techniques,




and be able to identify most tree species in Georgia.






      Several  aerial photography interpreters are required for the post  field   season photo




interpretation tasks which will  begin after  the false color transparencies of the photography  are




acquired. At least one of the interpreters should also have operational experience with an analytical




stereo  plotter. All of these interpreters should  have extensive experience with interpreting large-.




scale aerial photography and forest cover types.






16.4.2 Training






      The field person will be trained on how to take field and mapping notes, what  to identify, and




how extensive the notes need to be.
                                            16-5

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      The aerial photography interpreters will be trained with 10 of the 20 pilot photo plots with the



enhanced classification. Photo keys will be developed from these 10 photo plots.






16.4.3 Estimated Time on Plot






      The estimated time on the 400 hectare area for taking field notes is 3 hours, however, this



activity can easily be integrated with the GPS coordinate determination (Section 15) for efficient plot




time activities.






16.4.4 Transportation Requirements






      The field person will need to get  to and from the plot by vehicle.  A mountain bike may be



useful in getting around the 400 hectare area.






16.4.5 Equipment and Consumable Supply Procurement Need






      A notebook for field notes is required.  Additionally, sets of existing photography for each of



the field plot locations are required with transparency covers.  Fine-tipped color waterproof pens for



making notes on the existing photography are also required. Computer plots of the 400 hectare area



depicting  the transportation network  and  hydrography  will be provided with a  same  scale



transparent  base overlay  of the full  Landscape Pilot design.  This overlay  will  indicate  the full



configuration of the field plot, photo plot circles, and measurement locations.






      The post field photo interpretation work will require at least 2 stereoscopes and light tables



and 1 analytical stereo plotter.






16.4.6 Inventory and Storage Requirements






      The computer plots, photography, and field  notes will need to  be  organized and  labeled



appropriately for their respective plot locations.
                                            16-6

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16.5 INFORMATION MANAGEMENT


16.5.1  Paper Field Form


      The field notes should be identified by the appropriate plot location, the longitude/latitude

EMAP hexagon-ID. The computer plots and the photography will be prelabeled with the same ID

system.


16.5.2  Electronic Data Recording Capability


      The photo interpretations will be mapped and entered into a GIS via digital capture with the

analytical stereo plotter with standard photogravi metric mapping techniques.


16.5.3  Lists of Acceptable Code


      Codes will be developed for the enhanced EMAP Landscape Characterization classification.

These codes should be consistent with the Characterization's coding conventions.


16.5.4  Description of Codes


      Detailed land use, land cover codes, and specific interpretation codes for defoliation, mortality,

and such will be developed.


16.5.5  Computer Hardware and Software  Needs  and Availability  for  Data  Quality  Assurance,
       Summarization, and Analysis


      A workstation with ARC/INFO GIS software and an analytical stereo plotter with an appropriate

hardware and software interface are required.


16.6 REPORTS


      Data will be reported in the manner described in the general section  on  Data Reporting.

Contributions will be made to  the QA report, the Methods Manual,  and the Field  Study summary
                                           16-7

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report.  A separate report will detail the procedures used in the aerial photo interpretation and



linkages to other indicators.
                                             16-8

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                             17. LANDSCAPE CHARACTERIZATION


                               K. Hermann3 and R. Czaplewskib



      The Landscape Characterization group of EMAP focuses on the documentation of the physical

pattern of ecosystem components and land uses. This documentation, in the form of CIS coverages,

will provide the materials to analyze the pattern and the changes of the pattern over time.  The basic

products of EMAP-Landscape Characterization will support all of the resource groups with small-scale

remotely sensed materials and auxilary digital data.  This level of characterization is focused on full

hexagon characterization.


      The EMAP-Forests component of EMAP will utilize the materials provided by EMAP-Landscape

Characterization in the FHM program, however, there will be additional characterization work done

jointly between the two groups that will utilize higher resolution remotely sensed materials and will

focus more on specifics of the forested ecosystems.


      Given that the FHM program is being designed as a multiagency cooperative endeavor, it is

desirable that the systematic EMAP grid sampling design be linked within some type of framework to

existing forest health and management monitoring programs  such as the FS-FIA and FPM programs.

Linkages between these existing sampling frameworks can be facilitated through the application of

multilevel landscape characterization monitoring.


      The first level of the multilevel sample would be designed to permit stratification on landscape

features such as landform, and forest/nonforest. Several strata could occur in  any one 40 square

kilometers  EMAP  hexagon.  Landform-forest-cover  delineations would then be used  to select a

sample framework for high-resolution, second-level photoplots.  For example,  nonforested strata
  a ManTech Environmental Technology, Inc., Research Triangle Park, NC
  b Forest Service Rocky Mountain Forest Experimental Station, Fort Collins, CO
                                            17-1

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might be sampled at a lower intensity to monitor aforestation, or deal with errors in detecting forest




cover on low-resolution aerial images.  Habitat, forest type, or other criteria that are expensive to



apply to entire hexagons, might be used to provide a framework for developing extent estimates for




hexagons from plot-level indicator measurement data.






      The second level would be designed for inexpensive remeasurements of a few basic indicators



of forest health.  For example, tree mortality and defoliation may be measured using high-resolution



aerial photography and/or videography. Because high-resolution imagery has a narrow field of view,



complete coverage of each 40 square kilometer primary sampling unit with high-resolution imagery is



impractical.  A second-level sample plot is proposed using 3 to 10 second-level photoplots in each



40 square kilometers first-level sample unit to accurately estimate tree mortality and tree defoliation.



