EPA/600/R-93/135
July 1993
GLOBAL CHANGE RESEARCH PROGRAM
NORTH AMERICAN LANDSCAPE CHARACTERIZATION
(NALC) - PATHFINDER PROJECT
RESEARCH PLAN
US Environmental Protection Agency
Office of Research Development
Prepared by:
Remote and Air Monitoring Branch
Environmental Monitoring Systems Laboratory
Las Vegas, Nevada
Wayne N. Marchant, Laboratory Director
Bill Forte', Branch Chief
Ross Lunetta, NALC Technical Director
702-798-2175
re&> Printed on Recycled Paper
-------
LIST OF AUTHORS
Ross S. Lunetta, US Environmental Protection Agency (EPA),
Environmental Monitoring Systems Laboratory (EMSL), Post Office Box
93478, Las Vegas, NV 89193-3478.
John G. Lyon, Ohio State University, Department of Civil
Engineering, 2070 Neil Ave., Columbus, OH 43210-1275.
James A. Sturdevant, US Geological Survey, EROS Data Center,
Sioux Falls, SD, 57198.
John L. Dwyer, Hughes STX Corporation, EROS Data Center, Sioux
Falls, SD, 57198.
Christopher D. Elvidge, Desert Research Institute and University
of Nevada-Reno, Post Office Box 60220, Reno, NV 89506.
Lynn K. Fenstermaker, Desert Research Institute, Post Office Box
19040, Las Vegas, NV, 89132-0040.
Ding Yuan, Desert Research Institute, Post Office Box 19040, Las
Vegas, NV, 89132-0040.
Steve R. Hoffer, Lockheed Environmental Systems and Technology,
980 Kelly Johnson Drive, Las Vegas, NV, 89119.
Ridgeway Weerackoon, Desert Research Institute, Post Office Box
19040, Las Vegas, NV, 89132-0040.
-------
NOTICE
The information in this document has been funded wholly by the
U.S. Environmental Protection Agency under a contract. It has been
subjected to Agency review and approved for publication.
ACKNOWLEDGMENT
The assistance of Dorsey Worthy, U.S. EPA/EMSL Las Vegas, Len
Gaydos, U.S. Geological Survey, Ames Research Center, CA, David
Peter.son, NASA Ames Research Center, CA, and Russell Congalton,
University of New Hampshire with the review of this document is
greatly appreciated.
The NALC Peer Review Panel is also acknowledged for their
assistance and thoughtful comments. The Panel includes: James
Lawless, Chair, Jack Estes, Ed Sheffner, David Peterson, Len
Gaydos, Douglas Muchoney, John Townshend, and Charles Dull.
-------
-------
CONTENTS
NOTICE/ACKNOWLEDGMENT
PROJECT OVERVIEW
Introduction
Areas of Coverage
Satellite Data Acquisition
Standardized Data and Products
Methods Development
Project Schedule
Global Change Science Issues
References
Page 1
CHARACTERISTICS OF SENSORS AND NALC-PATHFINDER DATA SETS
Page 11
Landsat Program History
Multispectral Scanner (MSS) Data Characteristics
NALC Landsat MSS Triplicate Image Data Sets
Image Composites
Categorized Images of Land Cover
Change Detection Images
-------
METHODS AND RESPONSIBILITIES FOR PRODUCTION OF DATA PRODUCTS
Page 14
Acquisition of Current Data Products
Identification of Archival Data Products
Image Processing of Triplicate Scene Data Products
Landsat MSS Data Characteristics
Image Processing for NALC MSS Triplicate Production
Background
Data Acquisition
Triplicate Processing
Image Rectification Analysis Forms
IMAGE CATEGORIZATION AND DEVELOPMENT OF LAND COVER DATA PRODUCTS
Page 28
Training Set Development
Evaluation of Training Sets
Categorization
Identification of Land Cover and Classification System
Data Production and Distribution
Quality and Accuracy Assessment
NALC Products
-------
METHODS FOR MANAGING AND DISTRIBUTING NALC DATA AND PRODUCTS
Page 34
NALC Data Archive, Management, and Distribution
NALC Data Distribution
Query Procedures
Product Media Available to the User
Standard Product Ordering Procedures
Alternative Products and Ordering Procedures
IDENTIFICATION OF CHANGE DETECTION METHODS
Page 48
Introduction
Change Detection Literature Review
Optimizing for Digital Change Detection
NALC Scene Selection and Preprocessing
NALC Change Detection Strategy
NALC Change Detection Procedure
NALC Change Detection Evaluation Criteria
NALC Change Detection Research: Technical Work Plan
Study Sites
Methods to be Tested and Evaluation Criteria
Post-Categorization Change Detection
Enhancement Techniques for Change Detection
Change Detection Through Vegetation Index Differencing
Change Detection Through Principal Components Analysis
Change Detection Through Regression Analysis
-------
Page 63
Change Detection Study Time Frame
Change Detection Correspondences
References
NALC QUALITY ASSURANCE AND QUALITY CONTROL
Standard Operating Procedures
Quality Control
Data Analysis Tracking
Spatial Accuracy Verification
Thematic Accuracy Assessment
Field Verification
Sampling Design
Sampling Scheme
Sample Size
Verification Procedures
Thematic Accuracy Assessment Reporting
References
APPENDICES
Appendix I:
NALC Technical Review
NALC Technical Review Session Agenda
List of Invited Participants
NALC Review Panel
Report of the Technical Review Panel, North American
Landscape Characterization, February, 1993
NALC Project, Response to the NALC Panel Review Report
Page 76
-------
Appendix II:
Page 111
Agreement Between INEGI and USEPA
Appendix III:
Page 128
Path/Row Data Acquisition List, Triplicate Assembly Priorities
Appendix IV:
Triplicate Image Selection for All Path/Rows
Page 140
Caribbean
Central America
Chesapeake Bay Watershed
Mexico
Hawaii
Alaska
Western US
Eastern and Southern US
Midwest and Great Plains US
Appendix V:
Interagency Agreement (IAG) Documentation
Page 344
-------
Appendix VI:
Page 356
Mexico Worksheet for Acquisition and Determining Temporal
Windows for Acquisition of Remotely Sensed Imagery
Appendix VII:
Page 363
NALC / GCRP Coordination / Collaboration Documents
Appendix VIII:
NALC Product Refinement Workshop
Agenda
Participants
Letter Reports
Houghton
Lawrence
Salas
Page 378
Appendix IX:
Page 396
Landsat Pathfinder Initiative and the Canada Centre for
Remote Sensing (CCRS), and Documents from the December,
1992 Meeting with CCRS.
Appendix X:
Page 406
Land Cover Categorization System for Use in the NALC-Pathfinder
Proj ect
-------
FIGURES
Number
5.
Concept
*
Schematic Scatter Diagram of Date 1 versus
Date 2 for a Given Band
Paoe
1.
2.
3.
4.
Processing Flow for NALC - Pathfinder, Landsat
MSS Triplicate Production
NALC Pathfinder Data Flow
NALC Pathfinder Categorization
Landsat Pathfinder (LP) Program,
System
Data Management
21
22
33
38
62
TABLES
Number
2. Interim NALC IMS Metadata Database Schema
3. Definitions for NALC Interim IMS Database Schema
4. 8mm Tape Mapper for NALC Triplicate
5. Example of Data Descriptor Record (DDR)
6. Explanation of NALC Tape Mapper
7. NALC MSS Interpretation Verification Form
8. NALC Photographic Interpretation Verification Form
9. An Example Error Matrix Table
Page
1. Schedule for Triplicate Assembly and Application
Project Starts
5
39
40
43
44
47
72
73
74
-------
-------
NORTH AMERICAN LANDSCAPE CHARACTERIZATION (NALC)
LANDSAT-PATHFINDER
PROJECT OVERVIEW
Introduction
The North American Landscape Characterization (NALC) project
is a component of the National Aeronautics and Space Administration
(NASA) Landsat Pathfinder program of experiments to study global
change issues. The NALC program is funded principally by the U.S.
Environmental Protection Agency's (EPA's) Office of Research and
Development (ORD) Global Warming Research Program (GWRP), and by
the U.S. Geological Survey EROS Data Center.
The purpose of the project is to produce land cover and land
cover change data products at a 3.2-to-5.8 hectare (8.0-14.2 acre)
spatial resolution across a major portion of the North American
continent (Central America, Mexico, Caribbean and Hawaiian Islands,
and the United States) . The NALC - Pathfinder is designed to
contribute to an important objective of the U.S. Global Change
Research Program by "documenting global change." In support of the
EPA's GWRP, NALC will assist in providing primary land cover type
and extent data for use in national inventories of terrestrial
carbon stocks and trace greenhouse gas (methane, nitrogen
compounds) emissions, and support the carbon cycle and trace gas
dynamics assessment objectives of the Intergovernmental Panel on
Climate Change (Anonymous 1993) . Principal U.S. NALC collaborators
include the EPA's Environmental Monitoring Systems Laboratory at
Las Vegas, Nevada (EMSL-LV), the U.S. Geological Survey's EROS Data
Center (EDC) in South Dakota, and NASA Ames Research Center at
Moffett Field, California.
EPA research funds will be made available to support
application project research beginning in Fiscal Year (FY) 1993.
Application project research will focus on the development of land
cover and land cover change digital data base products and on the
assessment of product accuracies. Two remote sensing consortia are
being developed to provide application project support in Mexico
and Central America. Funding for application project research in
the United States will be determined on a competitive basis
beginning in Fiscal Year 1995. A total of seven funding assistance
agreements are expected to be initiated in support of the NALC -
Pathfinder project by 1996.
-------
The objectives of the NALC project are to produce standardized
remote sensing data sets, develop standardized analysis methods,
and derive standardized land cover change products for the majority
of the North American continent. All data, methods and derivative
products will be made available to the global change research
community. Data will be vended at the cost of duplication and
distribution. All data and products will be archived by EDC and
listed in their Information Management System (IMS) so as to
provide user access and data distribution support.
Information about NALC products and ordering may be obtained
from: Customer Services, EROS Data Center, USGS, Sioux Falls, SD,
57198, (605) 594-6151.
Areas of Coverage
The NALC project includes all of the North American continent,
defined as the area north of the Panama-Colombia border including
the Caribbean and the Hawaiian Islands. Due to funding restraints
and limited Landsat data holdings in U.S. archives, NALC is
currently not funding the assembly of standard NALC data sets for
Canada. Canadian archives contain an extensive collection of
Landsat Multispectral Scanner (MSS) data from the 1990's, 1980's,
and 1970's that could be used in support of a NALC/Canada effort.
Currently, work is under way on a Great Lakes Watershed Pilot Study
under the leadership of the Canada Centre for Remote Sensing
(CCRS). The future expansion of NALC to other priority watershed
locations in Canada will be dependent on the results of the CCRS
Great Lakes Watershed Pilot Study.
Satellite Data Acquisition
Satellite data used in the NALC project include Landsat MSS
data from the years of 1991, 1986, and 1973, plus or minus one year
(whenever possible) . MSS data are the principal source of
satellite measurements for the NALC program. This is for several
reasons, including the 20 year MSS digital data archive and the
relatively low data costs as compared to Landsat Thematic Mapper
(TM) or SPOT Multi-Spectral (MS) data. A total of 803 Landsat MSS
scenes were acquired during 1992 to help complete the twenty year
MSS data archive. Data collections for the 1990's epoch included:
Mexico (102 scenes); Central America (34 scenes); the Caribbean
islands (48 scenes); Alaska (182 scenes); Western U.S. (138
scenes); Chesapeake Bay Watershed (18 scenes); Eastern and Southern
U.S. (142 scenes); Midwest and Great Plains (131 scenes); and
Hawaiian islands (8 scenes) . In addition to the 803 MSS scenes
acquired for 1990's by NALC, a comprehensive review of the Landsat
-------
MSS archive has been conducted, and historical MSS data have been
selected from the 1970's and 1980's to complement the 1990"s data
acquisitions.
Standardized Data and Products
Standardized data sets used in the NALC project feature
Laridsat MSS triplicate images that have been georeferenced and
presented in earth coordinates. In addition, image to image
coregistration between the triplicate image scenes has been
stressed to provide high quality and standard data sets for land
cover change analysis. The NALC standard triplicate data sets will
include the following: 1) Coregistered/georeferenced Landsat MSS
triplicates; 2) Digital Elevation Model (DEM) data; 3) spectral
cluster or categorized data coverages corresponding to individual
NALC triplicate MSS scenes; and 4) change detection images. All
data sets will be clipped to the Landsat World Reference System 2
(WRS2) image sampling frame. In the case of the 1970'a images,
which conform to the World Reference System 1 sampling frame,
multiple scenes are being assembled and clipped to the WRS2 sample
frame boundaries to provide complete coverage.
An important aspect of the NALC project is the generation of
standardized data products for the North American continent. These
derivative products include: A nearly border-to-border land cover
(vegetation) type categorization for priority North American
locations using the 1990's MSS imagery; and land cover change data
from the early 1970's to the early 1990's. Land cover change data
sets will be processed only for those areas where vegetation change
has occurred between successive dates of the triplicate. . For
example, scene by scene change detection analysis will yield two
products: 1) A change detection image indicating the locations of
change within a scene between 1990'a to 1980's and 1990's to
1970's; and 2) a raster data coverage identifying the land cover
type(s) that existed at specific locations and that had undergone
change.
It should be noted that the CCRS Great Lakes Watershed Pilot
Study differs from non-Canadian NALC project activities in that the
Great Lakes Pilot will result in the development of a 50-meter MSS
mosaic data product. This study is actually a parallel project
being conducted in association with NALC and using NALC products.
Subsequent to the completion of the pilot, the CCRS will evaluate
the utility of a NALC type effort for Canada. EPA is hopeful that
the Great Lakes pilot will eventually develop into a larger
NALC/Canada effort.
-------
Methods Development
Specific issues related to development of NALC remote sensing
standard methods have been addressed. Methods issues include
determining: 1) The time periods when satellite data should be
acquired for most of North America (by path/row image scenes) ; 2)
the characteristics of automated Landsat MSS scene georegistration
and image-to-image coregistration; 3) the procedures necessary to
develop cloud reduced MSS image composite products; 4) the
algorithm for spectral clustering; 5) standard methods development
for land cover class labeling; 6) image difference algorithms,
post-categorization procedures, and standard methods to be used for
land cover change analyses; 7) data indexing/archiving and
metadata Information Management Systems (IMS); 8) NALC Quality
Assurance/Quality Control (QA/QC) procedures; 9) accuracy
assessment methods; 9) land cover classification system; and 10)
Landsat Thematic Mapper (TM) swath data acquisition and data
processing methods.
Whenever possible NALC will utilize existing remote sensing
data analysis methods that have wide acceptance in the remote
sensing community. A specific goal is to draw upon the most
conservative methods that will accomplish NALC data processing
objectives. This emphasis towards conservative methods is to
provide standard methods that can be employed by as many
researchers as possible. However, there are numerous remote
sensing methods development issues that will require new approaches
or significant improvement to existing methods. Methods currently
under development include: 1) The automated georeferencing and
coregistration of Landsat MSS imagery; 2) the development of
accuracy assessment procedures for large area accuracy assessment;
3) the development of a NALC land cover classification system; and
4) the development of Landsat TM swath data acquisition and data
processing methods.
Project Schedule
The schedule for the assembly of NALC triplicates and
application project starts is presented in Table 1.
-------
TABLE 1.
SCHEDULE FOR TRIPLICATE ASSEMBLY AND APPLICATION PROJECT STARTS
Project Schedule
The following schedule is based on continued-level funding
through FY 98.
TRIPLICATE ASSEMBLY
1993 Mexico
Chesapeake Bay Watershed
Caribbean
Central America
1994 Alaska
Hawaii
Western U.S.
SCENES
102
18
48
34
202
182
8
138
328
APPLICATION START
1994
1995
1995 Midwest and
Great Plains
Eastern-Southern U.S
131
142
1996
273
-------
Global Change Science Issues
The principal clients of the NALC-Pathfinder project are the
U.S. Global Change Research Program (USGCRP) , and the U.S. EPA's
Global Wanning Research Program (GWRP). NALC fulfills an important
goal of the USGCRP by "Documenting Global Change". An important
objective of the USGCRP is to document global change through the
establishment of an integrated, comprehensive, long term program of
observing and analyzing earth systems change on a global scale
(Committee on Earth and Environmental Sciences 1993). The NALC
project is part of a larger global effort known as the Landsat
Pathfinder Science Working Group. Other companion projects
included in the Landsat Pathfinder series are being conducted in:
Amazonia (NASA), Central Africa (NASA), S.E. Asia (NASA/EPA),
Coastal Brazil (EPA) and Eastern Europe (NASA).
The Environmental Protection Agency's GWRP research projects
will benefit from NALC data products focused on science issues
related to carbon and trace greenhouse gas fluxes. The carbon
measurements will be used in global climate change models to refine
predicted changes in land cover distributions. These contributions
will be used to estimate changes in carbon stocks and carbon fluxes
from the terrestrial system to the atmosphere, and in attempts to
identify carbon sources and sinks.
NALC products will provide inventories of the current status
and changes over the past 20 years for specific land cover types.
These data will be used by global change research scientists in
combination with literature values or new field measurement data,
to calculate standing carbon stocks and estimate carbon and trace
greenhouse gas fluxes over the past 20 years. NALC land cover data
base products could also be applied in the identification of North
American locations suitable for growing land cover types
(vegetation) to capture or sequester carbon.
The general change in carbon abundance or flux estimates for
carbon in North America over the past 20 years will be based on the
changes in land cover from one type to another (i.e., forest cover
or "woody" to agriculture or "developed land") . NALC will also
attempt to categorize the different age classes during the process
of forest regrowth. These data will be particularly important for
providing more accurate estimates of carbon content for individual
forest stands.
Digital data products to be provided in support of carbon and
greenhouse gas studies include the NALC "triplicate data sets", and
nearly border-to-border land cover (LC) and LC-change data for
priority locations in North America. NALC standard land cover and
land cover change digital data base products will have both
immediate and future applications in support of global change
science issues. The 1991 +/- one year border-to-border, land cover
-------
product for North America will have the following applications to:
1) Provide data input for the calculation of standing carbon
stocks; 2) provide data to determine areas of anthropogenic change
in land cover; and 3) provide a baseline data set to evaluate
future shifts in land cover attributable to future changes in
global climate.
The importance of land cover type data for calculating carbon
stocks is illustrated in the following equation (Sheffner et al.
1993) .
Total North American Carbon (LCi) = A (LCi) x C (LCi)
for land cover type "i"
where:
A (LCi) = Area of land cover type i, and
C (LCi) = Carbon per unit area or carbon density
for land cover type i
The level of confidence of total NALC North American carbon
estimates will be no greater than the uncertainty associated with
either or both of the above terms, A or C. A general consensus
among researchers is that the uncertainty associated with the area
term, is at least as great as that of the carbon stock per unit
area or carbon density measure for a given land cover type (Skole
1993). Also, the amount of carbon contained in forest land cover
is much greater than other land cover types. Hence, measurement of
forest cover types has the highest priority (Houghton et al. 1992).
Current estimates of deforestation in the United States are
largely based on statistics data compiled by the U.S. Forest
Service and state government forest management agencies. Most of
these statistics provide limited information on the spatial
distribution of forest resources. Although important data (e.g.,
timber type and diameter breast height, DBH) for the calculation of
carbon stocks are typically compiled, data from state inventories
are not easily accessed and inventory data are not typically
collected for private land holdings. South of the U.S. border,
forest statistical data in Mexico and the Central American
countries are, for the most part, unavailable.
To provide a consistent source of data to support the study of
global deforestation rates and to provide data inputs to greenhouse
warming and global vegetation models requires a project such as
NALC. Scientists have been utilizing NOAA Advanced Very High
Resolution Radiometer (AVHRR) data for related purposes. AVHRR
data have and will continue to provide an excellent source of data
for global change research. The temporal frequency of data
-------
collections (daily) , the low data volume, and inexpensive costs of
acquiring these data contribute to its utility. EPA global change
modelers addressing terrestrial systems response and vegetation
redistribution find the 1.1 km by 1.1 km spatial resolution of
AVHRR data applicable to their models which require data input in
one-degree longitude by one-degree latitude cells. Although AVHRR
data meet many of the remote sensing data input requirements for
current global change modeling efforts, serious problems can occur
when limited to only one remote sensing data source.
An important factor in evaluating the utility of any data
source being considered as data input for monitoring, inventory, or
modeling studies is the accuracy and precision associated with the
data. In remote sensing studies data are validated as to their
accuracy and precision in one of three ways. The first two methods
involve comparing remote sensing results to in situ field
measurements or comparing the results to another remote sensing
data source of known accuracy and precision. The third method
involves comparing two remote sensing data results of unknown
accuracy and precision. By evaluating the agreement between two
data sets, a quantitative evaluation of data quality can frequently
be made. It is best to employ one of these three approaches, as
there is a significant potential for error, when data are assumed to
be accurate when in fact they have not been validated.
An analysis of various remote sensing data types and
approaches for monitoring tropical deforestation were evaluated
under a NASA-sponsored research project titled "Landsat Tropical
Deforestation Project". Results revealed that Landsat Thematic
Mapper (TM) and SPOT Multi-Spectral data provide comparable
estimates of standing forests (Skole and Tucker 1993). However,
when compared to AVHRR results, the AVHRR overestimated the area of
tropical forest stands by 40 to 90 percent. A comparison of the
rates of tropical deforestation between Landsat and AVHRR data
indicated that AVHRR results often overestimated the rates of
tropical deforestation by approximately 200 to 400 percent (Skole
and Justice 1992).
The NALC data products will provide data of known accuracy and
precision to facilitate the evaluation of other commonly used
remote sensing data for global change research, e.g., AVHRR. NALC
MSS data products will also facilitate the study of sub-kilometer
scale science issues, i.e., "patch" sizes and the change or
migration of land cover types. In particular the NALC data
products will be useful in the study of "landscape pattern change"
or "early indicators" of global change, such as induced shifts in
biogeographic regions or spatial distribution changes of land cover
within a given region. These changes that would not be detected
with coarse resolution sensors, and they could have an impact on
ecological function prior to migration events.
-------
The Landsat MSS "triplicate data sets" or "triplicates"
represent the NALC standard data set. These data sets alone will
represent a substantial contribution to the study of global change
and will do so well into the next century. In addition to their
application in global change research, NALC triplicate data sets
will provide an unparalleled data record and will potentially
contribute to the study of numerous science issues in North
America. Examples of future science applications include: 1) The
documentation of changes in biodiversity; 2) contributions to the
future mapping of North American biotic community zones; and 3)
the analysis of future geographic shifts in vegetative communities
related to changes in global climate.
The standard NALC LC-change products will provide important
data for global change scientists as they provide a retrospective
"picture" or view in time useful in evaluation of a number of
important global change science issues. Issues applicable to NALC
LC-change products include: 1) The measurement of change in land
cover or change in carbon abundance (carbon flux) over the past 20
years; 2) estimating the potential for storing carbon in the form
of land cover (carbon sequestration) in North America; and 3)
estimating the flux of trace greenhouse gases using change in land
cover as an indicator. Carbon and trace greenhouse gas fluxes are
defined as the net exchange between terrestrial sinks and the
atmosphere over time.
The development of a border-to-border baseline land cover data
set .for much of North America will potentially be a major
accomplishment. The current Landsat Pathfinder series is the first
attempt at processing high resolution land cover type data sets for
significant portions of the global land mass. Border-to-border
baseline triplicate data sets are not currently being developed for
Canada. However, subsequent to the completion of the Great Lakes
Watershed Pilot study by the CCRS, it is hoped that Canada will
embark on a NALC/Canada effort to complete the border-to-border
baseline data assembly for North America.
REFERENCES
Anonymous, 1993, U.S. EPA Global Change Research Program:
Strategic Plan, April, 1993.
Committee on Earth and Environmental Sciences, 1993, Our Changing
Planet: The FY 1993 U.S. Global Change Research Program, 79 pp.
Houghton, J., Callander, B., and Varney, S., Editors, 1992,
Climate Change 1992: The Supplementary Report to the IPCC
Scientific Assessment, Intergovernmental Panel on Climate Change,
WMO/UNEP, Cambridge University Press, UK, 198 pp.
-------
Sheffner, E., Dull, D., Estes, J., Gaydos, L., Lawless, J.,
Muchoney, D., Peterson, D., Skole, D., and Townshend, J., 1993,
Report of the Technical Peer Review Panel: North American Landscape
Characterization Project, Unpublished Report, 19 pp.
Skole, D., 1993, Tropical Deforestation Project, Briefing Document,
U.S. EPA Headquarters, January, 1993.
Skole, D. and C. Justice, 1992, NASA Landsat Pathfinder:
Tropical Deforestation Project, Briefing Document, NASA
Headquarters, February, 1992.
Skole, D. and C. Tucker, 1993, Tropical Deforestation and Habitat
Fragmentation in the Amazon: Satellite Data from 1978 to 1988,
Science 260:1905-1909.
10
-------
CHARACTERISTICS OF SENSORS AND
NALC - PATHFINDER DATA SETS
Landsat Program History
The Landsat program of satellites gathered digital
Multispectral Scanner (MSS) remote sensor data from July, '1972
through September, 1992. The result is a twenty year time span of
data and the opportunity to evaluate earth resources from space.
Because the archive is composed of digital data, these
products present a useful source for image processing and analysis.
These data will support evaluations of change in landscapes or land
cover over time. No other digital archive of similar time length,
similar resolution, or of similar potential information content is
available for North America.
Landsat data have been indexed and archived in a number of
facilities throughout the world including North America. Data
collection and indexing has been discontinuous for most areas,
however, and has .required the NALC Project to select particular
years in the past twenty for analysis. These certain years hold
many scenes of interest in the archive.
For the United States the historical Landsat MSS data resides
at the National Satellite Land Remote Sensing Data Archive
(NSLRSDA) at the EROS Data Center. In particular, the years 1973
and 1986 have very complete coverage of North America. They have
been selected as focal years to include in the production of NALC-
Pathfinder images. The archive from these years, plus or minus a
one year period, will provide a unique opportunity to develop a
NALC data set for North America.
To assure that high quality MSS data would be available for
the current period of time, a major acquisition program was
executed. The program involved purchase of MSS coverage for 1992
based on the existing purchase agreement between EDC and EOSAT.
From February through September 1992 a number of acquisitions were
made. This action successfully populated the archive and will
support production of images for the current time period.
11
-------
Multispectral Scanner (MSS) Data Characteristics
Multispectral Scanner (MSS) instruments have flown on Landsats
using the same or similar spectral and spatial resolution. This
consistent period of instrument deployment provides a unique
opportunity to conduct comparisons over time.
The MSS has four spectral bands or spectral bandpass windows
that are measured by detectors. The bands include a green band or
band 1 (0.5 - 0.6 urn) , a red band or band 2 (0.6 - 0.7 urn) , a near
infrared band 3 (0.7 - 0.8 urn) and a second near infrared band 4
(0.8 - 1.1 urn). Bands 1 through 3 are digitized on a scale of 0 to
127, and band 4 is digitized from 0 to 63. On later Landsats the
band labelling was changed so that old system bands 4 through 7 are
now new system bands 1 through 4. The new system is used here.
The MSS sensor measures spectral radiance over a nominal
instantaneous field of view (IFOV) of approximately 80 m by 80 m.
The pixel resolution was resampled to 57 m by 80 m during ground
processing as that was the true geometric or spatial resolution.
The difference resulted from a systematic over-sampling of radiance
along the scan line. This action served to sharpen or convolute
the image. The difference between the spectral and spatial
resolution has been addressed and corrected in the standard NALC
processing procedures by pixel resampling. The different pixel
dimensions are rectified in the course of generating the geocoded
triplicates, and the resultant output pixels are resampled to 60 m
by 60 m. This procedure is described later.
NALC Landsat MSS Triplicate Image Data Sets
A major goal of NALC-Pathfinder was the development of a
"triplicate" product of historical and current data from the MSS
instruments. The triplicate would include a scene from 1973 plus
or minus one year, 1986 plus or minus one year, and 1991 plus or
minus one year. The triplicate is a "stack" of three image scenes
for each individual path/row scene of North America. The path/row
scenes form a world wide coverage that can be indexed via the
Landsat World Reference System (WRS). For each individual path/row
scene of the WRS for MSS sensor, the areal coverage on the ground
is approximately 185 km by 185 km.
The NALC program is dedicated to building coverage of North
America in triplicate MSS data sets. From this triplicate product
it will be possible to evaluate change between three dates over the
last twenty years.
12
-------
Image Composites
The standard NALC products are to be thirty percent or less
cloud covered. In some areas of North America it will not be
possible to obtain a given scene that meets the standard. Hence,
reduced cloud cover composites (RCCC) will be made from two scenes.
Composite scenes will be necessary to provide appropriate land
coverage by path/row image scene and to supply similar information
for evaluations of change between 1986 and 1991 scenes in a
triplicate. This product will be made by EROS Data Center (EDC)
and is described later.
Categorized Images of Land Cover
To address variables of interest to Global Change Researchers,
it is necessary to process NALC triplicates into derivative
products. Of particular utility is the Land Cover (LC) thematic
map that displays the land cover types found in a given scene. The
land cover classes are keyed to a land cover categorization system
of standard design (see section on image categorization for the
system).
Evaluations of single-date NALC scenes provide inventory data
on land cover or landscape characteristics. Comparisons of two or
more dates of NALC scenes allows determination of change in land
cover classes.
Change Detection Images
The determination of change in land cover over time is one
goal of the GCRP. Comparisons of land cover change between the
1970's, 1980's and 1990's image scenes will be undertaken by NALC.
The products are described in the section on change detection.
13
-------
METHODS AND RESPONSIBILITIES FOR
PRODUCTION OF DATA PRODUCTS
Acquisition of Current Data Products
Landsat MSS data products for use in the NALC - Pathfinder
project have already been acquired between February and the end of
September, 1992. This effort was initiated early to populate the
recent archive with suitable MSS images before the cessation of MSS
data acquisition. Past activities by other MSS clients had not
resulted in acquisitions on a continental basis for North America.
Thus, the NALC project acted quickly to avoid a similar deficiency
in the 1992 timeframe.
The acquisition program was developed between USEPA, USGS and
EOSAT. EOSAT formerly had copyright on the raw MSS data for a two-
year period after acquisition, and they formerly operated much of
the system for data collection and initial data processing. Their
role diminished with the transfer of the existing processing
capability and MSS archive to EDC in March, 1993.
The coordination and implementation of data collection
activities involved an Interagency Agreement (IAG) between USGS and
USEPA (Appendix V) and subsequent use of the;standard USGS-EOSAT
agreement covering Landsat data purchases and processing
activities.
The MSS data were scheduled for acquisition based on a number
of land and cloud-cover criteria. Considerable study was made of
historical periods of low cloud-cover acquisitions throughout the
NALC area of interest. Details on the seasonality of forest cover
and crop cover, the seasonality of snow cover, and the timing of
growing or rainy seasons were used to select periods for data
acquisition. This effort is further described in Appendix VI.
Identification of Archival Data Products
The MSS triplicate images make use of a 1991 +/- one year
period data set and two archival image scenes or "path/rows" from
the archive. The archival images were obtained from the National
Satellite Land Remote Sensing Data Archive (NSLRSDA) at EROS Data
Center in Sioux Falls, South Dakota.
14
-------
Selection of two historical scenes for each triplicate was
made from the 1970's and 1980's archive. The selection has been
made by efforts of EMSL-LV, cooperators and EROS Data Center (EDC)
personnel. The criteria for selection included: 1) The three
scenes should be from similar seasonal periods; 2) they should
contain thirty per cent or less cloud cover; 3) one image scene
should be from 1973 plus or minus one year; 4) one scene should be
from 1986 plus or minus one year; and 5) the images should be of
high quality.
The years 1973 and 1986 were selected based on the population
of the MSS data archive. A complete or majority coverage of North
America was not commonly achieved. These years represent the best
examples in terms of data availability.
The main objective is to obtain the highest quality images
from the three periods and to have the images match closely in
seasonal or phenological time. Naturally, the varying conditions
of weather, image collection priorities, population of the archive
and other factors detract from this objective. A best effort was
made to meet the criteria and produce high quality products.
Image Processing of Triplicate Scene Data Products
A variety of activities are involved in production of the
triplicate products from three MSS data scenes. Data processing
activities include geometric corrections, radiometric corrections.,
data formatting, scene-to-ground image registration or
"georegistration", image-scene-to-image-scene registration, data
indexing and archiving, and data distribution.
An overall objective is to develop an optimal data product for
the user. Efforts have been focused to provide a uniform and
standard product that resulted from both geometric and radiometric
corrections. In general, the corrections were conservative in
design and necessary to reduce sources of noise and to present the
image as a map-like product. The procedures were intentionally
conservative to ensure that they represented approaches with
demonstrated capabilities based on widespread current and
historical use.
Landsat MSS Data Characteristics
Landsat data have been archived and delivered in a variety of
formats over the years. The initial format for purchase was the
"X" format. This gave way to the "A" format that included
additional processing to yield a partially corrected product. The
most recent and current standard is the "P" format.
15
-------
Landsat MSS data in X format have been corrected for
variations in detector gains and offsets. They have also been
corrected for line length variations by the addition of extra
pixels at regular intervals at the ends of lines.
Additional X format characteristics include a pixel storage
format which was band-interleaved-by-pixel. The individual pixels
("n") were stored as pairs Cn, n + 1) for the four bands, and the
next two pixels were stored (n + 2, n + 3) for each band. The data
were also stored in four strips or tracks of approximately 810 to
830 columns. These strips need to be patched together to develop
a whole image product.
Landsat MSS data were subsequently stored in A format. These
data were available in either band-interleaved-by-line or band-
sequential formats. Band-interleaved-by-line format had all the
pixels in one scan line (n, . . ., n + 3200) stored one band at a
time. Band-sequential images stored all the pixel rows and
columns, by individual band, as individual files.
Landsat MSS data of current origin are stored as a P format
product (band-interleaved-by-line or band-sequential formats). The
data have either been rectified with ground-control points or
ground-control chips (precision corrected)' or without use of ground
control reference points in a systematic correction based on
satellite orbital characteristic data. The P format data are
provided in either Universal Transverse Mercator, Hotine Oblique
Mercator, or the Space Oblique Mercator projections.
Due to the variety of formats over the years and the
historical nature of the NALC effort, it was necessary to address
all these differences and organize processing procedures for them.
For NALC, the systematic corrections applied to the triplicate
images by EDC included:
Band-to-band geometric offsets;
Line-length adjustments;
Detector-to-detector radiometric offsets;
Correction for earth rotation during image acquisition;
Non-linear acceleration / deceleration of scanning mirror; and
Satellite ephemeris or orbital characteristics.
16
-------
Image Processing for NALC MSS Triplicate Production
Background
Restoration of geometric fidelity and accurate registration of
entire images, pairs or triplicates of images, or component parts
of Reduced Cloud Cover Composites (RCCC) raises important issues.
The use of ground control points and image rectification procedures
are used to transform the image data to a map projection. The use
of ground control point library (GCPLIB) image chips can expedite
the process of georeferencing and scene-to-scene registration.
A number of processing procedures have been established to
facilitate the production of standard NALC triplicates. These
include the systematic radiometric and geometric corrections and
precision image-to-map registration. The procedures presented here
have been implemented by the EROS Data Center for production of
standard triplicates. These standard procedures have , been
developed from more than twenty-years of experience in the digital
processing of Landsat data. A number of these procedures are well
known and have been documented in the literature.
The 1970's data selected for use in triplicate production are
pre-processed to correct for line length adjustments, variable
detector response, band registration, nonlinear mirror-scan
velocity, earth-rotational skew, and detector-to-detector offsets.
Both .the line length adjustments and detector-to-detector offsets
require a resampling in the along-scan direction (one-dimensional
cubic convolution). The parameters for these systematic
radiometric and geometric corrections are derived from the
satellite ephemeris and payload correction data. The individual
bands are also "destriped" to remove noise due to variable detector
response. The resultant product is analogous to the "P" product
currently generated by the EROS Digital Image Processing System
(EDIPS). In accordance with the Landsat Pathfinder Program
concept, the Pathfinder "basic data sets" will be composed of data
which have been radiometrically and geometrically processed to the
"P" level. NALC triplicates, however, will be processed beyond the
"P" level as described below.
Data Acquisition
The generation of NALC triplicates is initiated once the
appropriate scenes have been identified by EPA-EMSL and purchased
from EDC. EPA staff are responsible for determining all scenes to
be purchased, both current and historical acquisitions, in
accordance with the scene selection criteria described in a
previous section. This process involves searching databases and
reviewing browse or microfiche imagery to assess cloud cover and
image quality. EPA submits the scene-identification numbers to EDC
17
-------
for the subsequent submission of CCT orders. The original scenes
are then archived into the Landsat Pathfinder Archive (LPA) under
a project code which uniquely identifies the scenes as NALC
Pathfinder data.
In parallel with the ordering of scenes, the geographic corner
points for the selected scenes are used with EDC database software
to identify the topographic map sheets corresponding to the
respective scene path/rows. A subset of these maps is retrieved
for use in ground control point selection for image-to-map
registration. EDC has developed a relational database to
facilitate archiving and retrieving all U.S. 1:24,000 scale
quadrangle maps as well as the 1:250,000 and 1:50,000 maps received
from Mexico's National Institute for Statistics, Geography, and
Information (INEGI) . This database will be augmented as maps are
acquired for Central America and the Caribbean.
Triplicate Processing
The process of generating the triplicates involves a multi-
stream approach (Figures 1 and 2). Ground control points are
selected from the 1986 images and maps for use in developing the
model for geometric transformation (geocoding). The map control
points contain X and Y positional values and elevation values to
correct for relief displacement. A cubic convolution registration
is used to rectify and resample the image to a UTM projected output
image. The image is comprised of 60 m x 60 m pixels, with an Root
Mean Square Error (RMSE) of less than 1.0 pixel.
Concurrent with this activity, the 1970's scenes are
radiometrically and geometrically pre-processed to convert the CCT-
X data to a P-level product. The images are "destriped" to
compensate for variations in the radiometric response of the
individual detectors prior to geometric registration because the
noise is scan-line dependent. An interim systematic correction is
applied, using the satellite ephemeris data and platform navigation
model, to generate a UTM projected output image that is oriented
north-up.
Automated cross-correlation procedures are then implemented to
extract control points from the 1970's and 1980's images to compute
coefficients for image-to-image registration. This involves the
use of a single band from each of the 1970's input images. Once an
accurate transformation is developed, the grid for the interim
systematic correction is convolved with the image-to-image
transformation grid to produce coefficients that facilitate a
single step registration of the 1970's P-product with the map
registered 1980's image. The net result is an image registration
procedure that only involves one step of resampling. The target
RMSE for the image-to-image registration is 0.5 pixels or less.
Previous studies have shown that the use of polynomial
18
-------
transformations alone on CCT-X format data yields only a 1.5-2.0
pixel internal image accuracy, once map projected. The use of a
satellite model overcomes this problem and assists in meeting the
registration criteria.
The last step involves creation of a pixel identity image to
accompany each of the 1970's images. Each pixel value identifies
the specific 1970's scene used to fill-out the WRS-2 path/row scene
or "tile". This step is necessary due to the east-west shift in
scene position from WRS-1 and WRS-2.
The procedures for the 1990's image registration are similar
to those for the 1970's data, except that the 1990's data are
acquired as P-level products. An interim systematic correction is
applied, based on the satellite orbital data platform navigation
model, to generate a north-up UTM projected image. Automated
cross-correlation procedures are used to select control points, and
a transformation grid is computed for the image-to-image
registration. The two grids are convolved into a single grid which
is then applied to the 1990's data to create an equivalent of the
1980's image. Similar to the 1970's processing effort, this action
involves only one resampling step. The RMSE objective for this
registration is 0.5 pixels or less.
Reduced Cloud Cover Composites (RCCC) are made after all data
have been coregistered. This step is only performed in cases where
19907s scenes with 30% or less cloud cover are not available. To
minimize the amount of cloud cover in the 1990's triplicate
component, EDC has adapted AVHRR cloud-reduction compositing
procedures for use in NALC triplicate generation. The compositing
process operates on image pairs. The procedure is based on the
Normalized Difference Vegetation Index (NDVI):
(band 4 - band 2)/(band 4 + band 2)
This index is sensitive to variations in surface
characteristics, such as the presence or absence of green biomass,
and other scene characteristics such as clouds. The NDVI is
computed for each of the images to be used for compositing. The
maximum NDVI value determines which input image pixel brightness
values (BV) will be used to constitute the output image. This
maximum NDVI decision rule is efficient for computing and yields
consistent results as judged by comparisons of image and ground
surface features. A 1990's triplicate component which has been
composited will have two additional bands, a pixel identity image
and the resulting RCCC or maximum NDVI composite image.
The final task in triplicate production involves making
mosaics and a projection transformation of the Digital Elevation
Model (DEM) data. The DEM data being used is derived from the DMA
DTED data which were digitized from the standard NTMS 1:250,000
scale topographic maps, for which there is complete coverage for
19
-------
the United States and Mexico. These data, often referred to as the
3 arc-second DTED data, are made available as grid files
corresponding to blocks in dimensions of 1° of latitude by 1° of
longitude. These blocks are formed into a mosaic for the path/row
of interest, and then re-projected and resampled to 60 m x 60 m
pixels in UTM projection.
Image Rectification Analysis Forms
As explained previously, the image registration process will
use ground control points or the ground control point library
(GCPLIB) or "chips". The georegistration process will quantify the
errors associated with the modelled transformation. An independent
set of ground control points will be used to validate the accuracy
of georegistration.
The following forms document the results of the registration
of a NALC image. The forms will provide part of the documentation
of quality and heritage of the image. These forms will be stored
at the origin of production and may be accessed for quality
assurance and quality control (QA/QC) requirements.
A sample of the Image Rectification Analysis Forms will be
evaluated as part of the QA/QC efforts. It is anticipated that
every tenth rectification will be evaluated by QA/QC personnel.
Images or groups of images that fail the criteria will be re-
processed. It is anticipated that the acceptance criteria for
rectification will be + or - 1.0 pixel in comparison to ground
position and + or - 0.5 pixel in image - to - image registration.
20
-------
CD
o
OS "D
0. O
OQ.
co
a)
I
PL,
'co
U- (O
c +-
"co 5
CO
CD
O
O
21
-------
«
•a
*-» *-
o o
•a «j
1 ii
—I "- ra
/' 'jJ U j-
CCT-X |
Reprocessing j
GO
(0
T3
13 o
=> 9
•% £
{ '• LLCP 3480
:v.v.^
• ; Browse Pn
f.V:.:JData Base
0.
o
<
(h
D.
Q
ai
O
Reprocessing
O
(A
TJ
1 8
•So?
{ } LLCP 3480
'.'.'.I'.v:
• I Browse Pr
r';;JData Base
EDIPS-A to -P
O
Reprocessing
D
o
o
"5"
D)
OT
OT
-
o il
HI HI
Q:3i=
0)
DC
22
-------
o
0,
IMAGE RECTIFICATION ANALYSIS FORMS
o
•MO
O I)
O.CS
The following forms record the characteristics of the image
registration and rectification activities. These documents will
be held at the point of origin.
CO
o>
CO
via
o
§
o
CM
JC
u
o
o
irt
in
f-
m at,
»-4J a
>o •-«.
a -a u
C t) L.
a.t- o v
V U 4J
« c « x u
I V E
E E XC t.
•»- .00 o
O t- O.
u «o c
u
u
c
M O.
O L. U
U 41 U
O w O
i. 3 e
a i
xti
«i e
E a •'
act)
U a
-> C
w—<—> t.
tfo «*-
U O 3 tl
O E C.Q
£. a
at> e t>
j= -a
O*->T3 O
„ c «o
cu t)*eEwt- IJCQ.CI
•c-«r- t._J*r* O t)«f t) a
O4-> «J.Q E t-Xl- "D t- O^J
O. O « **-J3 O O 3 OH-
•a c t-i c_ B-O a
c t) a ow L. i (i
•r-4-i o a—> ct)t>ut>c.v>
W O O E O «>i- I Of U U-r-
• c u a •
V V_J _j
*-t-D.ejl.C
w O*J • CO t.
C « t- M L.
•*-» BBSO
o a c 3 o u
O.T3T- act)
-
•O t. U C'T-'B 3
o>
o
u ••
veo
a
00
00
oo
oo
C OU. OO
Oi- • .
•»-4JT3 00
a u c.
C M4J
X C «J
O U V « a C_i O.OLC -
o a au o.an B4-> x*j a.c
_«-x_a • o —i c
•—--• "• -• ^~ K ••— tr^'l ' ^% «iJT—
_^ X—> O XOT3 34->.a BJQ «l 01
"- 04J«JC_.^ X-r-XoH
X-r- OT-4J a X4J—«4-._. V O
t)-. W-i-a QIC « 0-> B-.-rn
> -j a t)T- coecaeaOM
•»• «j E t. « e a 3 o a t. «
*-• 3 O t-J3 C. a t) C C *• C 4J
o c<-> o 3*»f-.c.o e 3 a xa
< a 3 U «• M-D4JT- 6 a E—< U
1 «J I I I I will B-r-
i I i I a i i i
23
-------
IMAGE RECTIFICATION ANALYSIS FORMS (continued)
o
0,
I
J
o
o.
ur-
ea
o
• M
O
•r*
Ui
U ••
oca
3 B
4-r f
C f O*—tSIMi^* irvOf-^co O.O*wc\lKK~*trt*O f~cOO>p
^~ f vpj fi^^ifi-O'^. co o-^~ *"~*^*~^^^~^~*~«~ ^t\j ivj csx<\nvj-* r a,a.0.0.0.0.0.a.da;0.0,0.0.0.0.0.0.a0.0.0.0.0.0.0.0.0.0.0.
o i
o
—i-a i KI •oco -OOOT)'nTIt3T3TIT31Ot3-DT3-aT3OKlWXjTI
t» I 33333333333033 333333333 3 3««««««•««««« M CI-OOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
a, »> c o i oooooooooooooooooooooooooooooo
C o t-l'--» ...........................
^- OX>- 4J I OOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
C I
o > <
4J tl I
«^ I
AJ U] *
Q. OCVJ«- t OOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
4j ,_ OOO I OOOOOOOOOOOOOOOOOOOOOOOOOO OOO O
3 + i i I OOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
O ««« |..»*.».........................
„ O^T-^- I OOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
a, c ^O^ X f
g ^* ^xCO«4'^O O I
*J COKIKI C r
•k o ^^O^**'^* O (
C i- O.PJO I
U X MO-CO " >
•v- O CO*OOt —' '
.r_ o O*O*Ot CM X I OOOOOOOOOOOOOOOOOOOOOOOOOO OOOO
«. a. ... t- i oooooooooooooooooooooooooooooo
,»_ a -o<-o> 0- i - - * ' -
o >. >_ 1 .. >- I OOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
O t_ *~- "• 4JOOOS C "S. I
O O (V CM •>-»-«- ••- ** I
»- ^^ ^x oOKt t- a i
r < m OOO c _i t
t u + + i e •• - «> i
O -I- + VOOT-X>* > I
c t. o*t*.co "** i
O Or XX ^-»- M'r"T~ „ i ooooppppppppppopoooocDooooooooo
3EB t- «JI
C. V OV M UEI.. .-
^ ^ »OOr*- C €> to t. <3 a I v-«-«—CMCMCMKIKkK>****-*«rt ^-1 « (M CM CJ K> K> K> *
« ,- ^ ^-0*<- -r-C-t- O CLUI
c a «»o o MOM O*-H- c- co i
O C. 4^ ^.^ ^.^ ^^ O *£* O- C- '
t. « c< m ... s-»- c w
Ha,o O.T-V »•— o o 3
^-«i i o —• a.
„ 5 ^_ ^^ L.tof> 3 3CI****'**'^''^""*^***'**'**''^'^"**'"*^*'^'COC^O3COCOOO0OOOOOCOOOCOOOCO
CC C H-X >- «€)*» TOO-^I'
> x uu. u. ecu a H
z a o 3 u u v
••• =c o zaa <£
24
-------
IMAGE RECTIFICATION ANALYSIS FORMS (continued)
o
o.
o
o
O V
O.O
V a
eo
o>
CO
vin
o
a M
4J _»
«-J
a a
u.
ui o
»
CJ
o^
Ox
o ••
II CO
o
• V
3 •
.^^.CO 00 CO CO CO 00
Q.Q.Q.o.Q.o.o.o.o.cxa.Q.o.a.o.Q.a.a.Q.cxo.Q.cxa.Q.a.o.a.a.o.Q.Q.a.cxc
Om CMKl^O *o*o mo. o 4n*--*
CMm OOCOO ^.**- ^in r- m*o*o
K>CM K»»— «O KlfO CM «- «- CMK»«-
oo ooo o«- oo o ooo
««««««««*« « «««««««««« «« «««««««««« « ««««««««
««*««««««« « «««««««««« « «• «<««««««<« « <««<««««
•****««*««* « «««««««««« «« «««««««««« « ««•««*««
"OT3T) T3"OT3"O TJTJT3 "D "OT3"D^*DT3T3^"O"n "D"O ~O~UT3T3T3"a^"OT3"a T3 "D"X3T3*O "OT3"OT3
ooooooooooooooooooooooooooooooooooooooooooooooc3"o^j^jT)^jTj7)7j
CCCCCCCCCC C ICCCCCCCCCCIICC CCCCCCCCCCIC ICCCCCC'CC
4J 4J 4J 4-» 4J 4J 4J 4J 4J 4J 4J 4_* 4J 4J 4^ 4J ) • | • 4^ | • ^ . 4J4J 4-I4J4J4J4J4J 4J 4J 4J 4J 4_| xj 4.1 4.1 4^ 4^ 4_i jj 4^
OOOOOOOOOO O OOOOOOOOOO OO .OOOOOOOOOO O OOOOOOOO
CM** ^TKIN • h-IA O>K» . tw inKlin
**********CMf\J* Ki«~ l/\ 4t4I4I4I4I«I«4I4I«K1O*4:«—O*******«4I4IO*CMCM«— «««**«««
««*«««**««OO«OOO*'«««4i«««««O«^««OO«««««««4t«4iO«OOO««««<«««
III III lit
O OOOOOOOOOO*-OOtin«—OOO OOOOOOOO «— OOO«OcoOOOOOOOOOO^O*4-COm OOOOOOOO
—_ ._ Jr^l^oaaoooao
30
-*-«-CMCMCMIOK>»O-*-*-»-in «-^-
OOOOOOOOOOSDCJOCOCOT-OOOOOOOOOO-OI—OOOOT-OO OOOOOOOO OCOCOmOOOO OOO O
OOOOOOOOOOKt(MOK)O>OOOOOOOOOOOO>N-OO4-OOOOOOOOOOOinOr>.OOOOOOOO
t -f -» -4- ~*
J r- »- T- CM CM CM 00 CO CO CO CO
•kKifOfOFO
DOOOO
DOOOO
3OOOO
DOOOC
3000C
DOOOC
noooc
3000C
3OOOC
ooooc
ooooc
ooooc
ooooc
o n ** ** ** in T- «- V-CM CM CM fO K> Kl ** -* ** in «-»-«—CM CM CM KIM IO-* >
Co cocococo
25
-------
IMAGE RECTIFICATION ANALYSIS FORMS (continued)
t-
_j
o
,o
o
o «
D.O
co eo 00 eoo-o-o«cs o-oo-o-o-<>.»
O. O. Q. Q. O. Q. O. O. Q. O. O. O. Q. a O. D. CL O. O. Q. Q. Q. O. D. Q. [J. 0.
CU**«*'r-KI«-«-«r-*J-O'O>O
KI «-»»O
o ooo
T3 T3 "O •* "D O M »- 13 TJ T3 "D "O "O T3 T3 TJ TJ 13 "D T3 T3 T3 TI T3 TJ T3
uuoouooouuuuuuuuuuuouuuuuuu
CCC Cl 1 CCCCCCCCCCCCCCCCCCC
ooo o
ooooooooooooooooooo
: O« OOO<
I I
CO
o>
oooeoo«-r~«-ooooooooooooooooooo
OOOOOK1K10OOOOOOOOOOOOOOOOOOO
CO
out
o
OOOI^-OK>-cocococD(X>vr^-vr-j--j--T-i--T-i- M KI M Kl M M-»-*-a--*-*-»•-#-»•-»•-
ooooooooooooooooooooooooooo
ooooooooooooooooooooooooooo
ooooooooooooooooooooooooooo
ooooooooooooooooooooooooooo
ooooooooooooooooooooooooooo
ooooooooooooooooooooooooooo
ooooooooooooooooooooooooooo
D.D.
Old)
o o
a. a.
,
co^-
«-K»
o-o
I0~»
•OKI
c- t.
o o
L. i.
1. 1.
U1U1
r-UMM V V
irtQ^^ Kcc
Oif\~f
COf*-*^ CM O
ooooooooooooooooooooooooooo in*—o co-a-
• • • KICJ
OOO KIKI
•OCO
o
o
OOOOOOOOOOOOOOOOOOOOOOOOOOO
ooooooooooooooooooooooooooo
OOOOOOOOOOOOOOOOOOOOOOOOOOO
ooo
ooo
ooo
ooo
ooo oo
ooo oo
• • • oo
000 00
00
00
_. OO
" •>
3 "O *• -• a
f V -J 3
•a vtc. cxj
Cl O 3 *r*
4J CC V T3 M
a —i •• -r- ii
v«4-o o cxui Mce
U.CJ-J C EM t)
_i a.
C E
ooooooooooooooooooooooooooo
ooooooooooooooooooooooooooo
KI
u ••
ueo
o
. u
3 a
JC—
CO CJ in 00 KI KI KI KI K> KI KI-* ^"-J-•*-»-*•* ^^-*•*-»••*•*•*-»•-*
BT3
3-»-
T3 «•
•»- o
UK
v a.
C E
•*- a
•a .<•— • a
•^ «> a. — •
U C E O
o*»- « a.
— ' M L.
a v
C *-» 4-* 4->
t- 3 a c
Q.Q.Q. -c
oo'
o
a.
tl
4J
c
*-• t_ t_
V
a
EJQ.TI i.
o E E v
«j 3 3 >
19ZZ •<
26
-------
IMAGE RECTIFICATION ANALYSIS FORMS (continued)
V O
E-1
o
a
(W CM «*• *» O> m CO -i»- m in «- »- O KICMCMK»KllO»OCM«MtOCMCMtOCMK>CMMK>CVlCMCMCMCMCMM CWI-O
Via t • H «l. «J "O
t. i awo
v! t~- u •
MVI • "Oco
a i r-ooocoo-orj<^a-ooootoo»-Kiooo.»--*eoK>o.c>.oocymooo>Kieoo
1—• I r«I»OeOeOinC>t>CO»-lftCM-*T-l>.>OCMOCO«>>O«*-O«O-i a. i
c
•*-
II U
—* N
T- •»- E a
4- M
c —•
o o
i- X
4-1 «^
a a
u
f-
•r- «3
L. E
3 a
a. "
c 3
— o
IOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
CM ••*• V
I -O«- X
• T-
• • •* a.
•o
no c
>-i e I •OOm«OCOCMin«OO-*-*OOM'*i>-'rtacs01 aicsaaoacnadoucao»CDOocacaoiaicso
27
-------
IMAGE CATEGORIZATION AND DEVELOPMENT
OF LAND COVER DATA PRODUCTS
Training Set Development
The development of training sets for categorization requires
a method that will yield high quality and unbiased training set
characteristics in an automated fashion. The unsupervised training
set development approach has been proven successful in providing
good, homogeneous spectral clusters in applications such as land
cover or landscape characterization. The unsupervised approach
negates many systematic problems associated with atmospheric and
terrain radiometric distortions. This approach has been used
successfully for over twenty years, and it can also be implemented
with a minimum of effort. Consistent results can be expected for
most applications.
Unsupervised training set development will be conducted with
an automated spectral clustering algorithm. A common clustering
method has been selected from available choices in the literature
of remote sensing applications and has been developed intb an
algorithm by EMSL-LV personnel.
Training set development will be conducted with some
assumptions and input parameters. Individual scenes of the
triplicate will be clustered separately. Each scene will be
clustered on an iterative basis for as many as one-hundred or more
clusters or classes. Criteria for linking or breaking of clusters
will be established empirically.
Several approaches to unsupervised training set development
will be tested. These include clustering using an EMSL-LV
procedure and using procedures employed by the commercially
available image processing software currently in place at the EMSL.
These procedures will be tested as part of the standard methods
development activities that form the Chesapeake Bay Watershed and
State of Chiapas, Mexico Pilot Studies.
Evaluation of Training Sets
The results of unsupervised training set development will be
evaluated in several manners. A preliminary categorization image
will be produced from the unsupervised training set development or
clustering activity. This image will help indicate the location of
many clusters or classes.
28
-------
The preliminary categorized image will be used along with
other procedures to evaluate the quality of individual training
sets or clusters using aerial photo or other data. Procedures will
include divergence calculations, bi-variate scatter plots of
cluster means and variance-covariance, and comparison of the
preliminary categorized image with ground information or aerial
photographs.
Following analyses of the quality of training sets, the
contents may be edited to remove examples of poor clusters, e.g.,
clusters that represent very few pixels in the original image,
overlapping clusters, and clusters that are composed of more than
one distinct, spectrally homogeneous land cover class.
Categorization
The edited training sets will be used for categorization of
all three original scenes from the triplicate. The maximum
likelihood / Bayes decision rule approach to categorization will be
evaluated. There are a number of reasons to use this approach,
among which is the fact that it has been successfully used in
remote sensing applications for over twenty years. This approach
makes use of both Euclidean distance measurement and probability-
based criteria in the determination of the class identity of a
given pixel. The use of these criteria makes the categorizing
algorithm more sensitive to class characteristics as compared to
single-criterion approaches.
After evaluation of training set quality, the scene from the
triplicate will be categorized using all four bands as input to the
maximum likelihood classification algorithm. The work will be
completed at EMSL-LV and will employ the scene triplicates produced
by EDC.
The resulting product of one hundred or more classes will
represent a Preliminary Land Cover (PLC) product. It remains to
identify the land cover type of each cluster or class. This
activity is known as "labelling" and will be perform by using local
knowledge, ground information, aerial photographs and maps. The
PLC product will require that certain clusters or classes be
aggregated or "lumped" to render the best product. This
aggregation will allow inventory of several clusters or classes
that may represent the same functional land cover class as
represented in the NALC land cover categorization system.
29
-------
Identification of Land Cover and Classification System
The land cover classes in the PLC image will be labelled as to
cover type using a land cover classification system. The NALC
classification system was developed to specifically support NALC
project objectives and to be compatible with the other major land
cover classification systems. The NALC classification system is
compatible with the Anderson et al. (1976), Cowardin et al. (1979)
and Brown et al. (1979) systems. The system has been optimized to
support carbon inventory applications.
The standard NALC characterization data base products will be
based on Level 2 classes (Figure 3) . For a more complete
description of the NALC system and definitions of land cover types,
refer to Appendix X.
The assignment of class types to PLC classes will be performed
by NALC cooperators. The scheme is presented below, with Level 2
as the final land cover type detail to be identified. This
approach will supply sufficient detail while using the capabilities
of MSS data to their best measure. It is recognized that
differentiation between forest and shrub/scrub classes at Level 2
will be difficult and in some cases not possible without the aid of
ancillary data. However, a best effort will be made throughout
North America to accomplish this differentiation.
The resulting product will be the Land Cover thematic data
coverage products (LC) for each path / row. It will be supplied to
EMSL-LV for validation. Subsequent to data validation, LC products
will be archived at EMSL-LV. After validation, the products will
be archived and distributed by EDC. The image will be accompanied
by details on the training sets including cluster or class means in
each band, variance-covariance matrix, class identities, and
details related to quality and accuracy.
Data Production and Distribution
The data are to be processed by three groups, USGS-EDC, EPA
EMSL-LV, and the cooperators. The triplicates produced by EDC will
be distributed initially to EMSL-LV. EMSL-LV will conduct image
pre-processing, training set selection, training set evaluation,
and maximum likelihood categorization. EMSL-LV will distribute
NALC triplicate scenes, the categorized or clustered image scenes
(Preliminary Land Cover, PLC), as well as documents on standard
processing methods to the cooperators. Cooperators will identify
the clusters or classes of the PLC maps, aggregate and assign land
cover types from the categorization system, and create the Land
Cover (LC) products. The cooperators will supply the LC products
to EMSL-LV for archive. The LC products will be tested against
standards and for quality (data validation), and then sent to EDC
for archive and distribution to users.
30
-------
Quality and Accuracy Assessment
The evaluation of data quality has been addressed in two
manners: the systematic sources of errors in production; and the
accuracy of derived products. Once the systematic or within-system
evaluations have been performed and the characteristics of the
triplicates and derived products determined to meet the standards,
the data characteristics will be recorded and stored at the point
of origin (EDC).
The accuracy of derived products, such as the Land Coyer
products, will be tested. The results will be stored as an
attribute file and maintained at the point of origin. Accuracy of
each land cover class, the error matrix, and other' details will be
included.
The accuracy assessment information of the LC data sets must
pass validation. EMSL-LV personnel will take the data and develop
the accuracy assessment calculations and results. The criteria are
described in the section on QA/QC.
The LC data sets will be re-evaluated if they fail the above
criteria. The evaluation will focus on determining whether the
initial classes were incorrectly aggregated, were incorrectly
labelled, or were subject to other sources of errors. If the
source of error can be identified and a correction can be made, the
image will enter the stream for further evaluation. If it fails
again, a new attempt will be made at categorization, and results
will be evaluated for accuracy until a solution of the problem is
identified and the criteria are met.
NALC Products
Products provided by the NALC project will include post-
processed remote sensor data, post-processed digital elevation
model data (DEM), intermediate categorization products, and final
database categorization products. All NALC products will be
"clipped" to the World Reference System 2 (WRS 2) path/row scene
configuration.
NALC standard data products will include the following:
triplicate data sets georeferenced and coregistered Landsat MSS
imagery (1991, 1986, 1973 plus or minus one year); spectral
clustered categorized images for each Landsat MSS image; and
coregistered DEM data. Categorization database products will
include .complete (100%) image categorizations for Landsat MSS
scenes from 1991 plus or minus one year, and partial image
categorization products for Landsat MSS scenes from 1986 and 1973
plus or minus one year. For the Landsat MSS 1986 and 1973 scenes,
only the areas that have undergone significant levels of spectral
change will be available in categorized form.
31
-------
Anderson, J., Hardy, E., Roach, J., and Witmer, R., 1976, A Land
Use Classification System for Use With Remote-Sensor Data, U.S.
Department of the Interior, U.S. Geological Survey Professional
Paper 964, Washington, DC, 28 pp.
Brown, D., Lowe, C., and Pase, C., 1979, A Digitized
Classification System for the Biotic Communities of North America,
With Community (Series) and Association Examples, for the Southwest,
Journal of the Arizona-Nevada Academy of Science 14:1-16.
Cowardin, L., Carter, V., Golet, F., and LaRoe, E., 1979,
Classification of Wetlands and Deepwater Habitats of the United
States, U.S. Department of Interior, U.S. Fish and Wildlife
Service, Rep. No. FWS/OBS-79/31, Washington, DC, 103 pp.
32
-------
Figure 3.
NALC Pathfinder Categorization System
LEVEL 0
Land
LEVEL 1
1 .0 Barren or Developed
Land
LEVEL 2
1.1 Exposed Land
1.2 Developed Land
2.0 Woody
2.1 Forest
2.2 Scrub/Shrub
3.0 Herbaceous
3.1 Herbaceous
4.0 Arid
4.1 Arid Vegetation
4.2 Riparian
5.0 Snow/Ice
5.1 Snow/Ice
Water
6.0 Water & Submerged
Land
6.1 Ocean
6.2 Coastal
6.3 Near-Shore
6.4 Inland
Other
7.0 Other
7.1 Cloud
7.2 Shadow
7.3 Missing
7.4 Indeterminable
33
-------
METHODS FOR MANAGING AND DISTRIBUTING
NAIiC DATA AND PRODUCTS
The NALC-Pathfinder products will be identified, indexed and
archived. The NALC product characteristics will be stored in an
Information Management System (IMS) or "meta" data set housed at
EROS Data Center (EDC). This will allow identification and
tracking of NALC products even when archive products are stored at
different physical locations.
These new products require new identification numbers to be
assigned. This is due to the variety of Pathfinder products that
will be stored in the archive and the variety of data
characteristics. The Information Management System (IMS) will
require additional fields of meta data beyond the fields specified
in existing systems (Figure 4).
The population of image scenes in the archive will include
derivative products created outside of the EDC environment. These
products need to be produced with NALC standard processing
procedures. They must be of known and documented quality as per
NALC standard quality characteristics. To be included in the
archive, products must be uniform and adhere to NALC standards.
NALC Data Archive, Management, and Distribution
EDC has responsibility for the archive, management, and
distribution of NALC Pathfinder products (original Landsat MSS
CCTs, geocoded triplicates, OEMs, and derivative products). EDC
also has this responsibility for other Landsat Pathfinder projects.
Consequently, a long-term strategy for data archive, management,
and distribution must address project-specific needs in the context
of the overall Landsat Pathfinder Program goals.
Towards this end, an interim Information Management System
(IMS) approach is being developed to facilitate the indexing,
archiving, and distribution of NALC data sets. This approach is
designed to develop a database that is populated by metadata which
describe NALC data sets. It is also important that this IMS be
upwardly compatible with the Version 0 IMS at the Land Processes
Distributed Active Archive Center (LPDAAC) for EOSDIS.
34
-------
The Version 0 IMS at the LPDAAC will ultimately be used to
manage all Landsat Pathfinder Project data (source data and
derivative products). This IMS will be separate from the current
Global Land Information System (6LIS), which currently facilitates
query, browse, and product ordering from the National Satellite
Land Remote Sensing Data Archive (NLSRSDA) . GLIS is linked to the
NSLRSDA production database and is not currently structured to
handle higher level data products, such as those being generated by
NALC.
The types of metadata being compiled and tracked along with
the triplicate data sets are listed in Table 2 and described in
Table 3. These particular attributes are considered essential for
describing the NALC triplicate components. The higher level data
products which will be produced later (cluster data sets, land
cover products, etc.) can be expected to have different or expanded
metadata requirements. Currently, this metadata is managed using
a relational database management system (RDBMS), specifically D-
Base IV. However, in response to the Peer Review Panel Meeting in
New Orleans, EDC has investigated the IMS developed by the
University of New Hampshire for the Humid Tropical Forests
Inventory Project (HTFIP), which is another Landsat Pathfinder
activity. The HTFIP IMS is based on ARC/INFO capabilities, and
provides graphic displays of data coverage, browse images of
selected scenes, and the ability to query some metadata
attributes.
Upon evaluation of the HTFIP IMS, EDC has concluded this
capability can be developed for NALC in a more efficient manner
using ARC VIEW capabilities. The D-Base IV information which has
been compiled would be upwardly compatible, so no effort to date
has been wasted. The following elements are functional
capabilities which an IMS should support: Graphical display of the
extent of data coverage; geographic query; interrogation of
metadata attributes; data richness or status tracking; viewing of
selected browse products; linkages between NALC triplicates and
their source in GLIS; and the ability to execute product orders.
The interim IMS is not likely to provide immediately open
.access to the general public, but it should serve the internal
needs of EDC for product development and tracking and be portable
to EMSL-LV. EDC will provide EMSL with regular, periodic updates
to the metadata. In addition to providing support to NALC
production and data management activities, the interim IMS will aid
in defining the functional requirements of the Version 0 IMS. EDC
staff are currently investigating IMS requirements for the Landsat
Pathfinder projects as part of a larger effort to assess the
resources required to upgrade GLIS as a phased approach to the
LPDAAC Version 0 IMS development.
35
-------
NMiC Data Distribution
EDC is currently distributing the NALC triplicate products to
EMSL on 8mm Exabyte cartridges. These tapes contain the data
description record (DDR) files in ASCII format, and the image data
in band-sequential format (Table 4) .
The DDRs (Table 5) contain information describing the image
projection parameters and UTM coordinates. The order in which
files are written to tape (Table 6) is always the same, except the
number of files depends on the number of scenes used for each of
the 1970's and 1990's components.
The.image files, in turn, vary in size (i.e., number of bands)
depending on the requirements for cloud composite and mosaic
products. A DDR file is written before each image file, and the
geographic coordinates are always based on the 1986 scene which was
map-registered. All files (images and DEM) have the same
dimensions (NL, NS) ; the MSS images are 8-bit (BYTE) data, whereas
the DEM is 16-bit (INTEGER*2).
Query Procedures
The information on NALC-Pathfinder products in the Information
Management System will be available for a number of
characteristics. The details on a given image may be queried from
fields set up to index NALC-triplicate and derivation image product
characteristics.
Users would enter the IMS via communication link from a
workstation. The query sequence can be called up and executed, and
the user could examine the fields by individual scene. The fields
could also be used to search the database for a given set of
criteria. Searches by location, date, cloud cover, and so on could
be searched to identify scenes of interest.
These and other data fields (Tables 2 and 3) are still to be
evaluated. It may be desirable to add or subtract field types from
the list above to optimize the information content while minimizing
redundant, unnecessary/ or costly information fields.
Product Media Available to the User
The NALC-Pathfinder products will be available from EDC.
Products will be sold at the cost of duplication. The media for
distribution will include a number of types. This is due to the
variety of hardware owned by users and due to the fact that media
types change over time. NALC products such as scene triplicates
will be available on nine track tape, on 8mm tape, and on 3480 type
magnetic tape cartridges.
36
-------
The media type mix may change over the life of the program.
This could be caused by a change in industry standards for media or
media hardware. It could also be caused by a change in user
selection of storage devices or by experience with media that does
not meet the requirements of the project.
EDC archives the NALC image data on 3480 cartridge media and
only uses the 8mm cartridges for distribution to EMSL. EDC
recommends that users verify all 8mm cartridges upon delivery and
that the data be backed-up on 9-track tapes or other media. The
Exabytes may not be reliable long-term storage media. On several
occasions EMSL has not been able to read the 8mm cartridges, even
though EDC has been able to successfully run tape mappers on them
prior to shipping. In addition, there have been a number of calls
from other users who have been unable to read these tapes.
Standard Product Ordering Procedures
Data orders from the archive will be accomplished by standard
ordering procedures at EROS Data Center. Orders are currently made
by correspondence, or through electronic ordering procedures
available through the EDC's IMS or GLIS query systems.
Alternative Products and Ordering Procedures
Cooperators will receive data from EMSL-LV, and return project
related products to EMSL-LV. Cooperators with EPA may obtain data
from EMSL-LV.
37
-------
S
O) Q.
O JJ
9U O
CL C
*-* o
DL O
~ O)
03
S
03
C/5
O.
CO
.2
O
E
o.
05-g
"X a
§Q
"O O)
o
J=
05 0
•§ «
O a)
a 8
fcoc
x: o
0?
C3
JC
O
05
CD
e §
o g
as
1?
— CD o
811
UJ < QL
• • *•
38
-------
Table 2.
Interim NALC IMS Metadata Database Schema
Structure for database: C:\PATHFIND\NALC\NALC,
Number of data records: 4
Date of last update : 04/22/93
Field Field Name Type Width Dec
DBF
1 TAPEID
2 LEVEL
3 PATH
4 ROW
5 DECADE
6 ENTERDT
7 PROJCODE
8 FORMAT
9 RESAMP
10 SCENE1
11 CTLPTS1
12 RMSERR1
13 ACQDATE1
14 SUNELEV1
15 SUNAZIMUT1
16 SCENE2
17 CTLPTS2
18 RMSERR2
19 ACQDATE2
20 SUNELEV2
21 SUNAZIMUT2
22 SCENES
23 CTLPTS3
24 RMSERR3
25 ACQDATE3
26 SUNELEV3
27 SUNAZIMUT3
28 SCENE4
29 CTLPTS4
30 RMSERR4
31 ACQDATE4
32 SUNELEV4
33 SUNAZIMUT4
34 SOURCE
35 CONTACT
36 PHONE
Character 6
Character 1
Numeric 3
Numeric 3
Numeric 2
Date 8
Character 1
Character 4
Character 1
Character 16
Numeric 4
Float 4
Date 8
Numeric 2
Numeric 3
Character 16
Numeric 4
Float 4
Date 8
Numeric 2
Numeric 3
Character 16
Numeric 4
Float 4
Date 8
Numeric 2
Numeric 3
Character 16
Numeric 4
Float 4
Date 8
Numeric 2
Numeric 3
Character 16
Character 3 0
Character 14
Index
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
N
Example
31051
G
16
31
70
04/22/93
U
CCTX
C
8104515240500
97
0.65
09/06/72
47
139
0
0.00
00/00/00
0
0
0
0.00
00/00/00
0
0
0
0.00
00/00/00
0
0
121
TBD
TBD
** Total **
238
39
-------
Table 3.
Definitions for NALC Interim IMS Database Schema
Field Definitions:
TAPE ID = EDC archive storage location, the tape id serves as a
unique reference
identity combined with the path, row and decade fields,
Valid formats are:
A#### - 1 space, 1 alpha character and 4 numbers.
AA#### - 2 like alpha characters and 4 numbers.
###### - 6 numbers.
LEVEL = level of processing performed.
Valid codes are:
P - processed, G - geocoded/georegistered, M-mosaic,
C - composite, E - DEM.
PATH = a World Wide Reference System defined nominal Landsat
satellite track
(path). All processed NALC data is referenced to WRS2.
Valid values are:
1 - 233
ROW = a World Wide Reference System defined nominal
latitudinal center line
of a Landsat image. The row indicator represents scene
centers that
are chosen at 25-second (LS 1-3) and 23.92-second
(LS4/5) increments
along the orbital track in either direction of the
equator.
Valid values are:
1 - 59 = Northern Hemisphere (Descending)
60 = Equator (Descending)
61 - 119 = Southern Hemisphere (Descending)
120 - 122 = Southern Polar Zone (Descending)
123 - 183•= Southern Hemisphere (Ascending)
184 = Equator (Ascending)
185 - 246 = Northern Hemisphere (Ascending)
247 - 248 = Northern Polar Zone (Ascending)
DECADE = acquisition decade of set.
Valid codes are:
70 - 1970's.
80 - 1980's.
90 - 1990's.
ENTERDT = the date the metadata record is added to the data base.
40
-------
PROJCODE= map projection, default is 'U'.
Valid codes are:
U - UTM or Universal Transverse Mercator.
S - SOM or Space Oblique Mercator.
H - Hotine.
FORMAT = data's current input format.
Valid codes are:
CCTX - EDC computer compatible tape in X format.
EDIP - EDC Enhanced Digital Image Processing System's
format.
DEM - EDC digital elevation model format.
FAST - EOSAT's fast format.
RESAMP = resampling technique used to radiometrically process
the data.
Valid codes are:
C = cubic convolution.
N = nearest neighbor.
processing.
B = bilinear.
SCENE1 = a systematically corrected scene id or the first scene
id used in
generating a mosaic or composite.
SCENE2-4= 2 to 4 scene ids used to generate a mosaic or
composite.
CTLPTS1 = total number of geographic control points used for
scene 1.
CTLPTS2-4= total number of geographic control points used for
each scene 2-4.
RMSERR1 = the average root-mean-square error made using each
scene's control
points (i.e. RMSERR1 applies directly to SCENE1 and
CTRLPTS1 just as
...RMSERR2/3/4 applies directly to their respective
SCENE/CTRLPT
numbers).
RMSERR2-4=.the average root-mean-square error made using each
like numbered
scene's control points.
ACQDATE1= the acquisition date applicable to the first scene id.
ACQDATE2-4= the acquisition date applicable to the like numbered
41
-------
scene id.
SUNELEV1 = the elevation angle of the sun above the horizon.
(Valid values = 0 to 90 degrees)
SUNELEV2-4=the sun elevation applicable to the like numbered
scene id.
SUNAZIMUT1 = the azimuth angle of the sun measured clockwise from
north in
degrees. (Valid values = 0 to 360 degrees)
SUNAZIMUT2-4=the sun azimuth applicable to the like numbered
scene id.
SOURCE = base source used in registration.
Valid sources are:
24kUSGS = l:24k USGS topo map.
SOkMEXC = l:50k Mexico topo map.
121 = image to image registration.
CONTACT = a contact person available offsite whom fully knows
the projects.
Valid contact is:
TBD
PHONE = contact person's office phone number.
Valid phone number is:
TBD
42
-------
Table 4.
8mm Tape Mapper for NALC Triplicate
********** Mapper of tape cot mounted on DRIVE 4 **********
1 RECORDS 3428 BYTES LONG
END OF FILE #1 >»» 1 TOTAL RECORDS.
25000 RECORDS 5000 BYTES LONG
END OF FILE #2 >»» 25000 TOTAL RECORDS.
1 RECORDS 3428 BYTES LONG
END OF FILE #3 >»» 1 TOTAL RECORDS.
25000 RECORDS 5000 BYTES LONG
END OF FILE #4 >»» 25000 TOTAL RECORDS.
1 RECORDS 3032 BYTES LONG
END OF FILE #5 >»» 1 TOTAL RECORDS.
20000 RECORDS 5000 BYTES LONG
END OF FILE #6 >»» 20000 TOTAL RECORDS.
1 RECORDS 3824 BYTES LONG
END OF FILE #7 >»» 1 TOTAL RECORDS.
30000 RECORDS 5000 BYTES LONG
END OF FILE #8 >»» 30000 TOTAL RECORDS.
1 RECORDS 1849 BYTES LONG
END OF FILE #9 >»» 1 TOTAL RECORDS.
5000 RECORDS 10000 BYTES LONG
END OF FILE #10 >»» 5000 TOTAL RECORDS.
********** END OF TAPE **********
43
-------
Table 5.
Example of Data Descriptor Record (DDR)
IMAGE NAME:85068515574x0.trimp
NS:5000
DATE:10-Feb-93
NLtSDOO
DTYPE:BYTE
LAST MODIFIED:
SYSTEM:ieee-std
PROJ. CODE:(1)UTM
Valid:VALID
ZONE CODE:15
Valid:VALID
DATUM CODE:0
Valid:VALID
PROJ. PARM:
Valid:VALID
A: -1.61239334063410E+00
9.99600000000000E-01
B: O.OOOOOOOOOOOQOOE+00
O.OOOOOOOOOOOOOOE+00
C: 5.00000000000000E+05
O.OOOOOOOOOOOOOOE+00
D: O.OOOOOOOOOOOOOOE+00
O.OOOOOOOOOOOOOOE+00
E: O.OOODOOOOOOOOOOE+00
O.OOOOOOOOOOOOOOE+00
CORNER COOR:
Valid:VALID
ULcorner:1.70538000000000E+06
URcorner:1.70538000000000E+06
•40544000000000E+06
•40544000000000E+06
PROJ. DIST:6.00000000000000E+01
Valid:VALID
PROJ. UNITS:METERS
Valid:VALID
INCREMENT:1.OOOOOOOOOOOOOOE+00
Valid:VALID
MASTER COOR:1 1
NB:4
TIME:1643; 01
LLcorner:1.
LRcorner:1.
2.52200076913181E-01
-9.30000000000000E+07
O.OOOOOOOOOOOOOOE+00
0.OOOOOOOOOOOOOOE+00
O.OOOOOOOOOOOOOOE+00
4.44960000000000E+05
7.44900000000000E+05
4.44960000000000E+05
7.44900000000000E+05
6.OOOOOOOOOOOOOOE+01
1.OOOOOOOOOOOOOOE+00
44
-------
IMAGE NAME:85068515574x0.trimp
BAND NO:1
MINIMUM:0.OOOOOOOOOOOOOOE+00
Valid:INVALID
MAXIMUM:0.OOOOOOOOOOOOOOE+00
Valid:INVALID
DATA SOURCE:Landsat 5
SENSOR TYPE:MSS
CAPT. DIRECTION:descending
DATE:15-JAN-86
TIME:1557:4
IMAGE. NAME: 85068515574x0.trimp
BAND NO:2
MINIMUM:0.OOOOOOOOOOOOOOE+00
Valid:INVALID
MAXIMUM:0.OOOOOOOOOOOOOOE+00
Valid:INVALID
DATA SOURCE:Landsat 5
SENSOR TYPE:MSS
CAPT. DIRECTIONDescending
DATE:15-JAN-86
TIME:1557:4
IMAGE NAME:85068515574x0.trimp
BAND NO:3
MINIMUM:0.OOOOOOOOOOOOOOE+00
Valid:INVALID
MAXIMUM:0.OOOOOOOOOOOOOOE+00
Valid:INVALID
DATA SOURCE:Landsat 5
SENSOR TYPE:MSS
CAPT. DIRECTION:descending
DATE:15-JAN-86
TIME:1557:4
45
-------
IMAGE NAME:85068515574x0.trimp
BAND NO:4
MINIMUM:0.OOOOOOOOOOOOOOE+00
Valid:INVALID
MAXIMUM:0.OOOOOOOOOOOOOOE+00
Valid:INVALID
DATA SOURCE:Landsat 5
SENSOR TYPE:MSS
CAPT. DIRECTION:descending
DATE:15-JAN-86
TIME:1557:4
46
-------
Table 6.
Explanation of NALC Tape Mapper
File #1: Data Descriptor Record (DDR) file which is based on the
georeferencing parameters for the 1980's data image to map
registration. The projection information is the same for the
1990s and 1970s data. This is an ASCII file.
File #2: image data for the first 1970s scene used in the
triplicate. This contatins four bands of MSS and the
pixel identity image. The pixel value used to identify
this scene can be cross referenced to the Dbase records
which will follow separately.
File #3.: DDR for the next 1970s scene (if more than 2 are used)
File #4: the next 1970s scene; four MSS bands and a pixel
identity image *(as above)
File #5: DDR for the 1980s scene
File #6: 1980s image data; four MSS bands; no pixel
identity image required
File #7: DDR for the 1990s data
File #8: the 1990s image data; four MSS bands, NDVI image
resulting from cloud reduced, compositing, and the pixel
identity image. The last two bands only exist when
compositing is done. Band 5 is the maximum NDVI image
resulting from the multiple inputs. The pixel with the
maximum NDVI value is used to determine which input pixel
DN values are used to create the output. Bnad 6 is the pixel
identity image. These pixel values are indexed to the scene
from which the output pixel values were taken. The pixel
values correspond to the scene number in the dBASE record
(dBASE information isdistributed only to EPA-EMSL).
File #9: DDR for the DEM generated for that Path/Row. The
projection parameters should match those of the MSS data.
File #10: DEM data for the triplicate; same dimensions as the
MSS image data, except that it is INTEGER*2
47
-------
IDENTIFICATION OF CHANGE DETECTION METHODS
Introduction
The goal of NALC change detection methods development research
is to select or develop standard change detection methods for both
pre- and post-categorization delineations of change in land covers.
For the pre-categorization change detection technique, only those
areas that have undergone significant spectral change will be
categorized. The pre-categorization technique will be utilized as
the preferred NALC change detection method. The post-
categorization change detection technique will only be used when
pre-categorization is not technically feasible. This method will
be a raster-ba^ed Geographic Information System (GIS) operation and
would require complete (100%) categorized digital database products
for multiple dates. Because of the emphasis on carbon issues, the
NALC-Pathfinder will place a special emphasis on detecting land
cover changes involving forests, which hold more carbon per above-
ground area than other major land cover types. NALC is interested
in both the losses and gains in forest area plus the detection of
forests which have been placed into a regrowth stage due to
disturbance.
Change Detection Literature Review
Early efforts at using Landsat MSS data for the detection of
land cover change were largely based on visual interpretation of
the multitemporal images. This is essentially a
photointerpretation process, using techniques developed in the
decades preceding the launch of Landsat in 1972 (Shepard 1964).
This method is still widely used for change detection. It is
perhaps the most direct and fastest change detection technique,
particularly in the initial stages of a change detection project.
Following the use of visual interpretation for change
detection, digital methods began to be employed. Change detection
methods have been broadly divided into either enhancement (pre-
categorization) or post-categorization methods (Nelson 1983, Pilon
et al. 1988, Singh 1989).
Enhancement change detection techniques involve the
transformation of two original images to a new single-band or
multi-band tineategorized image in which the areas of land cover
change are readily detected. The change-enhanced data can be
further processed by other analytic methods, such as by using a
categorizer, to produce a labeled change detection output product.
Most of the enhancement techniques are based on the concepts of
image differencing or image ratioing (Weismiller et al. 1977, Toll
48
-------
et al. 1980). Differencing of vegetation indices has been a
popular form of enhancement change detection (e.g., Nelson 1983).
It has been shown that ^ image equalization in the data pre-
processing stage usually improves the results of change detection
(Hall et al. 1991). Techniques like band-to-band regressions and
principal components analysis have been used to simultaneously
perform the image-to-image equalization and the detection of change
areas (Ingram et al. 1981 and other references).
In post-categorization change detection two images from
different dates are independently categorized. The area of change
is then extracted through the direct comparison of the
categorization results (e.g., Colwell and Weber 1981). The
advantage of post categorization change detection is that it
bypasses the difficulties in change detection associated with the
analysis of images acquired at different times of year or by
different sensors. The disadvantages of the post-categorization
approach include greater computational and labeling requirements,
high sensitivity to the individual categorization accuracies, plus
the difficulties in performing adequate accuracy assessment on
historic data sets.
The Landsat Pathfinder Humid Tropical Forest Inventory
Project, being performed at the University of New Hampshire and the
University of Maryland, uses a post-categorization change detection
method. Because of the small number of classes being used (primary
forest, non-forest, secondary forest, water, cloud, and shadow),
the project is able to achieve high categorization and change
detection accuracies.
Because all enhancement methods are based on pixel-wise
operations or scene-wise plus pixel-wise operations, accuracy in
image registration and co-registration is more critical for these
methods than for other methods.
OPTIMIZING FOR DIGITAL CHANGE DETECTION:
NALC SCENE SELECTION AND PREPROCESSING
The basic requirement for remote sensing land cover change
detection is the availability of two dates of imagery upon which
the same area of land can be observed. Depending on the
characteristics of the two data sets, change detection can be
either an easy or difficult task. The NALC data sets have been
selected and assembled in order to simplify the detection of change
using digital image processing. Below is a description of some of
the complicating factors which tend to make change detection more
difficult, with indications of the design features of the NALC
project which address the difficulties.
49
-------
The accurate detection of land cover change using remotely
sensed data can be complicated by the following factors:
1) Spatial resolution and spectral bandpass differences between
images acquired with two sensors complicates the direct comparison
or the digital analysis of the data to detect change. With the
spectral bandpass differences, the detectable land cover classes
for the two dates of imagery may not be comparable. Land cover
classes that are distinct when observed with one sensor may be
indistinguishable with a sensor having broader or fewer numbers of
spectral bands. If there are substantial spatial resolution
differences between the two input images, ground features may be
visible in one data set and undetectable in the other. In the NALC
project we eliminate problems that could be introduced due to
spatial resolution and bandpass differences by working exclusively
with Landsat MSS, a sensor series that has provided consistent
spatial and spectral characteristics for a twenty-year period.
2) Variations in the radiometric response of a sensor can
complicate change detection by requiring some form of image-to-
image equalization prior to the change detection or by requiring
the use of a post-classification change detection approach. This
factor is important to the NALC project because we are working with
data spanning a twenty-year period and collected by five different
MSS sensors (Landsats 1-5) . Although each of the MSS sensors were
calibrated before launch, it is known that their response drifted
over time and that there is no adequate way to calibrate the data
to radiance units without extensive ground based efforts. It is
clear that the change detection procedures used by the NALC program
must explicitly address the radiometric differences to be
encountered in performing change detection without calibrated input
data.
3) If clouds are present in images from one or both dates, it is
impossible to detect land cover change between the two dates of
imagery where clouds occur. To minimize the effects of clouds, low
cloud cover was one of the primary factors directing the selection
of scenes for the NALC project.
4) Variations in solar irradiance, solar zenith angle, and solar
azimuth will affect scene brightness levels and the location of
shadows. The NALC project has attempted to reduce these effects by
selecting scenes for the three time periods which were acquired at
or near the same Julian date in order to match the solar conditions
as far as possible. In general, high sun angles (low amount of
shadowing) are better than low sun angles for the detection of land
cover change.
5) Variations in atmospheric effects (scattering and absorption)
can affect scene characteristics sufficiently that they must be
considered in evaluating change detection methods. The NALC data
sets have been visually screened to remove scenes where within-
50
-------
scene atmospheric variations are obvious. By assuming that the
atmospheric effects on the selected scenes are uniform across the
entire scene area, it is possible to partially compensate for
scene-to-scene variations in atmospheric effects by scene-to-scene
brightness equalization. The coregistration of the NALC MSS
products will also help to compensate for topographic variations
which alter atmospheric path length.
6) Phenological variations in vegetation result in large changes in
the reflectance patterns of the land surface. If images of leaf-on
and leaf-off conditions are compared, whole regions can appear to
have been "deforested". The scene selection process of the NALC
project has attempted to minimize this problem by selecting scenes
from the same time of year for each of the three time periods. In
some cases it was not possible to do this due to the lack of
appropriate archive data.
7) Spatial misregistration of images will tend to reduce the
accuracy of any digital change detection effort. These effects are
most severe on the change detection techniques using enhancement.
The NALC data sets are digitally coregistered, with accuracies on
the order of half a pixel or less, creating data sets that can be
analyzed for change .with minimal errors due to misregistration.