These conditions are often rare  and  not spatially contiguous (although  there are many exceptions),



and large photoplots would more efficiently quantify mortality and defoliation than smaller field



plots. The least expensive indicator would be the number of dead or defoliated trees per unit area



(status and extent).  However, to estimate the rate of change in mortality and defoliation extent, the



number of trees in each second-level photoplot might have to be estimated from the high-resolution



imagery, perhaps via subsampling the imagery. Rate estimation  requires that each individual sample



tree must be found on two dates of imagery taken 12 months apart, possibly requiring a reduction in



the size of  the  second-level photoplots to save  interpretation time.  Detection error  may  be



significant, especially for large plot sizes, and methods should be adopted to estimate the proportion



of dead or defoliated trees that  are not detected with interpretation of aerial imagery. It might be



desirable to use aerial  photography once every 5 to  10 years for estimating forest type, tree heights,



tree species,  regeneration, fuel loading, habitat type, stocking density, and stand development, and



to use aerial videography for the same plots in intermediate years for less expensive measurements of



tree mortality and defoliation. An interpenetrating  rotation between aerial photography and aerial



videography is also possible.
                                            17-2

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      FHM plots would be nested within the framework of the one square kilometer second level




plots to take advantage pf the annual monitoring for tree mortality and defoliation at the second-




level. Disturbance history for each plot interpreted from remote sensing, the need to quantify the




error in detecting tree mortality and defoliation with remote sensing at the second level, and would




permit extrapolation of FHM indicator data to the more extensive spatial framework. This integration




within the extensive framework would also provide a mechanism for comparative evaluation  of FIA,




FPM, and FHM data.






17.1 CONCERNS






      Efficiencies and  precision are gained  by  emphasizing remote sensing,  but  there is limited




infrastructure in place to acquire,  coordinate, interpret, and archive this source of data. To  ensure




consistency and quality, the  remote-sensing  activities would have to be institutionalized.  Ideally,




there would be a small  number (maybe one) of units that have direct responsibility for this function.




The unit(s) might be branches of existing units with related  missions, such as FIA, FPM, or State




Forestry agencies.






17.2 SYNERGIST1C BENEFITS






      FPM currently produces annual assessment reports on insects and diseases in the West. It might




be  possible to produce these same reports using annual defoliation estimates from high-resolution




aerial photography, and less frequent field examinations of FHM plots.  FPM might be able to make




minor adjustments to its current program to contribute to FHM, while meeting its current objectives




in a perhaps more efficient and  rigorous manner.  Similarly, there are  several new monitoring




initiatives in  the West:  detection  of possible effects  from global  climate change, and changes  in




condition of wilderness areas.  It might be possible to design one or two compatible sampling  frames




that more efficiently serve several different sets of objectives.
                                            17-3

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      The use of the PROGNOSIS model as the baseline for growth and mortality can also be used to




validate and  improve  this model.   PROGNOSIS is commonly used by the FS-NFS for their strategic



planning  (e.g.,  FORPLAN),  and  improvement of  planning  models  will  directly  improve  NFS



management. As part of Forest Plan monitoring, assumptions used in the planning process must be




verified.   Models such as PROGNOSIS are regional  in nature,  and are collections of numerous



assumptions on  growth and  mortality rates that directly affect the land management planning



process. Likewise, the use of fuel  loading and forest insect and disease risk models as forest health



indicators  will lead  to improvements in those models, with a  potential to  improve very expensive



management actions for fuels, insects, and diseases.






      High-resolution  aerial  photography  could be used  to reliably interpret  forest type, crown



closure, and stand development on a sample of FHM photoplots. A subsample of FHM  plots could be



very useful for labeling or training digital classifiers of satellite data (e.g., Landsat), and  for quality



control in the production  of vegetation cover maps. Another subsample of FHM  plots could be used



to estimate statistical calibration models that correct for misclassification bias in areal estimates.  This



would be valuable to national forests and other agencies for reliable mapping of wildland resources



in the West, and unbiased  aerial estimates used in local land-management strategic planning.






      High-resolution  aerial  photography  may  be suitable  for  estimating leaf area index or



photosynthetic efficiency, which are measurements related to other potential indicators of forest



health. This might be tested in future research studies.
                                            17-4

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                                  18. AIR AND DEPOSITION


                         D. Shadwick.a R. Baumgardner.b and L. Smith*



      The FHM program Air and Deposition Group has been examining  air constituent monitoring

data that will have relevance for regional forest health monitoring. Monitoring data from a variety

of sources is currently being examined. The data sources include the following: the Acid Deposition

System (ADS), the National Acid Deposition Program (NADP), the National Dry Deposition Network

(NDDN), and state monitoring systems for wet deposition ions and precipitation amount; NDDN and

National  Oceanic  and Atmospheric  Association  (NOAA)  for concentrations of dry deposition

constituents; and the Aerometric Information and Retrieval  System (AIRS) and  NDDN for hourly

ozone concentrations.  Only monitoring sites that are in close proximity to forested areas and not

located within urban areas have been selected for data summarization thus far.


      Relative to the EMAP sampling frame, the monitoring data, in general, is off frame data. For

direct application  to the  FHM program,  suitable interpolation and/or  summarization  of the

monitoring data for regions of interest will have to be carried out. Currently, maps of wet deposition

ion concentration  and deposition amount on  an annual basis over large geographic areas are

produced by NADP. There  are not any corresponding maps for dry deposition constituents. A few

interpolated maps of selected summary ozone  statistics have been produced and are available.

Interpolated maps specific to the FHM program interests have not been produced at the present time.


      Recently, the FHM program Air and Deposition Group cooperated with the National Forest

Service New England FHM Program to supply summary air constituent information and descriptions

for an annual report on the New England region. Sulphate and nitrate ion wet deposition values on

an annual and quarterly basis, sulphate and nitrate dry deposition concentrations on an annual basis,
  3 ManTech Environmental Technology, Inc., Research Triangle Park, NC
  b U.S. EPA, Atmospheric Research and Exposure Assesment Laboratory, Research Triangle Park, NC
                                            18-1

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and a summary ozone statistic (SUM06 - the sum of all concentrations greater than or equal to



0.060ppm) on a seasonal (April-October)  basis over the history of selected .sites were provided in



tabular or graphical form.  In addition, isopleth maps  of 1989 annual  sulphate and nitrate  wet




deposition amount were obtained from NADP for inclusion in the report.
                                           18-2

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                                         19. CLIMATE


                            E. Cooter.a P. Finkelstein.a and S. LeOuca



19.1 BACKGROUND


      Climate  conditions  impact forest  health and productivity  directly through disturbance

phenomena such as windthrow, hail, flooding, or drought events.  Secondary impacts are seen in soil

building and erosion processes, nutrient cycling and pest and pathogen outbreaks (Kozlowski, 1985;

Henry and Swan, 1974; Pickett and White, 1985; Solomon etal., 1984).