NALC Change Detection Strategy
The basic NALC data set consists of three dates of Landsat MSS
data, from the early 1970's, mid-1980's, and early 1990's, plus
ancillary data such as digital terrain. In performing the change
detection for the NALC project two output products will be
generated: 1) Land cover change from the early 1990's to the early
1970's; and 2) Land cover change from the early 1990's to the mid-
19 80' s. The output products should show the location of the
changes as well as the nature of the change (e.g., conversion of
woody to herbaceous).
NALC Change Detection Procedure
The NALC Landsat MSS triplicates have been selected from the
available archive of data held by the USGS EROS Data Center. A
considerable effort has been put into selecting scenes with low
cloud cover and at or near the same date for all three time periods
in order to have data sets optimized for digital change detection.
However, in the extreme southern portions of the project area
(southern Mexico, Central America, and Caribbean Islands) it was
frequently not possible to find low cloud cover scenes at the same
time of year. Cloud reduction compositing is being used to assist
in the development of triplicates which are matched to the same
time of year. However, there are numerous triplicates which will
not be closely matched.
51
-------
An example is given below for Path 21, Row 47 in southern Mexico:
21/47
21/47
22/47
23/47
92/04/29
84/11/25
74/02/15
78/09/17
LE84357515342xO
LM85026915590xO
LM8157215552500
LM83019615545xO
In this circumstance, it may be necessary to use a post-
classification change detection approach to derive the land cover
change products.
For the majority of the NALC triplicates, it has been possible
to find data in the archive having low cloud cover and closely
matched dates for use in the triplicates. An example of this type
of triplicate is given below for Path 45, Row 29 in Oregon:
45/29
45/29
49/29
48/29
92/08/03
86/08/19
72/09/02
72/07/27
LM85307718130xO
LM85090118114xO
LM8104118265500
LM8100418210500
In this case, and in the case of the majority of the NALC
triplicates, it should be possible to use an enhancement technique
to identify areas where change has occurred. Then the change areas
would be labeled using the same labeling procedures used in
developing the current land cover product.
The first choice of the NALC change detection will be to use
an enhancement method to locate pixels where land cover change has
occurred. However, because of the presence of triplicates which
contain data from diverse times of the year, it will be necessary
to have a post-categorization method developed. We are therefore
developing a dual-track research plan to develop a suitable
enhancement method and protocols for post-categorization change
detection.
NALC Change Detection Evaluation Criteria
NALC has two groups of criteria: programmatic criteria and
technical criteria, for evaluating change detection techniques.
For the change detection procedure using enhancement, the
criteria are as follows:
Programmatic criteria: 1) The method must be relatively
sensitive to deforestation and/or loss of vegetation - the primary
target of the NALC project; 2) the method must be simple in concept
and have been successfully tested widely, so that it can be
accepted by a wide spectrum of application scientists; 3) the
computation should be straightforward and unambiguous to perform in
52
-------
various computational environments; and 4) this method should
require a minimal amount of expert (human) intervention so that it
can be applied to a large number of scenes.
Technical criteria: 1) The method must be accurate; 2) the
method must be relatively insensitive to sensor effects and
atmospheric effect; and 3) the method must work in a wide range of
geographic regions.
Since the post-categorization method will only be applied to
a limited number of scenes and areas, the speed and simplicity of
the method will no longer be emphasized. Instead, the following
will addressed: 1) This method must be able to provide detailed and
accurate class to class change information; 2) this method should
require minimal human intervention so to optimize automated
processing; and 3) this method should provide highest possible
categorization accuracy.
NALC CHANGE DETECTION RESEARCH:
TECHNICAL WORK PLAN
This work plan describes the study sites and change detection
methods to be used in developing the standard procedures to be used
in the rest of the NALC project.
Study Sites
Three diverse areas have been selected for conducting change
detection research for the NALC program.
Washington, B.C. - Path. 15, Row 33: This is an area that has
undergone substantial urban and suburban expansion during the past
two decades. Many forest areas surrounding Washington, B.C. have
been converted to residential, commercial, and industrial sites.
The exact magnitude of these land cover changes has yet to be
determined. This Path/Row image scene lies within the Chesapeake
Bay Watershed, which has been an area of active USEPA Environmental
Monitoring and Assessment Program (EMAP) research. Buring the past
two years a current land cover map was produced by EMSL-LV using
recent Landsat Thematic Mapper (TM) data as part of the EMAP
Landscape Characterization program. The NALC project change
detection research pilot in the Washington, B.C. triplicate will
make use of the EMAP land cover data set as a source of validation
data. As an additional validation source, we will obtain the
coastal land cover change data for this area produced from Landsat
data by the NOAA Coastwatch-Change Analysis Program (C-CAP), which
was produced using a pre-categorization change detection method.
53
-------
Central Oregon - Path. 45, Row 29: This scene covers a forested
transect across the Western flank and crest of the Cascade Range,
plus arid areas, with irrigated agriculture to the east of the
Cascades. The forests of the Cascades include some old growth
forests, plus numerous secondary forests which have been disturbed
through forest harvesting. All conditions of forestation exist,
ranging from barren to closed canopy forests.
Southern Mexico - Path. 21, Row 48: This scene centers on a
mountainous tropical forest region, on the Mexico-Guatemala border.
On the Mexican side there has been extensive conversion of forest
for use in agriculture and timber production. There has been much
less activity on,the Guatemalan side. At the northern end of the
scene there are extensive wetland forests which have been cut and
drained. The area is covered by detailed 1:250,000 scale Land Use
- Vegetation maps produced in the early 1980's by the Mexican
government.
Methods to be Tested and Evaluation Criteria
In each of the three study areas, photo interpretation will be
used to outline and categorize areas of land cover change. We will
use the digitized photointerpretation results as a basis for
comparing the results from the digital change detection methods.
Because of the dual track approach required for change detection in
the NALC project, the use of both enhancement and post-
categorization methods will be investigated.
The areas of change delineated by each of the investigated
approaches will be digitally compared to the land cover change
results obtained through photointerpretation of the same scene area
to produce percent accuracy values. The change detection methods
will be evaluated based primarily on criteria stated above.
Post-Categorization Change Detection
Protocols for the post-categorization change detection will
involve the repeat of the NALC standard categorization results,
which are being developed to produce current land cover data sets.
These procedures will be repeated for the other two time periods.
Then the areas of change will be extracted by comparing the early
1970'8 and mid-1980's land cover categorizations with the current
land cover.
54
-------
Enhancement Techniques For Change Detection
Because most of the NALC triplicates have been optimized for
use in enhancement change detection methods, an emphasis will be
placed on experimentation with these approaches. Because of
atmospheric and sensor related radiometric response differences, we
will focus on procedures which have some capacity to normalize the
radiometry of the data prior to locating change pixels. Our review
of the literature suggests that three enhancement techniques have
built in capability for normalizing or equalizing the radiometry of
scenes: Vegetation index differencing; regression analysis; and
principal components analysis.
Change Detection Through Vegetation Index Differencing
The development of vegetation indices from spectral
reflectance values is based on the differential absorption and
reflectance of energy by vegetation in the red and near-infrared
portion of the electromagnetic spectrum (Derring and Haas 1980).
In general, green vegetation absorbs energy in the red region and
is highly reflective in the near-infrared region (Anderson and
Hanson 1992). A number of vegetation indices have been formulated
and utilized for monitoring vegetation change. Of these vegetation
indices, NDVI has been used most widely for monitoring terrestrial
vegetation dynamics (Townshend and Justice 1986, Eidenshink and
Haas 1992, Tappan et al. 1992) . The NDVI compensates for some
radiometric differences between images; however, it does not
completely remove radiometric images that are being compared. The
difference in the NDVI values of two images in certain cases
responds to changes in land cover (Nelson 1982 and 1983, Banner and
Lynham 1981). Singh (1989) concluded that NDVI differencing was
among the most accurate of change detection techniques.
In our change detection pilot data sets, the NDVI values will
be computed for each date of imagery. The early 1970's and the
mid-1980's NDVI images will be subtracted from the 1990's NDVI
image to create two NDVI difference images. These will be compared
to the photointerpretation results in order to calculate the
accuracy of the NDVI differencing.approach.
Change Detection Through Principal Components Analysis
Principal Components Analysis (PCA) involves the reorientation
of axes of an input data set, creating output Principal Components
(PC) data sets. In our case two coregistered MSS images will be
input as an eight band or eight axis image. Because of the
autocorrelation of the original data, there is an elongated cloud
of distribution of data located in the axis of each data set. The
PCA will orient the first axis of the output data through the
central core of the input data cloud. The second axis will be
55
-------
perpendicular to the first and will be directed through the next
major direction of variance in the data set. The PGA can continue
to establish new axes, until the number of output axes equals the
number of input axes. The first PC normally contains the overall
scene brightness variations which are in common between all the
input bands. The second, third, and fourth (and sometimes higher)
PC images frequently contain information on pixels which changed in
reflectance between the two dates of imagery (Byrne et al. 1980,
Richardson and Milne 1983, Fung and LeDrew 1987). The last PC
image would be expected to contain random noise that existed in one
image relative to the other.
In scenes with cloud cover and associated shadowing, it will
be necessary to screen out the cloud and shadow areas to exclude
them from the PGA. Clouds or shadows which are present in one date
and absent from the second date will tend to redirect the axes
which would otherwise be established due to reflectance changes due
to land cover change.
Part of the PGA algorithm involves the normalization or
equalization of the input data, thereby reducing atmospheric and
sensor radiometric response differences from the output PC images.
This is one of the characteristics required for the NALC change
detection, making PGA one of the leading candidates for the
standard change detection method for use in the NALC project.
Color composite images will be made from combinations of the
second, third, fourth, and higher PC's. Areas of change will be
identified from these color composites and the accuracy of these
PGA based change detection areas will be compared with the results
from the photointerpretation.
Change Detection Through Regression Analysis
The results of Hall et al. (1991) indicated that band-to-band
radiometric rectification or equalization, as a distinct
preprocessing step, can substantially improve the accuracy of the
image differencing approach to change detection. The methods of
Hall et al. relied on 'the manual extraction of pixels which were
consistently bright and dark pixels from the image, to obtain
digital number values for use in regression analysis. The
regression line between the bright and dark pixels was taken to be
the line of "no-change". Pixels which departed from the regression
line were taken to be pixels in which some type of land cover
change occurred.
We plan to experiment with a modification of the approach
described by Hall et al. to make the procedure automated, and
remove the reliance on bright and dark ground targets which occur
in each date of imagery. For each pair of MSS scenes, four band-
to-band (e.g., band 1 versus band 1) scatter diagrams will be
56 .
-------
prepared using a random subsampling of the image pixels. Early
experiments with this approach indicate that in each scatter
diagram a diagonal axis of data points will appear. This is the
axis of "no-change". Clouds and cloud shadow present in one image
and absent in the second image will tend to form their own axes,
parallel to the diagram axes (Figure 5). Pixels which change in
reflectance between the two dates due to land cover change will not
fall on the diagonal scatter diagram axis (the "no-change" axis).
By determining the location of the "no-change" axis with linear
regression, it is possible to calculate the orthogonal distance
from the "no-change" axis for each pixel in the image, creating an
output image depicting the reflectance changes which occurred
between the two dates for a particular band. By repeating this
procedure with all four bands, four output change images will be
created from the eight input bands. As with the PGA approach, the
cloud and cloud shadow areas would need to be excluded from the
regression analysis.
As with the PGA approach, color composite images will be made
from regression analysis output images. With four output change
images there are only two three-band color composite combinations
possible: 1, 2, and 3 or 1, 2, and 4. Areas of change will be
identified from these color composites and the accuracy of these
PGA based change detection areas will be compared with the results
from the photo interpretation. It may also be possible to utilize
a statistical test such as Chi-square.
Change Detection Study Time Frame
NALC change detection study is designed to investigate and
select the optimal change detection techniques for the NALC
project. The time frame for change detection study is as follows:
September 1992 - November 1992: Change detection literature
review. During this period, an extensive literature review on
change detection had been conducted. A detailed internal report on
present change detection techniques has been accomplished.
November 1992 - February 1993: Commence the testing of
techniques with small scale testing. During this period, majority
of the present change detection techniques has been tested in a
subscene Chiapas, Mexico. The NALC project has become conceptually
familiar with many of present change detection techniques. Selected
testing results have been internally reported to NALC.
February 1993 - April 1993: NALC change detection strategy and
procedure design and final refinement. This is done through more
review, testing and personal contacts with change detection
community.
57
-------
May 1993 - November 1993: NALC pilot change detection
experiments. The selected change detection techniques are to be
tested in three NALC pilot areas. A draft report regarding the
result of these experiments will be completed by November 12, 1993.
Change Detection Correspondences
During our process of soliciting feasible change detection
techniques for NALC, the following scientists have shared with us
their knowledge and experience on change detection. Many of them
have kindly offered us their suggestions on possible change
detection techniques for NALC.
Ford A. Cross, National Marine Fisheries Service, (919) 728-
8724. Cross is the Director of the CoastWatch Change Analysis
Project (C-CAP), NOAA Coastal Ocean Program. The change detection
analysis is primarily based on a post-classification comparisons.
Jerry E. Dobson, Oak Ridge National Laboratory, (615) 574-
5937. Dobson is the principal investigator of NOAA CoastWatch
Change Analysis Project and designed the change analysis procedure
for the program.
Charles R. Larson, Hughes STX Corporation, EROS Data Center,
(605) 594-6504. Larson had performed raw image differencing
technique to monitor surface change during the 1991 Persian Gulf
War period.
Bill Lawrence, Geography, University of Maryland at College
Park, (301) 405-6809. Lawrence is currently involved in the NASA
Landsat Pathfinder - Tropical Deforestation Project in Central
Africa. This project is planning to conduct post-classification
change detection on classified image maps obtained by a multiple
threshold technique.
Richard A. McKinney, Hughes STX Corporation, EROS Data Center,
(605) 594-6500. McKinney had conducted NDVI differencing change
detection for continental America for USGS.
Douglas Muchoney, The Nature Conservancy, (703) 841-5300.
Muchoney suggested that principal component analysis could
significantly reduce sensor and atmospheric effects from the raw
images, and therefore it would worth to test some principal
component analysis in some NALC pilot areas.
David Peterson, NASA/Ames Research Center, (415) 604-5899.
Peterson had used image regression technique with chi-square for
detecting changes.
58
-------
Paul M. Seevers, Hughes STX Corporation, EROS Data Center,
(605) 594-6010. Seevers had applied a sequential change detection
techniques to the Dallas - Ft. Worth region as well as in the Mt.
St. Helens region.
Ed Sheffner, TGS Technology, NASA/Ames Research Center, (415)
604-6565. Sheffner suggested that image regression might serve the
purpose of change detection of NALC, or may be used as a temporal
equalization technique.
Ashbindu Singh, GRID, United Nations Environmental Program,
EROS Data Center, (605) 594-6105. Singh has reviewed (1989) many of
the present change detection techniques and suggested that raw
image differencing, NDVI differencing, image regression, and
principal component differencing produced higher accuracies for
change detection.
David Skole, Complex System, University of New Hampshire,
(603) 862-1792. Skole is the leader of NASA Landsat Pathfinder -
Tropical Deforestation Project in Southeast Asia. This project will
use change detection methods similar to the Central Africa project
methods.
John Townshend, Geography, University of Maryland at College
Park. (301) 405 -4050. Townshend had suggested to us that we have
more intense contact with the scientists in .the change detection
community.
REFERENCES
Anderson, G. L., and Hanson, J. D., 1992, Evaluating Hand-Held
Radiometer Derived Vegetation Indices for Estimating Above Ground
Biomass, Geocarto International 1:71-77.
Banner, A. V., and Lynham, T., 1981, Multitemporal Analysis of
Landsat Data for Forest Cut Over Mapping - a Trial of Two
Procedures, Proceedings of the 7th Canadian Symposium on Remote
Sensing, Canadian Remote Sensing Society, pp. 233-240.
Byrne, G. F., Crapper, P. F., and Mayo, K. K., 1980, Monitoring
Land Cover Change by Principal Component Analysis of Multitemporal
Landsat Data, Remote Sensing of Environment 10:175-184.
Colwell, J. E., and Weber, F. P., 1981, Forest Change Detection,
Proceedings of the 15th International Symposium on Remote Sensing
of Environment, Environmental Research Institute of Michigan, Ann
Arbor, MI, pp.65-99.
59
-------
Derring, D. W. and Haas, R. H., 1980, Using Landsat Digital Data
for Estimating Green Biomass, NASA Technical Memorandum #80727, 21
PP-
Eidenshink, J. C., and Haas, R. H., 1992, Analyzing Vegetation
Dynamics of Land System With Satellite Data, Geocarto International
1:53-61.
Fung, T., and LeDrew, E., 1987, Application of Principal Component
Analysis to Change Detection, Photogrammetric Engineering and
Remote Sensing 53:1649-1658.
Hall, F. G., Strebel, D. E., Nickeson J. E., and Goetz, S. J.,
1991, Radiometric Rectification: Toward a Common Radiometric
Response Among Multidate, Multisensor Images, Remote Sensing of
Environment 35:11-27.
Ingram, K., Knapp, E., and Robinson, J. W., 1981, Change Detection
Technique Development for Improved Urbanized Area Delineation,
technical memorandum CSC/TM-81/6087, Computer Sciences Corporation,
Silver Springs, MD.
Nelson, R. F., 1982, Detecting Forest Canopy Change Using Landsat,
NASA technical memorandum 83918, Goddard Space Flight Center,
Greenbelt, MD.
Nelson, R. F., 1983, Detecting Forest Canopy Change Due to Insect
Activity Using Landsat MSS, Photogrammetric Engineering and Remote
Sensing 49:1303-1314.
Pilon, P. G., Howarth, P. J., and Bullock, R. A., 1988, An
Enhanced Classification Approach to Change Detection in Semi-Arid
Environments, Photogrammetric Engineering and Remote Sensing
54:1709-1716.
Richardson, A. J., and Everitt, J. H., 1992, Using Spectral
Vegetation Indices to Estimate Rangeland Productivity, Geocarto
International 1:63-77.
Richardson, A. J., and-Milne, A. K., 1983, Mapping Fire Burns and
Vegetation Regeneration Using Principal Component Analysis,
Proceedings of IGARSS'83 held in San Francisco (New York: IEEE),
pp.51-56.
Singh, A., 1989, Digital Change Detection Techniques Using
Remotely-Sensed Data, International Journal of Remote Sensing
10:989-1003.
Shepard, J. R., 1964, A Concept of Change Detection, Proceedings
of the 30th Annual Meeting of the American Society of
Photogrammetry, Washington, D.C., pp. 648-651.
60
-------
Tappan, G. G., Tyler, D. J., and Wehde, M. E., 1992, Monitoring
Rangeland Dynamics in Senegal with Advanced Very High Resolution
Radiometer Data, Geocarto International 1:87-98.
Townshend, I. R. G., and Justice, C. O.,1986, Analysis of the
Dynamics of African Vegetation Using the Normalized Difference
Vegetation Index/ International Journal of Remote Sensing 7:1435-
1446.
Toll, D. L., Royal, J. A., and Davis, J. B., 1980, Urban Area
Up-Date Procedures Using Landsat Data, Proceedings of the Fall
Technical Meeting of the American Society of Photogrammetry, Falls
Church, VA, pp.RS-El-17.
Weismiller, R. A., Kristoof, S. J., Scholz, D. K., Anuta, P. E.,
and Momen, S. A., 1977, Change Detection in Coastal Zone
Environments, Photogrammetric Engineering and Remote Sensing
43:1533-1539.
61
-------
•o
c
03
0>
5
03
03
P
CO
1
0)
0>
I
CM
O
03
L.
OB
03
*Q
t_t
•-
s
CO
03
O>
I
in
a)
.5 (D
^ t3'5
-------
NALC QUALITY ASSURANCE AND QUALITY CONTROL
Quality assurance and quality control (QA/QC) procedures will
be developed and implemented to insure and verify that NALC
products meet the project's data quality objectives stated herein.
Initial QA/QC procedures have been established and will be tested
in the pilot projects. The QA/QC procedures will be evaluated and
revised during the pilots. At the completion of the pilots a final
QA/QC guidance document will be prepared. The principal elements
of the NALC QA/QC include: Standard operating procedures for all
aspects of the data processing; quality control checks during data
analysis; implementation of data tracking forms to document all
procedures executed on each MSS scene; verification and validation
of spatial accuracies; acquisition of verification data; and
accuracy assessment of categorizations.
Standard Operating Procedures
Each organization participating in NALC is in the process of
developing standard operating procedures and a report documenting
those procedures. It is anticipated and expected that these
reports will be revised frequently during the first year of the
project. The NALC components that require standard operating
procedures include triplicate selection and production,
georeferencing, image categorization and change detection.
Quality Control
Quality control procedures will include the validation of
initial image spectral quality, location and date of image
collection, and adequate georectification. Both EMSL-LV and the
EROS Data Center have developed internal quality control checks for
previous projects that will be applied to NALC. Aspects of the
quality control procedures cross over into data analysis tracking
and spatial accuracy validation, both of which are discussed in
more detail after this section. The importance of quality control
procedures is to establish checks to minimize and identify data
degradation at the earliest stage of analysis. It is important to
document the source of data degradation and probable impacts on the
final products.
Data Analysis Tracking
Data analysis tracking includes documentation of all analyses
implemented on the data including quality control checks. Forms
designed to track the data can provide a means of post-analysis
error evaluation. A series of these forms have been developed at
EMSL-LV for the NALC pilots. An example of these tracking forms is
located at the end of the Image Categorization Section. All data
63
-------
tracking forms will be archived (either in digital or hardcopy
format) , by each organization participating in NALC, for the
duration of the project and for a minimum of two years after
proj ect completion.
Spatial Accuracy Verification
Spatial accuracy of the MSS georectification includes both a
verification of each scene and a validation of every tenth scene.
The EROS Data Center performs a verification check of their
georectification process on every scene as part of their internal
quality control. They are continuing to perform the same
verification process for NALC. After the MSS scenes are received
at EMSL-LV, every tenth scene's accuracy will be independently
validated.
The EROS Data Center uses USGS 7.5 minute quadrangle maps or
the best available maps to select control points for image-to-map
georectification. In the U.S., every ninth map across the scene is
selected for the georectification process. A total of 25 to 40
maps are available for control point selection per MSS scene. One
point per map is selected in both the image and map. Only those
points for which the.residuals are less than one second (i.e., +/-
80 meters or approximately 1.5 pixels) are used for
georectification. A minimum of 6 control points are required per
scene, although 10 to 12 points are preferred. For the
verification process different maps are randomly selected and a
total of 6-7 points are randomly selected. The map and
georectified image coordinates are statistically compared for these
points. Again residuals must be under 1.5 pixels.
Georectification for NALC involves image-to-image as well as
image-to-map rectification. The 1980s MSS scene is first
registered to USGS maps; then it is used as the "map base" for
georectification of the 1970s and 1990s scenes. The image-to-image
rectification is performed in a different manner than the image to
map, but the verification process is the same as that stated above.
Initially for the pilot areas, verification of every fifth scene
will be performed for the image to image georectification. If the
spatial data quality objectives are met for the pilot areas, then
the number of scenes that will be validated will be reduced to
every 10th scene.
The validation begins by examining a list of map control
points used by EROS Data Center to georectify the scene. This will
prevent selection of a georectification control point for the
validation, i.e., use of georectification control points in the
validation process would result in a positive bias. Then a random
selection of at least four maps that were not used by EDC in the
rectification process is made. At least three points per map that
are identifiable in the imagery are randomly selected. The map and
64
-------
image coordinates for the point are statistically compared. An
RMSE of +/-1.0 (60m) pixel has been established as the spatial
accuracy data quality objective (DQO) for the image to map
rectification and +/-0.5 pixel (30m) for the image-to-image
rectification. Therefore/ in comparing an image rectified by
another image to a map base, the total spatial error should not
exceed +/-1.5 pixels (i.e., +/- 1.0 pixel for the image to map plus
+/-0.5 pixel for the image-to-image). If the RMSE is larger than
+/-1.5 pixels, the EROS Data Center will be contacted. If, after
evaluating the georectification process, it is determined that
there are insufficient identifiable points to produce a better
rectification, the scene will be used, but it will be flagged to
identify that the spatial accuracy is less than the DQO.
Thematic Accuracy Assessment
The purpose of performing an accuracy assessment of the NALC
categorized data is twofold. General scientific ethics and EPA
policy require that all final products have a stated known
accuracy. Most importantly for NALC, the accuracy assessment will
enable a more precise estimate of the areal extent of each
category; in other words, an assessment of categorical accuracy is
more important than, and prevails over the necessity for an
assessment of overall map accuracy. Categorical accuracy
information will be critical in the development of realistic and
reliable continental carbon and climate models. Several methods
have .been presented in the literature for improving area estimates
of categorized data (Card 1982, Hay 1988, Jupp 1989, Conese and
Maselli 1992, Czaplewski and Catts 1992). All methods presented by
these authors use the error matrix derived from an accuracy
assessment to calculate corrected or unbiased estimates of
individual category areal extent. Each of these authors also
states that the choice of sample design and sample size is critical
to the valid use of an error matrix for improving area estima'tes.
The following text describes proposed methods for conducting a
pilot accuracy assessment that will result in a statistically valid
error matrix. (NOTE: These methods are not necessarily the
methods that will be used for all NALC categorization accuracy
assessments. They are test methods for the pilot that will enable
the NALC team to evaluate categorical error variances, reasons for
errors, the applicability of different scale verification sources,
and the overall efficiency and cost effectiveness of the assessment
approaches tested. Future pilots based on this initial assessment
will further test modified accuracy assessment procedures.)
Field Verification
As stated previously, Landsat MSS data will be used for the
categorization. The resolution of these data combined with the
level of categorization indicate that a detailed field verification
65
-------
may not be required to perform a statistically sound accuracy
assessment. Therefore, the verification effort will be based
primarily on MSS image * interpretation and aerial
photointerpretation with very limited examination of actual field
sites. A three-band color composite of the MSS images will be used
with ancillary data as a primary verification source. While high-
resolution aerial photographs would be the optimum verification
source, it is recognized that for some areas of the U.S. and almost
all of Central America, aerial photographs from a compatible time
period will not exist and acquisition of new aerial photographs is
cost prohibitive. Whenever possible, aerial photographs will be
used as the primary verification source. Field sites will only be
examined as a validation check of the MSS/photographic
interpretation sites. Whenever possible, all interpretations will
be performed by experienced image interpreters or photointerpreters
who are not directly involved in the NALC project. However, there
may be some cooperators from Central America who will not have the
resources available for an independent interpretation. In either
case, care will be taken to ensure that the interpreters do not
have access to the categorized image as this may cause bias in the
interpretation. During the pilot projects, testing of the
different sources and scales of verification data will be-
evaluated. In particular, the Chesapeake Bay pilot area will be
verified with MSS image interpretation, aerial photointerpretation,
and a field validation. The results and conclusions of this effort
will aid in the determination of a final sample scheme for NALC
accuracy assessments.
Sampling Design
Sampling design includes the choice of sampling scheme, i.e.,
random or systematic, and identification of an appropriate sample
size. The remote sensing literature states advantages and
disadvantages of both random and systematic schemes and is divided
in its support of each scheme (see Congalton, 1991 for a review of
this literature). However, it is recognized that the choice of an
appropriate sampling scheme is based on the goals of the accuracy
assessment. As stated previously, the goals of the NALC thematic
accuracy are to state the accuracy of the mapped categories as well
as to provide the necessary statistics to improve categorical area
extent estimates. Prisley and Smith (1987) state that "A crucial
assumption in this use of error matrices," i.e., to improve areal
extent estimates, "is that the distribution of error in the
contingency table is representative of the types of
misclassification made in the entire area classified." Card (1982)
also states that the sampling design employed in the collection of
verification data dictates how the error (or confusion) matrix may
be composed to provide conclusions about the entire population.
66
-------
The choice of a sampling scheme for the NALC accuracy
assessment is based on the following requirements and assumptions:
1) Individual category accuracies must be determined,
including errors of omission and commission;
2) the variance of categorical misclassification is unknown,
i.e., no previous pilots have been performed to provide an estimate
of the variances;
3) the existence and magnitude of misclassification spatial
autocorrelation is unknown, but one is assumed to be present;
4) the possability of visiting 100% of the verification sites
on the ground is logistically and cost prohibitive;
5) the availability of aerial photographs for verification
from a similar date or season as the MSS acquisition is highly
improbable for large portions of the NALC project•area;
6) the MSS data has already been acquired, therefore it is
impossible to acquire simultaneous "in the field" data;
7) each MSS scene will be categorized individually for the
Chesapeake Bay pilot, i.e., each scene will have cluster statistics
generated for it independently of the other scenes within the pilot
area. This will probably result in different misclassification
variances for each scene;
8) and the result of the accuracy assessment must be a
statistically valid error matrix that may be effectively used to
better estimate the area of individual categories.
Sampling Scheme
While numerous papers may be found that discuss sampling
schemes in the remote sensing literature, only two studies to date
(Congalton 1988 and Stehman 1992) have compared several sampling
schemes using the same categorized remotely sensed data. Congalton
recommends the use of a simple random or stratified random sample
while Stehman is a proponent of systematic sampling schemes. The
two papers do, however, have similar conclusions. First, both
authors state that the choice of sample scheme must be based on
project objectives. The objectives of the assessment and the
statistical approach drive the choice of sampling scheme.
Secondly, both authors indicate that periodicity or spatial
autocorrelation of misclassification errors can significantly and
negatively impact the precision of a systematic sample. Stehman
(1992) states that systematic sampling designs should not be used
if periodicity is suspected, unless the nature of the periodicity
is understood well enough to avoid an unfavorable sampling
interval. Congalton (1988) examined three images of differing
landscape pattern types, namely, forest, range and agriculture. In
each case the misclassif ication errors were found to be significant
and positively spatially correlated. The spatial autocorrelation
of NALC categorization errors will be unknown until after the first
accuracy assessment is performed. Therefore, until accuracy
assessment data is acquired and estimates of spatial
67
-------
autocorrelation may then be calculated, spatial autocorrelation
must be assumed.
Another concern in selecting a sampling scheme is how well
that scheme estimates variance. Stehman (1992) states that
estimation of error variance may be problematic for systematic
samples. Cochran (1977)_ states that there is "no trustworthy
method for estimating VfVjy) from the sample data is known" for
systematic samples. V(ysy) is the variance of the error mean.
Rosenfield (1982) agrees with and sites this statement from
Cochran. Congalton (1988) states that simple random sampling
provided good estimates of the mean error and variance for each
image type (forest, agriculture and range). He further states that
"stratified random sampling also performed well and should be used
especially when it is necessary to make sure that small, but
important, areas are represented in the sample," and that
systematic samples should only be used with caution, i.e., in
situations where the variance and spatial autocorrelation are well
understood.
As stated previously, the primary objective of the accuracy
assessment is to provide estimates of individual category accuracy
in the form of an error matrix. The error matrix must address
errors of omission and commission so that it may be used to
calculate better estimates per category. Given these constraints,
the sample design selected must provide the most precise and
unbiased estimator of individual class accuracy. Also, the NALC
categorization process will cluster each Landsat MSS scene
independently. Therefore, a between-scene error variance should be
accounted for in the pilot sample design. Based on the remote
sensing and statistical literature, a stratified random sampling
scheme will best meet the needs of the first pilot and will be used
for the Chesapeake Bay pilot on a scene by scene basis. After the
assessment data is acquired, a careful evaluation of scene to scene
and categorical variances will be made to determine the most
appropriate and cost effective sampling scheme for future NALC
accuracy assessments. Until the variances resulting from the NALC
categorization procedures are estimated and understood, the choice
of a systematic sample may result in an imprecise and/or biased
estimate of the error.-
Sample Size
The number of samples to be selected for verification is
dependent on the goals of the project. If only a right-wrong
assessment is needed then the binomial distribution or normal
approximation equation may be used to calculate the sample size
(van Genderen and Lock 1977, Rosenfield and Fitzpatrick-Lins 1982).
However, the binomial distribution does not provide a sufficient
sample size to construct a statistically valid error matrix.
68
-------
Fitzpatrick-Lins (1981) and Borella et al. (1982) used the normal
approximation equation and binomial distribution, respectively, to
calculate sample size for their accuracy assessments. In both
cases, the sample sizes were insufficient to construct complete
error matrices. An appropriate distribution to calculate sample
size for development of an error matrix is the multinomial
(Rosenfield 1982). However, correct usage of the multinomial
distribution requires information on the expected error and the
variance associated with that error. Since the categorization
methodology has not been thoroughly tested prior to this pilot
there are no variance estimates available. Initial calculations
using the multinomial equation presented in Rosenfield (1982) and
the preliminary results from a previous EMSL-LV categorization
indicate that a sample size between 137 to 163 samples per category
is appropriate. The equation is depicted below.
n = the sample size
X2 = the Chi Square distribution
.6, = the half width of the desired confidence interval for a
particular category j.
a = the Chi Square distribution tail; this definition is not
directly stated in Rosenfield (1982), but is surmised
from the numbers presented
k = the number of categories
E, = the estimated proportion of correctly categorized pixels
for a particular category j_; this definition is not
directly stated in Rosenfield (1982), but is surmised
from the text
However, the EMSL-LV data does not provide a sufficient
measure of error variance and questions arose over the proper use
of the multinomial equation presented by Rosenfield (1982).
Further examination of the multinomial distribution and
confirmation of the multinomial equation presented by Rosenfield
(1982) will be performed prior to finalization of the NALC pilot
accuracy assessment procedures. As a minimum, Hay (1979) and
Congalton (1991) recommend that sample size per class should be at
least 50 to test the accuracy of determinations, i.e., not only
right versus wrong but examine the multiple classes of wrong.
Another approach used to determine sample size is an area
weighted sample. This may be an overall area weighting or area
weighting by category. The overall area weighting specifies a
total sample size that represents a given percentage of the entire
population or image. The categorical area weighting is a
stratification of the overall area weighting wherein the class with
the largest area has the largest number of samples again based on
a given percentage. Intuitively, the random or systematic
69
-------
selection of points from a population should yield similar results
whether or not the selection process is stratified by category.
Congalton (1988) recommended that at least one percent of the
entire image be sampled for agricultural, range, and forested image
subsets. He also states that his results indicate a larger sample
size should be used if a stratified systematic unaligned or
systematic sampling scheme is used.
Based on the information above the NALC Chesapeake Bay pilot
sample size will contain no fewer than 50 points per category with
a maximum number of points per category as determined by the
multinomial distribution. If the error variances for individual
categories or scenes are higher than anticipated, additional
samples will be taken (i.e., a double sample) to better define the
variance. Based on the final results of the pilot, a
recommendation will be made for the appropriate choice of sample
sizes for future NALC categorization accuracy assessments.
Verification Procedures
After the verification sites have been selected, MSS Landsat
three-band color composites will be interpreted. The pixel closest
to the accuracy assessment sample site will be used as the
verification center point. A square area consisting of two pixels
per side from the center of the "sample" pixel will be interpreted,
i.e., approximately a 5x5 pixel area. A draft form for the MSS
verification is displayed in Table 7. EMSL-LV has in its
possession aerial photographs covering a significant portion of the
Chesapeake Bay watershed. Any aerial photographs coinciding with
a sample site will be interpreted to evaluate the effect of scale
and as a validation check of the MSS interpretations. Location of
the coordinates on the photographs may be performed by appropriate
scaling, and using maps and analytical stereoscopes. Table 8 lists
the information that will be documented for photointerpreted site.
The verification forms should be filled out as completely as
possible for each site. There will probably be some areas where
changes have occurred since the date of photograph acquisition or
field visit. For areas where changes are known to have occurred,
the interpreters should consult with people familiar with the ares.
to collect up-to-date information. The source of this type of
information should be documented on the verification forms.
A field check will be performed for four randomly selected
verification points per category as a validation check of the
interpreted data. Therefore, only those points that were included
in both the MSS and photo interpretation will be used for this
selection. The field check may be performed using one of two
methods: walking the site area or flying over the site at a low
altitude. Low altitude aircraft will primarily be used in areas
with no access or prohibitively dense vegetation. Maps, GPS units,
and/or other surveying techniques may be used to identify
70
-------
locations. The method of field checking will depend upon the site
conditions, access, and availability of resources. Therefore,
choice of method will be based on local conditions and the
capability of other NALC cooperators. If access to any of the
sites is prohibitive, then another site will be randomly selected
to replace it (i.e., sampling with replacement). A verification
form displayed will be used to record as much detailed site
information as possible and will be compared to the MSS and photo
interpretation forms for that site. In addition, 35mm photographic
slides will be acquired at field sampling sites. The field form
level of detail will provide more information than is necessary for
a validation check. However, previous experience has shown that
this level of detail is beneficial for evaluating sources of error.
Field crews will consist of at least two individuals per team; at
least one of the team members will have expertise operating GPS and
one with expertise on the local biota. If there are significant
differences among the different scales of verification data, the
field data and slides will be used to evaluate possible reasons for
the differences. If possible, a procedure for correcting the
differences will be developed.
All individuals employed to perform the verification /
validation of thematic accuracy will have expertise in the
appropriate areas and whenever possible, will be external to the
NALC project. In addition, these individuals will not have access
to the categorized MSS images to reduce the likelihood of positive
or negative biases.
71
-------
Table 7.
NALC MSS Interpretation Verification Form
Analyst:
Date of Interpretation:
Verification Site Information
MSS Scene ID#: Scene Date:
Verification Site ID#:
Scene Coordinates of Verification Site:
Bands and enhancements used for image color composite: Red
Green Blue
Ancillary Data (list any maps, photographs, local experts/ etc.)
Interpretive Information
Describe the size/ shape/ color/ texture, and types of features
within the verification site:
List Appropriate Category Name:
Second choice category name (if applicable)
72
-------
Table 8.
NALC Photographic Interpretation Verification Form
Analyst:
Film type:
Date of Interpretation:
Imagery Date:
Frame #:
Scale: Source:
Stereo pairs: yes no
Verification Site Information
MSS Scene ID#: Verification Site ID#:
Photograph Frame #:
Method of locating verification site on the photograph:
Interpretive Information
Describe the size, shape, color, texture, and types of features
within the verification site:
List Approprite Category Name:
Second Choice Category Name
(if applicable):
73
-------
Thematic Accuracy Assessment Reporting
A confusion (error) matrix and its associated parameters,
errors of commission and omission and overall accuracy, will be
calculated as described by Story and Congalton (1986). In the
matrix, the verification data will be compared to the categorized
data as columns and rows, respectively. The cells within the
matrix indicate the number of pixels categorized as category a
through n that correspond to the verification sites as labelled
category a through n. For instance, in the example error matrix
(Table 9) for the 100 verification sites examined for category a,
65 were correctly categorized in the remotely sensed data, 25 were
incorrectly categorized as category b, and 10 incorrectly
categorized as category c. The diagonal from the upper left to the
lower right represents the number of image pixels correctly
categorized and, when divided by the total number of pixels
verified, yields the overall accuracy of the categorization. There
are two ways to calculate the accuracy of individual categories and
they are termed user's and producer7 s accuracies or errors of
commission and omission, respectively.
Table 9. An Example Error Matrix Table
CATEGORIES
Image a
Image b
Image c
Producer7 s
Accuracy
Reference a
65
25
10
65/100 =
0.65
Reference b
5
85
10
85/100 =
0.85
Reference c
10
15
75
75/100 =
0.75
User7 s
Accuracy
65/80 =
0.81
85/125 =
0.68
75/95 =
0.79
225/100. =
0.75
Sum of major diagonal = 225 Overall accuracy = 225/300 = 75%
User7s accuracy or errors of commission may be defined as
whether a categorized pixel within an image actually represents
what is on the ground or not, or how accurately or reliably the
categorized map represents the area. Producer7s accuracy or errors
of omission approach the question of categorized accuracy from an
opposite direction, that is how well a sample site or area can be
represented by a categorized image or map. The example error
matrix demonstrates that the two calculations do not yield the same
results. Both of these types of individual category accuracies
will be presented to provide a more complete identification of the
types of errors present in the categorization. In addition, the
Kappa coefficient (Congalton 1991) will be calculated. Kappa
74
-------
measures the relationship of non-random categorization agreement
versus expected disagreement. It will be used to monitor trends in
categorization reliability from one categorization to another. The
Kappa coefficient equals zero when the agreement between the
categorized data and ground truth equals chance or random
agreement. Kappa increases to one as chance agreement decreases,
and becomes negative as random assignment of categories occurs.
Kappa equal to one occurs only when there is perfect agreement.
After accuracy assessment calculations are completed an evaluation
will be performed to determine where the errors occurred. In
particular, comparisons will be made between the error matrices
derived from MSS and aerial photograph interpretation, and between
aerial photointerpretation and field data. It is essential that
errors identified by the assessment be evaluated to: address the
adequacy of differing scales of verification data; identify error
sources; separate categorization errors from other errors; and
correct the errors, if possible.
REFERENCES
Borella, H.M., J.E. Estes, C.E. Ezra, J. Scepan, and L.R. Tinney,
1982, Image Analysis for Facility Siting: A Comparison of Low-
and High-Altitiude Image Interpretability for Land Use/Land Cover
Mapping, A final report prepared for Office of Nuclear Regulatory
Research, the U.S. Nuclear Regulatory Commission, NUREG/CR-2861 S-
744-R RE, 61p.
Card, D.H., 1982, Using Known Map Category Marginal Frequencies to
Improve Estimates of Thematic Map Accuracy, Photogrammetric
Engineering and Remote Sensing 48:431-439.
Cochran, W.G., 1977, Sampling Techniques, John Wiley & Sons, New
York, 428 p.
Conese, C. and F. Maselli, 1992, Use of Error Matrices to Improve
Area Estimates with Maximum Likelihood Classification Procedures,
Remote Sensing of Environment 40:113-124.
Congalton, R.G., 1988, Using Spatial Autocorrelation Analysis to
Explore the Errors in Maps Generated from Remotely Sensed Data,
Photogrammetric Engineering and Remote Sensing 54:587-592.
Congalton, R.G., 1988, A Comparison of Sampling Schemes Used in
Generating Error Matrices for Assessing the Accuracy of Maps
Generated from Remotely Sensed Data, Photogrammetric Engineering
and Remote Sensing 54:593-600.
Congalton, R.G., 1991, A Review on Assessing the Accuracy of
Classifications of Remotely Sensed Data, Remote Sensing of
Environment 37:35-36.
75
-------
Czaplewski, R.L. and G.P. Catts, 1992, Calibration of Remotely
Sensed Proportion or Area Estimates for Misclassification Error,
Remote Sensing of Environment 39:29-43.
Fitzpatrick-Lins, K., 1981, Comparison of Sampling Procedures and
Data Analysis for a. Land-Use and Land-Cover Map, Photogrammetric
Engineering and Remote Sensing 47:343-351.
Hay, A.M., 1979, Sampling Designs to Test Land-Use Map Accuracy,
Photogrammetric Engineering and Remote Sensing 45:529-533.
Hay, A.M., 1988, The Derivation of Global Estimates from a
Confusion Matrix, International Journal of Remote Sensing 9:1395-
1398.
Jupp, D.L.B., 1989,
Confusion Matrices,
10:1563-1569.
The Stability of Global Estimates from
'International Journal of Remote Sensing
Prisley, S.P. and J.L. Smith, 1987, Using Classification Error
Matrices to Improve the Accuracy of Weighted Land-Cover Models,
Photogrammetric Engineering and Remote Sensing 53:1259-1263.
Rosenfield, G.H. and K. Fitzpatrick-Lins, 1982, Sampling for
Thematic Map Accuracy Testing, Photogrammetric Engineering and
Remote Sensing 48:131-137.
Stehman, S.V., 1992, Comparison of Systematic and Random Sampling
for Estimating the Accuracy of Maps Generated from Remotely Sensed
Data, Photogrammetric Engineering and Remote Sensing 58:1343-1350.
Story, M. and R.G. Congalton, 1986, Accuracy Assessment: A User's
Perspective, Photogrammetric Engineering and Remote Sensing
52:397-399.
Van Genderen, J.L. and B.F. Lock, 1977, Testing Land-Use Map
Accuracy, Photogrammetric Engineering and Remote Sensing 43,: 1135-
1137.
76
-------
APPENDICES
77
-------
Appendix
I:
NALC Technical Review
NALC Technical Review Session agenda
List of Invited Participants
NALC Review Panel
Report of the Technical Review Panel, North American
Landscape Characterization, February, 1993
Comments on the Report
78
-------
5:30 pm
6:00
6:45
7:15
8:00
NALC TECHNICAL REVIEW SESSION
JAMES LAWLESS, MODERATOR
Wednesday, February 17, 1993
OPENING REMARKS
JAMES LAWLESS, NALC PEER REVIEW CHAIR
NASA HEADQUARTERS
THE "MISSING CARBON" STUDY:
IDENTIFYING GAPS IN AVAILABLE DATA
ALLAN AUCLAIR
SCIENCE AND POLICY ASSOCIATES
NALC PROGRAM OVERVIEW
ROSS LUNETTA, NALC TECHNICAL DIRECTOR
USEPA, EMSL LAS VEGAS
NALC USGS/EDC PROGRAM OVERVIEW
JAMES STURDEVANT, USGE/EDC NALC PROGRAM MANAGER
USGS EROS DATA CENTER
ADJOURN
79
-------
NALC-PATHFINDER TECHNICAL SESSION
ROSS LUNETTA, MODERATOR
Thursday, February 18, 1993
8:30 AM
9:00
9:30
10:00-10:30
NALC OVERVIEW
ROSS LUNETTA, NALC TECHNICAL DIRECTOR,
US EPA, EMSL Las Vegas
NALC LANDSAT MSS DATA ACQUISITIONS
JAMES LOVE, EOSAT
NALC DATA PURCHASES
AND PROCESSING PRIORITIES
JOHN LYON, OHIO STATE UNIVERSITY
BREAK
10:30
USGS NALC OVERVIEW
JAMES STURDEVANT, EROS DATA CENTER
11:00
IMAGE SELECTION, COMPOSITING AND GEOREFERENCING
JOHN DWYER, EROS DATA CENTER
11:30
IMAGE CATEGORIZATION
JOHN LYON, OHIO STATE UNIVERSITY
80
-------
5:30 pm
6:00
NALC TECHNICAL REVIEW SESSION
JAMES LAWLESS, MODERATOR
Wednesday, February 17, 1993
OPENING REMARKS
JAMES LAWLESS, NALC PEER REVIEW CHAIR
NASA HEADQUARTERS
THE "MISSING CARBON" STUDY:
IDENTIFYING GAPS IN AVAILABLE DATA
ALLAN AUCLAIR
SCIENCE AND POLICY ASSOCIATES
6:45
7:15
NALC PROGRAM OVERVIEW
ROSS LUNETTA, NALC TECHNICAL DIRECTOR
USEPA, EMSL LAS VEGAS
NALC USGS/EDC PROGRAM OVERVIEW
JAMES STURDEVANT, USGE/EDC NALC PROGRAM MANAGER
USGS EROS DATA CENTER
8:00
ADJOURN
79
-------
NALC-PATHFINDER TECHNICAL SESSION
ROSS LUNETTA, MODERATOR
Thursday, February 18, 1993
AM
9:00
9:30
10:00-10:30
NALC OVERVIEW
ROSS LUNETTA, NALC TECHNICAL DIRECTOR,
US EPA, EMSL Las Vegas
NALC LANDSAT MSS DATA ACQUISITIONS
JAMES LOVE, EOSAT
NALC DATA PURCHASES
AND PROCESSING PRIORITIES
JOHN LYON, OHIO STATE UNIVERSITY
BREAK
10:30
USGS NALC OVERVIEW
JAMES STURDEVANT, EROS DATA CENTER
11:00
IMAGE SELECTION, COMPOSITING AND GEOREFERENCING
• JOHN DWYER, EROS DATA CENTER
11:30
IMAGE CATEGORIZATION
JOHN LYON, OHIO STATE UNIVERSITY
80
-------
5:30 pm
6:00
6:45
7:15
8:00
NALC TECHNICAL REVIEW SESSION
JAMES LAWLESS, MODERATOR
Wednesday, February 17, 1993
OPENING REMARKS.
JAMES LAWLESS, NALC PEER REVIEW CHAIR
NASA HEADQUARTERS
THE "MISSING CARBON" STUDY:
IDENTIFYING GAPS IN AVAILABLE DATA
ALLAN AUCLAIR
SCIENCE AND POLICY ASSOCIATES
NALC PROGRAM OVERVIEW
ROSS LUNETTA, NALC TECHNICAL DIRECTOR
USEPA, EMSL LAS VEGAS
NALC USGS/EDC PROGRAM OVERVIEW
JAMES STURDEVANT, USGE/EDC NALC PROGRAM MANAGER
USGS EROS DATA CENTER
ADJOURN
79
-------
NALC-PATHFINDER TECHNICAL SESSION
ROSS LUNETTA, MODERATOR
Thursday, February 18, 1993
8:30 AM
9:00
9:30
10:00-10:30
NALC OVERVIEW
ROSS LDNETTA, NALC TECHNICAL DIRECTOR,
US EPA, EMSL Las Vegas
NALC LANDSAT MSS DATA ACQUISITIONS
JAMES LOVE, EOSAT
NALC DATA PURCHASES
AND PROCESSING PRIORITIES
JOHN LYON, OHIO STATE UNIVERSITY
BREAK
10:30
USGS NALC OVERVIEW
JAMES STURDEVANT, EROS DATA CENTER
11:00
IMAGE SELECTION, COMPOSITING AND GEOREFERENCING
JOHN DWYER, EROS DATA CENTER
11:30
IMAGE CATEGORIZATION
JOHN LYON, OHIO STATE UNIVERSITY
80
-------
12:00-1:30
1:30 PM
2:00
2:30
LUNCH
DATA MANAGEMENT:
INDEXING, ARCHIVING AND DISTRIBUTION
JOHN DWYER, EROS DATA CENTER
OVERVIEW OF CHANGE DETECTION
DING YUAN, DESERT RESEARCH INSTITUTE
ACCURACY ASSESSMENT: QA/QC ISSUES
LYNN FENSTERMAKER, DESERT RESEARCH INSTITUTE
3:00-3:30 BREAK
3:30 METHODS DEMONSTRATION AND PILOT PROJECTS
OVERVIEW
JOHN LYON, OHIO STATE UNIVERSITY
4:00
4:30
5:00
6:00
CHESAPEAKE BAY WATERSHED PILOT PROJECT
DORSEY WORTHY, US EPA, EMSL Las Vegas
GREAT LAKES WATERSHED PILOT STUDY
BERT GUINDON, CANADA CENTRE FOR REMOTE SENSING
OTTAWA, ONTARIO, CANADA
STRAWMAN REPORT - CONTENTS/OVERVIEW
REVIEW PANEL MEMBERS
ADJOURN
81
-------
8:00 AM
10:30
11:30
1:00
NALC TECHNICAL REVIEW SESSION
JAMES LAWLESS, MODERATOR
Friday, February 19, 1993
REVXEW PANEL REPORT WRITING
JAMES LAWLESS
REVIEW PANEL MEMBERS
QUESTIONS FOR PARTICIPANTS
REVIEW PANEL MEMBERS
COMPLETE REVIEW OF DRAFT REPORT
JAMES LAWLESS
ADJOURN PANEL REVIEW
82
-------
LIST OP
INVITED PARTICIPANTS
NALC Technical Review, New Orleans
REVIEW PARTICIPANTS
Allan Auclair
Roman Alvarez
Roberto Bonifaz
John Dwyer
Jack Durham
Lynn Fenstermaker
James Love
Ross Lunetta
John Lyon
James Sturdevant
Ridgeway Weerackoon
Ding Yuan
Dorsey Worthy
PEER REVIEWERS
Chuck Dull
John Estes
Leonard Gaydos
James Lawless
Douglas Muchoney
David Peterson
Ed Sheffner
John Townshend
INVITED OBSERVERS
Peter Beedlow
Michael Cairns
Chris Elvidge
Chris Justice
Don Lauer
Gene Meier
Joel Morrison
Lee Mulkey
Courtney Riordan
Al Watkins
83
-------
NALC REVIEW PANEL
James G. Lawless, Review Chair
NASA Headquarters
Earth System Science Division
MS 239-20
Ames Research Center
Moffett Field, CA 94035
PHONE: 0415) 604-5900
FTS: 464-5900
FAX: (414) 604-1068
E-MAIL: iglawlessgnasamail
Ed Sheffner, Review Coordinator
TGS Technology
NASA/Ames Research Center
MS 239-20
Ames Research Center
Moffett Field, CA 94035
PHONE: (415) 60A-6565
FTS: 464-6565
FAX: (415) 604-1088
E-MAIL: ej sheffner@gaia.arc.nasa.gov
John Estes, Reviewer
University of California
Santa Barbara
c/o Al Watkins
Chief USGS/NMD
Reston, VA 22092
PHONE: (703) 648-5752
FAX: (703) 648-57-92
E-MAIL: lestes@nasamail
estes@pollux.geog.ucsb.edu
John Townshend, Reviewer
University of Maryland
Department of Geography
1113 LeFrank Hall
University of Maryland
College Park, MD 20742
PHONE: (301) 405-4050
FAX: (301) 314-9299
E-MAIL: JT59@UMAIL.UMD.EDU
84
-------
David Peterson, Reviewer
NASA
AMES Research Center
MS 239-20
Ames Research Center
Moffett Field, CA 94035
PHONE: (415) 604-5899
FTS: 464-5899
FAX: (415) 604-4680
Leonard Gaydos, Reviewer
USGS
MS 242-4
Ames Research Center
Moffett Field, CA 94035
PHONE: (415)604-6368
FTS: 464-6368
FAX: (415) 604-4680
E-MAIL: leng@gala.arc.nasa.gov
.Chuck Dull, Reviewer
USDA Forest Service
14th & Independence Ave., SW
Washington, DC 20250
Phone: (202)205-1416
FAX: (202)205-0861
E-MAIL:
Douglas Muchoney, Reviewer
The Nature Conservancy
1850 N. Lynn St.
Arlington, VA 22209
PHONE: (703) 841-5300
FAX: (703) 841-1283
85
-------
Report of the
Technical Review Panel
North American Land Characterization Project
February, 1993
86
-------
Summary
A technical review of the North American Landscape Characterization
(NALC) project, an element of the Landsat Pathfinder, was held in New
Orleans, Louisiana on February 17-18, 1993. The members of the review
panel emphatically and unanimously endorse the goals, objectives and
technical approach of the NALC. Specific recommendations are made to
improve the utility and scientific merit of the products derived during the
course of the project, to assure that the project will meet the
information gathering requirements and performance specifications of
EPA, and to establish mechanisms for integrating the experience gained,
and the data generated by this project into other, large scale, global
change research programs.
1.0 Introduction
The North American Landscape Characterization (NALC) project is an
excellent example of the type of pathfinder envisioned by the National
Aeronautics and Space Administration (NASA). Personnel from the
Environmental Protection Agency (EPA), United States Geological Survey
(USGS), NASA, Earth Observation Satellite Corporation (EOSAT) and other
cooperating organizations have formed a team involved in a scientifically
significant, technologically challenging project. NALC is being conducted
on a scale that stretches our ability to facilitate science given today's
understanding of image acquisition, archiving, data processing,
verification and data distribution systems, and it is providing the science
community with essential data products. The NALC-Pathfinder's
experience is also providing lessons in the creation of large area,
multitemporal, high spatial resolution data sets - lessons of great value
in the development of EOS-DIS and a -variety of global change research
efforts. The data sets, archiving, and techniques developed can, and will,
make important contributions to resource management not only in the
United States and Canada, but Mexico, Central America and the Caribbean
as well. The NALC-Pathfinder is important. The project is needed. The
review panel supports the effort and believes the program will provide a
tremendous benefit to cooperators throughout North America.
The review panel supports, fundamentally, the goals, objectives, and
methods of the NALC. As with all reviews, there are areas where the
panel members feel improvements can be made. The panel applauds the
NALC-Pathfinder's efforts in technique development, and, while comments
87
-------
directed toward specific techniques suggested or recommended by the
NALC staff are presented, the comments are intended to call attention to
ways in which the final products of the NALC may be improved in an area
where continuing research is urgently required.
The goal of the NALC program is to provide scientists with the
information they require to conduct global change research. The Pathfinder
specifically focuses on the development of techniques and methods which
can be applied to the development of standard land cover categorization
products in support of global change research efforts of EPA Laboratories
in Corvallis, Oregon, and Athens, Georgia. From a science perspective, the
Pathfinder seeks to reduce the uncertainty in the area term for carbon
stocks and atmospheric trace gas emissions associated with North
American land cover type classes. To accomplish this the NALC program
will produce hard copy and digital image products from Landsat MSS data
suitable to inventory carbon stocks, determine location of carbon sources
and sinks and identify areas where the potential exists for carbon
sequestration.
The importance of the land cover type categorization of the NALC-
Pathfinder should not be underestimated. If we take the equation:
(i_ci)
A(LC1) + C(LC1) = ^ota' Glob3' Carbon
where:
A(LC1) = Area of land cover class 1, and
C(LC1) - Carbon per unit area for land cover class 1,
the reviewers believe strongly that the uncertainty present in the area
term of the equation is at least as large, if not larger (and much harder to
address,) than the current uncertainty in our knowledge of carbon stock
per unit area for given land cover classes. From that perspective, the
NALC
project contributes toward meeting the goals of global change science.
Technique development initiated within the scope of the NALC is
also significant. There is a need to develop advanced feature extraction
procedures for the processing of earth observing satellite data. The NALC
is addressing aggressively the issues of categorization, change detection,
and quality assurance/quality control (QA/QC) development for large area
cover type mapping. The development of a land cover type product, for
example, is very much a research area, and any approach taken is subject
88
-------
NALC Review
Panel Report 2/93
to criticism.
The review panel received oral presentations on the NALC based on a
draft document, the "Landsat Pathfinder Technical Work Plan" prepared
by the staff of the EPA's Environmental Monitoring Systems Laboratory.
The findings of the review panel are presented in detail below. The
organization of this report follows, in general, the organization of the
draft document. Specific suggestions and recommendations are made for
data acquisition and processing, archiving and analysis techniques, and
quality control/quality assurance. Comments are also directed toward
project management and the criteria for success. The report concludes
with a brief summary and recommendations. This report is a response to
the draft document and the material presented during the review. It is not
intended as a summary of the NALC.
2.0 Goals and Objectives
The stated goal of the NALC, "...assessing terrestrial biosphere
management options as they influence land cover, carbon pools,
atmospheric trace gases, and feedbacks to atmospheric conditions...,"
while broad, is made tractable by the careful selection and prioritization
of areas for study that address the uncertainties associated with sources
and sinks of carbon in North America. The proposal by the NALC team to
address the goal through the use of "triplet" Landsat Multispectral
Scanner System (MSS) data sets, is appropriate and probably essential for
cost effectiveness. Further, the success of the program is enhanced
through the leveraging of the resources of other agencies, universities and
the private sector. Compiling the necessary MSS data sets and initiating
the effort through pilot study areas to test the working scientific
hypotheses and to develop and test methodologies are appropriate
objectives. The use of Landsat images from three dates will provide
information on the current distribution of the land cover types, rates of
change within and among the land cover types, and trends in the rates of
change. Such information will be of value not only to EPA and the
collaborators in the NALC but to the US Global Change Research Program
as a whole. The involvement of cooperators outside the EPA takes
advantage of the best skills of the nation and helps assure successful
accomplishment of the objectives of the program in a timely and cost
89
-------
effective manner.
3.0 Image Categorization and Development of Land Cover
Products
Two key products will provide the bases for the applications - the
geographically registered, multi-epoch, multispectral data being
assembled at EDC, and the clustered data. NALC is on target to realize the
creation of both. Specifically, its proposal to test alternative clustering
methodologies and implementations is a good approach. The review panel
recommends that these tests be conducted using triplets from
representative environments and that measures of success be established.
The success criteria might include ability to separate classes of interest,
minimization of the border class problem, and ease in labeling.
No categorization technique can be successful without substantial
involvement of human interpreters. NALC is correct in stressing
techniques that rely on human expertise and judgement in the labeling
stage and in trying to minimize the subjectivity that comes with
techniques that require selection of training fields, numbers of clusters,
etc.
The panel is concerned about the qualifications of the cooperators
performing the cluster labeling, and it recommends that the program make
sure that the individuals performing that function are adequately trained
in multispectral categorization. Supplying the cooperators with measures
of inter-cluster separability, divergence and variance is insufficient if
the information cannot be used intelligently. Experience indicates that
spectral plots are very useful in visualizing the categorization process.
In that regard, selection of software that eases class labeling is
absolutely essential. It should be possible to highlight classes one at a
time, or in groups, with easily selected color codes! Systems that allow
viewing of the multispectral data while labeling are especially helpful.
The selection of a categorization system is obviously important and
difficult. While the proposed system appears to be one that is achievable
with Landsat MSS data, it does not seem to be "ecologically oriented" as
presented. Since measurement of closed forest canopy and its change with
respect to clearing and regrowth is critical to the Global Change Research
Program, a closed forest class should be included. There also appears to
be a gap with respect to savanna or scrub/shrub categories. Since NALC
data can be interpreted in concert with ancillary data, more consideration
90
-------
NALC Review
Panel Report 2/93
should be given to how such additional information can aid in the
categorization or refinement. There is also the problem of clear
definition and procedure. How is an important phenomenon like forest
regrowth handled? Is a patch that has just been cleared "barren," or is it
"herbaceous?" Due to seasonality inherent in the data, such a cleared
patch may appear barren one month, herbaceous the next. At which stage
does it get classified as woody? In an evergreen forest (as in the Pacific
Northwest), does the regrowth start out "deciduous" or. "mixed?" These
stages, and how they are handled in the categorization, are important to
consider carefully because maps and statistics on land cover change will
result from a coarse temporal sampling of the data over 20 years. Much
work needs to be done, prior to actual labeling, in categorization,
standardization, description of and developing application protocols, and
in documenting clear guidelines for cooperators to apply to the
categorization system. The present categorization system would benefit
from additional review and testing. In particular, a review of results of
other categorizations applied to MSS data and like techniques is needed.
There are robust examples from which to choose.
Strong consideration should be given to preparation of several
categorizations for specific science users. Though science users of NALC
have been consulted with regard to the categorization system, it may be
feasible to achieve finer categorizations for specific objectives (like
tracking forest regrowth or burns) if multiple categorizations was
offered.
The NALC is creating a data base of wide applicability. In addition to
the specific land cover type and land cover change products that will
emerge from the NALC to meet the science and operational requirements
of EPA, the variety of other potential products is quite large. These
products may include various image enhancements to expedite photo
interpretation or image mosaicking, vegetation indices, categorizations
optimized for specific purposes (such as identifying stages of forest
regrowth), or categorizations mapping different classes than those
proposed by NALC. Furthermore, a variety of measurements related to
patch shape and size, as well as spatial autocorrelation, may be
calculated based on either the multispectral or categorized data.
Combining NALC data with other sources, such as AVHRR time-series, TM,
91
-------
SPOT, or even digital orthophotos, may also prove valuable.
The ability to use NALC data in a geographic information system
(GIS) is quite promising, both for enhancing the ability to achieve desired
categorizations, and as a way of quantifying measures over specific areas
(such as forest districts) or in association with other factors (such as
steep slopes). While these may be beyond the needs or ability of the NALC
program or EPA at present, it should be noted that the program will
provide a solid foundation for others to work from.
4.0 Methods for Managing and Distribution of NALC Data
4.1 MSS Data Processing
Preprocessing of MSS data ceased at the Norman OK ground station
after September 19, 1992. It is uncertain that all MSS scenes acquired in
1992 during the acquisition window were pre-processed prior to the
failure. It is also uncertain if (and when) EOSAT will bring the capability
for MSS pre-processing back on-line, and/or if any of the foreign ground
stations retain the capability. Given the current status of MSS pre-
processing, arid the ongoing effort by EPA to bring the Canadians in to the
NALC project as a scientific collaborator, EPA is encouraged to inquire of
CCRS:
1. the extent of MSS coverage of Canada for 91-92
2. if the Canadian Ground Station at Prince Albert has maintained the
capability to pre-process MSS data, and
3. if the HDT's from Norman can be pre-processed by CCRS.
EPA is encouraged to determine, as soon as possible, if all MSS
data required for NALC has been acquired and pre-processed,
and to make any unfulfilled needs for MSS data acquisition or
pre-processing known to the Landsat program managers at
NASA HQ.
4.2 Triplet Processing
Current output of triplets at EDC is about one every 1.5 days on a
multi-tasking system. Although the rate of data processing may be
adequate to meet the applications schedule as described, increasing the
throughput rate would be advantageous as it would allow for pilot testing
and technique evaluation sooner and in a broader ranger of environments.
92
-------
NALC Review
Panel Report 2/93
4.3 IMS
Procedures for comprehensive collection of ancillary meta-data
describing the images has been put in place by EDC and is ready for placing
these data in an Information Management System. The panel recommends
that the IMS is implemented as soon as possible and that it is not treated
as a retrospective exercise after image preprocessing is complete.
The project would benefit now from the availability of an
operational IMS so that the progress of the project in developing triplets
with know characteristics can be assessed comprehensively. In the
absence of an IMS, project management, currently, has to rely on overly
qualitative assessments of many important image characteristics.
The experience of the Landsat Pathfinder in the humid tropics in the
construction of an IMS should be used to help guide the development of the
NALC-Pathfinder IMS.
4.4 Data Standards
Compilation of land cover and land cover change standard products to
repopulate the data archive must be closely 'monitored and analyzed with
the cooperation of the appropriate land management organization.
Standards for the data products, must also be developed in close
coordination with the Federal Geographic Data Committee.
4.5 Field Inventory/Field Data Management
Field inventory should include the consistent and proper use of
appropriate field forms and data collection and management techniques. A
plan for management of field data information should be developed and
implemented. Sources of guidance for field inventory and field information
management are the US Forest Service and The Nature Conservancy.
4.6 Sampling Allocation/Sampling Frame Development
Field verification methods have been .developed by the NALC-
Pathfinder, but methods for guiding field evaluations for cluster labeling
activities have been neglected. Even when analysts with knowledge of
local conditions are available, labeling benefits from field work. A
sampling frame based on cluster categories should be developed and field
sites selected so as to maximize observations along gradients. Sampling
93
-------
should be nominally proportional to class or strata area while keeping
potential access restrictions in mind. Collection of verification data
should be made concurrently.
4.7 Ancillary Data - Biotic Community
Biotic community categories and scales need to be defined to
evaluate their utility. The most appropriate use of this type of data needs
to be defined. Biotic community data may be useful, but research needs to
be performed on data availability and integration techniques.
5.0 Change Detection Methodology
The panel agrees strongly that change detection is an important
activity that can aid in the characterization and monitoring of land cover.
Retrospective change detection provides timely estimates on the extent
and type of change as a prelude to monitoring. The panel's
recommendation to refine the change detection methods primarily address
technique, rather than program. Specific areas of concern are
multitemporal radiometric normalization, atmospheric correction, change
detection techniques, test site activities, and most importantly,
reconciling program objectives with change detection methods and change
categories.
The science requirements of NALC for change detection information
have not been developed and/or reported. The objectives of change
detection are not clearly developed. Change categories have not been
defined and reconciled with the land cover categorization scheme. In
addition, the means of incorporating change detection information into
categorization, categorization, stratification and sampling frame
development and verification need to be further developed.
It is not apparent that change detection techniques, applications and
results have been researched fully. The categorization of change
detection techniques presented to the reviewers may not be appropriate.
It does not convey balanced information on categories of "techniques.
Generally accepted categories of change detection techniques are:
• Spectral-temporal change detection (layered-temporal)
• Principal components analysis
• Image differencing (image ratioing inclusive)
• Post-categorization change detection differencing
Vegetation index differencing (VID), the technique selected by NALC, is
94
-------
NALC Review
Panel Report 2/93
most appropriately a sub-group of image differencing or post
categorization change set-differencing since derived, single data
categorizations (VI) are subtracted on a pixel-by-pixel basis. At best, VID
belongs at the same level as the other categories.
Change detection method development in NALC has not been
adequately undertaken (or documented). VID was apparently chosen prior
to testing. It has several serious flaws and other techniques should have
been included in testing and analysis.
One problem with VID is the possibility that a number of land cover
types, given their natural variability and high level of abstraction as
classes, will map across virtually the entire range of Vis. This can be the
result of either ecological factors which vary across the landscape and
control biotic parameters, e.g. leaf area development, or seasonal
variation in the leaf development of many communities as varied as
"deciduous" or "cultivated land, or both. VI, therefore, may have difficulty
distinguishing corn fields from deciduous forest - a potential confusion
between land cover types of some significance.
The need and methods for performing multitemporal equalization
(normalization) and atmospheric corrections are in question. There are
techniques such as principal components analysis (PCA or Karhunen-Loeve
Transformation) which obviate normalization and provide useful
information on sensor calibration and atmospheric effects. Image
differencing would characterize the per-band distribution of inter-image
change, thereby providing information on change due to factors other than
object (feature).
Vis are particularly sensitive to variations in atmospheric
conditions. These indices are typically based on the contrasting biological
influences on the near-infrared versus red reflectance patterns
(scattering versus absorption, respectively). Given Rayleigh and Mei
scattering effects, particularly strong in the visible (red), the
atmospheric effects are manifested largely through the red band
(denominator). No VI can correct for atmospheric effects since they are
additive not multiplicative. The project should carefully re-consider the
atmospheric correction issue.
It is recommended that extensive testing of change detection
techniques be performed at each of the pilot sites - especially in areas
95
-------
where prior research data are available such as the Virginia Coast Reserve
LTER which is within the Chesapeake Bay Pilot area.
The panel recommends that NALC cooperate with the larger technical
science community, including experts at EDC, for evaluation and
development of change detection techniques and methods. Change
detection would be significantly helped by determining, with the aid of
the scientific community, which land cover transitions are most
important and which have little impact on the scientific objectives. This
should lead to substantial simplification of this part of NALC.
There is also a need to look at the effect of the long temporal
windows anticipated for some areas. Because of cloud cover, triplets will
be created using data obtained at different tinies in the growing season.
The inevitable variation in phenology and vegetation condition may make
characterization of condition difficult and obscure changes in land cover.
Efforts should be directed toward developing techniques that address
these anticipated problems.
There should also be a closer tie with the GCRP program scientists
to ensure that change detection products meet their specific, scientific
goals.
6.0 NALC Management. Scheduling and Coordination
Based upon discussions at the management overview and
presentation by Allan Auclair, it appears that the primary focus of NALC
should address and identify fluctuations in and to better understand the
carbon flux in the temperate/boreal forest. Preliminary conclusions
suggest the temperate/boreal forest are not at climatic equilibrium or at
carbon steady-state and appear to drive the net biospheric flux of the
carbon cycle. Suggested research and policy applications included further
action to acquire and verify data on forest depletions and inventory
available databases. Therefore the development of a terrestrial carbon
monitoring system to track sinks, sources and net carbon budget annually,
with subsequent development of criteria to regulate terrestrial biospheric
carbon fluxes, is predicated upon change detection analysis, specifically
forest change detection and monitoring.
The NALC-Pathfinder primary objectives include; acquisition of
current observations for North America to complement existing historical
data sets; to provide easy access and integration with past, present, and
future data sets; and derive land cover change output products using
96
1-0
-------
NALC Review
Panel Report 2/93
standardized data sets and standardized methods. -Close coordination with
major land management organization will be required to implement these
objectives most effectively.
The NALC-pathfinder program has the potential to provide a wealth
of needed information to land management agencies such as the USDA
Forest Service, BLM, National Park Service, TVA, state agencies and
private individual land managers. Their cooperation and participation will
be instrumental, if not absolutely essential, in the development of
reliable and credible outputs for the NALC-Pathfinder program in support
of global change programs. Use of existing databases established by the
organizations will be necessary to support and validate output products.
These organizations also comprise the primary users, or clients, the
program will support. Proposed benefits of the NALC-Pathfinder program
must be coincident with the data needs of these organization to ensure the
success of this interagency, cooperative program.
The EPA, USDA, NASA, & DOI have already contributed substantial
efforts to support the USGCRP. Data acquisitions and production of co
registered MSS triplets will provide a national archive of tremendous
utility and importance to the global change research community. However,
the data base must also address the needs, and meet the requirements, of
those agencies having legislative authority to affect the change
necessary to "regulate" terrestrial biospheric carbon fluxes.
Data acquisition and assembly of MSS triplets is a substantial task
which will continue to require close cooperation between EPA, NASA,
EOSAT, and EDC. Production of the land cover and land cover change
products is a major task requiring substantial cooperation between the
aforementioned land management organizations. Other agencies such as
ASCS, SCS, F&WS, FAS and international forestry and conservation
organizations (to name a few) should support, and receive benefit from,
this program. Standard algorithms and methods manuals should be
provided to these organizations along with the MSS triplets and
appropriate technical and financial support.
Given the complexities of interagency, interdisciplinary
organizations, the NALC is being managed with a high degree of success.
The project has faced significant logistical and research tasks that have
necessitated a flexible, innovative approach in project management. This
97
11
-------
quality was well demonstrated in the review by the effective way task
managers responded to questions from the review panel.
An area where strengthening of the management structure would be
beneficial is in closer coordination between EPA scientists, for whom the
data sets are being created, and the remainder of the project. For
example, representatives of this community participate in the NALC
Science Working Group, but closer involvement in the development of
technical aspects of the project, possibly through the NALC-Pathfinder
Technical Work Group, is desirable. Closer involvement is required so that
the technical development of the project can respond to improved
specification of scientific requirements and to revised scientific
priorities. Specifically, the review panel recommends that the NALC-
Pathfinder Technical Working Group is expanded to include representatives
of EPA and other global change science groups.
It is expected that it will be necessary for the Technical Work Group
to maintain a relatively high frequency of meetings to manage the large
range of technical developments anticipated in the work plan of NALC-
Pathfinder.
The review panel also recommends that those responsible in NASA
and other agencies for the overall management of Pathfinder projects
develop active mechanisms to compile and analyze the experience of NALC-
Pathfinder, and other Pathfinder projects, so that EOS-DIS will benefit
from the Pathfinder activities. One possible mechanism might be to
create an overall Pathfinder Science Committee containing agency
managers, chairs of the individual science teams and managers of the
pathfinder projects.
Another area where the input of the EPA science community is
especially desirable at present is in the sequence of areas to be analyzed
by the project It would appear that by modifying the order in which
different regions are analyzed, a more timely response to scientific
priorities could be achieved. For example, an earlier analysis of the
Pacific Northwest than that currently planned, would seem to be needed.
Also, the order in which areas are analyzed for testing and development of
methodology should be reconsidered so that a wider range of environments
is examined than those currently proposed. It is therefore recommended
that a review of the sequence of data processing and analysis be
implemented.
98
1 2
-------
NALC Review
Panel Report 2/93
7.0 Quality Assurance and Quality Control
QA/QC in the NALC is in the developmental stage. The review panel
is pleased to see that this topic is being addressed and that NALC
participants are researching cost efficient methods for implementing
QA/QC procedures in all aspects of the Pathfinder. One major success
criteria, the attainment of the 85%/85% goal (85% correct/85% of the
time) for categorization accuracy can only be achieved if innovative
statistical spatial sampling procedures are implemented. Other QA/QC
activities, while challenging, are somewhat more straight forward (e.g.
cloud cover estimation, RMS, geometric registration verification.)
The purpose of the quality assessment (particularly the accuracy
assessment) effort is uncertain, both as to its intended uses and its
motivation. Since such efforts can be very costly and time consuming, the
effort should be designed to provide more than a "figure of merit" such as
class-by-class categorization accuracy. This depends on whether, for
example, its motivation is to improve area proportion estimates or to
provide "hard" data for improved spatial mapping using the "soft" remote
sensing data (e.g., spatial statistics or geostatistical uses.) The latter
motivation would imply a design which avoids introducing too much bias
into the accuracy assessment sample through use of multistage sampling
techniques. To the degree this is done, and the assessment data are
preserved, the continued re-use of these data by the broader scientific
community will be assured.
The panel believes that a good start has been made, but more still
needs to be done. The choice of 85%/85% seems to be arbitrary and the
consequences of failing to meet it should be developed from a science
perspective. Examination of spatial sampling that have been accomplished
in projects such as those done by the United States Geological Survey, and
the University of California Santa Barbara, need to be examined. Several
panel members feel that the project should look at a stratified,
systematic, unaligned sampling strategy with double sampling to, as best
as practical, cover under-represented categories. In addition to the
procedures discussed, the panel believes that Pathfinder project personnel
should consider the use of independent analyst interpreter verification of
the accuracy of sample locations directly from the MSS imagery employed
in the categorization process. Done with proper care and diligence, this
99
13
-------
should not introduce significant bias into the verification process, and
studies have shown it to be an effective and efficient method of accuracy
verification.
Use of qualified interpreters analyzing the MSS data does not
necessarily rule out the use of local expertise but can make such use more
effective and cost efficient by focusing attention on categories that are
more difficult to verify.
The panel questions whether the data form categories proposed are
realistic using photointerpretation when land cover goals are more
modest. The use of photointerpretation as a basis for accuracy assessment
may be fraught with logistical difficulties. In addition, the goals
described in the intended interpretation data form may be unrealistic, e.g.,
species types. The procedure is probably sufficient for the limited
purposes of the categorization scheme.
The panel recommends that efforts continue on the development of
cost-effective, large-area categorization verification sampling
procedures. It also recommends that the panel, or some other independent
review board, review the procedures developed prior to implementation.
8.0 Global Change Science and Methodology
To achieve a balance between meeting global change science goals
and meeting the technical goals of NALC, the program must strike a
reasonable balance between the diverse needs of a changing scientific
customer base and the desire to standardize procedures. Land cover type
and change in land cover, features o.f the landscape resolvable with
Landsat MSS, have been established as the data needs by NALC's primary
customers - the studies being conducted at the Corvallis and Athens EPA
labs on carbon cycle questions. The NALC team should continue to work
with these two EPA labs to arrive at an appropriate and translatable
scheme for categorizing, the data set into land cover type classes. This
cooperation is the basis for the scientific value of tracing a 20-year
change in land cover for North America. Once it has addressed the needs
of its primary customers, the data set developed by the NALC will be
suitable for applications pilot projects throughout North America. The
pilots projects will help maintain the balance between science and
technical objectives by evaluating the trade-offs between desired
information and its dependence on particular techniques.
100 14
-------
NALC Review
Panel Report 2/93
9.0 Measures of Success
Listed below are four criteria for success that the review panel
members believe represent the goals and objectives of the NALC program:
1. Provide data, currently unavailable, to drive existing EPA land
processes models, which, in turn, will help formulate agency
policy decisions.
2. Compilation of a well designed standardized set of data products,
including wall-to-wall triplet raw MSS and ancillary data,
land cover images and statistics, tabular data, and evaluation
of land cover change. This data set is designed for, and must
be made readily available (as evaluated by physical access and
cost) to the science and application user community. As such,
it will have the very-real potential of providing return on
investment will beyond anything currently envisioned. The
degree to which the data base is acquired and used in
meaningful scientific investigations will measure how well
this is accomplished.
3. Scientific and technical publications in the peer reviewed
literature.
4. Concrete examples of how high spatial resolution (MSS) data
improve our understanding of land cover change and its effects
on global change phenomena versus other approaches, e.g. point
data, traditional mapped data, and coarse resolution data.
10.0 Conclusions and Recommendations
The NALC is truly a "pathfinder." It is developing a data base and an
operational use for remotely sensed data that will meet the needs .of EPA
and that will serve as a model for other -global change research efforts.
The review panel concurs with the goals, objectives and approach of the
program, and presents the following recommendations to improve the
efficiency of the data processing and the robustness of the results.
10.1 General Considerations
• The direct links between specific science questions on carbon
cycles and the information needed to solve them, or improve EPA's
understanding with the NALC technical approach and goals, need more
101
1 5
-------
clarity and definition.
• The purpose of some activities, e.g., accuracy assessment, is
unclear and difficult to tie to specific science goals.
• The scientific arguments as to why the specific land cover
categorization scheme will or will not satisfy important gaps in
understanding and predicting carbon cycle questions are not sufficiently
made and need better definition.
* The relationship between the schedule for data delivery and the
applications projects with major scientific uncertainties suffers from
the lack of a clear scientific rationale.
• NALC's efforts are a key driving force in the overall Landsat
Pathfinder program. As such, the potential customer or user base is likely
much larger even within EPA than the current EPA base. The NALC
management clearly recognizes this and has been motivated to remain
sensitive to potential changing information needs by seeking to
standardize products, to retain useful analyses, such as clustering, at
levels that permit reinterpretations, and by seeking to retain accuracy
assessment data together with the Landsat data, all archived at the EROS
Data Center. The team is attempting to define an information management
systems for the NALC data base that will allow access to the greater
scientific community. EPA should proceed with caution, once the NALC
technical procedures are set, in deviating significantly from their path if
needed or requested by other science users. Different users may want to
alter the priorities on scheduling. This should be weighed, with caution,
against the need to complete the wall-to-wall, 3-epoch data set and the
needs of NALC's original EPA Global Change customer base. EPA could
avail itself, as NALC has already begun to do, of collaborative
opportunities for "application projects" so as to leverage their significant
NALC effort.
; NASA, and the other agencies participating in the Pathfinder
Program, should develop mechanisms to assimilate the NALC-Pathfinder
experience, and that of other Pathfinder projects, in the development of
EOS-DIS.
• One area, where the input of the EPA science community is
especially desirable at present is in the sequence of areas to be analyzed
by the project. It would appear that by modifying the order in which
different regions are analyzed, a more timely response to scientific
priorities could be achieved. For example an earlier analysis of the
102
1 6
-------
NALC Review
Panel Report 2/93
Pacific Northwest, than that currently planned, would seem to be needed.
• The NALC should specify criteria for success so that the progress
and accomplishments of the project are readily apparent.
10.2 Technical Recommendations
• Categorization:
1. Measures of success should be established for testing
categorization techniques, e.g. ability to recognize categories
of interest, ease of labeling, etc.
2. Categorization tests should be conducted using triplets from
representative environments.
3. Alternative categorization techniques should be tested before
a categorization methodology is chosen for the duration of
the program.
• Labeling of categorized pixels is crucial to the success of the program.
The individuals performing the labeling must be trained adequately
in multispectral categorization techniques.
• Immediate implementation of an IMS will greatly improve the efficiency
of the program and its chances to meet all its objectives.
• Increasing throughput of compilation of the triplets would allow for
pilot testing and technique evaluation sooner and in a broader range
of environments.
• Efforts should be taken immediately to assure all required MSS scenes
were collected and preprocessed, or that a mechanism exists to
complete the preprocessing.
• Compilation of land cover and land cover change standard products
should be monitored and analyzed with the cooperation of the
appropriate land management organizations
• Field inventories should include the consistent and proper use of field
forms and data collection and management techniques.
• A sampling frame approach for cluster labeling activities should be
developed and implemented.
• Change detection methodology:
1. Evaluation of alternative change detection methods should be
performed. The cooperation of the appropriate elements of the
science community should be sought for that effort.
103
1 7
-------
2. Extensive testing of change detection techniques should be
performed at each of the pilot sites.
3. Explicit procedures for change detection given the temporal
variation in the triplets should be developed and evaluated.
Membership in the NALC-Pathfinder Technical Working Group should
include the science community within EPA and other research
programs directed toward global change.
QA/QC needs to be fully developed and evaluated. A rationale for the
selection of the "85/85" criterion should be developed based on
science requirements. Evaluation of other QA/QC should be
attempted.
Efforts should continue toward the development of cost-effective
categorization verification sampling procedures. The procedures
must be reviewed prior to implementation.
104
1 8
-------
NALC Review
Panel Report 2/93
Panel Review Members
Dr. Chuck Dull
Dr. John Estes
Dr. Leonard Gaydos
Dr. James G. Lawless
(Chair)
Mr. Douglass Muchoney
Mr. David Peterson
Mr. Edwin Sheffner
Dr. David Skole
Dr. John Townshend
US Forest Service
Washington, D.C.
Dept. of Geography
University of California, Santa Barbara
USGS, Ames Research Center
Moffett Field, California
NASA Headquarters
Washington, D.C.
The Nature Conservancy
Arlington, Virginia
NASA Ames Research Center
Moffett Field, California
TGS Technology
Ames Research Center
CSRC/EOS
University of New Hampshire, Durham, N.H.
Dept. of Geography
University of Maryland, College Park, MD.
105
1 9
-------
NORTH AMERICAN LANDSCAPE CHARACTERIZATION (NALC) PROJECT
RESPONSE TO THE NALC PANEL REVIEW REPORT
The organization of this response follows the format provided in the NALC Panel
Review report. The order is patterned after the format of the NALC Technical Work
Plan and order of presentations at the NALC Technical Review session in New
Orleans.
The responses to comments by the Technical Review Panel specifically address
those areas identified as requiring additional attention. The objectives of the
responses are to identify how we intend to act upon the comments.
3.0 IMAGE CATEGORIZATION AND DEVELOPMENT OF LAND COVER PRODUCTS
In response to comments in this section of the Panel Report, we intend to develop
measures of success, establish them programmatically, and characterize the methods
to evaluate them as to their appropriateness. These issues include addressing the
separation of classes, the border class problem, and methods to ease the labor
involved in labeling.
It is desirable to devote further attention to the qualifications of associated-
researchers as related to capabilities in computer categorization and labelling of
classes resulting from analysis of Landsat data. This will help assure their
performance and adherence to standard procedures. We will assist them, by
developing routines which supply spectral plots and which will highlight individual
classes to further assure high quality work in labeling land cover classes. Attention
will be devoted to better describing the classification system classes, including closed
forest and savanna or shrub/scrub classes. We will address and clarify methods to
handle the question of land cover condition or type following harvest.
Further efforts will examine the use of ancillary data along with NALC products,
such as AVHRR time series, TM, SPOT and/or digital orthophoto data. These efforts
will be conducted directly to insure their characterization early on.
These efforts will also entail further review of literature as to' previous results, levels
of accuracy achieved, and further documentation on background of the criteria
selected for the NALC Project.
106
-------
4.0 METHODS FOR MANAGING AND DISTRIBUTION OF NALC DATA
In 1992 over five hundred and sixty MSS scenes were acquired by EOSAT and pre-
processed in support of the project. These scenes along with those in the archive are
being identified and scheduled for triplicate production. To augment this source, EPA
and USGS are in contact with the Canada Centre for Remote Sensing. The goal is to
determine the extent of 1990/91/92 MSS data that have been acquired from
Canadian sources, and that can be used for NALC. We will also determine their
capabilities to pre-process EOSAT HDT-R products using their HDT-A processing
stream.
As part of these efforts we will evaluate options to increase the throughput of MSS
scenes through the processing procedures by the EROS Data Center.
The NALC Information Management System (IMS) is currently being implemented.
The characteristics will be evaluated with a perspective of serving the needs of
production level data and metadata management, and later requirements important to
public access and browsing. Work has been conducted to learn from the experience
of the IMS of Landsat Pathfinder on the Humid Tropics (HTRIP), and see how this
Arc/Info based approach may be of assistance.
Data processing standards will be further coordinated with interested federal
agencies. This will take the form of further consultations, and inclusion of additional
personnel in the Technical Work Group.
Field and aerial photo sampling techniques and data record sheet will be further
developed. This will be accomplished with an eye toward assisting in the
field/laboratory labeling of categorized images, and towards the capture of a variety
of land cover conditions during field studies.
5.0 CHANGE DETECTION METHODOLOGY
The approach to detection of change in land covers will be refined following the
comments provided by the Panel. This effort will include evaluating other change
detection approaches, such as principal components analysis, regression-Chi square
analysis, image difference methods, and post-classification change detection methods.
The methods and approach will be evaluated on a variety of sites representing the
variability in land covers to be encountered during the Project. The determination of
methods to be used will be made with a focus on the final products and science
issues related to detection of change.
107
-------
In particular, we will focus on relating change detection methods to identifying land
cover changes important to the science objectives, and insure there is a fully
reconciled land cover classification system for change detection. This effort will be
conducted in cooperation with the science clients within the Agency and others. This
effort will involve additional, exhaustive evaluations of available methods. This work
will be conducted with the assistance of researchers actively involved in applications.
The experience of governmental, industrial and academic personnel will be tapped.
The issues of image pre-processing with regard to systematic corrections and other
radiometric corrections will be re-visited. Issues will include atmospheric corrections,
sun angle corrections, and the long temporal windows encountered in analysis of
some scenes.
6.0 NALC MANAGEMENT, SCHEDULING AND COORDINATION
Further cooperation will be fostered with Agency science clients and with other land
management agencies that have an opportunity to respond to the results through
modification in management practices. Cooperation with the USGS, DOI agencies,
USDA agencies, TVA and others is envisioned. It is anticipated that vehicle for such
cooperation will be meetings with interested parties and membership on the Technical
Work Group.
An additional goal of this cooperation will be to identify existing databases held by
these groups. Use of such ancillary data may be of great value to the work of NALC,
and its scientific clients.
It is also necessary to further develop the links with the Agency science clients. It
is anticipated that attendance of more clients at the Technical Work Group sessions
would assist this process. Further visits to Agency Laboratories, as a follow on to
previous visits, will also help to further appraise clients of opportunities, project
progress, and allow feedback on requirements related to scientific objectives.