      The most important ecological characteristic of a  disturbance is its time lag or periodicity.

When an  environmental  factor such as temperature  or precipitation  oscillates regularly, species

distributions change until, at some point this factor can no longer be considered a disturbance. Thus,

the distribution through time of climate conditions, including natural variability, can be one measure

of ecosystem stability (Forman and Godron, 1986; Woodward, 1987).


      These events become  disturbances (stressors) when they  are  extreme or their patterns of

recurrence begin to change.  Catastrophe theory suggests that a gradually changing system (with its

characteristics)  converges on and crosses particular points. Only  a slight change in the immediate

vicinity of such a point will divert the system in a quite different direction.  Major  alterations in

landscape development  can take place in this way.  An  abrupt change in the distribution of

climatological events  is one factor that, alone or in combination with other factors, can push an

ecosystem beyond some critical threshold point.  For  instance, tree species that normally tolerate

degraded  air quality  conditions  may  experience a precipitous  decline when the frequency  and

intensity of severe winter conditions changes. The determination of the short- or long-term nature of
  a Atmospheric Sciences Modeling Division, Air Resources Laboratory, National Oceanic and Atmospheric Administration,
  on assignment to the Atmospheric Research and Exposure Laboratory, U.S. Environmental Protection Agency, Research
  Triangle Park, NC
                                             19-1

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such changes in climatologies! variability will influence present status and monitored trends in forest




health (Johnson etal., 1988; Adams and Eagar, 1989).






19.2 CAPABILITIES






      For the past year, the AREAL Global Processes Research Branch has been investigating sources



of climate data and methods of analysis and presentation that facilitate research into climate/forest



interactions - particularly those affecting forest health status and trends. This has been an entirely



voluntary effort in support of EMAP-forests and the Forest Health Monitoring (FHM) program.






      A digitized national database of severe weather related events such as high winds, large hail



and  tornados has been obtained.  A  digitized archive of National Weather  Service  Cooperative



weather data has been regularly accessed. A digitized time series of climate division drought index



values has been acquired.






      Examples of derived statistics such as occurrence of drought, late spring freezes, and cool



growing seasons have been computed from these data.  Standard climatological products  such  as



maps of mean annual temperature and precipitation have been provided to the New England Forest



Health Monitoring (NEFHM) program (Brooks et al., In Press; Brooks et al., In Review).






      Additional  products  have  been developed to characterize  the  spatial  extent  of  climate



disturbance phenomena.  Figure 19-1 illustrates the reported occurrence of tornados, winds in excess



of 50 kts, and hail in  excess of .75" diameter across the New England region during the last 30 years.



A  Geographic Information  System (GIS) is then used  to  illustrate the relationship  between these



widely dispersed events and potential FHM sampling locations (Figure 19-2). Figures 19-1 and 19-2



show that although  many storm events occur over time throughout the region, the probability of



noting the effects of these events at a particular forest sampling point is  quite small. This illustrates



the   importance   and   difficulties  associated   with  constructing   accurate  landscape-scale



characterizations from point observations. The figures also illustrate the potential value of remote



sensing products (aircraft and satellite) to the description of forest landscape/climate interactions.
                                            19-2

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                                                              1 1 SO
                                                              ( a r 1 4 t < r  t h a .1
                                                                0 1 .1 o ( i )

                                                              HAIL EVENT

                                                                      [YE.fl
                                                              Spot
                                                              ( T o f ,1 a d o
                                                               a f i  i ,id i 03 t
                                                               i i th 4  lint)
Figure 19-1. Digitized Location of Severe Weather Events. As Reported by the National Weather
            Service National Severe Storms Forecast Center, Kansas City, Missouri, 1961-1990.
                                            19-3

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Figure 19-2. Intersections of Digitized Severe Weather Events with NEFHM Program Sampling
           Hexagons, 1961-1990.
                                           19-4

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      The GIS has also been used to identify those sampling areas of relative dimatological stability



and stress.  Figure 19-3 illustrates for each year from  1981  through 1990, the percent of the New



England region that experienced drought (Palmer Drought Severity Index value equal to or less than -



3.00), late spring freezes, late spring snowfall, and small growing degree day (GDD) accumulations.



"Late" and "small" are defined for each FHM sampling hexagon as a likelihood of the event occurring



fewer than once in 20 years.  This regional summary is related to the FHM network by highlighting



those hexagons experiencing the greatest number of climate stress events.  Based  on the drought-



freeze-snowfall-GDD criteria and the data available, the sampling regions highlighted in Figure 19-4



represent the most climatologically stressful locations within.the NEFHM study area for  the period



1981-1990.
     1OO
      eo
      6O
o
cz.

ri
     . 20
                                                                          dl freeze
               _na_
                            sncw

                            drcucnt
jfc.	a
                                                                Ik!
               1981   1982  1983  19S4  1985  1985  1987  1988   1989   199O



                                             YEAR




Figure 19-3.  Percent of New England Region Impacted by Climate Stress, 1981-1990.
                                           19-5

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 Figure 19-4. Location of Hexagons Reporting Five or More Intersections with Climate Disturbances,
            1981-1990.

      Finally,  climate  information  for individual sampling  hexagons can be used  to  support

associative studies or model development.  Figure19-5 illustrates a partial analysis template for a

hexagon located in southwestern New Hampshire.  Only the climate data are available at this time.