Stronger links will be forged with the image processing and image application
communities. The Project can use their input to assure high quality results and assure
acceptable standard methods for data processing. This effort is on-going with the
advent of talks on the Project presented to university audiences, non-profit and
industrial groups, and additional federal audiences. Linkages have been developed to
date with Agency scientist including EMAP personnel, USGS Center for Inter-American
Mineral Resource Investigations (CIMRI) scientists, USGS Water Division scientists,
USFWS Gap Project personnel and others.
108
-------
7.0 QUALITY ASSURANCE AND QUALITY CONTROL (QA/QC)
The need for quality assurance procedures and quality control mechanisms is
mandated by Agency policy. It is an important aspect of the Project due to the high
level of interest in accuracy assessments, and methods are also an important result
of the work.
The QA/QC plan is being further developed to track the production and document
the quality of triplicate products. The quality goals have established based on Agency
policies and the work is on-going. The procedures are currently being reviewed to
determine their adequacy.
The determination as the categorization accuracy is an important Agency QA/QC
goal. It is also important to the user community and to the Technical Review Panel.
We are currently developing the protocol to be applied. Recommendations from the
Panel such as reconciliation of science objectives and accuracy assessment objectives
are being implemented. We are also re-evaluating the categorization accuracy quality
objectives to further ground them in scientific experience.
The land categorization methods and survey sampling methods to accomplish the
accuracy assessment are undergoing further study and exhaustive documentation.
This effort will be thoroughly grounded in theory and literature, and draw upon
experience in the government and in other research arenas. The approaches of
stratified systematic sampling, and double sampling will be further evaluated.
Methods for systematic sampling in general require further attention, as does the level,
of effort devoted to each MSS scene, and the use of a combination of photo
interpretation and ground sampling methods.
Further attention will be devoted to developing photo interpretation sheets for
accuracy assessments. The issue of independent photo interpretive assessment of
accuracy will be addressed. Photo interpretive keys will be developed to guide the
interpreters in their assessments. The photo interpretative keys will also be optimized
recognize the difference in detail between photos and the land cover categories.
The Panel will have the opportunity to review these procedures during the next
Technical Work Group meeting in May 1993 and in the next draft of the Technical
Work Plan appearing at about the same time.
8.0 GLOBAL CHANGE SCIENCE AND METHODOLOGY
In additional to the comments addressed above, the Project will develop procedures
to further communicate the elements of the work and further the dialogue between
the user community and NALC personnel.
109
-------
9.0 MEASURES OF SUCCESS
The measures of success articulated in the Technical Panel Report are particularly
clear, and germane to the effort. These measures include: NALC products and results
will augment EPA inventory and modeling efforts; the standard NALC products will be
a valuable resource for the general scientific community; the characteristics of
products and result of NALC will be disseminated in the peer reviewed literature; and
the results will demonstrate the value of area-based sampling as compared to
traditional sampling approaches alone.
10.0 CONCLUSIONS AND RECOMMENDATIONS
As addressed previously, the NALC Project will continue to refine the link between
the specific scientific questions that drive the Project and the technical approaches
and goals.
110
.
-------
Appendix II:
Agreement Between INEGI and USEPA
111
-------
UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF RESEARCH AND DEVELOPMENT
ENVIRONMENTAL MONITORING SYSTEMS LABORATORY-LAS VEGAS
P.O. BOX 93478
LAS VEGAS. NEVADA 89193-3478
(702/798-2100 - FTS 545-2100)
MAR 24 I393
Nestor Duch Gary
Director de Geographia
Institute Nacional de Estadistica, Geografia e Inforiaatica
Heroe de Nacozari No. 2301
Puerto 7 Anexo
C.P. 20290 CD, Industrial
Aguascalientes, Mexico
Dear Mr. Duch:
The Office of Research and Development of the U.S.
Environmental Protection Agency has undertaken a project known as
the North American Landscape Characterization (NALC) as part of
its Global Change Research Program. The objective of this
project is to produce land cover and land cover change datasets
for use in the inventory of terrestrial carbon stocks and for
estimating the flux of carbon which has occurred due to land
cover change. Project areas include Central America, Mexico,
Caribbean Islands, and the U.S.A.
The NALC land cover and land cover change data sets are to
be derived from Landsat MSS "triplicates" which are currently
being assembled for the project areas by the U.S. Geological
Survey, EROS Data Center (EDC). The triplicates consist of three
dates of georeferenced Landsat MSS data, with scenes from the
early 1970's, mid-1980's, and early 1990's. Where available we
are also adding digital terrain model (DTM) data and digital
ecoregion or biotic community data sets. To cover all of Mexico
will require the assembly and analysis of 99 of these
triplicates.
Our primary collaborator in the U.S.A. is the EROS Data
Center. This is a very natural collaboration for several
reasons. EDC is an operational arm of the USGS National Mapping
Division, which has responsibility for coordinating national
mapping projects and producing national map products. In
addition, the federal archive of Landsat MSS data resides with
EDC.
The Data Center is assembling the NALC triplicates in
support of the NALC project. The topographic maps and digital
terrain datasets for the USA triplicates are being provided by
EDC as one of their contributions to the "project. EDC will
establish an open archive for the NALC datasets, which will be
available to the wider scientific community. While the specific
112
-------
details on distribution media and costs are still to be worked
out, the intent is that the NALC datasets will be distributed by
EDC at the marginal cost of filling individual orders.
Our relationship with the EROS Data Center is very
productive and represents a long term commitment of EPA and USGS
to work together in producing high quality land cover and land
cover change datasets. We would like to establish a similar
relationship with the Institute Nacional de Estadistica Geografia
e Informatica (INEGI). This is in recognition of the special
role INEGI plays in producing and distributing geographic
datasets for Mexico. As the Technical Director of the NALC
project, I am able to commit .to the following terms of agreement,
which can be the basis for am expanding collaboration between the
EPA's Environmental Monitoring Systems Laboratory - Las Vegas
(EMSL-LV) and INEGI. All data distribution commitments by EMSL-
LV are subject to the availability of NALC project funds.
DATA EXCHANGE:
1) EMSL-LV will provide a complete set of the georeferenced
Landsat MSS triplicates and ancillary data sets to INEGI in
digital form.
2) INEGI will provide the topographic maps and digital terrain
model data sets required for the assembly of the Landsat MSS
triplicates for Mexico.
3) The objective of the NALC is to derive land cover and land
cover change datasets for large portions of North America. EPA
will invite INEGI participation in the analysis of the Mexican
data sets. Particular attention will be paid to producing output
products which are compatible with existing INEGI land cover maps
for Mexico.
4) INEGI is free to use the NALC data sets they receive for
other projects.
DATA DISTRIBUTION:
1) No copyright or trade secret restrictions will constrict the
distribution of the NALC Landsat data sets. Any holder of those
data sets may utilize, copy, and distribute those data sets. At
INEGI's request, restrictions will be placed on the distribution
of INEGI provided DTM data sets of Mexico.
2) The central archive and distribution center for the NALC
project is the USGS EROS Data Center. In determining a price for
the data sets, EDC plans to simply recover the costs associated
with filling a specific request.
113
-------
3) EMSL-LV invites INEGI to become an archive and distribution
center for NALC data sets of Mexico. INEGI is free to set their
own pricing and distribution system.
TERMS FOR USE -OF THE DTM DATA:
The DTM data will be coregistered to the Landsat MSS scenes
and these combined data sets will be archived and distributed
from EROS Data Center. Based on our agreement with EDC terms
set by INEGI for the distribution of the Mexican DTM data sets
will be respected. In working with EDC on this issue it would be
possible to restrict access of the Mexican DTM data sets to non-
commercial applications. This would involve the submission of
signed statements from any organization requesting the data sets,
attesting to their non-commercial status and non-commercial
applications for the DTM data of Mexico.
Sincerely,
s.
Ross S. Lunetta
NALC - Technical Director
114
-------
Institute National de Estadlstica,
Geograiia e Informaiica
DIRECCION GENERAL DE GEOGRAEEA
A.93.72.082
April 5, 1993
Mr. Ross S. Lunette
Technical Director - NALC
U.S. Environmental Protection Agency
En.vironmCT.tal Monitoring Systems Laboratory
944 E. Harmon Street
Las Vegas, Nevada 89193
ILS-A.
Dear Mr, Lunetta,
Thank you for your letter of March. 23,1993 regarding the EPA-USGS North American
Landscape Characterization (NALQ, which is a- component of the- ELS. Global Change
Research. Program. In your letter yon have proposed an exchange of data, with INEGI
providing map and digital elevation data for nse in producing" the basic NALC data sets for
Mexico. To. addition, yon have invited the participation of INEGI in the conduct of the NALC
project in Mexico.
In recognition of the scientific merit of the NALC project and its close match to
several of INEGTs technical objectives for the 1990's, I am pleased to inform you that INEGI
agrees to the data, exchange outlined in your letter.
Specifically, INEGI agrees to provide:
1) The digital terrain models (DTMs) for an of Mexico. Please note mat these data sets are
being provided Sor non-commercial use only, under terms-described later in this letter
agreement.
115
-------
2) Topographic maps at 1:50,000 and 1:250,000 scales for all of Mexico.
3) Land use / land cover maps of Mexico at 1:250,000 scale.
4) Selected aerial photographic prints of Mexico from existing archives held by -INEGL
Tlie data sets -to be provided to INEGI by EPA include:
1) The NALC Landsat MSS data sets of Mexico for three time periods (1970's, 1980's, and
1990*s) on the form of georefererieed and coregistered triplicates.
2) The DTM data for the Mexican MSS triplicates, resampled to the MSS spatial resolution
and clipped to the MSS scene coverages.
3) The digital land cover and land cover change data sets produced through the analysis of
the NALC triplicates of Mexico.
Regarding the DTM data, 3NEGI is providing these data sets for use in the scientific
research to "be conducted with the NALC data sets. In order to preserve the commercial
markets that INEGI lias established for these data sets, we request that the EPA and USGS
restrict the access of these data sets to government entities, universities., and non-profit
organization. We request that the users of the DTM data sign agreements binding- their usep
of the data to noa-K^rnrnercial projects and preventing 'the unauthorized dispersion of the data.
Attached for your consideration are draft copies of the style of apjph'cafion and agreement
which we would like to have executed by each group receiving the Mexican DTM data. It
is our understanding that the USGS EROS Data Center has agreed to administer this
application and agreement system and that INEGI win be provided with copies of 1he
completed forms on a periodic basis.
INEGI frirenffe to maintain art archive of the NALC data sets, which wJH be used for
our internal projects. We expect to also establish a system for distributing these data sets to
government entities and scientific investigators in Mexico, in response to data requests. We
recognize that rnese data sets will also be available from the USGS EROS Data Center.
INEGI is the principal agency in Mexico responsible for the production, maintenance,
and distribution of cartographic and spatial data sets. As such, we are pleased to accept your
invitation for INEGI participation in the NALC project for Mexico. I believe that INEGTs
participation win mate a valuable contribution to the NALC project Please top us informed
of the meetings, reviews, and other activities for the NALC project so that our staff has an
to attend..
116
-------
At the present time our staff has commenced the assembly of the topographic maps,
land use - land cover maps, and DTM data sets you have requested for the NALC project
We look forward to collaborating with. EPA on the NALC project and on other research
projects in the coming years.
Sincerely,
DIRECTOR
LIC. NESTOR DUCH GARY
117
-------
Bear Investigator,
The data you have requested of Mexico from the North American Landscape
Characterization (NALQ project contains digital elevation data which was provided by
Mexico's National Infinite for Statistics, Geography, and Informatics (INEGI)* These data
sets have been provided by INEGI for use in scientific investigations and projects conducted
by government agencies, universities, and non-profit organizations. These data, provided at
the marginal costs of distribution, are not to be used for commercial applications. These data
sets can be purchased from INEGI by any group wishing to use them in a commercial project-
In order to preserve the commercial markets INEGI has developed for these data sets,
we request that you complete the foUowing application and agreement. Your cooperation in
abiding by the terras outlined in the agreement win ensure the continued availability of these
data, sets for scientific research.
The Landsat MSS data sets that yon have requested of Mexico are not restricted in
their Tise or distribution-
Please send th& completed application and agreement forms to the following address:
Customer Services
USGS EROS Data Center
Mundt Federal Building
Sioux Falls, South Dakota 57198
U.S-A-
TeL 605-594-6961
No facsimiles of the forms will be accepted.
To obtain the Mexican. DIM data for use in a commercial project, please contact INEGI at
the following address:
Servicio a Usarios
1NEGEI, Direccion de Integracion y Analisis
Subdireccion de Comercializacion
Heroe de Nacozari 2301 Pueita 11
CoL del Parqne OP. 20290
Aguascalientes, AGS
MEXICO
TeL 52-49-181948
FAX 52-49-180739
118
-------
APPLICATION FOR USE OF THE DIGITAL TEKRAM DATA OF MEXICO
Applicant Name (Last, First, MJ):
Title:
Organization:
Division:
Mailing Address:
TeO.epi.one:
Fax:
Electronic Mail:
Title of Reseatca:
Purpose of Usage:
119
-------
AGREEMENT FOR USE OF THE DIGITAL TERRAIN DATA OF MEXICO
The digital terrain data you have requested of Mexico has been provided by the Mexico's
National Institute for Statistics, Geography., and Informatics
(INEGI) for use in non-commercial scientific research.
The researcher agrees to the following conditions for use of the Mexican digital terrain
1) The data -will not be used for commercial purposes.
2) The source of the data (ENEG3) mQ. be acknowledged in any derived data-product, reports,
or publications.
3) .The data win not be distributed to third parties. Investigators affiliated with, your project
who •wish to obtain the digital t^tr-miy data must complete their own application and
agreement forms-
4) The undersigned agree to take appropriate measures to restrict the transfer of the data to
commercial entities.
RESEARCHER
Name
ORGANIZATION
Name of Superintendent
Signature / Date
Address
Signature / Date
Address
120
-------
United States Department of the interior
GEOLOGICAL SURVEY
EROS Data Center
Sioux Falls, South Dakota 57198
INR£PLYR£FERTO: OC 6-4
June 8, 1993
Mr. Nestor Duch Gary
Director d Geographia
Institute Nacional de Estadistica,.Geografia e Informatica
Heroe de Nacozari No. 2301
Puerto 7 Anexo
C.P. 20290 CD, Industrial
Aguascalientes, Mexico
Dear Mr. Duch:
We are pleased to learn of your participation in the North American Landscape
Characterization (NALC) Program and your contribution of several data sets,
including digital terrain models (DTM's) for all of Mexico. We understand
that Mexico's National Institute for Statistics, Geography, and Informatics
(INEGI) is providing DTM's for use in the scientific research to be conducted
with the NALC data sets. Per your request, the Environmental Protection
Agency and the U.S. Geological Survey (USGS) will restrict the access of these
data sets to government entities, universities, and non-profit organizations.
This will be administered by the USGS EROS Data Center as follows:
— A user requesting Mexican DTM data will be sent three items (enclosed):
• a letter explaining data use restrictions,
• an application for use of the data, and
• an agreement restricting use and distribution of the data.
— Upon receipt of both completed forms and proper payment, the Data Center
will fill the order.
— The Data Center will provide copies of completed application and
agreement forms to INEGI on a quarterly basis.
121
-------
Mr. Nestor Ouch Gary . , 2
We hope that this procedure meets'with your approval. We look forward to
collaborating with INEGI on the NALC project and on other research projects in
the coming years.
Enclosures
cc: R. Lunetta, EPA
Sincerely,
/
/AjDonald T. Lauer
Chief, EROS Data Center
122
-------
Dear Investigator,
The data you have requested of Mexico from the North American Landscape
Characterization (NALC) project contains digital elevation data which was-
provided by Mexico's National Institute for Statistics, Geography, and
Informatics (INEGI). These data sets have been provided by INEGI for use
scientific investigations and projects conducted by government agencies,
universities, and non-profit organizations. These data, provided at the
marginal costs of distribution, are not to be used for commercial
applications. These data sets can be purchased from INEGI -by any group
wishing to use them in a commercial project.
In order to preserve the commercial markets INEGI has developed for these data
The Landsat MSS data sets that you have requested of Mexico are not restricted
in their use or distribution.
Please send the completed application and agreement forms to the following
address:
Customer Services
USGS EROS Data Center
Mundt Federal Building
Sioux Falls, South Dakota 57198
USA
Tel. 605-594-6151
No facsimiles of the forms will be accepted.
To obtain the Mexican DTM data for use
INEGI at the following address:
in a commercial project, please contact
Servicio a Usarios
INEGI, Direccion de Integracion y Analisis
Subdireccion de Comercializacion
Heroe de Nacozari 2301 Puerta 11
Col. del Parque C.P. 20290
Aguascalientes, AGS
MEXICO
Tel. 52-49-181948
Fax 52-49-180739
123
-------
APPLICATION FOR USE OF THE DIGITAL TERRAIN DATA OF MEXICO
Applicant Name (Last, First, M.I.):
Title:
Organization:
Division:
Mailing Address:
Telephone:
Fax:
Electronic Mail:
Ti tl e of "Research:
Purpose of Usage:
Latitude and Longitude Coverage Limits of Data Requested:
124
-------
AGREEMENT FOR USE OF THE DIGITAL TERRAIN DATA OF MEXICO
The digital terrain data you have requested of Mexico has been provided by
Mexico's National Institute for Statistics, Geography and Informatics (INEGI)
for use in non-commercial scientific research.
The researcher agrees to the following conditions for use of the Mexican
digital terrain data:
1) The data will not be used for commercial purposes.
2) The sources of the data (INEGI) will be acknowledged in any derived data
product, reports, or publications.
3) The data will not be distributed to third parties. Investigators
affiliated with your project who wish to obtain the digital terrain data
must complete their own application and agreement forms.
4) The undersigned agree to take appropriate measures to restrict the
transfer of the data to commercial entities. . v
RESEARCHER
Name
ORGANIZATION
Name of Superintendent
Signature/Date
Address
Signature/Date
Address
125
-------
UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
cwv/m^K, OFFICE OF RESEARCH AND DEVELOPMENT
ENVIRONMENTAL MONITORING SYSTEMS LABORATORY-LAS VEGAS
P.O. BOX 93478
LAS VEGAS. NEVADA 89193-3478
(702/798-2100 - FTS 545-21 OO)
JUN I 6 I993
Mr. Nestor Duch Gary
Director General de Geografia
Institute Nacional Estadistica, Geografia e Informatica
Heroe de Nacozari N. 2301
Puerto 7 Anexo
C.P. 20290 CD, Industrial
Aguascalientes, ACS. Mexico
Dear Mr. Duch:
Thank you for your letter of April 5, 1993 regarding the
data exchange agreement between the Institute Nacional
Estadistica, Geografia e Informatica (INEGI) and the
Environmental Monitoring Systems Laboratory - Las Vegas (EMSL-
LV). The data exchange is being conducted in support of the
North American Landscape Characterization (NALC) project, which
is a component of the U.S. Global Change Research Program.
In accordance with our data exchange agreement, enclosed
please find 19 NALC triplicate data sets for Mexico. We will
continue to ship triplicate data sets, in increments of 12 or
greater, until all 102 path/rows for Mexico are provided to
INEGI.
Also in accordance with your reguest, the Mexico Digital
Terrain Data provided by INEGI will be restricted to
noncommercial users of NALC triplicate data sets. The U.S.
Geological Survey (USGS), EROS Data Center (EDC) has provided you
with correspondence and a copy of the forms "Application or
Agreement for the use of the Digital Terrain Data of Mexico", to
comply with your distribution restrictions.
To meet the current NALC triplicate data set assembly
schedule we would appreciate receipt of Digital Terrain Data for
the following coordinates by July 12, 1993.
N 27°, W 99° to N 23°, W 96°
N 33°, W 117° to N 16°, W 99°
We request that the digital terrain data for the remaining
locations in Mexico be provided by August 13, 1993.
126
-------
I have also enclosed a letter request from Ms. Susan K.
Jenson of the EDO. Ms. Jenson would like permission from INEGI
to produce a 15 arc-second generalized elevation data set for
Mexico. This data set would complement similar efforts completed
for the United States and Canada. If you would like additional
details please contact Ms. Jenson directly at (605)594-6011.
My current travel plans provide an opportunity for me to
visit Aguascalientes on September 30, 1993. If this is a good
tir.c for you, I would appreciate the opportunity to again visit
INEGI. During the visit I could brief you and your staff on the
status of the NALC project. Also, I am interested in the
progress of your facility upgrades and potential utilization of
NALC data sets.
I would like to invite you or member(s) of your staff to
attend the next "NALC Technical Work Group" at EMSL - Las Vegas,
Nevada on December 8-9, 1993. We could also use the opportunity
to tour our facility and brief you on the environmental
monitoring research activities at EMSL-LV.
Thank you for your interest and collaboration on the NALC
project. I look forward to seeing you on September 30, 1993, in
Aguascalientes.
Enclosures
Sincerely,
f^fp&
Ross S. Lunetta
NALC Technical Director
CC:
Lauer, USGS/EDC
Sturdevant, USGS/EDC
Jenson, USGS/EDC
127
-------
Appendix Illi
Path/Row Data Acquisition List, Triplicate Assembly Priorities
128
-------
NALC MSS DATA ACQUISITIONS BY PATH/ROW
{* =PATH/ROWS PURCHASED FROM EOSAT USING EDC BROKERAGE AGREEMENT)
Caribbean
48 scenes.
1/49-50-51-52
2/48
4/47-48
5/47-48
6/47
7/46-47
8/46-47-48
9/45-46-47
10/44-45-46-47
11/43-44-45-46-47-48
12/43 -45-46-47-48
13/42-43-44-45-46
14/41-42 -44-45
15/44-45
16/44-45
17/44-45
*48
*47
*47
*47,48
*45,46,47
*44,45,46,47
*44,45,46,47
*43,45,46,48
*45
*45
Central America
10/54-55
11/54-55
12/53-54-55
13/54-55
14/53-54
15/50-51-52-53
16/49-50-51-52-53
17/49-50-51-52
18/49-50-51
19/48-49-50-51
20/50
32 scenes.
*55
*53
*52,53
*51,52
*49,50,51
*50
Chesapeake Bay Watershed
18 scenes.
14/30-31-32-33-34
15/30-31-32-33-34
16/30-31-32-33-34
17/31-32 -34
*30,31,32,33,34
*30,31,32,33,34
*30,31,32,33,34
*31,32,34
129
-------
Mexico
119 scenes.
18/45-46
19/45-46-47
20/45-46-47-48-49
21/46-47-48-49-50
22/47-48-49
23/47-48-49
24/46-47-48-49
25/45-46-47-48-49
26/42-43-44-45-46-47-48
27/42-43-44-45-46-47-48
28/40-41-42-43-44-45-46-47
29/39-40-41-42-43-44-45-46-47
30/40-41-42-43-44-45-46-47
31/39-40-41-42-43-44
32/38-39-40-41-42-43
33/38-39-40-41-42-43-44
34/38-39-40-41-42-43-44
35/38-39-40-41-42-43
36/38-39-40-41-42
37/38-39-40-41
38/37-38-39-40
39/37-38-39
40/37-38
*46
*45,46,47
*46, 47,48, 50
*47,49
*48,49
*48,49
*46,47,48
*45,46,47
*46,47
*40, 41, 42, 43, 44, 45, 46
*40, 41, 42,43
*39,40,41,42,43
*39,40,41,42
*39
*39,40,41,42
*40,41
*38
*38
Brazilian MSS Acquisitions
83 scenes.
214/64-65-66-67
215/64-65-66-67-68-69 -71-72-73-74
216/63-64 -69-70-71-72-73-74-75-76
217/63 -70-71-72-73-74-75-76
218/62-63 -70-71-72-73-74-75-76
219/74-75-76-77
220/74-75-76-77-78-79-80-81
221/71 -74-75-76-77-78-79-80-81-82-83
222/74-75-76-77-78-79-80-81-82-83
223/75-76-77-78-79-80
224/75-76-77
(* sPATH/ROWS PURCHASED FROM EOSAT USING EDC BROKERAGE AGREEMENT)
130
-------
Hawaii
62/46-47
63/46-47
64/45-46
65/45
66/45
8 scenes.
Alaska
54/21-
55/21-
56/20-
57/19-
58/19-
59/18-
60/19
61/18
62/17-
63/17-
64/16-
65/15-
66/14-
67/13-
68/12-
69/11-
70/11-
71/11-
72/11-
73/11-
74/10-
75/10-
76/10-
77/10-
78/10-
79/10
80/10
81/10
82/10
83/12
180 scenes.
22
22
21
20-21
20
19-20
18
18
17-
16-
15-
14-
13-
12-
•12-
•12-
•12-
•12-
•11-
•11-
•11-
•11-
-11-
•11-
•11
-11
•11
•13
18
17-18
16-17-18
15-16-17-18
•14-15-16-17-18
-13-14-15-16-17-18-19-20
•13-14-15-16-17-18-19-20
•13-14-15-16-17-18-19-20
•13-14-15-16-17-18-19-20-21
•13-14-15-16-17-18-19-20-21
•12-13-14-15-16-17-18-19-
•12-13-14-15-16-17-18-19-
•12-13-14-15-16-17-18-19-
•12-13-14-15-16-17-18-
•12-13-14-15-16-17-18-
-12-13-14-15-16-17-18-
•12-13-14-15
•12-13-14-15
•12-13-14
•14
*19
*16
-21-22
-22
-22
-23
-23
*12,13,14,15,16,17
(* =PATH/ROWS PURCHASED FROM EOSAT USING EDC BROKERAGE AGREEMENT)
131
-------
Western US
133 scenes.
33/33-
34/31-
35/31-
36/29-
37/29-
38/27-
39/27-
40/27-
41/26-
42/26-
43/26-
44/26-
45/26-
46/26-
47/26-
48/26-
34-35-
32-33-
32-33-
30-31-
30-31-
28-29-
28-29-
28-29-
27-28-
27-28-
27-28-
27-28-
27-28-
27-28-
27-28-
27
•36-37
34-35-
34-35-
32-33-
32-33-
30-31-
•30-31-
30-31-
29-30-
29-30-
29-30-
29-30-
29-30-
29-30-
29-30
36-37
36-37
34-35-36-37
34-35-36-37
32-33-34-35-36
32-33-34-35-36
32-33-34-35-36
31-32-33-34-35-36-37
31-32-33-34-35-36
31-32-33-34-35-36
31-32-33-34-35
31-32-33
31-32
*34
*31,35,36,37,38
*31,32,33,34,35,36,37,38
*37
*30,33,34,35
*35,36,37
*27,34,36
*28,29,30,31,32,33
Eastern and Southern US
141 scenes.
06/38
10/29
11/27-
12/27-
13/28-
14/29-
15/29-
16/35-
17/30-
18/31-
19/31-
20/29-
21/28-
22/27-
23/27-
24/27-
25/26-
28-29- -31
•28-29-30-31
•29-30-31-32
• 35-36
• 35-36-37 -41-42-43
•36-37-38-39-40-41-42
33- 35-36-37-38-39-40-41
•32-33-34-35-36-37-38-39
•32-33-34-35-36-37-38-39
•30-31-32-33-34-35-36-37-38-39
•29-30-31-32-33-34-35-36-37-38-39-40
•28-29-30-31-32-33-34-35-36-37-38-39-40
•28-29-30-31-32-33-34-35-36-37-38-39-40
•28-29-30-31-32-33-34-35-36-37-38-39
•27-28-29-30-31-32-33-34-35-36-37-38-39-40
(* =PATH/ROWS PURCHASED FROM EOSAT USING EDC BROKERAGE AGREEMENT)
132
-------
Midwest and Great Plains
122 scenes
26/27
27/27
28/26
29/26
30/26
31/26
32/26
33/26
34/26
35/26
36/26
37/26
38/26
39/26
40/26
-28-29-30-31-32-33-34-35-36-37-38-39-40-41
•28-29-30-31-32-33-34-35-36-37-38-39-40-41
-27-28-29-30-31-32-33-34-35-36-37-38-39
-27-28-29-30-31-32-33-34-35-36-37-38
•27-28-29-30-31-32-33-34-35-36-37-38-39
-27-28-29-30-31-32-33-34-35-36-37-38
-27-28-29-30-31-32-33-34-35-36-37
-27-28-29-30-31-32
-27-28-29-30
-27-28-29-30
-27-28
-27-28
TOTAL NON-BRAZILIAN NALC SCENES = 801
(* =PATH/ROWS PURCHASED FROM EOSAT USING EDC BROKERAGE AGREEMENT)
133
-------
EPA 1992-1993 MSS DATA TRIPLICATE PRIORITIES BY PATH/ROW
Priorities are indicated by numbers following the "#" symbol.
1993 efforts
#1 Southeastern Mexico 39 scenes.
18/45-46
19/45-46-47
20/45-46-47-48-49
21/46-47-48-49-50
22/47-48-49
23/47-48-49
24/46-47-48-49
25/45-46-47-48-49
26/44-45-46-47-48
27/45-46-47-48
#1 Chesapeake Bay Watershed 21 scenes,
14/30-31-32-33-34
15/30-31-32-33-34
16/30-31-32-33-34
17/31-32-33-34
18/33-34
#1 Oregon Transect 2 scenes,
45/29
46/29
Priority total = 62 scenes
134
-------
#2 Central America
10/54-55
11/54-55
12/53-54-55
13/54-55
14/53-54
15/50-51-52-53
16/49-50-51-52-53
17/49-50-51-52
18/49-50-51
19/48-49-50-51
20/50
32 scenes.
#2 Caribbean
48 scenes,
1/49-50-51-52
2/48
4/47-48
5/47-48
6/47
7/46-47
8/46-47-48
9/45-46-47
10/44-45-46-47
11/43-44-45-46-47-48
12/43 -45-46-47-48
13/42-43-44-45-46
14/41-42 -44-45
15/44-45
16/44-45
17/44-45
#2 Hawaii
8 scenes.
62/46-47
63/46-47
64/45-46
65/45
66/45
Priority total =88 scenes,
135
-------
$3 US Great Lakes Watershed
35 scenes.
15/29
17/30
18/31-32
19/31-32
20/29-30-31-32
21/28-29-30-31
22/27-28-29-30-31
23/27-28-29-30-31
24/27-28-29-30
25/26-27-28
26/27-28
27/27-28
(note that some triplicates made for the Chesapeake Bay Watershed
also include portions of the US Great Lakes Watershed).
#3 US/Mexico Border
20 scenes.
26/42-43
27/42
28/40-41
29/40
30/40
31/40
32/39
33/38-39
34/38
35/38
36/38
37/38
38/38
39/37-38
40/37-38
#3 Southeastern US
16/36-37
17/35-36-37
18/35-36-37 -39
19/34-35-36-37
20/34-35-36-37
21/35-36
Priority total = 74 scenes.
19 scenes,
136
-------
#4
Mexico Forested Regions 37 scenes,
28/46-47
29/47
30/45-46-47
31/44
32/40-41-42-43
33/40-41-42-43-44
34/39-40-41-42-43-44
35/39-40-41-42-43
36/39-40-41-42
37/39-40-41
38/39-40
39/39
#4 Western USA
130 scenes.
31/39
33/33-34-35-36-37
34/31-32-33-34-35-36-37
35/31-32-33-34-35-36-37
36/29-30-31-32-33-34-35-36-37
37/29-30-31-32-33-34-35-36-37
38/29-30-31-32-33-34-35-36-37
39/29-30-31-32-33-34-35-36
40/26-27-28-29-30-31-32-33-34-35-36
41/26-27-28-29-30-31-32-33-34-35-36-37
42/26-27-28-29-30-31-32-33-34-35-36
43/26-27-28-29-30-31-32-33-34-35-36
44/26-27-28-29-30-31-32-33-34-35
45/26-27-28 -30-31-32-33
46/26-27-28 -30-31-32
47/26-27-28-29-30
48/26-27
Priority total = 167 scenes
Year total = 391 scenes.
137
-------
1994 efforts
#5
Alaska
180 scenes.
54/21-
55/21-
56/20-
57/19-
58/19-
59/18-
60/19
61/18
62/17-
63/17-
64/16-
65/15-
66/14-
67/13-
68/12-
69/11-
70/11-
71/11-
72/11-
73/11-
74/10-
75/10-
76/10-
77/10-
78/10-
79/10-
80/10-
81/10-
82/10-
83/12-
22
22
21
20-21
20
19-20
18
18
17-18
16-17-18
15-16-17-
14-15-16-
13-14-15-
12-13-14-
12-13-14-
12-13-14-
12-13-14-
12-13-14-
11-12-13-
11-12-13-
11-12-13-
11-12-13-
11-12-13-
11-12-13-
11-12-13-
11-12-13-
11-12-13-
13-14
18
17-18
16-17-
15-16-
15-16-
15-16-
15-16-
15-16-
14-15-
14-15-
14-15-
14-15-
14-15-
14-15-
14-15
14-15
14
18
17-18-19-20
17-18-19-20
17-18-19-20
17-18-19-20-21
17-18-19-20-21
16-17-18-19- -21-22
16-17-18-19- -22
16-17-18-19- -22
16-17-18- -23
16-17-18- -23
16-17-18
138
-------
#6 Eastern and Southeastern US 88 scenes,
06/38
10/29
11/27-28-29 -31
12/27-28-29-30-31
13/28-29-30-31-32
14/29- 35-36
15/35-36-37 -41-42-43
16/35- 38-39-40-41-42
17/38-39-40-41
18/38
19/33 -38-39
20/33 -38-39
21/32-33-34 -37-38-39-40
22/32-33-34-35-36-37-38-39-40
23/32-33-34-35-36-37-38-39-40
24/31-32-33-34-35-36-37-38-39
25/29-30-31-32-33-34-35-36-37-38-39-40
#7 Non-Forested Mexico 19 scenes,
27/43-44
28/42-43-44-45
29/41-42-43-44-45-46
30/41-42-43-44
31/41-42-43
#8 Midwest and Great Plains
123 scenes.
26/29-
27/29-
28/26-
29/26-
30/26-
31/26-
32/26-
33/26-
34/26-
35/26-
36/26-
37/26-
38/26-
39/26-
30-31-
30-31-
27-28-
27-28-
27-28-
27-28-
27-28-
27-28-
27-28-
27-28-
27-28
27-28
27-28
27-28
32-33
32-33
29-30
29-30
29-30
29-30
29-30
29-30
29-30
29-30
-34-35-36-37-38-39-40-41
-34-35-36-37-38-39-40-41
-31-32-33-34-35-36-37-38-39
-31-32-33-34-35-36-37-38-39
-31-32-33-34-35-36-37-38-39
-31-32-33-34-35-36-37-38
-31-32-33-34-35-36-37-38
-31-32
Year total = 410 scenes.
Grand total = 801 scenes.
139
-------
Appendix IV:
Triplicate Image Selection for all Path/Rows
Caribbean
Central America
Chesapeake Bay Watershed
Mexico
Hawaii
Alaska
Western US
Eastern and Southern US
Midwest and Great Plains US
140
-------
The following lists provide suggested triplicate images for
production. The entries are listed by region, and identified by
the path / row scene identification numbers for Landsat World
Reference System 2 (WRS-2). Also listed are the selected scene
dates, Landsat scene identification numbers, and for 70's epoch
scenes the WRS-1 identifiers that are- correlated to the WRS-2
scenes are provided.
Some comments are included. They include: suggestions as to
making Reduced Cloud Composites, "Composite" or "Composite with" or
"Composite 3" when a third image may be necessary; suggestions for
EDC to examine the images again, "EROS Check"; suggestions that a
second WRS-1 scene may be unnecessary, "Second WRS-1 scene not
required ?"; indications that the microfiche image is unsuitable to
judge image quality, "fiche missing" or "bad fiche" or "black
fiche" or "faded fiche"; or suggestions to check for available
scenes using the EDC metadata system, "GLIS all 90's" or "GLIS for
ID".
141
-------
NALC MSS Triplicates - Caribbean
Path 01 Row 49
01/49
92/04/01
86/04/25
86/03/24
72/10/14
LM84354713304xO
LM85078513512xO
LM85075313521xO
LM8108314000500
Second WRS-1 not required?
Path 01 Row 50
92/09/08
92/07/22
86/01/19
86/01/03
251/50 73/03/24
LM84370713361xO
LM84365913345xO
LM85068913535xO
LM85067313541xO
LM8124413553500
Second WRS-1 not required?
Path 01 Row 51
92/04/17
86/01/19
86/01/03
251/51 73/03/24
LM84356313320xO
LM85068913542xO
LM85067313544xO
LM8124413560500
Second WRS-1 not required?
Composite with
Composite with
Composite with
Composite with
Path 01 Row 52
92/04/17
86/01/19
86/01/03
251/52 75/12/18
LM84356313322xO
LM85068913544xO
LM85067313550xO
LM8233013415500
Composite with
Second WRS-1 not required?
142
-------
Path 02 Row 48
01/48
92/03/07
86/03/15
86/01/10
73/02/17
LM84352213361xO
LM85074413580xO
LM85068013593xO
LM8120914001500
Second WRS-1 not required?
Composite with
Path 04 Row 47
04/47
92/08/12
85/01/21
73/03/10
73/01/15
LM84368013525xO
LM85032614142xO
LM8123014171500
LM8117614162500
Composite with
Second WRS-1 not required?
Path 04 Row 48
04/48
92/08/12
92/06/25
83/02/09
73/03/10
LM84368013531xO
LM84363213514xO
LM84020814134xO
LM8123014174500
Second WRS-1 not required?
Composite with
Path 05 Row 47
92/05/31
83/06/24
LM84360713564xO
LM84034314200xO
04/47
05/47
73/03/10
73/01/15
78/03/30
LM8123014171500
LM8117614162500
LM82116313442xO
Composite with
143
-------
Path 05 Row 48
04/48
05/48
Path. 06 Row 47
06/47
92/08/19
85/01/12
73/03/10
78/03/30
92/04/20
92/04/04
92/03/03
86/01/06
79/01/31
LM84368713593xO
LM85031714205xO
LM8123014174500
LM82116313445x0
LM84356614012xO
LM84355014005x0
LM84351814003xO
LM85067614235xO
LM82152414062xO
Second WRS-1 not required?
Path 07 Row 46
No 1990's
86/01/13 LM85074714281xO
07/46 79/02/01 LM82152414052xO
Second WRS-1 not required?
Path 07 Row 47
07/47
92/05/13
85/05/02
73/12/08
LM84358914082xO
LM85042714324xO
LM8150314311500
Composite 3
Second WRS-1 not required?
144
-------
Path 08 Row 46
08/46
09/46
Path 08 Row 47
08/47
09/47
Path 08 Row 48
08/48
92/05/04
86/07/31
72/08/28
75/02/06
92/05/20
92/05/04
86/01/20
73/12/09
75/02/06
92/05/20
86/03/09
73/12/09
LM84358014135xO
LM85088214303xO
LM8103614384500
LM8201514332500
LM84359614145x0
LM84358014141xO
LM85069014360xO
LM8150414365500
LM8201514334500
LM84359614151xO
LM85073814352xO
LM8150414372500
Second WRS-1 not required.
Path 09 Row 45
09/45
92/07/30
86/01/11
78/09/03
LM84366714223xO
LM85068114413xO
LM83018214333xO
Composite with
Second WRS-1 not required?
145
-------
Path 09 Row 46
09/46
10/46
92/06/12
86/01/11
75/02/06
74/01/16
LM84363514215xO
LM85068114420xO
LM8201514332500
LM8154214470500
Path 09 Row 47
09/47
10/47
Path 10 Row 44
11/44
92/08/15
86/09/08
74/02/06
74/01/16
t
92/09/07
86/08/14
76/03/28
LM84368314235xO
LM85092114354xO
LM8201514334500
LM8154214472500
LM84370614293xO
LM85089614413xO
LM8243114393500
Second WRS-1 not required?
Path 10 Row 45
10/45
92/05/02
85/06/08
78/11/15
LM84357814254xO
LM85046414502xO
LM83025514394xO
Second WRS-1 not required?
146
-------
Path 10 Row 46
11/46
92/06/03
92/05/02
85/06/08
76/03/28
LM84361014272xO
LM84357814260xO
LM85046414505xO
LM8243114402500
Second WRS-1 not required.
Path 10 Row 47
10/47
92/03/31
85/06/08
74/01/16
LM84354614253xO
LM85056414511xO
LM8154214472500
Second WRS-1 not required?
Path 11 Row 43
DOUBLET !!!
86/03/14 LM85074314514xO
12/43 73/04/05 LM8125615013500
Second WRS-1 not required?
Path 11 Row 44
11/44
92/04/23
85/04/28
76/03/28
LM84356914311xO
LM85042314561xO
LM8243114393500
Second WRS-1 not required?
Composite with
147
-------
Path 11 Row 45
12/45
Path. 11 Row 46
11/46
12/46
Path 11 Row 47
12/47
92/08/29
85/02/07
80/06/24
92/04/23
86/02/10
76/03/28
73/02/10
.92/08/13
87/05/04
73/02/10
LM84369714354x0
LM85034314565xO
LM82198014482xQ
LM84356914320xO
LM85071114534xO
LM8243114402500
LM8120215023500
LM843 681143 60x0
IiM85115914504xO
LM8120215023500
Second WRS-1 not required?
Path 11 Row 48
92/02/19
92/03/22
84/08/15
LM84350514324xO
LM84353714315XO
LM850167145572xO
79/02/15
78/11/17
12/48
Second WRS-1 not required?
LM83034714521xO
LM83025714520xO
Composite with
OR
EROS CHECK
148
-------
Path 12 Row 43
12/43
92/03/29
86/01/16
73/04/05
LM84354414361xO
LM85068614591xO
LM8125615013500
Second WRS-1 not required?
Path 12 Row 45
12/45
13/45
Path 12 Row 46
12/46
13/46
Path 12 Row 47
12/47
13/47
92/03/29
92/03/13
86/02/17
80/06/24
78/12/24
92/03/29
86/02/17
73/02/10
73/01/06
92/07/19
86/07/27
73/02/10
73/01/06
LM84354414370xO
LM84352814365xO
LM85071814591xO
LM82198014482xO
LM83029414563xO
LM84354414372xO
LM85071814594xO
LM8120215023500
LM8116715075500
LM84365614413xO
LM85087814554xO
LM8120215025500
LM8116715081500
Composite with
149
-------
Path 12 Row 48
92/04/30
87/02/04
LM84357614391xO
LM85107014541xO
79/02/15
78/10/30
12/48
Second WRS-1 not required?
LM83034714521xO
LM83023914522xO
Path 13 Row 42
14/41
14/42
Path 13 Row 43
14/43
92/03/20
86/04/29
72/10/09
73/03/20
92/03/20
86/04/29
73/03/20
LM84353514415xO
LM85078915024xO
LM8107815111500
LM8124015123500
LM84353514422xO
LM85078915030xO
LM8124015130500
Second WRS-1 not required?
Path 13 Row 44
13/44
92/02/25
86/04/29
72/09/02
LM85291715023xO
LM85078915033xO
LM8104115063500
OR
EROS Check
Second WRS-1 not required?
150
-------
Path 13 Row 45
13/45
14/45
Path 13 Row 46
13/46
92/02/25
86/02/24
78/12/24
75/06/26
92/02/25
86/02/24
78/12/24
LM85291715030x0
LM85072515052xO
LM83029414563xO
LM8506814501500
LM85291715032xO
LM85072515054xO
LM83029414563xO
Second WRS-1 not required?
Path 14 Row 41
15/41
92/03/19
85/05/03
73/04/26
LM85294015073xO
LM85042815133x0
LM8127715174500
Second WRS-1 not required?
Path 14 Row 42
14/42
15/42
92/03/19
86/04/20
73/03/20
75/02/03
LM85294015080xO
LM85078015090xO
LM8124015123500
LM8192515015500
151
-------
Path 14 Row 44
14/44
15/44
Path 14 Row 45
14/45
15/45
Path 15 Row 44
15/44
16/44
92/08/02
92/07/01
86/09/27
86/09/11
75/06/26
80/09/07
92/08/02
92/07/01
87/09/30
75/06/26
76/04/01
92/03/10
86/02/22
80/09/07
72/08/18
LM84367014531xO
LM84363814521xO
LM85092415051xO
LM85092415051xO
LM8506814501500
LM82205515061xO
LM84367014533xO
LM84363814523xO
LM85130815121xO
LM8506814501500
LM8243515025500
LM85293115150xO
LM85072315172xO
LM82205515061xO
LM8102615235500
Composite with
Composite with
Composite with
Path 15 Row 45
16/44
92/07/24
86/08/01
72/08/18
LM84366114592xO
LM85088315132xO
LM8102615235500
Second WRS-1 not required?
152
-------
Path 16 Row 44
16/44
17/44
Path 16 Row 45
17/45
92/05/28
86/04/18
72/08/18
79/02/02
92/07/15
92/06/13
85/03/30
85/03/14
73/07/09
LM84360415032xO
LM85077815222xO
LM8102615235500
LM83033415191xO
LM84365215052xO
LM84362015041xO
LM85039415274xO
LM85037815275xO
LM8135115301500
Second WRS-1 not required.
Path 17 Row 44
18/44
92/09/08
86/07/30
72/10/31
LM84370715124xO
LM85088115252xO
LM8110015354500
Second WRS-1 not required?
Path 17 Row 45
18/45
92/03/08
86/07/30
78/11/05
LM85292915274xO
LM85088115254xO
LM83024515253xO
Composite with
Composite with
Second WRS-1 not required?
153
-------
NALC MSS Triplicates - Central American
Path 10 Row 54 To be selected
Path 10 Row 55 To be selected
Path 11 Row 54
92/05/25 LM84360114362xO
85/02/07 LM85034315003xO
11/54 No low cloud data.
12/54 74/02/23 LM8158015004500
Path 11 Row 55
92/04/15
92/02/27
85/02/23
85/02/07
LE85296714542xO
LE85291914543xO
LM85035915005xO
LM85034315005xO
11/55
12/55
Path 12 Row 53
No low cloud 1970's data.
No low cloud 1970's data.
92/04/22
86/02/01
86/01/16
LE85297414594xO
LM85068615031xO
LM85068615031xO
12/53
No low cloud 1970's data.
Path 12 Row 54
12/54
13/54
92/04/22
87/01/19
87/01/03
74/02/23
74/02/24
LE85297415000xO
LM85105414560xO
LM85103814553x0
LM8158015004500
LM8158115063500
Composite with
Composite with
Composite with
Composite with
154
-------
Path 12 Row 55
12/55
13/55
Path 13 Row 54
92/04/22 LE85297415003xO
85/03/18 LM85038215070xO
No low cloud 1970'a data.
73/03/19 LM8123915121500
13/54
14/54
Path 13 Row 55
13/55
92/03/28
92/03/12
87/03/15
74/02/24
74/01/20
92/03/12
86/03/28
73/03/19
LE85294915063xO
LE85293315063xO
LM85110915041xO
LM8158115063500
LM8154615130500
LE85293315070xO
LM85075715084xO
LM8123915121500
Second WRS-1 not required?
Path 14 Row 53
15/53
92/05/14
92/03/27
87/02/18
87/02/02
74/05/09
LE84359014541xO
LE84354214523xO
LM85108415091xO
LM85106815083xO
LM8165515160500
Composite with
Composite with
Composite with
Second WRS-1 not required???
155
-------
Path. 14 Row 54
14/54
15/54
92/04/20
92/03/03
87/02/18
87/02/02
87/01/01
79/01/30
79/01/22
LE85297215123xO
LE85292415124xO
LM85108415093xO
LM85106815085xO
LM85103615075xO
LM83033115061xO
LM82146114582xO
Composite 3
Path 15 Row 50
16/50
92/08/09
86/02/22
86/02/06
78/04/01
LE84367715015xO
LM85072315194xO
LM85070715200xO
LM83002715141x0
Second WRS-1 not required.
Path 15 Row 51
16/51
92/05/05
87/10/07
78/04/01
LE84358114591xO
LM85131515205xO
LM83002715144xO
Second WRS-1 not required.
Path 15 Row 52
16/52
92/05/05
85/04/24
85/04/08
78/04/01
LE84358114593xO
LM85041915241xO
LM85040315242xO
LM83002715150x0
Second WRS-1 not required.
Composite with
Composite with
156
-------
Path 15 Row 53
15/53
16/53
Path 16 Row 49
16/49
17/49
Path 16 Row 50
16/50
17/50
Path 16 Row 51
16/51
17/51
92/05/13
86/02/06
74/05/09
75/03/03
92/04/02
86/01/28
78/04/19
78/04/02
92/04/02
92/03/01
86/01/28
78/04/01
76/03/16
92/04/26
92/03/01
86/01/28
78/04/01
78/04/02
LE85299515180xO
LM85072315205xO
LM8165515160500
LM8204015163500
LE85295415230xO
LM85069815260xO
LM83004515140xO
LM83002815193xO
LM85295415232xO
LM85292215233xO
LM85069815262xO
LM83002715141xO
LM8241915165500
LM84357215045xO
LM85292215235xO
LM85069815265xO
LM83002715144xO
LM83002815202xO
Composite with
Composite with
157
-------
Path 16 Row 52
16/52
17/52
Path 16 Row 53
16/53
17/53
Path 17 Row 49
92/03/01
86/04/02
78/04/01
78/04/02
92/03/01
86/03/01
75/03/03
78/04/02
17/49
18/49
Path 17 Row 50
92/04/01
92/03/08
86/03/08
78/04/02
73/12/19
17/50
18/50
92/03/08
85/02/01
76/03/16
75/03/23
LE85292215242xO
LM85076215255xO
LM83002715150xO
LM83002815205xO
LE85292215244xO
LM85073015265xO
LM8204015163500
LM83002815211XO
LE84354715093XO
LE85292915292xO
LM85073715312xO
LM83002815193xO
LM8151415345500
LE85292915294xO
LM85033715355xO
LM8241915165500
LM8206015263500
Composite with
158
-------
Path 17 Row 51
17/51
18/51
Path 17 Row 52
17/52
92/04/01
92/03/08
85/02/01
78/04/02
72/12/24
92/04/01
86/02/20
78/04/02
LE84354715102xO
LE85292915300xO
LM85033715362xO
LM83002815202xO
LM8115415385500
LE84354715104xO
LM85072115325xO
LM83002815205xO
Composite with
Second WRS-1 not required?
Path 18 Row 49
18/49
19/49.
Path 18 Row 50
18/50
19/50
92/03/23
86/03/15
73/12/19
78/12/21
92/03/23
86/03/15
75/03/23
74/03/02
LE84353815153xO
LM85074415372xO
LM8151415345500
LM82142915180xO
LE84353815160xO
LM85074415375xO
LM8206015263500
LM8158715391500
159
-------
Path 18 Row 51
18/51
19/51
Path 19 Row 48
92/03/23
86/03/31
72/12/24
75/03/06
92/06/02
92/03/30
86/08/29
86/07/28
20/48 75/03/25
LE84353815162xO
LM85076015375xO
LM8115415385500
LM8204315323500
LM84360915234xO
LM84354515213xO
LM85091115381xO
LM85087915392xO
LM8206215371500
Second WRS-1 not required.
Path 19 Row 49
20/49
92/05/01
92/03/30
87/12/14
75/03/25
LE84357715225xO
LE84354515215xO
LM84197715394xO
LM8206215373500
Second WRS-1 not required.
Path 19 Row 50
20/50
92/05/01
86/04/07
78/12/31
LE84357715231xO
LM85076715433xO
LM83030115385xO
Composite with
Composite with
Composite with
Second WRS-1 not required.
160
-------
Path 19 Row 51
20/51
92/05/01
86/03/22
73/02/18
LE84357715234xO
LM85075115441xO
LM8121015503500
Second WRS-1 not required?
Path 20 Row 50
21/50
92/03/21
92/04/22
86/03/13
76/03/02
LE84353615282xO
LE84356815291xO
LM85074215501xO
LM8240515402500
Composite with
Second WRS-1 not required?
161
-------
NALC MSS Triplicates - Chesapeake Bay Watershed
Path 14 Row 30
15/30
91/09/09
86/08/26
73/07/07
LM85274815025xO
LM85090815001xO
LM8134915123500
Second WRS-1 not required?
Path 14 Row 31
15/31
Path 14 Row 32
15/32
91/06/21
87/06/10
73/07/07
73/07/24
I
91/06/21
87/06/10
73/08/30
LM85266815022x0
LM85119615035xO
LM8134915125500
LM8136615065500
LM85266815025xO
LM85119615041xO
LM8140315123500
Second WRS-1 not required?
Path 14 Row 33
15/33
91/09/09
87/06/10
73/08/30
73/08/29
LM85274815040xO
LM85119615044xO
LM8140315125500
LM8140215071500
162
-------
Path 14 Row 34
15/34
91/09/09
85/06/20
73/08/12
LM85274815043xO
LM85047615104xO
LM8138515134500
Second WRS-1 not required?
Path 15 Row 30
16/30
17/30
90/09/13
87/06/17
73/08/30
72/08/19
LM85238715050xO
LM85120315095xO
LM8140315114500
LM8102715233500
Path 15 Row 31
16/31
90/08/12
87/06/17
73/07/08
LM85235515053xO
LM85120315101xO
LM8135015183500
Second WRS-1 not required?
Path 15 Row 32
16/32
90/07/27
87/06/17
73/07/03
LM85233915060xO
LM85120315104xO
LM8135015190500
Second WRS-1 not required?
163
-------
Path 15 Row 33
90/06/25
87/06/17
16/33 73/07/08
LM85230715063xO
LM85120315110xO
LM8135015192500
Second WRS-1 not required?
Path 15 Row 34
90/06/25
85/08/14
16/34 73/08/31
LM85230715070xO
LM85053115163xO
LM8140415190500
Second WRS-1 not required?
Path 16 Row 30
91/08/22
86/07/23
17/30 74/07/22
LM85273015150xO
LM85087415135xO
LM8172915153500
Path 16 Row 31
91/08/07
86/09/09
17/31 72/09/06
LM85274615154xO
LM85092215123xO
LM8104515240500
164
-------
Path 16 Row 32
17/32
91/09/07
86/09/09
72/09/06
LM85274615160xO
LM85092215125xO
LM8104515243500
Second WRS-1 not required?
Path 16 Row 33
17/33
91/09/07
86/09/09
76/09/12
LM85274615162xO
LM85092215132xO
LM8259915045500
Second WRS-1 not required?
Path 16 Row 34
17/34
90/08/30
87/06/08
75/08/22
LM85234615130xO
LM85119415172xO
LM8512514593500
Second WRS-1 not required?
Path 17 Row 31
18/31
92/06/10
87/06/15
75/06/04
LM85265715204xO
LM85120115223xO
LM8215015184500
165
-------
Path. 17 Row 32
91/08/13
85/08/12
18/32 75/06/21
LM85272115215xO
LM85052915280xO
LM8215015190500
Path 17 Row 34
92/09/14
86/07/14
18/34 73/09/02
LM85275315230xO
LM85086515215xO
LM8140615303500
166
-------
NALC MSS Triplicates - Mexico
Path 18 Row 45
To be selected
Path 18 Row 46
To be selected
Path 19 Row 45
20/45
90/11/04
86/07/28
86/07/12
76/01/07
LM84303315422xO
LM85087915381xO
LM85086315384xO
LM8235015333500
Second WRS-1 not required?
Path 19 Row 46
20/46
92/05/01
86/12/19
86/12/03
76/02/12
LE84357715214xO
LM85102315351xO
LM85100715344xO
LM8238615332500
Second WRS-1 not required?
Path 19 Row 47
EROS CHECK
Composite
Composite
20/47
90/11/20
84/11/11
75/12/02
LM84304915424xO
LM85025515463xO
LM8231415344500
Second WRS-1 not required?
167
-------
Path 20 Row 45
21/45
22/45
Path 20 Row 46
21/46
22/46
90/04/17
86/03/13
74/03/04
75/11/16
92/05/08
86/03/13
76/04/07
76/02/14
72/08/24
LM84283215531xO
LM85074215481xO
LM8158915483500
LM8229815453500
LE84358415281xO
LM85074215483xO
LM8244115373500
LM8238815445500
LM8103215585500
OR
EROS CHECK
Path 20 Row 47
21/47
22/47
Path 20 Row 48
90/04/17
86/04/14
86/03/13
78/01/15
74/02/15
21/48
90/04/17
86/04/14
74/02/14
LM84283215540xO
LM85077415482xO
LM85074215490XO
LM82108915121xO
LM8103215592500
LM84283215542xO
LM85077415484xO
LM8157115500500
Composite
Second WRS-1 not required?
168
-------
Path 20 Row 49
No 90's data? Doublet not triplet.
86/03/13 LM85074215495xO
21/49 79/02/15 LM83033815441xO
Path 21 Row 46
To be selected
Path 21 Row 47
22/47
23/47
Path 21 Row 48
92/08/27
90/12/20
84/11/25
74/02/15
78/09/17
80/11/17
22/48
23/48
92/08/27
84/11/25
74/02/15
75/12/05
LE85310115512xO
LM84307915540xO
LM85026915590xO
LM8157215552500
LM83019615545xO
LM83098815410xO
LE85310115515xO
LM85026915592xO
LM8157215554500
LM8231715522500
OR
EROS CHECK
OR
EROS CHECK
169
-------
Path 21 Row 49
22/49
23/49
Path 21 Row 50
90/12/20
86/01/15
74/02/15
75/12/05
22/50
90/10/01
90/08/06
86/01/15
74/02/15
LE84307915545xO
LM85068515571xO
LM8157215561500
LM8231715524500
LM84299915574xO
LM85234915501xO
LM85068515574xO
LM8157215563500
EROS CHECK
Second WRS-1 not required?
Path 22 Row 47
23/47
24/47
Path 22 Row 48
92/04/20
86/04/12
78/09/17
72/12/30
23/48
24/48
90/11/25
90/11/09
84/01/09
80/11/17
76/05/16
LE84356615401xO
LM85077216004xO
LM83019615545xO
LM8116016112500
LM84305416013xO
LM84303816015xO
LM84054216040xO
LM83098815412xO
LM8248015542500
Composite
170
-------
Path 22 Row 49
23/49
24/49
92/04/04
92/03/03
86/03/11
75/12/05
73/01/17
LE84355015403xO
LE84351815401xO
LM85074016021xO
LM8231715524500
LM8117816115500
Composite
Path 23 Row 47
24/47
25/47
90/12/18
86/03/18
72/12/30
76/04/11
76/03/24
LE84307716063xO
LM85074716072xO
LM8116016112500
LM8244516004500
LM8242716012500
EROS CHECK
Composite
Path 23 Row 48
24/48
25/48
90/12/18
86/05/05
86/04/03
76/05/16
79/02/28
LE84307716065xO
LM85079516064xO
LM85076316073xO
LM8248015542500
LM83036016063xO
EROS CHECK
Composite
Path 23 Row 49
24/49
25/49
92/04/11
86/04/19
73/01/17
72/12/30
73/11/20
LE84355715465xO
LM85077916073xO
LM8117816115500
LM8116016121500
LM8148516153500
Composite
171
-------
Path 24 Row 46
To be selected
Path 24 Row 47
25/47
26/47
Path 24 Row 48
90/08/27
87/08/19
87/08/03
73/02/05
73/05/25
25/48
26/48
92/04/18
92/04/02
86/04/10
86/03/25
79/03/09
79/02/28
73/01/19
73/05/25
Path 24 Row 49
92/04/18
86/03/25
25/49 73/11/20
26/49 74/02/01
Path 25 Row 45
89/10/10
86/01/27
27/45 72/11/09
LM85237016073xO
LM85126616134xO
LM85125016132xO
LM8119716172500
LM8130616231500
LE84356415530xO
LE84354815522xO
LM85077016133xO
LM85075416135xO
LM82150715551xO
LM83036016063xO
LM8118016231500
LM8130616233500
LE84356415532xO
LM85075416141xO
LM8148516153500
LM8155816192500
LM84265916261xO
LM85069716200xO
LM8110916275500
Composite
Composite
Composite
Composite
OR
EROS CHECK
172
-------
Path 25 Row 46
26/46
27/46
89/11/11
84/12/23
73/05/25
73/03/14
73/04/20
LM84267516263xO
LM85029716233xO
LM8130616224500
LM8123416230500
LM8127116284500
Composite
Path 25 Row 47
26/47
27/47
Path 25 Row 48
92/05/27
86/04/17
86/03/16
73/05/25
73/02/27
26/48
27/48
Path 25 Row 49
90/12/16
86/01/27
73/01/19
73/05/25
79/02/03
26/49
27/49
90/08/02
84/12/23
74/02/01
74/02/20
LE84360316001xO
LM85077716191xO
LM85074516195xO
LM8130616231500
LM8119916285500
LE84307516192xO
LM85069716212xO
LM8118016231500
LM8130616233500
LM82147316050xO
LM85234516143xO
LM85029716244xO
LM8155816192500
LM8157716245500
Composite
EROS CHECK
OR
EROS CHECK
173
-------
Path 26 Row 42
28/42
92/07/29
92/06/27
87/08/17
73/09/12
LE85312016200xO
LE85304016211xO
LM85126416240xO
LM8141616310500
Second WRS-1 not required.
Path 26 Row 43
28/43
90/10/20
85/10/30
74/09/07
LM84301816252xO
LM85060816271xO
LM8177616221500
Second WRS-1 not required.
Path 26 Row 44
27/44
28/44
Path 26 Row 45
27/45
28/45
90/10/20
85/10/30
72/11/09
73/05/27
90/10/20
85/10/30
72/11/09
73/05/27
LM84301816254xO
LM85060816273xO
LM8110916273500
LM8130816332500
LM84301816260xO
LM85060816275xO
LM8110916275500
LM8130816334500
Composite
EROS CHECK
174
-------
Path 26 Row 46
27/46
28/46
Path 26 Row 47
27/47
28/47
Path 26 Row 48
27/48
28/48
Path 27 Row 42
90/11/21
85/10/30
73/04/20
73/11/23
r
89/03/07
85/01/31
73/02/07
73/11/23
J
92/05/18
86/04/24
79/02/03
73/03/16
LM84305016253xO
LM85060816282xO
LM8127116284500
LM8148816312500
LM84242616325xO
LM85033616302xO
LM8119916285500
LM8148816315500
LE84359416063xO
LM85078416253xO
LM82147316050xO
LM8123616352500
Use as much of 73/03/16 as possible.
28/42
29/42
91/07/18
86/07/04
73/09/12
74/03/30
LE85269516273xO
LM85085516271xO
LM8141616310500
LM8161516324500
175
-------
Path. 27 Row 43
28/43
29/43
Path 27 Row 44
92/04/07
86/05/01
86/03/14
73/05/27
73/03/17
28/44
29/44
Path 27 Row 45
92/05/09
92/04/07
86/03/14
73/05/27
73/05/10
28/45
29/45
92/05/09
83/05/17
73/05/27
76/03/28
Path 27 Row 46
28/46
29/46
92/05/25
92/05/09
86/03/14
73/04/21
76/03/28
LE84355316090xO
LM85079116293xO
LM85074316304xO
LM8130816325500
LM8123716390500
LE84358516104xO
LE84355316093xO
LM85074316310xO
LM8130816332500
LM8129116391500
LE84358516110xO
LM84030516350xO
LM8130816334500
LM8243116231500
LE84360116120xO
LE84358516113xO
LM85074316315xO
LM8127216343500
LM8243116234500
Composite
Composite
Composite
176
-------
Path. 27 Row 47
28/47
29/47
Path 27 Row 48
28/48
29/48
92/09/06
86/03/14
73/11/23
76/03/28
J
DOUBLET
86/03/14
73/03/16
76/03/28
Path 28 Row 40
30/40
92/06/25
86/07/27
73/05/29
LE85311116282xO
LM85074316321xO
LM8148816315500
LM8243116240500
LM85074316324xO
LM8123616352500
LM8243116243500
LE85303816324xO
LM85087816315xO
LM8131016431500
Second WRS-1 not required?
Path 28 Row 41
29/41
30/41
92/06/25
86/07/27
74/09/08
73/05/29
LE85303816331xO
LM85087816321x0
LM8177716271500
LM8131016433500
177
-------
Path 28 Row 42
29/42
30/42
Path 28 Row 43
29/43
30/43
Path 28 Row 44
29/44
30/44
90/03/16
83/04/22
74/03/20
76/04/16
I
90/03/16
83/04/22
73/03/17
74/06/29
t
92/04/30
83/04/22
73/05/10
73/03/18
Path 28 Row 45
29/45
30/45
90/03/16
86/04/06
76/03/28
76/03/20
LM85220616305xO
LM84028016401xO
LM8161516324500
LM8245016271500
LM85220616311xO
LM84028016403xO
LM8123716390500
LM8170616361500
LE84357616162xO
LM84028016410xO
LM8129116391500
LM8123816451500
LM85220616320xO
LM85076616371xO
LM8243116231500
LM8533616053500
178
-------
Path 28 Row 46
29/46
30/46
Path 28 Row 47
92/04/30
86/04/06
76/03/28
73/05/11
76/03/20
29/47
30/47
Path 29 Row 39
31/39
90/03/16
86/04/06
76/03/28
76/03/20
)
91/07/16
85/07/31
73/07/05
LE84357616171xO
LM85076616373xO
LM8243116234500
LM8129216455500
LM8533616055500
OR
EROS CHECK
LM85220616325xO
LM85076616375xO
LM8243116240500
LM8533616062500
LE85269316384XO
LM85051716451xO
LM8134716480500
Second WRS-1 not required?
Path 29 Row 40
31/40
92/04/21
86/03/12
75/03/18
LE84356716204xO
LM85074116415xO
LM8205516370500
Second WRS-1 not required?
179
-------
Path 29 Row 41
31/41
Path 29 Row 42
31/42
32/42
92/04/21
86/03/12
73/12/14
i
92/04/21
86/04/14
76/09/08
76/05/06
Path 29 Row 43
30/43
31/43
Path 29 Row 44
30/44
31/44
92/04/21
86/04/13
73/07/22
72/08/15
I
92/04/21
86/04/1-3
73/03/18
76/03/21
LE84356716211xO
LM85074116421xO
LM8150916461500
LE84356716213xO
LM85077316420xO
LM8259516290500
LM8247016380500
LE84356716220xO
LM85077316422xO
LM8136416434500
LM8102316490500
LE84356716222xO
LM85077316424x0
LM8123816451500
LM8533716104500
180
-------
Path 29 Row 45
30/45
31/45
92/04/21
86/03/12
76/03/20
76/03/21
Path 29 Row 46
92/04/21
86/03/12
30/46 76/03/20
31/46 73/02/11
Path 29 Row 47
92/04/21
86/03/12
30/47 76/03/20
31/47 72/12/19
LE84356716224xO
LM85074116435xO
LM8533616053500
LM8533716111500
LE84356716231xO
LM85074116441xO
LM8533616055500
LM8120316513500
LE84356716233xO
LM85074116444xO
LM8533616062500
LM8114916514500
Path 30 Row 40
32/40
92/04/28
86/04/20
75/06/17
LE84357416271xO
LM85078016471xO
LM8214616424500
Second WRS-1 not required?
181
-------
Path 30 Row 41
32/41
92/04/28
86/04/20
73/03/02
LE84357416273xO
LM85078016473xO
LM8122216552500
Second WRS-1 not required?
Path 30 Row 42
32/42
92/04/28
86/04/20
76/05/06
LE84357416275xO
LM85078016480xO
LM8247016380500
Second WRS-1 not required?
Path 30 Row 43
32/43
92/05/14
86/04/20
76/05/06
LE84359016290xO
LM85078016482xO
LM8247016383500
Second WRS-1 not required?
Path 30 Row 44
32/44
90/05/25
86/04/20
73/03/02
LM84287016535xO
LM85078016485xO
LM8122216563500
Second WRS-1 not required?
182
-------
Path 30 Row 45
32/45
92/05/14
92/04/28
86/04/20
73/03/02
LE84359016295xO
LE84357416291xO
LM85078016491xO
LM8122216570500
Composite
Second WRS-1 not required?
Path 30 Row 46
32/46
92/05/30
92/05/14
86/04/20
73/03/02
LE84360616305xO
LE84359016301xO
LM85078016493xO
LM8122216572500
Composite
Second WRS-1 not required?
Path 30 Row 47
31/47
32/47
Path 31 Row 39
33/39
DOUBLET
85/05/19
72/12/19
76/05/24
)
92/06/30
86/06/14
72/07/30
LM85044416545xO
LM8114916514500
LM8248816394500
LE85304316505xO
LM85083516512xO
LM8100716590500
Second WRS-1 not required?
183
-------
Path 31 Row 40
33/40
92/05/21
87/06/17
74/05/27
LE84359716342xO
LM85120316524xO
LM8167316532500
Second WRS-1 not required?
Path. 31 Row 41
33/41
92/05/21
85/05/10
75/05/13
LE84359716344xO
LM85043516583xO
LM8211116484500
Second WRS-1 not required?
Path 31 Row 42
33/42
92/04/19
85/05/10
76/03/23
LE84356516335xO
LM85043516590xO
LM8533916211500
Second WRS-1 not required?
Path 31 Row 43
92/05/05
92/04/19
LE84358116345xO
LE84356516341xO
Composite
86/03/10 LM85073916552xO
33/43 76/03/23 LM8533916214500
Second WRS-1 not requied?
184
-------
Path 31 Row 44
33/44
92/05/05
92/04/19
86/03/10
73/03/03
LE84358116351xO
LE84356516344xO
LM85073916555xO
LM8122317022500
Second WRS-1 not required?
Path 32 Row 38
34/38
92/06/29
85/07/04
75/06/28
LE84363616405xO
LM85049017033xO
LM8507016420500
Second WRS-1 not required?
Path.32 Row 39
34/39
35/39
92/06/05
86/05/04
73/06/02
73/06/03
LE85301816572xO
LM85079416585xO
LM8131417053500
LM8131517112500
Composite
Path 32 Row 40
34/40
35/40
92/04/10
86/04/18
73/06/02
73/06/21
75/06/20
LE84355616385xO
LM85077816593xO
LM8131417060500
LM8133317113500
LM8214917000500
OR
EROS CHECK
185
-------
Path 32 Row 41
34/41
35/41
92/04/10
86/04/02
73/06/02
73/06/03
Path 32 Row 42
92/04/10
86/04/02
34/42 73/06/02
35/42 ' 75/07/08
Path 32 Row 43
34/43
35/43
92/04/10
84/04/04
73/04/09
73/02/15
LE84355616392xO
LM85076217002xO
LM8131417062500
LM8131517121500
LE84355616394xO
LM85076217004xO
LM8131417065500
LM8216717004500
LE84355616401xO
LM84062817022xO
LM8126017074500
LM8120717131500
Path 33 Row 38
35/38
36/38
92/06/04
86/07/30
73/06/21
74/06/17
LE84361116462xO
LM85088117015x0
LM8133317104500
LM8169417090500
186
-------
Path 33 Row 39
35/39
36/39
Path 33 Row 40
35/40
36/40
Path 33 Row 41
35/41
36/41
92/04/01
86/03/24
74/04/05
73/05/17
)
92/04/01
86/03/24
75/06/20
73/04/11
92/04/01
86/03/24
73/06/03
73/03/24
LE84354716442xO
LM85075317055xO
LM8162117054500
LM8129817171500
LE84354716445xO
LM85075317062xO
LM8214917000500
LM8126217175500
LE84354716451xO
LM85075317064xO
LM8131517121500
LM8124417182500
Path 33 Row 42
35/42
36/42
92/05/03
86/03/24
73/01/28
73/03/24
LE84357916464xO
LM85075317071xO
LM8118917123500
LM8124417184500
187
-------
Path 33 Row 43
35/43
92/06/04
86/03/24
73/02/15
LE84361116482xO
LM85075317073xO
LM8120717131500
Second WRS-1 not required.
Path 33 Row 44
36/44
92/05/03
86/03/24
73/04/11
LE84357916473xO
LM85075317075xO
LM8126217193500
Second WRS-1 not required?
Path 34 Row 38
36/38
37/38
92/06/11
86/05/18
74/06/17
73/06/05
LE85301617092xO
LM85080817102xO
LM8169417090500
LM8131717222500
Path 34 Row 39
36/39
37/39
92/06/11
83/06/03
73/05/17
73/06/05
LE84361816530xO
LM84032217155xO
LM8129817171500
LM8131717224500
188
-------
Path 34 Row 40
36/40
37/40
Path 34 Row 41
36/41
37/41
Path 34 Row 42
92/06/11
83/06/03
73/04/11
73/04/12
92/04/24
86/03/15
73/03/24
73/04/12
36/42
37/42
92/04/24
86/03/15
73/03/24
73/03/25
LE84361816533xO
LM84030617161xO
LM8126217175500
LM8126317234500
LE84357016521xO
LM85074417130xO
LM8124417182500
LM8126317240500
LE84357016523xO
LM85074417133xO
LM8124417184500
LM8124517243500
Path 34 Row 43
36/43
37/43
92/04/24
86/03/15
73/04/11
73/03/25
LE84357016530xO
LM85074417135xO
LM8126217191500
LM8124517245500
189
-------
Path 34 Row 44
36/44
Path 35 Row 38
37/38
38/38
Path 35 Row 39
92/04/24
86/03/15
73/04/11
5
92/06/02
86/06/10
73/06/05
74/06/01
37/39
38/39
92/06/02
83/04/23
73/06/05
73/06/06
LE84357016532xO
LM85074417141xO
LM8126217193500
LE84360916583xO
LM85083117155xO
LM8131717222500
LM8167817210500
LE84360916590xO
LM84028117222xO
LM8131717224500
LM8131817283500
Path 35 Row 40
37/40
38/40
92/05/01
83/04/23
73/04/12
75/03/16
LE84357716581xO
LM84028117224xO
LM8126317234500
LM8196617111500
190
-------
Path 35 Row 41
37/41
38/41
Path 35 Row 42
37/42
38/42
Path 35 Row 43
92/05/01
83/04/23
73/04/12
73/05/09
I
92/05/01
86/04/23
73/05/18
73/05/01
37/43
38/43
92/05/01
86/04/23
73/03/25
74/11/28
LE84357716583xO
LM84028117230xO
LM8126317240500
LM8130017293500
LE84357716590xO
LM85078317185xO
LM8129917241500
LM8128217301500
LE84357716592xO
LM85078317191xO
LM8124517245500
LM8185817170500
Path 36 Row 38
38/38
39/38
92/10/07
86/09/05
74/06/01
72/09/28
LE85314217194xO
LM85091817190xO
LM8167817210500
LM8106717330500
191
-------
Path. 36 Row 39
38/39
39/39
Path 36 Row 40
38/40
39/40
92/05/08
83/04/14
73/06/06
73/05/20
)
92/05/08
83/04/14
72/08/22
72/09/10
Path 36 Row 41
38/41
39/41
Path 36 Row 42
38/42
92/05/24
87/07/22
72/08/22
72/09/10
»
92/05/24
87/07/22
73/05/01
LE84358417042xO
LM84027217282xO
LM8131817283500
LM8130117342500
LE84358417044XO
LM84027217285xO
LM8103017280500
LM8104917340500
LE84360017054xO
LM85123817244x0
LM8103Q17283500
LM8104917342500
LE84360017060xO
LM85123817250xO
LM8128217301500
Second WRS-1 not required.
192
-------
Path 37 Row 38
92/10/14
86/10/14
39/38 72/10/16
40/38 72/09/29
?ath 37 Row 39
92/05/31
87/07/13
39/39 73/05/20
40/39 73/06/08
Path 37 Row 40
39/40
40/40
92/05/15
83/05/07
72/09/10
73/04/15
LE85314917255xO
LM85095717240xO
LM8108517332500
LM8106817385500
LE84360717112xO
LM85122917295xO
LM8130117342500
LM8132017395500
LE84359117111xO
LM84029517350xO
LM8104917340500
LM8126617405500
Path 37 Row 41
92/04/29
86/04/21
39/41 76/03/20
40/41 76/03/21
LE84357517105xO
LM85078117305xO
LM8242317191500
LM8242417245500
193
-------
Path 38 Row 37
40/37
41/37
Path 38 Row 38
40/38
41/38
92/08/02
86/09/03
72/09/29
72/09/30
>
92/07/17
86/06/15
73/06/08
73/07/15
Path 38 Row 39
40/31
41/39
Path 39 Row 40
Path 39 Row 37
92/04/20
87/07/20
73/06/08
73/04/15
76/03/31
LE85307617325xO
LM85091617310xO
LM8106817382500
LM8106917441500
LE85306017333xO
LM85083617341xO
LM8132017393500
LM8135717445500
LE84356617160XO
LM85123617361xO
LM8132017395500
LM8126617402500
LM8534717052500
To be selected
41/37
42/37
92/06/30
86/06/06
73/06/09
73/05/23
LE84363717235xO
LM85082717402xO
LM8132117445500
LM8130417504500
OR
EROS CHECK
194
-------
Path 39 Row 38
41/38
42/38
Path 39 Row 39
41/39
92/06/30
86/05/05
73/05/22
73/06/10
)
DOUBLET
92/06/30
76/06/11
Path 40 Row 37
42/37
43/37
Path 40 Row 38
92/08/16
87/09/04
72/09/13
73/09/27
LE84363717241xO
LM85079517414xO
LM8130317453500
LM8132217510500
LE84363717244xO
LM8534717052500
LE85309017450xO
LM85128217490xO
LM8105217495500
LM8143117543500
To be selected
195
-------
NALC MSS Triplicates - Hawaii
Path 62 Row 46
No good 90'8 data.
No 80's data.
67/46 73/02/11 LM8120320180500
Path. 62 Row 47
67/47
90/07/28
No 80's data.
73/02/11
75/07/22
LM84293420113x0
LM8120320182500
LM8218120061500
OR
Check fiche
Path 63 Row 46
92/07/24
92/05/21
LM84366119563xO
LM84359719542xO
No 1980's data.
67/46 73/02/11 LM8120320180500
Composite with
Path 63 Row 47
67/47
92/08/09
92/07/24
No 1980's data
73/02/11
75/07/22
LM84367719572xO
LM84366119565xO
LM8120320182500
LM8218120061500
Composite with
OR
Check fiche
196
-------
Path 64 Row 45
92/07/15
92/06/29
LM84365220020xO
LM84363620013xO
Composite with
68/45
No 1980'8 data
73/01/25 LM8118620230500
Path 64 Row 46
92/07/15 LM84365220022xO
No 1980's data
68/46 LM
Path 65 Row 45
92/08/07
92/07/22
92/07/06
92/06/20
Path 66 Row 45
92/07/29
92/06/27
No 1980's data
LM84367520085xO
LM84365920082xO
LM84364320075xO
LM84362720072xO
LM84366620144xO
LM84363420135xO
Composite 3 of 4?
Composite with
197
-------
NALC MSS Triplicates - Alaska
Path 54 Row 21
59/21
60/21
92/07/01
85/07/30
74/09/02
73/07/16
LM85304419055xO
LM85051619123xO
LM8177119111500
LM8135819264500
Path 54 Row 22
GLIS ALL 90'8
85/07/30 LM85051619125xO
59/22 GLIS
60/22 GLIS
Path 55 Row 21
GLIS ALL 90's
86/08/09 LM85089119104xO
60/21 73/07/16 LM8135819264500
Second WRS-1 not required?
Path 55 Row 22
GLIS ALL 90's
86/09/26 LM85093919092xO
60/22 GLIS
Second WRS-1 not required?
198
-------
Path 56 Row 20
GLIS ALL 90's
86/09/17 LM85093019150xO
61/20 74/09/22 LM8179119212500
Second WRS-1 not required?
Path 56 Row 21
GLIS ALL 90's
86/09/17 LM85093019153xO
61/21 GLIS at 0-50% cloud level
Second WRS-1 not required?
Path 57 Row 19
61/19
62/19
Path 57 Row 20
GLIS ALL 90's
86/06/04
84/08/17
GLIS
GLIS 0-50%
OR
GLIS ALL 90's
86/06/04
86/10/10
OR
61/20 GLIS 0-50%
62/20 74/07/31 LM8173819291500
199
-------
Path 57 Row 21
61/21
62/21
Path. 58 Row 19
GLIS ALL 90 's
86/06/04 GLIS for ID
GLIS 0-50%
GLIS
63/19
64/19
Path 58 Row 20
63/20
92/06/27
86/06/27
72/08/11
GLIS 0-50%
)
92/06/27
86/09/15
74/09/06
LM85304019295xO
LM85084819295xO
LM8101919430500
LM85304019301xO
LM85092819273xO
LM8177519333500
Second WRS-1 not required?
Path 59 Row 18
64/18
65/18
GLIS ALL 90's
86/09/06 LM85091919331xO
72/08/12 LM8102019480500
GLIS
check fiche
200
-------
Path 59 Row 19
GLIS ALL 90's
86/09/06 LM85091919333xO
64/19 GLIS 0-50%
65/19 72/09/18 LM8105719542500
Path 59 Row 20
GLIS ALL 90'8
86/09/06 GLIS for ID
64/20 GLIS
65/20 GLIS
Path 60 Row 19
92/06/25
86/06/25
65/19 72/09/18
LM85303819421xO
LM85084619422xO
LM8105719542500
Second WRS-1 not required?
Path 61 Row 18
66/18
67/18
92/09/04
84/08/13
73/07/22
73/07/23
LM85310919470xO
LM85016519542xO
LM8136419595500
LM8136520053500
201
-------
Path 62 Row 17
To be selected
Path 62 Row 18
67/18
68/18
Path 63 Row 17
92/08/10
86/09/11
73/07/23
72/09/21
LM85308419533xO
LM85092419513xO
LM8136520053500
LM8106020111500
68/17
69/17
Path 63 Row 18
GLIS ALL 90 's
84/09/12 LM85019520065xO
GLIS
74/07/02 LM8170920090500
68/18
69/18
GLIS ALL 90's
84/09/12 LM85019520071xO
72/09/21 LM8106020111500
72/09/22 LM8106120165500
Path 64 Row 16
GLISS ALL 90's
85/06/02 LM85045820123xO
70/16 76/06/13 LM8250820042500
Second WRS-1 not required?
check fiche
202
-------
Path 64 Row 17
GLIS ALL 90's
85/06/02 LM85045820130xO
70/17 74/06/15 LM8169220152500
Second WRS-1 not required?
Path 64 Row 18
GLIS ALL 90's
86/09/09 LM85092220040xO
70/18 73/09/18 LM8142220215500
Second WRS-1 not required?
Path 65 Row 15
92/06/28 LM85304120113xO
86/06/28 LM85084920113xO
71/15 72/09/24 LM8106320271500
Second WRS-1 not required?
Path 65 Row 16
71/16
92/06/28
86/06/28
75/07/08
LM85304120115xO
LM85084920120xO
LM8216720162500
check fiche
Second WRS-1 not required?
203
-------
Path. 65 Row 17
71/17
92/06/28
86/06/28
76/07/20
GLIS for ID
LM85084920122xO
LM8254520093500
Second WRS-1 not required?
Path 65 Row 18
71/18
92/06/12
86/06/28
76/06/14
LM85302520125xO
GLIS for ID
LM8250920105500
Second WRS-1 not required?
Path. 66 Row 14
72/14
73/14
Path 66 Row 15
72/15
73/15
92/07/05
86/07/05
76/08/26
72/08/21
92/07/05
86/08/22
86/09/07
76/08/26
72/08/21
GLIS for ID
LM85085620171xO
LM8258220125500
LM8102920381500
LM85304820173xO
LM85090420155xO
LM85092020151xO
LM8258220131500
LM8102920383500
OR
204
-------
Path 66 Row 16
72/16
92/07/05
86/07/05
73/07/10
GLIS for ID
LM85085620175xO
LM8135220333500
Second WRS-1 not required?
Path 66 Row 17
72/17
92/07/05
_/
86/07/05
75/07/09
LM85304820182xO
,LM85085620182xO
LM8216820223500
Second WRS-1 not required?
Path 66 Row 18
92/07/05
86/07/05
85/07/18
GLIS for ID
OR
72/18 75/07/09 GLIS for ID and X or N
Second WRS-1 not required?
Path 67 Row 13
73/13
74/13
92/08/29
84/09/24
76/08/27
76/07/23
LM85310320221xO
LM85020720295xO
LM8258320181500
LM8254820245500
205
-------
Path 67 Row 14
73/14
74/14
Path. 67 Row 15
GLIS ALL 90' s
84/09/24 LM85020720302xO
72/08/21 LM8102920381500
76/07/23 LM8254820252500
73/15
74/15
Path 67 Row 16
73/16
74/16
Path 67 Row 17
73/17
92/08/28
86/09/14
72/08/21
76/08/01
92/06/10
86/09/14
75/07/10
76/08/01
r
92/06/10
86/07/28
75/07/10
LM85310020230xO
LM85092720211xO
LM8102920383500
LM8547019553500
LM85302320242xO
LM85092720213xO
LM8216920275500
LM8547019560500
LM85302320245xO
LM8 5087920234x0
LM8216920281500
Second WRS-1 not required?
206
-------
Path 67 Row 18
73/18
92/08/29
86/08/13
86/07/28
75/07/09
LM85310320241xO
LM85089520233xO
LM85087920240xO
OR
GLIS for ID and X or N
Second WRS-1 not required?
Path 68 Row 12
74/12
75/12
Path 68 Row 13
92/06/01
86/07/03
86/06/01
72/08/22
76/07/06
74/13
75/13
Path 68 Row 14
92/07/03
86/07/03
85/08/01
76/07/23
76/08/11
74/14
75/14
92/07/03
85/08/01
76/07/23
76/07/06
LM85301420291xO
LM85085420284xO
LM85082220294xO
LM8103020430500
LM8253120304500
LM85304620291xO
LM85085420291xO
LM85051820355xO
LM8254820245500
LM8256720300500
LM85304620293xO
LM85051820361xO
LM8254820252500
LM8253120313500
OR
OR
207
-------
Path 68 Row 15
74/15
75/15
Path 68 Row 16
74/16
75/16
Path 68 Row 17
74/17
75/17
Path 68 Row 18
92/07/03
86/09/05
76/08/01
76/07/06
92/07/03
86/07/03
86/09/05
76/08/01
76/07/06
r
92/07/03
86/09/05
75/07/11
76/07/06
74/18
92/07/03
86/09/05
84/08/14
73/08/17
LM85304620295xO
LM85091820274xO
LM8547019553500
LM8253120320500
LM85304620302xO
LM85085420302xO OR
LM85091820280xO
LM8547019560500
LM8253120322500
LM85304620304xO
LM85091820282xO
LM8217020340500
LM8253120325500
LM85304620311xO
LM85091820285xO OR
LM85016620374xO
LM8139020452500
208
-------
Path 69 Row 11
GLIS ALL 90's
85/07/07 LM85049320412xO
76/11 74/06/21 LM8169820470500
Second WRS-1 not required?
Path 69 Row 12
GLIS ALL 90's
85/07/07 LM85049320414xO
76/12 74/09/01 LM8177020450500
Second WRS-1 not required?
Path 69 Row 13
75/13
,.76/13
Path 69 Row 14
75/14
76/14
92/09/04
84/09/22
76/08/11
75/08/18
t
92/09/04
84/09/22
76/07/06
76/09/17
LM84370320214xO
LM85020520422xO
LM8256720300500
LM8220820425500
LM84370320220xO
LM85020520424xO
LM8253120313500
LM8260420351500
check fiche
209
-------
Path 69 Row 15
75/15
76/15
Path. 69 Row 16
75/16
76/16
Path 69 Row 17
75/17
76/17
92/09/04
84/09/22
76/07/06
GLIS 0-50%
92/06/08
84/06/18
76/07/06
74/07/27
r
92/09/12
86/09/12
76/07/06
74/07/27
LM84370320223xO
LM85020520430xO
LM8253120320500
LM85302120365xO
LM85010920414xO
LM8253120322500
LM8173420480500
LM85311720355xO
LM85092520342xO
LM8253120325500
LM8173420482500
Path 69 Row 18
92/06/08
84/06/18
75/18 GLIS 0-50%
Second WRS-1 not required?
LM85302120374xO
LM85010920423xO
check fiche
210
-------
Path 69 Row 19
GLIS ALL 90'8
86/07/26 LM85087720365xO
75/19 GLIS 0-50%
Second WRS-1 not required?
Path 69 Row 20
GLIS ALL 90's
86/07/26 LM85087720372xO
75/20 74/09/18 LM8178720415500
Second WRS-1 not required?
Path 70 Row 11
77/11
92/07/17
86/07/01
73/08/02
LM85306020403xO
LM85085220404xO
LM8137520595500
Second WRS-1 not required?
Path 70 Row 12
77/12
92/07/17
86/07/01
73/08/02
LM85306020405xO
LM85085220411xO
LM8137521002500
Second WRS-1 not required?
211
-------
Path 70 Row 13
76/13
77/13
Path 70 Row 14
76/14
77/14
Path 70 Row 15
76/15
77/15
Path 70 Row 16
92/07/17
86/07/17
75/08/18
76/09/18
I
92/07/01
84/08/12
76/09/17
74/09/02
92/08/02
84/08/12
76/06/01
74/09/02
LM85306020412xO
LM85086820410xO
LM8220820425500
LM8260520403500
LM85304420415x0
LM85016420483xO
LM8260420351500
LM8177120513500
LM85307620415xO
LM85016420485xO
LM8249620384500
LM8177120515500
76/16
77/16
GLIS ALL 90'a
86/07/01 LM85085220424xO
74/07/27 LM8173420480500
72/08/25 LM8103321020500
check fiche
212
-------
Path 70 Row 17
76/17
77/17
92/09/03
85/09/16
74/07/27
72/08/25
LM85310820421xO
LM85056420491xO
LM8173420482500
LM8103321022500
Path 70 Row 18
76/18
77/18
Path 70 Row 19
GLIS ALL 90'-s
85/09/16 LM85056420493xO
GLIS 0-50%
72/08/25 LM8103321025500
76/19
92/06/07
84/08/12
74/07/27
LM84361420273xO
LM85016420502xO
LM8173420491500
Second WRS-1 not required?