To be complete, pertinent FIA and FHM observations must be included as well.  The template contains

the location of the forest sampling point within the hexagon, the distance and  direction to the

nearest climate observation location and the location and time of physical disturbance events such as

high wind and tornados.  Error limits have been estimated surrounding these event locations which

reflect imprecision in the National Weather Service digitized record. Stressful climate conditions are

summarized in the upper right of the Figure.  A filled circle represents a late spring freeze, or cool

growing season with  a 5%  or less probability of occurrence. Drought stress is indicated if at least one

monthly PDSI value of-3.0 or less is reported during the year.
                                            19-6

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              Hexagon    1477
                (southwestern New Hampshire)
YEX3 7HOS7
1531
1932
i932
1934
1935
1936
1987
1933
1939
1950
COO










DP.OUCKT !
•



•





                                                       wind  '. v en t
                                                  —   torno'i'j  track
                                                  o    FHM  samp I« sit;
                                                  Direction  and  distance
                                                  to  nsarest coop site
                                          SCALE 1:50,000
Figure19-5.  Climate Information for a Selected Hexagon, 1981-1990.


19.3 EXPANSION TO THE SOUTHEAST

     The assembled and analyzed data are targeted to forest ecosystems of the Northeastern United

States. Other geographic regions are expected to require a combination of shared and specialized

climate stress information.  For instance, while drought frequency and intensity  should be an
                                      19-7

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important forest health factor in the South, an index of fire frequency and occurrence could also be




helpful.   Landfall characteristics of  tropical  depressions  and hurricanes may be useful to the



description of coastal forest ecosystem status and trends in Southeastern and Gulf states.






      At present there are few limitations to the climatological factors that can either be directly




analyzed or indirectly estimated for use by the FHM program. The real value of these products to the



program will be determined by the requirements of FHM scientists. Application-specific issues that



will need to be addressed before these requirements can be met include: data access; data reduction;



selection  of  spatial  algorithms;  selection   of   derived  data   models  (e.g.,  soil  moisture,



evapotranspiration);  and  error estimation.  Although general background and  sampling season



products have been requested and supplied to the NEFHM program, specific analysis needs have not



been expressed by FHM participants at this time.  The issues just listed can and will be resolved when



the level of interest and support for climate-related activities by EMAP-forests and FHM becomes



more clearly defined.
                                            19-8

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                                20. INDICATOR DEVELOPMENT





                                        T. Strickland*
20.1 INDICATOR APPLICATION






      The EMAP  program seeks to (1)  describe current ecosystem status,  (2) identify long-term




changes in ecosystem status, (3) characterize the components of ecosystem change, and (4) suggest




avenues for diagnostic  research.  To complete these objectives, the program has  adopted an




indicator-based approach  to the assessment of ecosystem condition  (Knapp et al.,  1990).   This




approach  assumes that  (1) indicators of specific interrelationships between  ecosystem  functions




(e.g., rates of nutrient transfer, capacity for nutrient conservation, level of redundancy of function,




etc.) are known, (2) indicators can be related within an assessment framework to specific changes in




ecosystem condition (e.g., growth, morbidity, mortality), and (3) indicator measurement at a national




survey scale is logistically, economically, and technically  feasible.  When  the  above criteria for




indicators are not met, a procedure has been established to evaluate options for the development of




new indicators, to assess their  potential utility within the existing assessment framework,  or  to




evaluate the need to develop new or additional assessment frameworks.






      The FHM program will assess the effects of multiple stressors on forest ecosystem condition.




Because ecosystem  processes are linked to  spatial and temporal combinations of environmental




components (climate, soils,  topography, vegetation,  trophic structure,  etc.), the  success of an




indicator and  of  the  corresponding modeling and  assessment  program will depend  on the




development of an appropriate diagnostic  framework for identifying  major resources of concern,
   ManTech Environmental Technology, inc., EPA Environmental Research Laboratory, Corvallis, OR
                                            20-1

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suggesting research priorities, and defining attainable conditions of sustainable ecosystem health.




This framework will  be developed  around a  regional concept, recognizing that the nature of




problems and their solutions vary among definable, ecological regions. The framework is focused on




the development and application of  a suite of tested indicators and models that accurately predict




risk to specific ecosystem subpopulations; it should also provide guidelines for specifying the most




reliable models for determining ecosystem risk for various stressor-management scenarios.






20.2 DEFINING FOREST HEALTH






      A major use of indicators in  the  FHM  program will be  to assess  condition, or health, of




ecological  resources.   Rapport (1989) lists three approaches or criteria commonly used to assess




ecosystem  health:  (1) identification of systematic indicators of ecosystem functional and structural




integrity, (2) measurement of ecological sustainability or resiliency (i.e., the ability of the system to




handle stress loadings, either natural  or anthropogenic), and (3) an absence of detectable symptoms




of ecosystem disease or stress. Thus, ecological health  is defined as both the  occurrence of certain




attributes that are deemed  to be present in a  healthy sustainable  resource, and the absence of




conditions that result from known stressorsor problems affecting the resource.






20.3 INDICATORS AND ASSESSMENT






      The  FHM  program's  reports on  the condition  of  forested  ecosystems  will  be based  on




indicator(s)  response(s).  These responses  represent the quantifiable changes  occurring  in some




components of the forested ecosystem.  It is necessary to place the balance of indicator response (net




and relative magnitudes of change in  positive or negative direction) into a matrix reflecting the value




placed upon  forested  ecosystems by society. The FHM program's assessment framework recognizes




the differing uses to which forests  are placed.  Societal values can therefore be described as fitting




into one of the three following broad categories:
                                            20-2

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      •   Ecological  Integrity -  The concept of ecological integrity recognizes the importance  of
          maintaining ecosystem functional capacity, and considers both biological and abiological
          resources.
      •   Economic Value - Society places great import in the capacity of forested ecosystems  to
          provide livelihood.  This value represents the capacity  for the system to generate both
          direct (e.g., sales) and indirect (e.g., regulation of water availability for agriculture) sources
          of livelihood.
      •   Sociologic Value - This value incorporates the intrinsic desires of society to maintain some
          parts of the world in a "natural state" and includes recreational and aesthetic components.

      To  provide  a  structure  bridging  the  gap  between societal concepts  of value and the
measurement  of quantifiable  components of the ecosystem,  the FHM program has identified a
number of quantifiable assessment endpoints (Figure 20.1).  Using such a structure, it is possible (and
likely) that any individual indicator will be interpretable in the context of any of the societal values.
For example, soil chemical analysis data will be used in developing interpretations for the assessment
end points of soil productivity, soil weathering rate, soil contamination, and nutrient cycling balance.