Path 70 Row 20
GLIS ALL 90 's
84/08/12 LM85016420505xO
76/20 74/07/27 LM8173420494500
Second WRS-1 not required?
213
-------
Path 71 Row 11
78/11
92/06/30
86/07/08
76/08/14
LM84363720311xO
LM85085920464xO
LM8257020462500
Second WRS-1 not required?
Path 71 Row 12
92/08/01
92/07/16
LM84366920324xO
LM84365320321xO
86/07/08
78/12 GLIS 0-50%
Second WRS-1 not required?
LM85085920471xO
Path 71 Row 13
77/13
78/13
Path 71 Row 14
77/14
78/14
92/08/01
85/07/21
76/09/18
74/09/03
t
92/08/01
85/07/21
74/09/02
75/09/25
LM84366920330xO
LM85050720542xO
LM8260520403500
LM8177220565500
LM84366920332xO
LM85050720545xO
LM8177120513500
LM8224620542500
Composite with
214
-------
Path 71 Row 15
77/15
78/15
Path 71 Row 16
92/08/01
85/07/21
74/09/02
74/09/03
LM84366920335xO
LM85050720551xO
LM8177120515500
LM8177220574500
77/16
78/16
GLIS ALL 90 's
85/07/05 LM85049120554xO
72/08/25 LM8103321020500
73/07/16 LM8135821075500
Path 71 Row 17
77/17
78/17
Path 71 Row 18
GLIS ALL 90's
84/09/04 LM85018720561xO
72/08/25 LM8103321022500
74/09/03 LM8177220583500
77/18
78/17
92/06/30
GLIS 80's
76/06/20
73/07/16
LM84363720340xO
LM8251520451500
LM8135821082500
CHECK FICHE
215
-------
Path. 71 Row 19
77/19
78/19
Path, 71 Row 20
77/20
78/20
GLIS ALL 90 's
85/07/05 LM85049120565xO
76/07/08 LM8253320450500
76/07/09 LM8253420504500
)
GLIS ALL 90's
84/09/04 LM85018720572xO
76/07/08 LM8253320453500
76/07/09 LM8253420511500
Path 72 Row 11
79/11
92/07/23
86/07/15
74/09/04
LM84366020381xO
LM85086620524xO
LM8177321014500
Second WRS-1 not required?
Path 72 Row 12
92/06/05
85/06/10
79/12 GLIS 0-50%
Second WRS-1 not required?
LM84361220370xO
LM85046621002xO
216
-------
Path 72 Row 13
79/13
92/06/05
86/06/13
73/06/29
LM84361220372xO
LM85083420543xO
LM8134121123500
Second WRS-1 not required?
Path 72 Row 14
78/14
79/14
Path 72 Row 15
92/06/05
86/06/13
75/09/25
73/06/29
LM84361220375xO
LM85083420545xO
LM8224620542500
LM8134121130500
78/15
79/15
Path 72 Row 16
GLIS ALL 90's
86/06/13 LM85083420552xO
73/07/16 LM8135821073500
73/06/29 LM8134121132500
78/16
79/16
92/06/05
86/06/13
73/07/16
73/06/29
LM84361220383xO
LM85083420554xO
LM8135821075500'
LM8134121135500
217
-------
Path 72 Row 17
78/17
79/17
Path 72 Row 18
92/06/05
86/06/13
73/07/16
73/06/29
LM84361220390xO
LM85083420560xO
LM8135821082500
LM8134121141500
78/18
79/18
Path 72 Row 19
78/19
79/19
GLIS ALL 90's
86/05/28 LM85081820570xO
76/07/09 LM8253420502500
76/07/10 LM8253520560500
)
GLIS ALL 90's
86/05/28 LM85081820572xO
76/07/09 LM8253420504500
GLIS 0-50%
Path 72 Row 20
78/20
79/20
Path 72 Row 21
GLIS ALL 90's
86/05/28 LM85081820575xO
76/07/09 LM8253420511500
74/07/30 LM8173721064500
To be Selected
218
-------
Path 73 Row 11
80/11
81/11
92/08/31
85/08/04
74/06/25
76/07/12
LM84369920453xO
LM85052121055xO
LM8170221095500
LM8253721043500
Path 73 Row 12
GLIS ALL 90's
85/08/04 LM85052121061xO
80/12 74/09/05 LM8177421074500
Second WRS-1 not required?
Path 73 Row 13
GLIS ALL 90's
86/08/23 LM85090520581xO
80/13 73/06/30 LM8134221182500
Second WRS-1 not required?
Path 73 Row 14
GLIS ALL 90's
85/07/19 LM85050521071xO
79/14 73/06/29 LM8134121130500
80/14 76/07/11 LM8253621000500
219
-------
Path 73 Row 15
GLIS ALL 90 's
84/09/02 LM85018521074xO
79/15 74/09/04 LM8177321032500
80/15 75/09/09 LM8223021063500
Path 73 Row 16
GLIS ALL 90's
84/09/02 LM85018521081xO
79/16 74/09/04 LM8177321034500
80/16 72/08/10 LM8101821191500
Path 73 Row 17
79/17
80/17
Path 73 Row 1.8
79/18
80/18
GLIS ALL 90's
84/09/02 LM85018521083xO
73/06/29 LM8134121141500
72/08/10 LM8101821193500
3
GLIS ALL 90's
84/09/02 LM85018521090xO
76/07/10 LM8253520560500
74/08/18 LM8175621110500
220
-------
Path 73 Row 19
GLIS ALL 90's
84/09/02 LM85018521092xO
79/19 GLIS 0-50%
80/19 GLIS Q-50%
Path 73 Row 20
GLIS ALL 90's
84/09/02 LM85018521094xO
79/20 74/07/30 LM8173721064500
80/20 72/09/15 LM8105421205500
Path 73 Row 21
GLIS ALL 90's
84/09/02 LM85018521101xO
79/21 GLIS 0-50%
80/21 GLIS 0-50%
Path 74 Row 10
GLIS ALL 90's
85/08/27 LM85054421112xO
81/10 74/09/06 LM8177521124500
82/10 GLIS
221
-------
Path 74 Row 11
81/11
82/11
Path 74 Row 12
GLIS ALL 90's
85/08/27 LM85054421115xO
76/07/12 LM8253721043500
GLIS
81/12
82/12
Path 74 Row 13
81/13
92/09/07
85/07/26
74/09/06
GLIS
5
92/09/07
85/07/26
76/07/12
LM84370620521xO
LM85051221123xO
LM8177521133500
LM84370620524xO
LM85051221125xO
LM8253721052500
Second WRS-1 not required?
Path 74 Row 14
81/14
92/09/07
85/07/26
76/07/12
LM84370620530xO
LM85051221132xO
LM8253721055500
Second WRS-1 not required?
222
-------
Path 74 Row 15
81/15
92/09/07
85/07/26
72/08/29
LM84370620533xO
LM85051221134xO
LM8103721243500
Second WRS-1 not required?
Path 74 Row 16
80/16
81/16
Path 74 Row 17
80/17
81/17
Path 74 Row 18
92/09/07
85/07/26
72/08/10
75/09/28
r
92/06/03
86/05/26
72/08/10
GLIS 0-50%
LM84370620535xO
LM85051221141xO
LM8101821191500
LM8224921123500
LM84361020512xO
LM85081621090xO
LM8101821193500
80/18
81/18
GLIS ALL 90's
86/05/26 LM85081621092xO
74/08/18 LM8175621110500
75/08/05 LM8219521140500
223
-------
Path. 74 Row 19
80/19
81/19
92/08/06
GLIS 80's
74/08/18
75/08/05
LM84367420541xO
GLIS for ID and X or N
LM8219521142500
Path 74 Row 21 To be selected
Path. 74 Row 22 To be selected
Path. 75 Row 10
GLIS ALL 90's
86/07/04 LM85085521111xO
82/10 GLIS
83/10 74/07/16 LM8172321260500
Path. 75 Row 11
82/11
83/11
92/07/28
85/08/02
GLIS
74/07/16
LM84366520565xO
LM85051921181xO
LM8172321262500
224
-------
Path 75 Row 12
82/12
83/12
92/07/28
85/07/17
GLIS
76/08/01
LM84366520571xO
LM85050321184xO
LM8255721155500
check fiche
Path 75 Row 13
GLIS ALL 90's
86/07/04 LM85085521122xO
82/13 GLIS
Second WRS-1 not required?
Path 75 Row 14
92/07/12
86/07/04
82/14 GLIS
Second WRS-1 not required?
LM84364920573xO
LM85085521124xO
Path 75 Row 15
92/07/28
92/07/12
LM84366520582xO
LM84364920575xO
Composite with
85/08/02
82/15 GLIS
Second WRS-1 not required?
LM85051921195xO
225
-------
Path. 75 Row IS
GLZS ALH 9d's
85/08/02 LM850.51921201xO
82/16 GLIS
Second WRS-1 not required?
Path 75 Row IT
81/17
82/17
Path 75 Row 18
CLOTS ALL 50's
85/0 8/02 LM8 5 0 51921204x0
GLXS a-50%
GILXS
81/18
82/18
Path 75 Row
GLIS ALE.
85/08/02
75/08/05
GIrTS
LM8 50 5192 12 10x0
LM8219521140500
81/19
82/19
ALL 9:0 's
a4/09/16 LM8 5 019 9 2 12 14x0
7e/a5/0€ LM8 2 531210 62 5 00.
GLIS
check fiehe
Path 75 Row 22 To be selected
-------
Path 76 Row 10
83/10
84/10
Path 76 Row 11
GLIS ALL 90's
86/08/12 LM85089421160xO
74/07/16 LM8172321260500
74/08/22 LM8176021302500
83/11
84/11
92/06/17
84/09/23
74/07/16
74/08/22
LM84362421014xO
LM85020621244xO
LM8172321262500
LM8176021305500
Path 76 Row 12
GLIS ALL 90's
86/09/13 LM85092621154xO
83/12 74/09/08 LM8177721245500
84/12 72/09/19 LM8105821403500
Path 76 Row 13
GLIS ALL 90's
84/06/03 LM85009421233xO
83/13 74/08/03 LM8174121264500
84/13 75/07/21 LM8218021293500
check fiche
227
-------
Path. 76 Row 14
GLIS ALL 90 's
84/08/06 LM85015821252xO
83/14 74/08/03 LM8174121270500
Second WRS-1 not required?
Path 76 Row 15
GLIS ALL 90's
85/05/21
84/06/03
LM85044621262xO
LM85009421242xO
83/15 76/08/19 LM8257521163500
Second WRS-1 not required?
Path. 76 Row 16
83/16
92/06/17
85/07/24
76/08/19
LM84362421034xO
LM85051021263xO
LM8257521170500
Second WRS-1 not required?
Path 76 Row 17
82/17
83/17
92/06/17
85/07/24
GLIS
75/06/14
LM84362421040xO
LM85051021265xO
LM8214321254500
OR
228
-------
Path 76 Row 18
GLIS ALL 90's
84/06/03 LM85009421253xO
82/18 GLIS
83/18 GLIS 0-50%
Path 76 Row 19
GLIS ALL 90' 8,
84/06/03 LM85009421255xO
81/19 75/08/05 LM8219521142500
82/19 GLIS
Path 76 Row 22 To be selected
Path 77 Row 10
GLIS ALL 90's
84/06/26 LM85011721285xO
84/10 74/08/22 LM8176021302500
85/10 73/06/17 LM8132921455500
Path 77 Row 11
GLIS ALL 90's
84/08/29 LM85018121305xO
84/11 74/08/22 LM8176021305500
85/11 75/07/22 LM8218121342500
229
-------
Path 77 Row 12
84/12
85/12
Path 77 Row 13
84/13
85/13
92/09/28
84/08/29
72/09/19
73/06/17
I
92/09/28
86/07/02
75/07/21
75/07/22
LM84372721112xO
LM85018121311xO
LM8105821403500
LM8132921464500
LM84372721114xO
LM85085321245xO
LM8218021293500
LM8218121351500
check fiche
Path 77 Row 14
GLIS ALL 90's
86/07/02 LM85085321251xO
84/14 75/07/03 LM8216221301500
Second WRS-1 not required?
Path 77 Row 15
GLIS ALL 90's
86/07/02 LM85085321253xO
84/15 75/07/03 LM8216221304500
Second WRS-1 not required?
230
-------
Path. 77 Row 16
GLIS ALL 90 's
86/07/02 LM85085321260xO
84/16 76/06/27 LM8252221242500
Second WRS-1 not required?
Path 77 Row 17
GLIS ALL 90's
84/08/29 LM85018121331xO
84/17 75/07/21 LM8218021311500
Second WRS-1 not required?
Path 77 Row 18
84/18
92/06/24
84/06/26
75/08/08
LM84363121105x0
LM85011721320xO
LM8219821311500
Second WRS-1 not required?
Path 77 Row 23
Path 78 Row 10
85/10
86/10
To be selected
GLIS ALL 90's
84/07/03 LM85012421351xO
73/06/17 LM8132921455500
GLIS
231
-------
Path. 78 Row 11
85/11
86/13
Path 78 Row 12
92/06/15
84/07/03
-.
73/06/17
GLIS
LM84362221140xO
LM85012421353xO
LM8132921462500
85/12
86/12
Path. 78 Row 13
GLIS ALL 90's
85/07/06 LM85049221372xO
73/06/17 LM8132921464500
GLIS
85/13
86/13
Path 78 Row 14
92/09/03
85/07/06
75/07/22
GLIS
LM84370221172xO
LM85049221374xO
LM8218121351500
85/14
86/14
GLIS ALL 90's
85/07/22 LM85050821380xO
75/07/04 LM8216321355500
GLIS
232
-------
Path 78 Row 15
GLIS ALL 90's
84/06/17 LM85010821365xO
85/15 73/06/17 GLIS for ID and X or N
Second WRS-1 not required?
Path 78 Row 16
GLIS ALL 90's
84/06/17 LM85010821372xO
85/16 76/06/10 LM8250521303500
Second WRS-1 not required?
Path 78 Row 17
GLIS ALL 90's
84/06/01 LM85009221373xO
85/17 76/06/10 LM8250521305500
Second WRS-1 not required?
Path 78 Row 18
GLIS ALL 90's
84/06/01 LM85009221375xO
85/18 76/06/10 LM8250521312500
Second WRS-1 not required?
Path 78 Row 23 To be selected
233
-------
Path 79 Row 10
GLIS ALL 90'8
86/06/30 LM85085121360xO
86/10 GLIS
87/10 75/07/06 LM8216521454500
Path 79 Row 11
92/07/08
86/06/14
86/11 GLIS
87/11 75/07/06
Path 79 Row 12
92/06/06
86/06/14
86/12 GLIS
87/12 75/07/06
Path 79 Row 13
86/13
87/13
92/06/06
86/06/14
GLIS
74/07/02
LM84364521205xO
LM85083521365xO
LM8216521461500
LM84361321202xO
LM85083521372xO
LM8216521463500
LM84361321204xO
LM85083521374xO
LM8170921504500
234
-------
Path 79 Row 14
86/14
87/14
Path 79 Row 15
86/15
87/15
92/06/06
86/06/30
GLIS
74/07/02
92/06/06
86/06/30
GLIS
74/07/02
LM84361321210xO
LM85085121374xO
LM8170921510500
LM84361321213xO
LM8508512l380xO
LM8170921513500 check X or N
Path 79 Row 16
92/06/06
84/08/27
86/16 GLIS
Second WRS-1 not required?
LM84361321215xO
LM85017921451xO
Path 79 Row 17
92/09/26
84/09/28
86/17 GLIS
Second WRS-1 not required?
LM84372521254xO
LM85021121454xO
235
-------
Path 79 Row 18
GLIS ALL 90's
84/09/28 LM85021121460xO
86/18 GLIS
Second WRS-1 not required?
Path 80 Row 10
87/10
88/10
Path 80 Row 11
87/11
88/11
Path 80 Row 12
87/12
88/12
92/07/15
84/07/01
75/07/06
76/08/06
92/07/15
86/07/07
75/07/06
74/07/03
>
92/07/31
86/07/07
75/07/06
GLIS 0-50%
LM84365221265xO
LM85012221473xO
LM8216521454500
LM8256221433500
LM84365221272xO
LM85085821422xO
LM8216521461500
LM8171021553500
LM84366821281xO
LM85085821424xO
LM8216521463500
236
-------
Path 80 Row 13
87/13
88/13
Path 80 Row 14
87/14
88/14
Path 80 Row 15
92/07/31
84/07/01
74/67/02
75/07/25
t
92/07/31
84/07/01
74/07/02
74/07/03
87/15
88/15
92/07/31
84/07/01
74/07/02
74/07/03
LM84366821284xO
LM85012221484xO
LM8170921504500
LM8218421522500
LM84366821290xO
LM85012221490xO
LM8170921510500
LM8171021565500
LM84366821292xO
LM85012221493xO
LM8170921513500
LM8171021571500
LM84364321325xO
Path 81 Row 10
92/07/06
GLIS
89/10 76/08/25 LM8258121484500
Second. WRS-1 not required?
check X or N
237
-------
Path 81 Row 11
89/11
92/07/06
84/06/06
72/08/01
LM84364321331xO
LM85009721534xO
LM8100922083500
Second WRS-1 not required?
Path 81 Row 12
88/12
89/12
Path 81 Row 13
88/13
89/13
Path 81 Row 14
92/07/06
84/06/06
GLIS 0-50%
72/08/01
!
92/06/20
84/06/06
75/07/25
72/08/01
88/14
89/14
92/07/06
92/07/22
84/06/06
74/07/03
72/08/01
LM84364321334xO
LM85009721540xO
LM8100922090500
LM84362721333xO
GLIS for ID
LM8218421522500
LM8100922092500
LM84364321334xO
LM84365921345xO
GLIS for ID
LM8171021565500
LM8100922095500
Composite with
238
-------
Path 81 Row 15
GLIS ALL 90's
84/06/06 GLIS for ID
88/15 74/07/03 LM8171021571500
Second WRS-1 not required?
Path 82 Row 10
GLIS ALL 90's
84/09/01 LM85018422012xO
90/10 72/08/02 LM8101022135500
Second WRS-1 not required?
Path 82 Row 11
90/11
92/07/29
84/06/29
75/08/14
LM4366621401xO
LM85012022001xO
LM8220422022500
Second WRS-1 not required?
Path 82 Row 12
89/12
90/12
92/07/29
84/06/29
72/08/01
72/08/02
LM84366621403xO
LM85012022004xO
LM8100922090500
LM8101022144500
239
-------
Path. 82 Row 13
89/12
90/12
Path 82 Row 14
92/07/13
84/07/15
72/08/01
72/08/02
89/14
90/14
Path 83 Row 12
92/07/29
92/07/13
84/07/15
72/08/01
72/08/02
90/12
91/12
92/07/04
84/09/08
72/08/02
73/06/23
LM84365021402xO
LM85013622011xO
LM8100922090500
LM8101022144500
LM84366621412xO
LM84365021405xO
LM85013622014xO
LM8100922095500
LM8101022153500
LM84364121455xO
LM85019122082xO
LM8101022144500
LM8133522210500
Path 83 Row 13
GLIS ALL 90's
84/09/08 LM85019122085xO
90/13 74/08/10 LM8174822063500
Second WRS-1 not required?
Composite with
240
-------
Path 83 Row 14
GLIS ALL 90's
84/06/04 LM85009522071xO
90/14 72/08/02 LM8101022153500
241
-------
NALC MSS Triplicates - Western USA
Path 33 Row 33
35/33
36/33
Path 33 Row 34
35/34
36/34
Path 33 Row 35
35/35
36/35
Path 33 Row 36
35/36
36/36
92/07/06
86/07/30
74/07/04
75/06/30
I
92/07/06
86/06/28
74/07/04
75/06/30
92/07/06
86/06/12
75/06/29
74/06/17
92/08/07
86/07/30
74/07/04
72/08/02
LM84364316452xO
LM85088116595xO
LM8171117004500
LM8507216511500
LM84364316454xO
LM85084917012xO
LM8171117010500
LM8507216514500
LM84364316461xO
LM85083317021xO
LM8507116462500
LM8169417074500
LM84367516473xO
LM85088117010xO
LM8171117015500
LM8101017145500
242
-------
Path 33 Row 37
35/37
36/37
Path 34 Row 31
92/08/07
86/07/30
73/06/21
72/08/02
36/31
37/31
Path 34 Row 32
92/07/21
92/07/05
85/07/02
74/07/23
75/07/01
36/32
37/32
Path 34 Row 33
36/33
37/33
92/07/05
85/06/16
73/07/10
75/07/01
\
92/07/05
83/07/05
75/06/30
75/07/01
LM84367516475xO
LM85088117013xO
LM8133317102500
LM8101017152500
LM85306417055xO
LM84304817061xO
LM85048817130xO
LM8173017045500
LM8507316561500
LM85304817063xO
LM85047217133xO
LM8135217134500
LM8507316563500
LM85304817065xO
LM84035417134xO
LM8507216511500
LM8507316570500
Composite with
fiche missing
243
-------
Path 34 Row 34
36/34
37/34
Path 34 Row 35
36/35
37/35
Path 34 Row 36
36/36
37/36
Path 34 Row 37
92/07/05
83/07/05
75/06/30
75/07/01
92/09/07
86/08/22
75/09/19
73/09/21
92/09/07
86/08/22
72/08/02
73/08/16
36/37
37/37
92/07/05
85/07/02
73/06/22
75/06/22
LM85304817072xO
LM84035417140xO
LM8507216514500
LM8507316572500
LM85311217065xO
LM85090417060xO
LM8224017022500
LM8142517193500
LM85311217071xO
LM85090417062xO
LM8101017145500
LM8138917204500
LM85304817083xO
LM85048817152xO
LM8133417160500
LM8215117101500
244
-------
Path 35 Row 31
37/31
38/31
Path 35 Row 32
37/32
38/32
92/06/02
85/06/07
75/07/01
73/06/06
i
92/08/13
86/07/28
74/08/11
75/07/01
74/08/30
Path 35 Row 33
92/06/18
86/07/28
37/33 75/07/01
38/33 73/06/06
Path 35 Row 34
92/06/18
85/06/23
37/34 75/07/01
38/34 75/06/05
LM84360916555xO
LM85046317192xO
LM8507316561500
LM8131817251500
LM85308717121xO
LM85087917115xO
LM8174917102500• OR (Fiche missing)
LM8507316563500
LM8176817152500
LM84362516570x0
LM85087917122xO
LM8507316570500
LM8131817260500
LM84362516572xO
LM85046317203xO
LM8507316572500
LM8213417143500
245
-------
Path 35 Row 35
37/35
38/35
Path 35 Row 36
37/36
38/36
Path 35 Row 37
37/37
38/37
Path 36 Row 29
38/29
39/29
92/06/18
85/06/23
73/06/05
73/06/06
92/06/02
85/07/09
73/06/05
75/06/05
r
92/06/02
86/06/10
73/06/05
74/06/01
)
92/07/27
86/07/19
74/07/25
74/07/26
LM84362516575xO
LM85047917205xO
LM8131717210500
LM8131817265500
LM84360916575xO
LM85049517211xO
LM8131717213500
LM8213417152500
LM84360916581xO
LM85083117153xO
LM8131717215500
LM8167817203500
LM84366417025xO
LM85087017171xO
LM8173217153500
LM8173317211500
246
-------
Path 36 Row 30
92/07/27
86/07/19
38/30 73/07/30
39/30 72/08/05
Path 36 Row 31
92/08/12
86/08/17
38/31 74/07/25
39/31 72/08/05
Path 36 Row 32
92/08/28
85/08/17
38/32 74/08/30
39/32 73/09/05
Path 36 Row 33
92/08/20
86/08/04
38/33 73/09/04
39/33 73/09/05
LM84366417032xO
LM85087017173xO
LM8137217240500
LM8101317294500
LM84368017041xO
LM85053417250xO
LM8173217162500
LM8101317300500
LM84369617050xO
LM85053417252xO
LM8176817152500
LM8140917300500
LM85309417184xO
LM85088617181xO
LM8140817244500
LM8140917303500
fiche missing
247
-------
Path 36 Row 34
92/08/28
86/09/05
38/34 73/09/04
39/34 74/08/31
Path 36 Row 35
92/09/05
86/09/05
, 38/35 73/09/04
39/35 72/09/10
Path 36 Row 36
38/36
39/36
Path 36 Row 37
38/37
39/37
92/07/19
85/06/30
74/07/25
75/06/24
r
92/07/19
85/06/14
75/06/23
73/06/25
LM84369617055xO
LM85091817172xO
LM8140817251500
LM8176917215500
LM85311017191xO
LM85091817175xO
LM8140817253500
LM8104917315500
LM85306217202xO
LM85048617272xO
LM8173217182500
LM8215317211500
LM85306217204xO
LM85047017275xO
LM8215217155500
LM8133717332500
fiche missing
fiche missing
248
-------
Path 37 Row 29
92/08/19
86/08/27
39/29 72/09/05
40/29 73/08/01
Path 37 Row 30
92/08/27
86/08/27
39/30 72/08/05
40/30 72/08/06
Path 37 Row 31
92/07/26
85/08/08
39/31 72/08/05
40/31 72/08/06
Path 37 Row 32
92/07/26
85/08/08
39/32 73/09/05
40/32 72/08/06
LM84368717095xO
LM85090917220xO
LM8140917285500
LM8137417351500
LM85310117233xO
LM85090917222xO
LM8101317294500
LM8101417352500
LM85306917242xO
LM85052517312xO
LM8101317300500
LM8101417355500
LM85306917245xO
LM85052517312xO
LM8140917300500
LM8101417361500
249
-------
Path 37 Row 33
39/33
40/33
Path. 37 Row 34
39/34
40/34
Path 37 Row 35
39/35
40/35
Path 37 Row. 36
39/36
40/36
92/08/27
85/08/24
73/09/05
72/08/06
t
92/08/27
85/08/24
74/08/31
72/08/06
92/08/27
85/08/24
72/09/10
72/08/24
92/08/27
86/07/26
72/08/23
72/08/24
LM85310117244xO
LM85054117315xO
LM8140917303500
LM8101417364500
LM85310117251xO
LM85054117322xO
LM8176917215500
LM8101417370500
LM85310117253xO
LM85054117324xO
LM8104917315500
LM8103217373500
LM85310117255xO
LM85087717255xO
LM8103117322500
LM8103217375500
250
-------
Path 37 Row 37
92/07/26
86/07/26
39/37 73/06/25
40/37 75/07/31
Path 38 Row 27
92/08/02
86/08/02
40/27 73/08/01
41/27 72/08/25
Path 38 Row 28
92/07/17
86/08/02
40/28 73/08/01
41/28 75/08/10
Path 38 Row 29
92/08/26
86/08/02
40/29 73/08/01
41/29 72/08/07
LM85306917265xO
LM85087717262xO
LM8133717332500
LM8219017265500
LM85307617285xO
LM85088417281xO
LM8137417342500
LM8103317400500
LM85306017293xO
LM85088417284xO
LM8137417344500
LM8511317152500
LM84369417161xO
LM85088417290xO
LM8137417351500
LM8101517404500
251
-------
Path 38 Row 30
40/30
41/30
Path 38 Row 31
40/31
41/31
Path 38 Row 32
40/32
41/32
Path 38 Row 33
40/33
41/33
92/08/26
85/08/15
72/08/06
72/08/07
92/07/17
86/08/02
72/08/06
72/08/07
i
92/07/17
86/08/02
72/08/06
72/08/07
5
92/08/26
85/08/15
72/08/06
72/08/07
LM84369417163xO
LM85053217370xO
LM8101417352500
LM8101517410500
LM85306017304xO
LM85088417295xO
LM8101417355500
LM8101517413500
LM85306017311xO
LM85088417301xO
LM8101417361500
LM8101517415500
LM84369417174xO
LM85053217381xO
LM8101417364500
LM8101517422500
252
-------
Path 38 Row 34
40/34
41/34
Path 38 Row 35
40/35
41/35
Path 38 Row 36
40/36
41/36
Path 39 Row 27
42/27
92/08/18
86/08/02
72/08/06
72/08/07
92/07/01
86/07/17
74/07/09
74/06/22
92/08/02
85/08/15
72/08/24
72/08/07
r
91/07/06
85/06/19
74/06/23
LM85309217312xO
LM85088417310xO
LM8101417370500
LM8101517424500
LM85304417323xO
LM85086817320xO
LM8171617300500
LM8169917362500
LM85307617323xO
LM85053217392xO
LM8103217375500
LM8101517433500
LM85268317353xO
LM85047517422xO
LM8170017384500
Second WRS-1 not required?
253
-------
Path 39 Row 28
42/28
91/07/22
85/07/21
73/07/16
LM85269917361xO
LM85050717424xO
LM8135817462500
Second WRS-1 not required?
Path 39 Row 29
42/29
92/08/09
85/07/05
75/08/11
LM85308317355xO
LM85049117431xO
LM8511417213500
Second WRS-1 not required?
Path 39 Row 30
41/30
42/30
Path 39 Row 31
41/31
42/31
92/07/16
85/07/05
72/08/07
73/07/16
92/07/16
86/06/22
74/06/22
74/06/23
LM84365317213xO
LM85049117433xO
LM8101517410500
LM8135817471500
LM84365317215xO
LM85084317372xO
LM8169917344500
LM8170017402500
254
-------
Path 39 Row 32
41/32
42/32
92/06/22
85/06/19
73/06/27
74/06/23
Path 39 Row 33
92/08/01
85/08/22
41/33 72/08/07
42/33 74/08/16
Path 39 Row 34
92/08/09
86/08/09
41/34 72/08/07
42/34 73/07/16
Path 39 Row 35
92/09/10
86/09/10
41/35 72/08/07
42/35 72/09/13
LM85303517373xO
LM85047517442xO
LM8133917423500
LM8170017404500
LM84366917231xO
LM85053917442xO
LM8101517422500
LM8175417392500
LM85308317375xO
LM85089117370xO
LM8101517424500
LM8135817485500
LM85311517374xO
LM85092317361xO
LM8101517431500
LM810521749050Q
255
-------
Path 39 Row 36
41/36
42/36
92/09/10
86/09/10
72/08/07
72/09/13
LM85311517380xO
LM85092317363xO
LM8101517433500
LM8105217493500
Path 40 Row 27
43/27
92/07/31
86/07/31
72/08/27
LM85307417412xO
LM85088217404xO
LM8103517513500
Second WRS-1 not required?
Path 40 Row 28
43/28
92/07/15
86/07/31
73/07/17
LM85305817415xO
LM85088217410xO
LM8135917521500
Second WRS-1 not required?
Path 40 Row 29
92/07/31
86/07/31
LM85307417420xO
LM85088217413xO
75/07/25
74/07/12
43/29
Second WRS-1 not required?
LM8509717281500
LM8171917443500
OR
fiche missing
256
-------
Path 40 Row 30
43/30
92/07/31
86/07/31
74/07/12
LM85307417423xO
LM85088217415xO
LM8171917450500
Second WRS-1 not required?
Path 40 Row 31
43/31
92/07/07
86/07/31
74/07/12
LM84364417275xO
LM85088217422xO
LM8171917452500
Second WRS-1 not required?
Path 40 Row 32
43/32
92/07/23
86/07/31
74/07/12
LM84366017284x0
LM85088217424xO
LM8171917455500
Second WRS-1 not required?
Path 40 Row 33
43/33
92/07/31
86/07/31
74/07/12
LM85307417434xO
LM85088217430xO
LM8171917461500
Second WRS-1 not required?
257
-------
Path 40 Row 34
43/34
92/07/23
86/07/31
74/07/12
LM84366017293xO
LM85088217433xO
LM8171917464500
Second WRS-1 not required?
Path 40 Row 35
42/35
43/35
92/06/05
86/06/13
74/06/23
73/06/29
LM84361217282xO
LM85083417453xO
LM8170017420500
LM8134117551500
Path 40 Row 36
42/36
43/36
Path 41 Row 26
92/06/21
85/06/10
74/06/23
73/06/29
LM84362817291xO
LM85046617521xO
LM8170017422500
LM8134117554500
92/07/22 LM85306517471xO
GLIS for composite
86/08/07 LM85088917461xO
44/26 72/08/10 LM8101817565500
Second WRS-1 not required?
Composite with
258
-------
Path 41 Row 27
44/27
92/07/22
92/06/20
86/08/07
72/08/28
LM85306517473xO
LM85303317480xO
LM85088917461xO
LM8103617571500
Composite with
Second WRS-1 not required?
Path 41 Row 28
44/28
92/07/22
92/07/14
92/06/20
86/07/19
74/07/13
LM85306517480xO Composite 2 of 3
LM84365117330x0
LM85303317482xO
LM85050517550xO
LM8172017495500
Second WRS-1 not required?
Path 41 Row 29
44/29
92/07/14
85/07/03
74/07/13
LM84365117332xO
LM85048917553xO
LM8172017502500
Second WRS-1 not required?
Path 41 Row 30
44/30
92/07/14
85/07/03
74/07/13
LM84365117335xO
LM85048917555xO
LM8172017504500
Second WRS-1 not required?
259
-------
Path. 41 Row 31
44/31
92/07/14
85/07/03
74/07/13
LM84365117341xO
LM85048917562xO
LM8172017511500
Second WRS-1 not required?
Path 41 Row 32
44/32
92/07/22
85/07/03
74/07/13
LM85306517493xO
LM85048917564xO
LM8172017513500
Second WRS-1 not required?
Path 41 Row 33
44/33
92/07/22
85/07/03
73/06/30
LM85306517500xO
LM85048917570xO
LM8134218001500
Second WRS-1 not required?
Path 41 Row 34
44/34
92/07/22
85/07/03
73/06/30
LM85306517502xO
LM85048917573xO
LM8134218003500
Second WRS-1 not required?
260
-------
Path 41 Row 35
44/35
92/07/22
85/07/03
73/06/30
LM85306517505xO
LM85048917575xO
LM8134218010500
Second WRS-1 no required?
Path 41 Row 36
44/36
92/07/06
85/07/03
74/06/25
LM85304917512xO
LM85048917582xO
LM8170217535500
Second WRS-1 not required?
Path 41 Row 37
44/37
92/07/06
85/07/03
74/06/25
LM85304917515xO
LM85048917584xO
LM8170217541500
Second WRS-1 not required?
Path 42 Row 26
45/26
46/26
92/08/14
85/07/26
74/07/14
75/07/10
LM85308817530xO
LM85051218002xO
LM8172117544500
LM8508217450500
261
-------
Path 42 Row 27
45/27
46/27
92/06/27
85/07/10
74/07/14
75/07/10
73/08/07
Path 42 Row 28
45/28
46/28
90/07/08
85/07/10
74/06/26
75/07/10
73/08/07
72/08/12
Path 42 Row 29
45/29
46/29
Path 42 Row 30
45/30
46/30
92/07/29
85/06/24
72/08/11
72/08/12
)
92/07/13
86/07/13
74/06/26
74/06/27
LM85304017540xO
LM85049618005xO
LM8172117551500
LM8508217453500
LM8138018084500
LM85232017512xO
LM85049618011xO
LM8170317561500
LM8508217455500
LM8138018090500
LM8102018085500
LM85307217543xO
LM85048018014xO
LM8101918035500
LM8102018092500
LM85305617550xO
LM85086417545xO
LM8170317570500
LM8170418024500
OR f. missing
fiche missing
OR
OR
all fiche missing
262
-------
Path 42 Row 31
45/31
46/31
Path 42 Row 32
45/32
46/32
Path 42 Row 33
92/07/21
86/07/13
74/06/26
74/06/27
>
92/07/29
86/07/29
73/08/06
74/06/26
72/07/25
45/33
46/33
Path 42 Row 34
45/34
46/34
92/07/29
86/07/29
75/08/05
72/07/25
t
92/07/29
86/07/29
75/08/05
72/07/25
LM84365817404xO
LM85086417552xO
LM8170317572500
LM8170418031500
LM85307217554xO
LM85088017551xO
LM8137918050500
LM8170317575500
LM8100218125500
LM85307217560xO
LM85088017553xO
LM8219517541500
LM8100218131500
LM85307217563xO
LM85088017555xO
LM8219517541500
LM8100218134500
OR £. missing
fiche missing
263
-------
Path. 42 Row 35
45/35
46/35
Path 42 Row 36
45/36
92/06/19
85/06/16
74/06/26
74/07/15
92/08/30
85/09/12
74/08/19
LM84362617411xO
LM84106617583xO
LM8170317590500
LM8172218041500
LM85310417565xO
LM85056018034xO
LM8175717574500
Second WRS-1 not required?
Path 43 Row 26
46/26
47/26
Path 43 Row 27
46/27
47/27
90/07/15
85/08/18
75/07/01
73/08/08
r
90/07/15
86/07/20
75/07/10
73/08/07
73/08/08
LM85232717564xO
LM85053518062xO
LM8508217450500
LM8138118140500
LM85232717571xO
LM85087117594xO
LM8508217453500
LM8138018084500
LM8138118142500
OR
fiche missing
264
-------
Path 43 Row 28
46/28
47/28
92/07/28
86/07/20
72/08/12
73/08/07
75/07/10
73/08/08
LM84366517455xO
LM85087118000xO
LM8102018085500
LM8138018090500
LM8508217455500
LM8138118145500
OR
OR
fiche missing
Path 43 Row 29
46/29
47/29
Path 43 Row 30
92/07/28
86/07/20
72/08/12
73/08/07
73/08/08
46/30
47/30
Path 43 Row 31
46/31
47/31
92/08/05
86/08/05
73/08/07
73/08/08
92/08/13
86/08/21
73/08/07
73/08/08
LM84366517461xO
LM85087118002xO
LM8102018092500
LM8138018093500
LM8138118151500
LM85307918010xO
LM85088718001xO
LM8138018095500
LM8138118154500
LM84368117473xO
LM85090318000xO
LM8138018102500
LM8138118160500
OR
fiche missing
265
-------
Path 43 Row 32
46/32
47/32
Path 43 Row 33
46/33
47/33
Path 43 Row 34
46/34
47/34
Path 43 Row 35
46/35
92/07/28
86/07/20
72/07/25
73/07/26
J
92/08/05
86/08/05
72/07/25
72/07/26
t
92/08/05
85/08/05
72/07/25
74/06/28
92/09/06
85/08/18
72/09/17
LM84366517472xO
LM85087118014xO
LM8100218125500
LM8100318170500
LM85307918021xO
LM85088718013xO
LM8100218131500
LM8100318173500
LM85307918023xO
LM85051918094xO
LM8100218134500
LM8170518100500
LM85311118023xO
LM85053518095xO
LM8102018115500
Second WRS-1 not required?
266
-------
Path 43 Row 36
46/36
92/09/06
85/08/18
72/09/17
LM85311118025xO
LM85053518101xO
LM8105618123500
Second WRS-1 not required?
check fiche
Path 44 Row 26
92/07/19
85/07/08
47/26 73/08/08
48/26 72/07/27
Path 44 Row 27
91/08/02
85/08/25
47/27 73/08/08
48/27 72/07/27
Path 44 Row 28
92/08/20
86/08/12
47/28 73/08/08
48/28 72/07/27
LM84365617510xO
LM85049418125xO
LM8138118140500
LM8100418194500
LM84330417591xO
LM85054218125xO
LM8138118142500
LM8100418201500
LM84368817524xO
LM85089418052xO
LM8138118145500
LM8100418203500
267
-------
Path 44 Row 29
47/29
48/29
92/08/20
85/08/25
73/08/08
72/07/27
LM84368817530xO
LM85054218133xO
LM8138118151500
LM8100418210500
Path. 44 Row 30
47/30
48/30
Path 44 Row 31
47/31
48/31
Path 44 Row 32
92/08/28
86/08/12
73/08/08
72/07/27
92/08/04
85/08/09
73/08/08
72/07/27
47/32
48/32
92/08/04
86/08/12
73/07/26
72/07/27
LM85310218065xO
LM85089418061xO
LM8138118154500
LM8100418212500
LM84367217532xO
LM85052618143xO
LM8138118160500
LM8100418215500
LM84367217535xO
LM85089418070xO
LM8100318170500
LM8100418221500
268
-------
Path 44 Row 33
47/33
48/33
Path 44 Row 34
47/34
92/08/04
86/08/12
72/07/26
73/07/22
I
92/08/04
85/07/08
74/06/28
LM84367217541xO
LM85089418072xO
LM8100318173500
LM8136418225500
LM84367217544x0
LM85049418160xO
LM8170518100500
Second WRS-1 not required?
Path 44 Row 35
47/35
92/09/13
84/09/07
72/08/13
LM85311818083xO
LM85019018163xO
LM8102118174500
Second WRS-1 not required?
Path 45 Row 26
49/26
48/26
92/08/11
86/08/19
73/09/15
72/07/27
LM84367917575xO
LM85090118103xO
LM8141918244500
LM8100418194500
Bad fiche
269
-------
Path 45 Row 27
49/27
48/27
Path 45 Row 28
49/28
48/28
Path 45 Row 29
49/29
48/29
Path 45 Row 30
49/30
48/30
92/08/03
86/08/19
73/09/15
72/07/27
92/08/03
86/08/19
72/09/02
72/07/27
92/08/03
86/08/19
72/09/02
72/07/27
92/08/03
86/08/19
72/09/02
72/07/27
LM85307718121xO
LM85090118105xO
LM8141918251500
LM8100418201500
LM85307718123xO
LM85090118112xO
LM8104118262500
LM8100418203500
LM85307718130xO
LM85090118114xO
LM8104118265500
LM8100418210500
LM85307718132xO
LM85090118120xO
LM8104118271500
LM8100418212500
270
-------
Path 45 Row 31
92/08/03
86/08/19
49/31 73/08/28
48/31 72/07/27
Path 45 Row 32
92/09/04
86/08/19
49/32 73/08/28
48/32 72/07/27
Path 45 Row 33
92/09/04
86/08/19
49/33 73/08/28
48/33 73/07/22
Path 46 Row 26
92/08/18
85/08/23
49/26 73/09/16
50/26 73/09/16
LM8530771834xO
LM85090118123xO
LM8140118271500
LM8100418215500
LM85310918134xO
LM85090118125xO
LM8140118273500
LM8100418221500
LM85310918140xO
LM85090118132xO
LM8140118280500
LM8136418225500
LM84368618041xO
LM85054018244xO
LM8141918244500
LM8142018303500
Fiche missing
Fiche missing
Fiche missing
EROS Check Film
271
-------
Path 46 Row 27
92/08/10
85/08/23
49/27 73/09/15
50/27 72/07/27
Path 46 Row 28
92/08/10
85/08/23
49/28 72/09/02
50/28 72/07/29
Path 46 Row 29
49/29
50/29
Path 46 Row 30
49/30
50/30
92/08/26
85/09/24
72/09/02
72/07/29
92/08/10
86/08/10
72/09/02
72/07/29
LM85308418181xO
LM85054018251xO
LM8141918251500
LM8100418201500
LM85308418184xO
LM85054018253xO
LM8104118262500
LM8100618315500
LM85310018185xO
LM85057218253xO
LM8104118265500
LM8100618322500
LM85308418193xO
LM85089218183xO
LM8104118271500
LM8100618324500
272
-------
Path 46 Row 31
49/31
50/31
Path 46 Row 32
49/32
92/08/10
84/07/03
73/07/23
73/07/24
92/07/09
84/07/03
73/08/28
LM85308418195xO
LM85012418254xO
LM8136518274500
LM8136618332500
LM85305218204xO
LM85015618270xO
LM8140118273500
Second WRS-1 not required?
Path 47 Row 26
50/26
51/26
Path 47 Row 27
50/27
51/27
92/07/16
86/08/01
73/09/16
74/09/12
92/07/16
c
86/08/01
75/07/23
74/09/12
LM85305918242xO
LM85088318233xO
LM8142018303500
LM8178118265500
LM85305918244xO
LM85088318235xO
LM8218218201500
LM8178118272500
Black fiche
Fiche missing
273
-------
Path 47 Row 28
50/28
51/28
Path 47 Row 29
50/29
92/07/16
86/05/29
75/07/23
74/09/12
92/07/16
86/05/29
72/07/29
LM85305918251xO
LM85081918262xO
LM8218218204500
LM8178118274500
LM85305918253xO
LM85081918265xO
LM8100618322500
Second WRS-1 not required?
Path 47 Row 30
50/30
92/07/16
86/06/30
72/07/29
LM85305918260xO
LM85305918260xO
LM8100618324500
Second WRS-1 not required?
Path 48 Row 26
51/26
52/26
92/09/09
86/08/08
74/09/12
74/09/13
LM85311418294xO
LM85089018293xO
LM8178118265500
LM8178218324500
274
-------
Path 48 Row 27
51/27
92/09/09
84/07/17
74/09/12
LM85311418301xO
LM85047418380xO
LM8178118272500
Second WRS-1 not required?