      An  example of the relationships  in the assessment framework is presented in Figure 20.2.
Reading the figure from right-to-left, the societal value, Quality of the Vegetative Biotic Resource,
serves as the focus through which the assessment endpoints, can be interpreted.  The assessment
endpoints encompass broad categories of ecosystem  component  characteristics  (i.e., indicator
distributions or statistical  representations thereof), the aggregation of which defines  ecosystem
status.     Indicators  may  comprise  individual  field  measurements  or  aggregations of  field
measurements and are  the technical base for quantifying the characteristics of the assessment
endpoints. Indicators carry no capacity to assign a value judgement. They serve as a "tag," markinga
point of condition in  time and space that can be applied to multiple perceptions of value. Thus, the
FHM program will provide  quantity  information  on  the condition of the  assessment endpoints
(i.e., status, and magnitude of change over time).
                                            20-3

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        SOCIETAL VALUE PLACED ON FORESTED ECOSYSTEMS
      VALUE
  ECOLOGICAL INTEGRITY
     ABIOTIC RESOURCE
     BIOTIC RESOURCE
    ASSESSMENT ENDPOINT
SOIL EROSION
SOIL PRODUCTIVITY
SOIL WEATHERING RATE
SOIL CONTAMINATION
SOIL WATER RETENTION
WATER QUALITY
WATER QUANTITY
AIR QUALITY

BIODIVERSITY
NUTRIENT CYCLING BALANCE
CONTAMINATION
ANIMAL QUALITY
VEGETATIVE QUALITY
LANDSCAPE DYNAMICS
  ECONOMIC VALUE
PRODUCT GNP
BIOMASS BY PRODUCT CATEGORY
WATER EXPORT
HABITAT PROVISION
TOURISM & RECREATION
 SOCIOLOGIC VALUE
DESIGNATED USE USABILITY
PRISTINENESS/AESTHETICS
Figure 20.1. Societal Value Placed on Forested Ecosystems.
                          20-4

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KJ
O
          INDICATOR        ASSESSMENT ENDPOINT      SOCIETAL VALUE
               t
          SOIL CHEMISTRY
          FOLIAR CHEMISTRY
          RADIAL GROWTH
          VIDEOGRAPHY
   t
DIVERSITY
                                       RESISTANCE
                                       LAI/PROD
                                       HIST./PROD
                                       LAI/PROD
                    QUALITY OF

                    VEGETATION
QUAL BIO

RESOURCE
     Figure 20.2. An Example of the Relationships in the Assessment Framework.

-------
      Based on assessment endpoint information, the FHM program will also provide interpretive
assessments as to the relative condition (direction and rate of change in  condition) for  regional
forested  ecosystems.  The interpretive  assessments will  thus  provide agency  policy offices with
technically based guidance as to the  potential  for  approaching  critical  conditions in  forested
ecosystems.  However, policy offices are responsible for making regulatory recommendations relating
to societal values (e.g., whether mitigative action should be required); such recommendations are not
the purview of the FHM program reporting.

20.4 INDICATOR SELECTION CRITERIA

      This section describes the criteria that must be applied in the adoption of indicators.  An
acceptable indicator must meet the following criteria, thus resulting in the selection of indicators on
an interim basis'while additional information is collected leading to the use of a more desirable set of
indicators.

      •  Societal Value - Changes in indicator status should result in a  willingness to  manage
         stressor sources.   Though policy-makers  can  be  advised of the significance of an array of
         technically relevant indicators, the willingness of society to accept regulation on the basis
         of indicator changes must also be considered. The values that society places on  forested
         ecosystems can be aggregated  into three categories: ecological integrity, economic value,
         and sociological value.  These three categories drive the FHM program.  All  indicators
         selected for implementation must be interpretable in an assessment context that has direct
         relationship to these values.
      •  Ecological Integrity - The ecological integrity of a forested ecosystem is  a function of the
         quality of, and interactions between, its component parts (i.e., abiotic and biotic  elements).
         There is a growing awareness that  the  "health and  quality" of the human condition  is
         inextricably linked to the "health" of the ecosystems people inhabit and the use to which
         ecosystems are  placed  (e.g.,  waste disposal).   Humankind  is learning that the term
         "ecosystem" is a function of multiple scales.  For example,  the source of atmospherically
         deposited stressors to a watershed may be thousands of square kilometers, the  affected
         vegetation in the watershed only a  few square kilometers, and the area affected by the
                                            20-6

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         watershed's export (larger streams and groundwater) may again be thousands of square
         kilometers in area.
      •  Economic Value - The economic value of forested ecosystems lies  in marketing the vast
         quantities of forest products each year as well  as exports from the forested ecosystem
         (e.g., air, water), management of forests for tourism (e.g., National Park System and private
         souvenir vending),  and many other services that are currently treated as external to the
         goals of forest management per se.
      •  Sociological Value  - The sociological (or aesthetic) value placed on an ecosystem is an
         intangible quality stemming from a sense of personal value found in nature.
      •  Conceptual Model Output - Because the FHM programs assessment of forest condition will
         be  made using  conceptual  models  as  hypotheses  of forest  structure, function,  and
         response, indicators included  in the  monitoring plan must be specifically included (or
         amenable to inclusion) in conceptual models of forest condition and response.
      •  Specificity and Sensitivity - Indicators adopted by the program must be sensitive to changes
         in stressor exposure and/or reflective  of the long-term changes in forest structure.  They
         must be operationally definable in  terms of some measurement or combination of
         measurements.
      •  Application - In addition to the  selection of an indicator, its form of expression must also
         be considered.  For example, an indicator such  as available N  may be expressed in the
           4
         following ways:   (1) as the percentage of samples  which  fall below or exceed some
         threshold value, (2) in terms of changes in the median value, or (3) in terms of percentage
         of map units which contain ecosystems below some threshold  value.  The  choice of an
         indicator and reporting format  will reflect the desire of decision makers as well as the
         ecological relevance of the information and the structure of available data bases.
      •  Detection Capability - The utility of an indicator  in detecting trends in condition will also
         depend upon the magnitude of  its remeasurement error. For example, there are specific
         procedures  that can be employed to  determine  whether the size of the  remeasurement
         error precludes  indicator use   because  the change that one  wishes to  detect  with
         confidence is too small relative to the remeasurement error.

20.5 INDICATOR CATEGORIES

      A key element  of the the FHM program's approach is the linkage of indicators to assessment
endpoints.  Potential  indicators are identified using conceptual  models of ecosystems, followed by
                                           20-7

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systematic evaluation and testing to ensure their linkages to the assessment endpoints and  their
applicability  within  the FHM  program.   The  models  used may be  based  either  on current
understanding  of the effects  of stresses  on ecosystems, or on  the structural,  functional  and
recuperative  features of "healthy" ecosystems.  Important information about assessment endpoints
falls into one of the  following categories:  condition of the ecosystem, exposure of the endpoint to
potential  stressors, and availability of conditions  necessary  to support the desired state of the
endpoint.  To provide appropriate linkage between assessment endpoints and indicators, indicator
development in the  FHM program will produce indicators that fall into  one of  the  following four
categories (Hunsaker and Carpenter 1990).

      1.  Response indicators represent characteristics of the environment measured to provide
          evidence of the biological condition of a resource at the organism, population, community,
          or ecosystem levels of organization.
      2.  Exposure indicators provide evidence of the occurrence or magnitude of contact of an
          ecological resource with a physical, chemical, or biological stressor.
      3.  Habitat indicators are physical, chemical, or biological attributes measured to characterize
          conditions  necessary to  support an organism,  population, community, or  ecosystem
          (e.g., availability of snags; substrate of stream bottom; and  vegetation type, extent and
          spatial pattern).
      4.  Stressor  indicators are natural processes, environmental hazards, or management actions
          that effect changes in exposure and habitat (e.g., climate fluctuations, pollutant releases,
          and species introductions). Information on stressors will often be measured and monitored
          by non-FHM programs.

20.6 INDICATOR DEVELOPMENT PROCESS

      The indicator development framework is designed to provide information about ecosystem
condition that is relatively free of interpretation bias.  This will provide user flexibility which is vital to
the differing needs and priorities of the large client base served by the FHM program.  The framework
is  designed in  the form of  a progressive  flow diagram  with  specific decision criteria driving
progression from one level to the next (Figure 20.3): The framework guides indicator development
                                            20-8

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through an assessment process that considers needs and objectives, acceptable  data  uncertainty,
appropriateness  of available  analytical procedures, data  management  procedures,  statistical
procedures, and the need for integrative assessment among multiple indicators.

      Indicators reflect the nature and application of assessment endpoints,  must characterize the
forest resource, and are the primary vehicle for reporting ecosystem status.  Because there are a
variety of levels at which assessments may be conducted, the FHM program's indicator development
framework is designed to foster comparability among disparate assessment approaches by distilling
the process to a common set of steps.  Selection of indicators for research and developmental  testing
will be a function of several interacting factors as follows.

      1.  Whether or not a linkage can be  made with  the assessment endpoints (Figure 20.1).
         Inclusion for development in the monitoring program will be tied specifically to how well
         the proposed indicator is expected to feed into and enhance the assessment framework.
      2.  The availability of data. Are data available that were collected in a manner appropriate for
         application in a national or regional context (i.e., represented in models, representative of
         regional resource distribution,  indicative of ecosystem change, etc.)?  Large quantities of
         data are already in existence that can be analyzed to characterize ecosystem condition and
         to develop response models.  The level of available analytical data will vary among regions
         because of disparate perceptions of the key operational processes at differing ecosystem
         scales and varying degrees of data base development for different regions.
      3.  The consequences of uncertainty.  There is always a component of uncertainty associated
         with an environmental assessment. Because the FHM  program's approach will require the
         linkage of multiple  components in the stressor-ecosystem relationship (estimation  of
         stressor exposure, assumption of processes mitigating or exacerbating ecosystem response,
         and variation in genetic response capabilities of receptor organisms), additive increases in
         the uncertainty accompanying the representation of system response will result.
      4.  The characteristics of the ecosystems under consideration.   This includes the  response
         characteristics  of ecosystems  and  their  spatial distribution.   For example,  it may  be
         necessary to use  different stand  biomass algorithms to  describe  the same  species
         depending upon soil depth, physical structure, chemistry, topography, hydrology, and
         such.  Within any region,  these  parameters may vary substantially.  Hypothetically, this
                                            20-9

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              IDENTIFY ISSUES/ASSESSMENT ENDPOINTS
           OBJECTIVES
              Develop Indicators of
              endpolnt status
                          METHODS
                       Expert Knowledge
                       Literature Review
                       Conceptual Models
                           EVALUATION
                         Workshops
                         Criteria met?
                 CANDIDATE INDICATORS
             Prioritize based
             on criteria
   I PHASE 3
                       Expert Knowledge
                       Literature Review
                       Conceptual Models
                         Criteria Met?
                         Peer Review
                                T
                 RESEARCH INDICATORS (Pilot scale testing)
             Evaluate Performance
   i PHASE 4
                                T
                       Analyze existing data
                       Simulations
                       Field tests
                       Statistical assessments
                       Conceptual models
                         Criteria Met?
                         Peer Review
             DEVELOPMENTAL INDICATORS (Regionaltests)
    PHASE 5
Evaluate Performance
Assess logistics
Cost effective?
Field tests
Statistical assessments
                                T
Criteria Met?
Peer Review
Agency Review
                     CORE INDICATORS
    PHASE 6
Implement regional
and national
monitoring
Data analysis
Value added?
Agency and
peer review
                                    Assess new Indicators
                                    Evaluate assessment endpolnts
Figure 20.3. The Framework of Specific Decision Criteria Driving Indicator Progression.
                                    20-10

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         would create a range of response potentials and diverse baseline conditions within the
         same region.
      5.  The spatial extent, magnitude, and temporal domains over which stressor exposure occurs.
         Exposure to a stressor may only be detrimental to forest condition  during certain times of
         the year,  and thresholds of critical exposures may differ both  spatially and temporally.
         Estimation of ecosystem condition requires  an understanding of  how an ecosystem  will
         respond over time to differing stressors and stressor loads.  This estimation must be based
         on an understanding of the physical,  chemical, and biological   processes involved in
         response  and will be  further  complicated  by synergistic  effects  between stressors
         (e.g., acidification  effects of nitrogen and sulfur).  In addition, because the geographic
         distribution of forest cover  types and responses,  stressor deposition  estimation,  and
         potential  for stress abatement may differ, special attention must  be given to the spatial
         scale of analysis and to the spatial representation of data.
      Forcing  formal consideration  of assumptions  is perceived  as  essential to  the uniform
development of indicators suitable for a national monitoring program because program design  and
selection of measurement criteria  are often based on the "cumulative learning"  and/or opinions of
the participating personnel.

20.7 INDICATOR ADDITION AND REPLACEMENT

      It is important to point out that the program  will not continually add new indicators to the
field program. As a national monitoring program, the FHM program will add  and/or delete indicators
depending  upon their capacity to provide necessary information to interpretation and assessment.
However, the number of indicators to be measured will be  strictly limited  and prioritized according
the value added in  characterizing ecosystem status  and trends in condition. Redundancy  among
indicators providing the same information will be perpetuated only as long as it takes to evaluate
their relative value.

      The objective of the development framework  process is to reduce the uncertainty associated
with interpretive assessments which are compiled from  indicator data (Figure  20.4).  The utility of an
indicator (or group of indicators) for forest ecosystem health characterization (and thus the decision
to retain the indicator in the program) will be a function of its:
                                           20-11

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Implementability - Can the samples or data required be collected in a time frame suitable
for a national monitoring program? the FHM program has set a 1-day limitation on all field
activities for each sampling site visit.
Interpretability - Does the indicator fit within the assessment and reporting criteria?  In
other words, does the inclusion of the indicator in the measured suite add a key piece of
information otherwise absent from the interpretive assessment framework, and can it be
evaluated unambiguously?
                                 20-1.2

-------
    LOW
                             INDICATOR UTILITY
INTERPRETABILITY
                                                                               HIGH
o
Ul
             U
             N
             C
             E
             R
             T
             A
             I
             N
             T
             Y
                  CANDIDATE
    RESEARCH
      (Pilots)
                     DEVELOPMENTAL
                     (Regional Demonstrations)
                                        CORE
    LOW   -«-
IMPLEMENTABILITY
                                                                               HIGH
Figure 20.4. Indicator Utility.

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                             21. INTEGRATION AND ASSESSMENT





                                        . K. Riittersa
                                »







21.1 INTRODUCTION AND RATIONALE






      In the FHM program, integration  refers to a process of coordinating and  blending the




monitoring  activities into a functioning and  unified whole  (Fabrizio et al.,  In Preparation)  and




assessment means the procedures by which data are converted into useful information (NRC,  1990).




Integration  and assessment processes are essential to  improve the conduct of environmental




monitoring  and to increase the relevance of reports for risk assessments (Streets, 1989; EPA, In




Preparation).






      Some aspects  of integration and assessment are addressed- simply  by the planning  and




reporting of the scientific elements of the FY91 field study. Others are addressed by  planning and




conducting  the field work  which sets  up an infrastructure for monitoring.   But many aspects of




integration  and assessment are beyond the scope  of the field study.  Thus, the objectives of this




section  are  to describe how the  field  study  is contributing to integration  and assessment within




EMAP-Forests, and to  suggest  how the data can  be  used  for development of integration  and




assessment processes after the field study.






      Because  the field study is concerned  mainly with issues  of  statistical  design,  indicator




evaluation,  and operations, there will be no  report of the integration and assessment of the field




study data,  per se. Rather, the results  of the integration and assessment processes will be evident




through  the success  of the coordinated field study  and  later, through  reports that  help analysts




decide how best to convert data into useful information.
   1 ManTech Environmental Technology, Inc., Research Triangle Park, NC
                                            21-1

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21.2 CONTRIBUTIONS TO INTEGRATION

      Fabrizio et al. (In Preparation) identified the integration  activities associated with policy,
program, and technical aspects of monitoring  ( refer  to  Table 21.1).   The field study will make
contributions to these aspects as described in this section.

Table 21-1  Policy, Program, and Technical Integration Issues in EMAP*

 Policy Integration Issues
      Identify and address needs of constituent groups  with interests in single- or multiple-resource
      categories.
      DQOs for EMAP.
 Program Integration Issues
      Coordinate  EMAP with cooperating agencies that  focus on  issues dealing with single- or
      multiple-resource categories.
      Coordinate acquisition of off-frame (stressor) data useful to multiple resource groups.
      Assessment of data availability and negotiations for acquisition of off-frame data that will be
      used by more than one resource group.
      Propose modifications of existing networks based on evaluations  of existing off-frame data
      for integration purposes.
 Technical Integration Issues
      Development of ecological indices for multiple resource groups.
      Sufficiency of spatial and temporal distribution for the FHM program indicators that  are
      considered as stressor indicators by other resource groups.
      Indicators applicable to multiple resource groups, including those not specific to Forests.
      A strategy to analyze and evaluate data from Forests and from multiple resource groups.
      Sampling unit density of EMAP grid points.
      Frequency of co-occurrence of Forests with other resource groups  in an EMAP sampling unit
      (hexagon).
      Frequency of landscape characterization to redefine the Tier  2 sample frame.
      Statistical power to  detect association between ecological  condition  and  corresponding
      landscape indicators.
      Interannual frequency of site visits.
      Guidelines for implementing Tier 3 or Tier 4 sampling.
 * Adapted from Fabrizio et al., In Preparation.
                                            21-2

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21.2.1 Policy Integration

                                                               v
      The field study is not designed to identify needs of constituent groups, nor to set DQO's for the

FHM program.


21.2.2 Program Integration


      The field study is an excellent test of coordinating the FHM program with the F5-FHM program

of the USDA FS. The study also involves individuals from other Federal Agencies (USDA SCS, USDI Fish

and Wildlife Service, TVA, USDI National Park Service)  and states which will help to set up  later

interagency coordination.


      The acquisition of off-frame stressor data is a continuing function of the FHM program Air and

Deposition, Landscape and CIS, and Information Management groups.  These groups are represented

in the field study, and so there.is a potential that acquisition of off-frame data will  be coordinated

among resource groups.  The field  study does not provide for the assessment of data availability or

negotiations for acquisition of off-frame data that will  be used by more than one resource group.

The field study also does  not provide for analyzing or proposing modifications to existing off-frame

data collection networks.


21.2.3 Technical Integration


      Data from the field study will be useful for developing ecological indicators and indices that

are not specific to forests, but this is  not a stated objective of the study.  Nearly  all of the forest

indicators can  potentially contribute to such indices for  terrestrial assessments.  The field study will

not address the use of forest indicators as stressor indicators by other resource groups.


      Each of the reports produced as a result of the field study will  help to define the strategy for

analyzing and evaluating  data from the Forest group. This includes indicator-specific reports and any
                                            21-3

-------
reports that combine information from more than one indicator. Data from the field study will be




available for follow-up studies to address this issue in more detail.






      The field study, particularly the large-scale demonstration of selected indicators, will provide




data that can be used to evaluate the sampling unit density of the FHM program grid points. Neither




the  frequency  of co-occurrence  of  resource  categories nor  the  frequency  of  landscape




characterization will be addressed by the field  study.  Data from the small-scale  pilot of selected




indicators may be useful for this purpose in later studies. The small-scale landscape pilot will provide




some limited information to evaluate the statistical power to detect association between ecological




condition and landscape indicators.  The interannual frequency of site visits can be evaluated by the




Forest group based on simulation studies using a single annual sample of all sites, or by resampling




analyses using several annual samples of all sites. This evaluation  is not a stated objective of the field




study.  Guidelines for implementing Tier 3 and Tier 4 sampling are not addressed in the field study.






21.3 CONTRIBUTIONS TO ASSESSMENT






      Palmer et al. (1991, Sections 2, 3, and 7) outlined an FHM program assessment strategy in the




context of the overall FHM program assessment strategy, the indicator development strategy, and the




assessment strategy.   The  field  test  may contribute  to developing the overall FHM program




assessment strategy but that is not a primary goal. In this section, the contributions of the field test to




key  elements  of  the  FHM  program  assessment  strategy - assessment  reports,  assessment




infrastructure, and assessment paradigm - are described.






21.3.1  Assessment Reports






      The field study is not  intended to  assess the condition of the sampled  forests in relation to




stresses.  However, data from the field  study will be used by the EMAP-lntegration and Assessment




team in subsequent demonstrations of assessment report formats and functions.
                                            21-4

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21.3.2 Assessment Infrastructure

      The assessment infrastructure refers to the arrangement of people and  facilities within the
FHM program,  and their coordination with other FHM program-wide support groups and other
agencies, to produce assessment reports. The field study is an excellent opportunity to identify the
key working groups, facilities, and communications that are needed to produce assessment reports in
an operational monitoring system.

21.3.3 Assessment Paradigm

      An assessment paradigm is a point of view for organizing, synthesizing, and interpreting data
(Palmer et al.,  1991, Section 2).   The field  study  contributes to the unique  elements that are
characteristic of the FHM program assessment paradigm.

      Elements of an FHM  program  assessment paradigm and  the relationship to the EPA risk
assessment model are described by Messer (1990; see also Riitters et al., In Preparation) and will not
be repeated here.  The field  study addresses this "long-term, large-scale, policy-relevant" paradigm
by emphasizing the following elements.

      1.  Suites of indicators rather than disconnected measurements.
      2.  Multistage, systematic sampling and linkage across spatial scales (pilot test only).
      3.  Distinction between indicators of condition and indicators of stresses.
      4.  Regional-scale testing, analysis, and reporting (demonstration test only).
      5.  Detection of important forest changes with a view towards subsequent identification of
         possible causes of those changes.
      6.  Indicators that have quantifiable relevance to both social values and biological processes.
      7.  Selection of indicators appropriate for Tiers 1 and  2 of an operational monitoring system.
                                            21-5

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21.4 CONTRIBUTIONS TO FUTURE INTEGRATION AND ASSESSMENT TASKS






      Data from the field study will be used for several purposes beyond the scope of the current




document. The FHM program Integration and Assessment group will utilize these and similar data to




prepare an example integrated  assessment in  FY92 (personal communication with Dan  Valero,




Technical Coordinator for  EMAP  Integration and Assessment, February 1991).  The field study data




will also be used to develop and test assessment techniques as part of the research and development




of the FHM program's assessment  capabilities in FY92 (Palmer et al., 1991, Section 7).
                                        .  21-6

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Proceedingsof the Society of American Foresters Annual Meeting, Washington, DC.
                                           22-19

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       TIMELINE FOR PRODUCTION OF EMAP-FORESTS FY91 INDICATOR EVALUATION FIELD





                                     STUDY PLAN:
Mar. 18           Compiled outlines sent out to EMAP-Forests Team members so they may help with




                 their areas of expertise.






Mar.25           Pilot writers conf. call K.Hermann sponsor 4:00 EST (202) 245-3613






Mar.26           Demo and Pilot Writers' Conf. Call  11:00 EST B.Kucera sponsor (202) 245-3622






Apr.3             Sections sent to AREAL RTP for editing and word processing.






Apr. 19           Send plan out for peer review and internal review.






May 3            Receive review comments-copy to editor and author






May 10           Reconciliation sent to editor from author






May 10-13        Editing and wordprocessing






May 14           Document sent to lab for approval






May 31           Lab approval






June 3            Pretraining Asheville for Pilot and Demo






June 10           Training for all SE and all (including NE) demo and pilot personnel. Asheville, NC






June 17           Training for NE FHM.






TIMELINE FOR REPORTING: to be developed.

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