275
-------
NALC HSS Triplicates - Eastern and Southern US
Path 6 Row 38
Path 10 Row 29
To be selected
11/29
91/07/11
91/06/25
86/08/14
75/07/02
LM85268814370xO
LM85267214370xO
LM85089614353xO
LM8216114374500
Second WRS-1 not required?
Path 11 Row 27
11/27
12/27
Path 11 Row 28
11/28
12/28
Path 11 Row 29
11/29
12/29
91/06/16
85/07/01
74/08/21
76/06/09
J
91/06/16
85/07/01
76/08/19
73/07/22
)
91/07/02
85/07/01
75/07/02
74/07/17
LM85266314421xO
LM85048714492xO
LM8175914384500
LM8250414361500
LM85266314423xO
LM85048714494xO
LM8257514285500
LM8136414541500
LM85267914431xO
LM85048714500xO
LM8216114374500
LM8172414463500
Composite with
276
-------
Path 11 Row 31
92/08/21
86/08/05
12/31 74/07/17
Path 12 Row 27
91/06/07
86/07/27
12/27 76/06/09
13/27 73/07/23
Path 12 Row 28
91/06/07
85/06/22
12/28 73/07/22
13/28 73/07/23
Path 12 Row 29
91/06/07
85/06/22
12/29 74/07/17
13/29 73/07/23
LM85309514432xO
LM85088714425xO
LM8172414472500
LM85265414481xO
LM85087814475xO
LM8250414361500
LM8136514593500
LM85265414484xO
LM85047814555xO
LM8136414541500
LM8136514595500
LM85265414490xO
LM85047814562xO
LM8172414463500
LM8136515002500
277
-------
Path 12 Row 30
13/30
92/08/12
86/09/13
73/07/23
LM85308614492xO
LM85092614471xO
LM8136515004500
Second WRS-1 not required?
Path 12 Row 31
12/31
13/31
Path 13 Row 28
14/28
91/06/07
85/08/09
74/07/17
73/07/23
(
91/06/14
84/06/10
75/06/26
LM85265414495xO
LM85052614564xO
LM8172414472500
LM8136515011500
LM85266114545xO
LM85010115001xO
LM8506814431500
Second WRS-1 not required?
Path 13 Row 29
14/29
92/06/16
84/06/10
74/06/13
LM85302914554xO
LM85010115004xO
LM8169014591500
Second WRS-1 not required?
278
-------
Path 13 Row 30
14/30
92/09/20
85/09/17
73/08/29
LM85312514545x0
LM85056515020xO
LM8140215060500
Second WRS-1 not required?
Path 13 Row 31
14/31
91/07/16
84/06/10
74/06/13
LM85269314563xO
LM85010115013xO
LM8169015000500
Second WRS-1 not required?
Path 13 Row 32
14/32
91/07/16
84/06/10
74/06/13
LM85269314570x0
LM85010115015xO
LM8169015002500
Second WRS-1 not required?
Path 14 Row 29
15/29
91/08/08
86/08/26
76/06/03
LM85271615021xO
LM85090814595xO
LM8541114274500
Second WRS-1 not required?
279
-------
Path 14 Row 35
91/07/23
86/07/09
15/35 75/06/18
LM85274815045xO
LM85086015040xO
LM8214715031500
Second WRS-1 not required?
Path 14 Row 36
15/36
90/09/06
86/09/27
73/08/30
LM85238015011xO
LM85094015013xO
LM8140315141500
Path 15 Row 29
16/29
17/29
92/07/16
85/07/13
75/06/10
72/08/19
LM85305915075xO
LM85049915144xO
LM8505214560500
LM8102715231500
Path 15 Row 35
15/35
16/35
91/10/18
85/09/15
73/08/30
72/10/11
LM85278715112xO
LM85056315163xO
LM8140315134500
LM8108015201500
280
-------
Path 15 Row 36
15/36
16/36
91/10/18
85/09/15
73/08/30
72/10/11
LM85278715114xO
LM85056315165xO
LM8140315141500
LM8108015203500
Path 15 Row 37
16/37
91/10/18
85/08/14
72/10/11
LM85278715121xO
LM85053115171xO
LM8108015210500
Second WRS-1 not required?
Path 15 Row 41
16/41
92/05/13
86/04/27
73/04/09
LM85299515132xO
LM8507871544xO
LM8126015233500
Second WRS-1 not required?
Path 15 Row 42
16/42
91/03/08
91/02/20
86/04/27
73/03/22
LM85256315111xO
LM85254715105xO
LM85078715151xO
LM8124215240500
Second WRS-1 not required?
Composite with
281
-------
Path 15 Row 43
16/43
92/05/13
86/04/27
75/02/13
LM85299515140xO
LM85078715153xO
LM8202215122500
Second WRS-1 not required?
Path 16 Row 35
17/35
91/10/09
85/09/06
75/08/22
LM85277815172xO
LM85055415224xO
LM8512514595500
Second WRS-1 not required?
Path 16 Row 36
17/36
90/10/06
85/09/06
72/10/12
LM85241015132xO
LM85055415231xO
LM8108115262500
Second WRS-1 not required?
Path 16 Row 37
17/37
92/10/06
86/09/25
72/10/12
LM85241015134xO
LM85093815142xO
LM8108115264500
Second WRS-1 not required?
282
-------
Path 16 Row 38
91/08/06
86/08/24
LM85271415180xO
LM85090615155xO
17/38
Second WRS-1 not required?
72/08/19
74/08/27
LM8102715265500
LM8176515173500
OR (EROS Check)
Path 16 Row 39
92/05/04
85/05/17
17/39 74/05/11
LM85298615184XO
LM85044215250xO
LM8165715213500
Second WRS-1 not required?
Path 16 Row 40
92/03/17
86/04/02
17/40 73/04/10
LM85293815193xO
LM85076215210xO
LM8126115285500
Second WRS-1 not required?
Path 16 Row 41
92/03/17
86/04/18
16/41 73/04/09
17/41 73/04/28
LM85293815200xO
LM85077815211xO
LM8126015233500
LM8127915291500
283
-------
Path. 16 Row 42
92/05/04
86/04/18
16/42 73/03/22
17/42 75/04/09
LM85298615195xO
LM85077815213xO
LM8122415240500
LM8207715173500
Path 17 Row 30
92/06/28
85/07/27
18/30 75/06/30
19/30 74/07/06
LM85304115204xO
LM85051315272XO
LM8507215064500
LM8171315273500
Path 17 Row 33
18/33
19/33
92/10/02
85/09/29
76/09/13
73/09/03
LM85313715203xO
LM85057715275xO
LM8260015103500
LM8140715355500
Path 17 Row 35
18/35
19/35
92/10/02
84/10/12
72/09/07
73/10/27
73/07/29
LM85313715211xO
LM85022515294xO
LM8104615313500
LM8146115352500
LM8137115371500
OR (if too late)
284
-------
Path 17 Row 36
91/09/30
86/10/18
18/36 72/09/07
19/36 73/09/03
LM85276915235xO
LM85096115192xO
LM8104615315500
LM8140715370500
Path 17 Row 37
90/09/27
86/10/18
18/37 74/10/21
19/37 74/10/04
Path 17 Row 38
90/10/29
86/10/18
18/38 72/10/31
19/38 74/10/04
Path 17 Row 39
91/10/16
84/10/12
18/39 72/10/13
19/39 72/10/14
LM85240115200xO
LM85096115195xO
LM8182015212500
LM8183915262500
LM85243315201xO
LM85096115201xO
LM8110015331500
LM8180315273500
LM85278515252xO
LM85022515311xO
LM8108215332500
LM8108315390500
285
-------
Path 17 Row 40
18/40
91/10/16
84/10/12
74/10/21
LM85278515254xO
LM85022515314xO
LM8182015223500
Second WRS-1 not required?
Path 17 Row 41
18/40
91/10/16
86/10/18
73/11/13
LM85278515260xO
LM85096115212xO
LM8147815315500
Second WRS-1 not required?
Path 18 Row 31
19/31
20/31
Path 18 Row 32
19/32
20/32
92/06/03
86/06/03
74/07/06
74/07/07
>
90/08/17
85/07/18
73/09/03
73/09/04
LM85301615274xO
LM85082415281xO
LM8171315275500
LM8171415334500
LM85236015243xO
LM85050415343xO
LM8140715352500
LM8140815410500
286
-------
Path 18 Row 33
19/33
20/33
91/09/21
84/09/17
73/09/03
73/09/04
LM85276015285xO
LM85020015350xO
LM8140715355500
LM8140815413500
Path 18 Row 34
91/09/21
84/09/17
19/34 73/09/03
20/34 74/10/05
LM85276015292xO
LM85020015352xO
LM8140715361500
LM8180415313500
Path 18 Row 35
90/10/10
84/10/03
19/35 73/10/27
73/07/29
20/35
72/10/15
LM85242415251xO
LM85021615355xO
LM8146115352500
LM8137115371500
LM8108415431500
OR (if too late)
Path 18 Row 36
90/10/20
84/10/03
19/36 73/09/03
20/36 72/10/15
LM85242415254xO
LM85021615361xO
LM8140715370500
LM8108415433500
287
-------
Path 18 Row 37
19/37
20/37
90/10/20
85/10/06
74/10/04
72/10/15
LM85242415260xO
LM85058415353xO
LM8180315271500
LM8104815434500
Path 18 Row 38
19/38
20/38
90/10/20
85/10/06
74/10/04
74/10/23
LM85242415263xO
LM85058415360xO
LM8180315273500
LM8182215331500
Path. 18 Row 39
19/39
20/39
90/09/18
85/10/06
72/10/14
74/10/23
LM85239215271xO
LM85058415362xO
LM8108315390500
LM8182215333500
Path 19 Row 31
20/31
21/31
91/06/08
85/06/23
74/07/07
73/06/07
75/07/30
LM85265515331xO
LM85047915402xO
LM8171415334500
LM8131915474500
LM8218915352500
OR (EROS Check)
288
-------
Path 19 Row 32
20/32
21/32
92/08/29
86/08/29
86/08/13
73/09/04
73/07/13
LM85310315330xO
LM85091115315xO
LM85089515322xO
LM8140815410500
LM8135515474500
Composite with
Path 19 Row 33
'92/08/29
84/09/08
20/33 73/09/04
21/33 74/10/06
Path 19 Row 34
20/34
21/34
Path 19 Row 35
20/35
21/35
90/09/25
84/09/08
74/10/05
74/10/06
92/09/30
86/09/14
72/10/15
74/10/06
LM85310315333xO
LM85019115,411xO
LM8140815413500
LM8180515365500
LM85239915311xO
LM85019115413xO
LM8180415313500
LM8180515372500
LM85313515334xO
LM85092715323xO
LM8108415431500
LM8180515374500
289
-------
Path 19 Row 36
20/36
21/36
91/09/28
84/09/08
72/10/15
74/10/06
LM85276715361xO
LM85019115422xO
LM8108415433500
LM8180515381500
Path 19 Row 37
91/09/28
83/10/16
20/37 72/10/15
21/37 74/10/06
LM85276715364xO
LM84045715421xO
LM8108415440500
LM8180515383500
Path 19 Row 38
20/38
21/38
91/10/14
86/10/16
74/10/23
72/10/16
LM85278315371xO
LM85095915324xO
LM8182215331500
LM8108515501500
Path 19 Row 39
91/10/14
86/10/16
20/39 74/10/23
21/39 72/10/16
LM85278315374xO
LM85095915330xO
LM8182215333500
LM8106715501500
290
-------
Path 20 Row 29
92/08/20
86/08/20
21/29 75/08/08
22/29 75/07/31
LM85309415381xO
LM85091215371xO
LM8511115211500
LM8219015401500
Path 20 Row 30
92/09/05
86/08/20
21/30 75/08/08
22/30 75/07/31
LM85311015382xO
LM85090215373xO
LM8511115214500
LM8219015404500
Path 20 Row 31
90/08/31
85/08/09
21/31 75/07/30
22/31 75/07/31
Path 20 Row 32
21/32
22/32
91/08/02
86/08/04
73/07/13
74/07/09
LM85237415362xO
LM84112015402xO
LM8218915352500
LM8219015410500
LM85271015402xO
LM85088615385xO
LM8135515474500
LM8171615453500
291
-------
Path 20 Row 33
21/33
22/33
91/08/02
86/08/04
75/08/08
76/07/25
LM85271015404xO
LM85088615392xO
LM8511115225500
LM8255015345500
Path 20 Row 34
21/34
22/34
92/10/07
86/10/07
74/10/06
76/08/30
LM85314215391xO
LM85095015373xO
LM8180515372500
LM8258615341500
Path 20 Row 35
21/35
22/35
90/09/16
86/10/07
74/10/06
75/10/20
LM85239015375xO
LM85095015375xO
LM8180515374500
LM8518415251500
Path 20 Row 36
21/36
22/36
90/09/16
86/07/19
74/10/06
75/10/02
LM85239015382xO
LM85087015410xO
LM8180515381500
LM8516615264500
292
-------
Path 20 Row 37
21/37
22/37
90/09/16
86/09/21
74/10/06
74/10/07
LM85229415390xO
LM85093415391xO
LM8180515383500
LM8180615442500
Path 20 Row 38
21/38
22/38
90/09/16
84/10/01
74/09/18
74/10/07
LM85231015393xO
LM85021415492xO
LM8178715100500
LM8180615444500
Path 20 Row 39
21/39
22/39
90/09/16
84/10/01
72/10/16
74/10/07
LM85239015393xO
LM85021415495xO
LM8108515503500
LM8180615451500
Path 21 Row 28
22/28
23/28
91/07/08
86/06/24
86/06/08
75/07/31
74/07/10
75/06/26
LM85268515444xO
LM85084515445xO
LM85082915453xO
LM8219015395500
LM8171715493500
LM8215515460500
OR
OR (EROS Check)
293
-------
Path 21 Row 29
22/29
23/29
91/08/09
85/08/08
75/07/31
75/08/01
LM85271715452xO
LM85052515513xO
LM8219015401500
LM8219115460500
Path 21 Row 30
22/30
23/30
Path 21 Row 31
22/31
23/31
91/08/25
85/07/23
75/07/31
75/08/01
91/08/25
85/07/23
75/08/27
74/08/15
LM85273315455xO
LM85050915521xO
LM8219015404500
LM8219115462500
LM85273315462xO
LM85050915523xO
LM8513015264500
LM8175315493500
Path 21 Row 32
22/32
23/32
91/06/06
84/06/02
73/06/08
73/06/09
LM85265315455xO
LM85009315511xO
LM8132015534500
LM8132115593500
294
-------
Path 21 Row 33
92/09/28
85/09/01
22/33 75/10/02
23/33 72/09/30
LM85313315452xO
LM84114315465xO
LM8516615252500
LM8106915591500
Path 21 Row 34
92/09/28
86/09/28
22/34 76/08/30
23/34 72/09/30
LM85313315454xO
LM85094115440xO
LM8258615341500
LM8106915594500
Path 21 Row 35
91/09/26
86/09/28
22/35 75/10/20
23/35 72/09/12
Path 21 Row 36
91/09/26
86/09/28
22/36 75/10/02
23/36 73/08/20
LM85276515481xO
LM85094115442xO
LM8518415251500
LM8105116000500
Faded fiche
(EROS Check)
LM85276515483xO
LM85094115445xO
LM8226215421500
LM8139316001500
Black fiche
(EROS check)
295
-------
Path 21 Row 37
22/37
23/37
91/09/26
86/09/29
74/10/07
74/09/20
LM85276515490xO
LM85094115451xO
LM8180615442500
LM8178915503500
Path 21 Row 38
22/38
23/38
Path 21 Row 39
91/09/26
86/09/28
74/10/07
72/09/12
22/39
23/39
92/10/14
84/09/06
74/10/07
74/10/08
LM85276515492xO
LM85094115453xO
LM8180615444500
LM8105116012500
LM85314915472xO
LM85018915560xO
LM8180615451500
LM8180715505500
Faded Fiche
(EROS Check)
Path 21 Row 40
22/40
23/40
92/10/14
85/09/09
75/10/11
74/10/08
LM85314915474xO
LM85055715554xO
LM8226215435500
LM8180715512500
296
-------
Path 22 Row 27
91/07/15
86/08/18
23/27 75/06/26
24/27 73/07/16
LM85269215503xO
LM85090015485xO
LM8215515454500
LM8135816024500
Path 22 Row 28
91/07/15
85/06/28
23/28 74/07/10
75/06/26
24/28
74/07/11
LM85269215510xO
LM85048415574xO
LM8171715493500
LM8215515460500
LM8171815551500
OR (EROS Check)
Path 22 Row 29
91/07/15
85/06/28
23/29 73/06/09
24/29 73/06/10
Path 22 Row 30
91/07/15
85/06/28
23/30 73/06/09
24/30 73/06/10
LM85269215512xO
LM85048415580xO
LM8132115581500
LM8132216042500
LM85269215514xO
LM85048415583xO
LM8132115584500
LM8132216042500
297
-------
Path 22 Row 31
23/31
24/31
91/07/15
85/07/22
73/06/09
73/06/10
LM85269215521xO
LM84110215530xO
LM8132115590500
LM8132216045500
Path 22 Row 32
23/32
24/32
92/10/05
85/10/02
75/10/12
74/09/21
LM85314015505xO
LM85058015582xO
LM8226315461500
LM8179015541500
Path 22 Row 33
23/33
24/33
Path 22 Row 34
23/34
24/34
92/10/05
85/10/02
74/10/08
74/10/09
[
92/10/05
85/09/16
72/09/30
74/10/09
LM85314015511xO
LM85058015584xO
LM8180715482500
LM8180815541500
LM85314015514xO
LM85056415592xO
LM8106915594500
LM8180815543500
298
-------
Path 22 Row 35
92/10/05
85/09/16
23/35 72/09/12
24/35 72/10/01
75/09/07
LM85314015520xO
LM85056415594xO
LM8105116000500
LM8107016055500
LM8222815533500
Prime
Alternate
Path 22 Row 36
91/07/31
86/07/17
23/36 73/08/20
24/36 74/07/29
LM85270815542xO
LM85086815533xO
LM8139316001500
LM8173615575500
Black fiche
(EROS Check)
Path 22 Row 37
92/10/05
86/10/21
23/37 72/09/12
24/37 75/10/13
Path 22 Row 38
92/10/05
85/08/31
23/38 74/10/08
24/38 75/10/13
LM85314015525xO
LM85096415504xO
LM8105116005500
LM8226415540500
LM85314015531xO
LM85054816011xO
LM8180715503500
LM8226415543500
299
-------
Path. 22 Row 39
23/39
24/39
92/10/05
85/08/31
74/10/08
72/10/01
LM85314015534xO
LM85054816013xO
LM8180715505500
LM8107016073500
Path 22 Row 40
23/40
24/40
92/10/05
85/08/31
74/10/08
74/10/09
LM85314015540xO
LM85054816015xO
LM8180715512500
LM8180815570500
Path 23 Row 27
24/27
25/27
Path 23 Row 28
24/28
25/28
91/06/04
85/06/03
73/07/16
74/06/24
(
91/06/04
86/08/09
74/07/11
75/07/16
LM85265115561xO
LM85045916033xO
LM813581602450d
LM8170116010500
LM85265115564x0
LM85089115554xO
LM8171815551500
LM8217515572500
300
-------
Path 23 Row 29
91/06/04
85/06/03
24/29 74/07/11
25/29 73/07/17
LM85265115570xO
LM85045916042xO
LM8171815554500
LM8135916091500
Path 23 Row 30
91/06/04
85/06/03
24/30 73/06/10
25/30 73/06/11
LM85265115572xO
LM85045916044xO
LM8132216042500
LM8132316100500
Path 23 Row 31
92/09/10
85/09/07
24/31 75/09/07
25/31 72/10/02
Path 23 Row 32
90/09/05
84/09/20
24/32 74/09/21
25/32 72/10/02
LM85311515571xO
LM85055516042xO
LM8222815515500
LM8107116095500
LM85237915551xO
LM85020316053xO
LM8179015541500
LM8107116102500
301
-------
Path 23 Row 33
24/33
25/33
90/09/05
84/09/20
74/10/09
72/10/02
LM85237915554xO
LM85020316060xO
LM8180815541500
LM8107116104500
Path 23 Row 34
24/34
25/34
90/09/05
84/09/20
74/10/09
72/10/02
LM85237915560xO
LM85020316062xO
LM8180815543500
LM8107116111500
Path 23 Row 35
24/35
25/35
Path 23 Row 36
90/09/05
84/09/20
75/09/07
73/08/21
72/10/02
24/36
25/36
92/10/12
84/09/20
74/07/29
72/10/02
LM85237915563xO
LM85020316064xO
LM8222815533500
LM8139416053500
LM8107116113500
LM85314715583xO
LM85020316071xO
LM8173615575500
LM8107116120500
OR (EROS Check)
302
-------
Path 23 Row 37
92/10/12
84/09/20
24/37 75/10/13
25/37 72/10/02
LM85314715585xO
LM85020316073xO
LM8226415540500
LM8107116122500
Path 23 Row 38
, 91/10/10
86/08/25
24/38 75/10/13
25/38 72/10/02
LM85277916015xO
LM85090715590xO
LM8226415543500
LM8107116125500
Path 23 Row 39
92/10/12
86/08/25
24/39 72/10/01
75/09/25
25/39
74/10/10
Path 23 Row 40
92/10/12
86/08/25
84/10/06
24/40 74/10/09
25/40 74/10/10
LM85314715594xO
LM85090715593xO
LM8107016073500
LM8224615550500
LM8180916022500
LM85314716001xO
LM85090715595xO
LM85021916084xO
LM8180815570500
LM8180916025500
OR (EROS Check)
OR (EROS Check)
303
-------
•.-pa-
24 Row 27
26/27
27/27
92/06/29
85/08/13
74/06/25
76/06/06
LM85304216025xO
LM85053016092xO
LM8170216065500
LM8250116022500
Path 24 Row 28
26/28
27/28
Path 24 Row 29
26/29
27/29
Path 24 Row 30
26/30
27/30
90/06/24
85/07/12
75/07/17
74/06/26
)
91/08/14
86/08/16
73/08/05
73/09/11
)
90/09/04
86/09/01
72/08/28
72/08/29
LM85230616001xO
LM85049816100xO
LM8217616030500
LM8170316125500
LM85272216035xO
LM85089816020xO
LM8137816144500
LM8141516195500
LM84297216090xO
LM85091416015xO
LM8103616152500
LM8103716210500
304
-------
Path 24 Row 31
25/31
26/31
92/10/03 LM85313816025xO
86/09/01 LM85091416021xO
GLIS for scene
74/09/05 LM8177416055500
Path 24 Row 32
25/32
26/32
92/10/03
85/10/16
72/10/02
73/10/16
LM85313816032xO
LM85059416103xO
LM8107116102500
LM8145016142500
Path 24 Row 33
92/10/03
85/10/16
25/33 72/10/02
26/33 73/10/16
Path 24 Row 34
91/10/17
85/10/16
25/34 72/10/02
26/34 72/08/28
LM85313816034xO
LM850594l6110xO
LM8107116104500
LM8145016145500
LM85278616063xO
LM85059416112xO
LM8107116111500
LM8103616165500
305
-------
Path 24 Row 35
25/35
26/35
92/10/03
85/10/16
72/10/02
74/07/31
LM85313816043xO
LM85059416114xO
LM8107116113500
LM8173816085500
Path 24 Row 36
25/36
26/36
92/10/19
85/10/16
72/10/02
75/09/27
LM85315416044xO
LM85059416121xO
LM8107116120500
LM8224816051500
Path 24 Row 37
25/37
26/37
Path 24 Row 38
25/38
26/38
92/10/19
86/10/19
72/10/02
75/09/27
!
92/10/19
86/09/17
72/10/02
72/10/03
LM85315416050xO
LM85096216030xO
LM8107116122500
LM8224816054500
LM85315416053xO
LM85093016043xO
LM8107116125500
LM8107216183500
306
-------
Path 24 Row 39
92/10/19
86/10/19
25/39 74/10/10
26/39 72/10/03
LM85315416055xO
LM85096216035xO
LM8180916022500
LM8107216190500
Path 25 Row 26
92/06/20
86/07/06
26/26 76/06/05
27/26 76/07/03
LM85303316084xO
LM85085716083xO
LM8250015561500
LM8544115325500
Path 25 Row 27
91/08/05
86/07/22
26/27 74/06/25
27/27 76/06/05
Path 25 Row 28
91/08/05
86/07/22
26/28 75/07/17
27/28 74/06/26.
LM85271316092xO
LM85087316082xO
LM8170216065500
LM8250116022500
LM85271316094xO
LM85087316084xO
LM8217616030500
LM8170316125500
307
-------
Path 25 Row 29
92/09/0a
86/09/08
26/29 7/3/0 8/Q5
27/29 73/09/11
LM85311316085xO
LM85092116072xO
LM8137816144500
LM8141516195500
Path 25 Row 30
92/09/08
86/09/08
26/30 72/08/28
27/30 73/09/11
Path 25 Row 31
92/09/08
86/09/08
26/31 74/09/05
27/31 72/08/29
Path 25 Row 32
26/32
27/32
92/09/24
86/09/08
73/08/05
74/08/19
LM85311316091xO
LM85092116074x0
LM8103616152500
LM8141516202500
LM85311316093xO
LM85092116081xO
LM8177416055500
LM8103716213500
LM85312916094xO
LM85092116083xO
LM8137816160500
LM8175716124500
308
-------
Path 25 Row 33
92/09/24
84/09/18
26/33 73/10/16
27/33 72/10/04
LM85312916100xO
LM85020116182xO
LM8145016145500
LM8107316221500
Path 25 Row 34
92/09/24
84/09/18
26/34 72/08/28
27/34 72/10/04
LM85312916100xO
LM85020116182xO
LM8103616165500
LM8107316224500
Path 25 Row 35
90/08/26
85/08/28
26/35 74/07/31
27/35 72/08/11
Path 25 Row 36
91/10/08
84/07/08
26/36 75/09/27
27/36 72/10/04
LM84296316172xO
LM84113916122xO
LM8173816085500
LM8101916225500
LM85277716132xO
LM84072316143xO
LM8224816051500
LM8107316233500
309
-------
Path. 25 Row 37
91/10/08
8.4/07/08
26/37 75/03/27
27/37 72/10/04
LM85277716135xO
LM84072316145xO
LM8224816051500
LM8107316235500
Path 25 Row 38
92/07/06
814/0 9/18
26/38 74/07/13
27/38 73/07/19
Path 25 Row 39
92/07/06
85/06/01
26/39 74/07/13
27/39 74/06/26
Path 25 Row 40
92/07/06
85/06/01
26/40 76/06/23
27/40 74/06/26
LM85304916132xO
LM85020116202xO
LM8172016105500
LM8136116243500
LM85304916134x0
, .V '"
LM85045716204xO
LM8172016111500
LM8170316173500
LM85304916140xO
LM85045716210xO
LM8251816014500
LM8170316175500
310
-------
NALC MSS Triplicates - Midwest and Great Plains
Path 26 Row 27
28/27
91/08/12
86/07/29
72/08/12
LM85272016153xO
LM85088016141xO
LM8102016252500
Second WRS-1 not required?
Path 26 Row 28
28/28
91/08/28
85/09/12
72/08/12
LM85273616160xO
LM85056016214xO
LM8102016255500
Second WRS-1 not required?
Path 26 Row 29
27/29
28/29
Path 26 Row 30
27/30
28/30
90/08/25
85/09/12
73/09/11
74/09/25
)
92/08/30
85/07/10
73/09/11
74/09/25
LM85236816124xQ
LM85056016220xO
LM8141516195500
LM8179416154500
LM85310416153xO
LM85049616231xO
LM8141516202500
LM8179416160500
311
-------
Path 26 Row 31
27/31
28/31
92/08/30
84/08/08
72/08/29
74/09/25
LM85310416155xO
LM85016016231xO
LM8103716213500
LM8179416163500
Path 26 Row 32
27/32
28/32
Path 26 Row 33
27/33
28/33
92/10/01
86/10/17
74/08/19
74/09/25
I
92/10/01
86/10/17
72/10/14
74/09/25
LM85313616154xO
LM85096016133xO
LM8175716124500
LM817941616.5500
LM85313616160xO
LM85096016135xO
LM8107316221500
LM8179416165500
Path 26 Row 34
27/34
28/34
92/10/01
86/10/17
72/10/04
72/09/17
LM85313616163xO
LM85096016142xO
LM8107316224500
LM8105616282500
312
-------
Path 26 Row 35
90/08/25
84/08/24
27/35 72/08/11
28/35 73/08/25
Path 26 Row 36
90/06/06
85/06/08
27/36 75/06/12
28/36 74/08/20
Path 26 Row 37
27/37
28/37
Path 26 Row 38
27/38
28/38
91/06/25
85/06/08
75/06/12
74/06/27
J
91/06/25
84/07/15
73/07/19
74/06/27
LM85236816150xO
LM85017616251xO
LM8101916225500
LM8139816282500
LM85228816154xO
LM85046416254xO
LM8214116122500
LM8175816200500
LM85267216190xO
LM85046416260xO
LM8214116124500
LM8170416222500
LM85267216192xO
LM84073016213xO
LM8136116243500
LM8170416225500
Black fiche
(EROS Check)
313
-------
Path 26 Row 39
27/39
28/39
90/07/08
84/07/15
74/06/26
74/06/27
LM85232016165xO
LM84073016215xO
LM8170316173500
LM8170416231500
Path 26 Row 40
27/40
28/40
Path 26 Row 41
28/41
92/10/01
86/10/17
76/09/22
75/10/17
L
92/10/01
86/10/17
73/10/18
LM85313616185xO
LM85096016164xO
LM9260916045500
LM8226816181500
LM85313616192xO
LM85096016170xO
LM8145216293500
Second WRS-1 not required?
Path 27 Row 27
29/27
91/06/16
84/06/28
73/07/03
LM85266316210xO
LM85011916264xO
LM8134516313500
EROS Check
Black fiche
Second WRS-1 not required?
314
-------
Path 27 Row 28
91/06/16
86/06/02
LM85266316212xO
LM85082316224xO
29/28
Second WRS-1 not required?
73/07/03
74/06/28
LM8134516320500
LM8170516242500
OR (EROS Check)
Path 27 Row 29
29/29
92/09/22
84/09/16
74/09/26
LM85312716205xO
LM85019916290xO
LM8179516212500
Second WRS-1 not required?
Path 27 Row 30
29/30
92/09/22
84/10/02
74/09/26
LM85312716212xO
LM85021516293xO
LM8179516214500
Second WRS-1 not required?
Path 27 Row 31
29/31
92/09/22
84/10/02
74/09/26
LM85312716214xO
LM85021516295xO
LM8179516221500
Second WRS-1 not required?
315
-------
Path 27 Row 32
29/32
92/09/22
85/08/10
74/09/26
LM85312716221xO
LM84112116235xO
LM8179516223500
Second WRS-1 not required?
Path 27 Row 33
29/33
92/09/22
84/08/15
73/08/26
LM85312716223xO
LM85016716302xO
LM8139916332500
Second WRS-1 not required?
Path 27 Row 34
29/34
92/09/22
85/10/05
74/09/08
LM85312716225xO
LM85058316300xO
LM8177716241500
Second WRS-01 not required?
Path 27 Row 35 To be selected
316
-------
Path 27 Row 36 To be selected
Path 27 Row 37 To be selected
Path 27 Row 38 To be selected
Path 27 Row 39 To be selected
317
-------
Path 27 Row 40 To be selected
Path 27 Row 41 To be selected
Path 28 Row 26
30/26
31/26
90/09/08
85/07/08
73/07/22
76/06/10
LM85238216235xO
LM85049416335xO
LM8136416364500
LM8250516245500
Path 28 Row 27
90/09/08
90/07/06
85/07/08
30/27 74/07/17
31/27 74/08/05
LM85238216241xO
LM85231816243xO
LM85049416342xO
LM8172416290500
LM8174316340500
OR
318
-------
Path 28 Row 28
30/28
91/08/26
85/08/25
74/07/17
LM85273416282xO
LM85054216342xO
LM8172416292500
Second WRS-1 not required?
Path 28 Row 29
91/08/26
85/09/26
30/29 73/09/14
LM85273416285xO
LM85057416342xO
LM8141816370500
Second WRS-1 not required?
Path 28 Row 30
30/30
92/09/29
85/09/26
73/09/14
LM85313416272xO
LM85057416344xO
LM8140016375500
Second WRS-1 not required?
Path 28 Row 31
92/08/28
84/08/14
30/31 73/08/27
LM85310216282xO
LM84076016305xO
LM8140016381500
Second WRS-1 not required?
319
-------
Path 28 Row 32
92/08/28
84/08/14
30/32 72/08/14
LM85310216284xO
LM84076016311x0
LM8102216384500
Second WRS-1 not required?
Path 28 Row 33
92/08/28
86/09/13
30/33 74/08/04
LM85310216290xO
LM85092616272xO
LM8174216305500
Second WRS-1 not required?
Path 28 Row 34
92/08/28
84/09/07
30/34 72/09/19
LM85310216293xO
LM85019016371xO
LM8105816395500
Second WRS-1 not required?
Path 28 Row 35
92/08/28
86/08/28
30/35 72/09/19
LM85310216295xO
LM85091016284xO
LM8105816401500
Second WRS-1 not required?
320
-------
Path 28 Row 36
92/08/28
85/08/09
29/36 72/07/26
30/36 74/07/17
Path 28 Row 37
92/08/28
85/08/09
29/37 73/07/21
30/37 72/09/19
LM85310216302xO
LM85052616374xO
LM810.0316355500
LM8172416324500
LM85310216304xO
LM85052616380xO
LM8136316353500
LM8105816410500
Path 28 Row 38
92/09/29
84/09/07
29/38 72/08/31
30/38 74/10/15
LM85313416303xO
LM85019016385xO
LM8103916355500
LM8181416304500
Path 28 Row 39
92/08/28
86/07/27
29/39 72/08/31
30/39 72/08/14
LM85310216313xO
LM85087816313xO
LM8103916361500
LM8102216414500
321
-------
Path 29 Row 26
31/26
32/26
90/08/30
85/07/31
76/06/10
74/08/06
LM85237316300xO
LM85051716395xO
LM8250516245500
LM8174416392500
Path 29 Row 27
31/27
32/27
90/07/13
86/07/18
74/08/05
74/08/06
LM85232516304xO
LM85086916331xO
LM8174316340500
LM8174416394500
Path 29 Row 28
31/28
32/28
90/07/13
84/07/20
73/08/28
72/07/29
LM85232516310xO
LM84073516360xO
LM8140116424500
LM8100616484500
Path 29 Row 29
31/29
32/29
90/07/13
84/08/13
73/07/05
72/07/29
LM85232516313xO
LM85016516410xO
LM8134716435500
LM8100616490500
322
-------
Path 29 Row 30
90/08/30
84/08/29
31/30 73/08/28
LM85237316313xO
LM85018116414xO
LM8140116433500
Second WRS-1 not required?
Path 29 Row 31
91/07/16
84/08/13
31/31 74/07/18
LM85269316352xO
LM85016516415xO
LM8172516362500
Second WRS-1 not required?
Path 29 Row 32
31/32
90/08/30
84/08/29
73/08/10
LM85237316322xO
LM85018116423xO
LM8138316444500
Second WRS-1 not required?
Path 29 Row 33
92/08/19
86/09/20
31/33 73/08/28
LM85309316352xO
LM85093316332xO
LM8140116444500
Second WRS-1 not required?
323
-------
Path 29 Row 34
92/07/18
86/08/03
31/34 73/07/05
LM85306116362xO
LM85088516352xO
LM8134716455500
Second WRS-1 not required?
Path 29 Row 35
91/08/01
86/07/18
31/35 73/07/05
LM85270916371xO
LM85086916362xO
LM8134716462500
Second WRS-1 not required?
Path 29 Row 36
92/08/03
86/07/18
31/36 75/08/09
LM85307716365xO
LM85086916364xO
LM8219916343500
Second WRS-1 not required?
Path 29 Row 37
30/37
31/37
92/08/03
85/07/31
72/09/19
75/08/09
LM85307716371xO
LM85051716442xO
LM8105816410500
LM8219916350500
324
-------
Path 29 Row 38
92/08/03
86/07/18
30/38 75/08/08
31/38 72/07/28
LM85307716374xO
LM85086916373xO
LM8219816294500
LM8100516470500
Path 30 Row 26
92/08/10
85/08/07
32/26 74/08/06
33/26 72/07/30
Path 30 Row 27
92/08/10
84/08/12
32/27 74/08/06
33/27 73/08/30
LM85308416390xO
LM85052416460xO
LM8174416392500
LM8100716533500
LM85308416392xO
LM84075816413xO
LM8174416394500
LM8140316534500
Path 30 Row 28
92/08/10
86/07/25
32/28 72/07/29
33/28 73/09/17
LM85308416394xO
LM85087616393xO
LM8100616484500
LM8142116534500
325
-------
Path 30 Row 29
32/29
33/29
91/08/24
84/08/12
72/07/29
72/09/04
LM85273216411xO
LM84075816422xO
LM8100616490500
LM8104316550500
Path 30 Row 30
32/30
33/30
Path 30 Row 31
32/31
91/08/24
84/08/12
72/07/29
72/08/17
90./09/14
84/08/28
72/09/21
LM85273216413xO
LM84075816424xO
LM8100616493500
LM8102516551500
LM84298216461xO
LM84077416431xO
LM8106016500500
Second WRS-1 not required?
Path 30 Row 32
32/32
92/09/27
86/09/27
72/09/21
LM85313216403xO
LM85094016385xO
LM8106016503500
Second WRS-1 not required?
326
-------
Path 30 Row 33
92/09/27
86/09/27
32/33 75/09/24
LM85313216405xO
LM85094016391xO
LM8515816231500
Second WRS-1 not required?
Path 30 Row 34
32/34
92/09/27
86/09/27
74/10/17
LM85313216412xO
LM85094016394xO
LM8181616403500
Second WRS-1 not required?
Path 30 Row 35
32/35
92/09/27
86/09/11
74/10/17
LM85313216414xO
LM85092416403xO
LM8181616405500
Second WRS-1 not required?
Path 30 Row 36
92/09/27
86/09/11
32/36 72/10/09
LM85313216421xO
LM85092416410xO
LM8107816522500
Second WRS-1 not required?
327
-------
Path 30 Row 37
32/37
92/10/13
86/10/13
72/10/09
LM85314816421xO
LM85095616402xO
LM8107816524500
Second WRS-1 not required?
Path. 30 Row 38
32/38
92/10/13
86/10/13
72/10/09
LM85314816423xO
LM85095616404xO
LM8107816531500
Second WRS-1 not required?
Path 30 Row 39
32/39
92/10/13
86/10/13
72/10/09
LM85314816430xO
LM85095616411xO
LM8107816533500
Second WRS-1 not required?
Path 31 Row 26
33/26
34/26
91/08/31
84/08/11
72/07/30
72/08/18
LM85273916461xO
LM85016316521xO
LM8100716533500
LM8102616592500
328
-------
Path 31 Row 27
91/08/31
84/08/11
33/27 73/08/30
34/27 72/08/18
Path 31 Row 28
91/08/31
84/08/11
33/28 73/09/17
34/28 72/08/18
Path 31 Row 29
92/09/02
84/08/27
33/29 72/09/04
34/29 73/10/06
LM85273916463xO
LM85016316523xO
LM8140316534500
LM8102616594500
LM85273916470xO
LM85016316530xO
LM8142116534500
LM8102617001500
LM85310716460xO
LM85017916534xO
LM8104316550500
LM8144016592500
Path 31 Row 30
92/09/02
84/08/27
33/30 72/08/17
34/30 73/10/06
LM85310716462xO
LM85017916541xO
LM8102516551500
LM8144016595500
329
-------
Path 31 Row 31
33/31
34/31
92/08/01
84/08/11
73/08/12
73/07/26
LM85307516471xO
LM85016316541x6
LM8138516554500
LM8136817014500
Path 31 Row 32
33/32
34/32
Path 31 Row 33
33/33
34/33
90/08/28
84/08/11
72/08/17
73/07/26
1
92/08/01
86/07/16
73/07/25
72/08/18
LM85237116444xO
LM85016316543xO
LM8102516560500
LM8136817020500
LM85307516480xO
LM85086716480xO
LM8136716564500
LM8102617021500
Path 31 Row 34
33/34
92/08/01
86/07/16
73/07/07
LM85307516483xO
LM85086716482xO
LM8134916572500
Second WRS-1 not required?
330
-------
Path 31 Row 35
33/35
90/09/13
84/08/27
72/09/22
LM85238716455xO
LM85017916560xO
LM8106116573500
Second WRS-1 not required?
Path 31 Row 36
33/36
90/08/28
84/08/27
72/09/22
LM85237116462xO
LM85017916563xO
LM8106116575500
Second WRS-1 not required?
Path 31 Row 37
33/37
92/06/30
86/08/01
72/07/30
LM85304316500xO
LM85088316490xO
LM8100716581500
Second WRS-1 not required?
Path 31 Row 38
33/38
92/07/16
86/07/16
72/07/30
LM85305916501xO
LM85086716500xO
LM8100716584500
Second WRS-1 not required?
331
-------
Path. 32 Row 26
92/08/08
84/07/01
34/26 72/08/18
35/26 74/07/22
Path, 32 Row 27
91/08/22
84/08/10
34/27 72/08/18
35/27 74/07/22
LM85308216512xO
LM85012216571xO
LM8102616592500
LM8172916570500
LM85273016524xO
LM84075616535xO
LM8102616594500
LM8172916573500
Path. 32 Row 28
91/08/22
84/08/10
34/28 72/08/18
35/28 74/07/04
LM85273016530xO
LM84075616542xO
LM8102617001500
LM8171116583500
Path 32 Row 29
92/09/09
85/09/06
34/29 73/10/06
35/29 72/10/12
LM85311416520xO
LM85055416591xO
LM8144016592500
LM813511706450
332
-------
Path 32 Row 30
92/09/09
85/09/06
34/30 73/10/06
35/30 72/08/19
Path 32 Row 31
34/31
35/31
92/09/09
85/09/06
73/10/06
72/08/19
LM85311416523xO
LM85055416594xO
LM8136817011500
LM8102717065500
LM85311416525xO
LM85055417000xO
LM8144017001500
LM8102717072500
Path 32 Row 32
92/09/09
85/09/06
34/32 73/10/06
35/32 72/08/01
LM85311416531xO
LM85055417003xO
LM81440170045QO
LM8100917073500
Path 32 Row 33
92/09/09
85/09/06
34/33 72/08/18
35/33 74/07/04
LM85311416534xO
LM85055417005xO
LM8102617021500
LM8171117004500
333
-------
Path 32 Row 34
34/34
35/34
Path 32 Row 35
34/35
92/09/09
85/09/06
72/07/31
75/07/26
92/09/09
85/09/06
74/10/19
LM85311416540xO
LM85055417011xO
LM8100817024500
LM8218516570500
LM85311416543xO
LM85093816525xO
LM8181816522500
Second WRS-1 not required?
Path 32 Row 36
92/09/25
86/09/25
34/36 74/10/19
LM85313016543xO
LM85093816525xO
LM8181816524500
Second WRS-1 not required?
Path 32 Row 37
34/37
92/09/09
85/09/06
74/10/19
LM85311416551xO
LM85055417023xO
LM8181816531500
Second WRS-1 not required?
334
-------
Path 33 Row 26
90/09/11
84/08/25
35/26 74/07/22
36/26 74/07/23
LM85238516544xO
LM85017717045xO
LM8172916570500
LM8173017025500
Path 33 Row 27
90/09/11
84/08/25
35/27 74/07/22
36/27 74/09/15
LM85238516550xO
LM85017717051xO
LM8172916573500
LM8178417011500
Path 33 Row 28
35/28
36/28
92/08/15
86/08/15
74/07/04
73/07/10
LM85308916584xO
LM85089716574xO
LM8171116583500
LM8135217120500
Path 33 Row 29
35/29
36/29
92/08/15
86/08/15
73/07/09
74/07/05
LM85308916590xO
LM85089716580xO
LM8135117064500
LM8171217044500
335
-------
Path 33 Row 30
92/08/15
86/08/15
35/30 72/08/19
36/30 74/07/05
LM85308916590xO
LM85089716580xO
LM8102717065500
LM8171217050500
Path 33 Row 31
35/31
36/31
92/08/15
86/08/15
72/08/19
73/08/15
LM85308916592xO
LM85089716583xO
LM8102717072500
LM8138817125500
Path 33 Row 32
92/08/15
86/08/15
35/32 72/08/01
36/32 73/08/15
LM85308916595xO
LM85089716585xO
LM8100917073500
LM8138817131500
Path 34 Row 26
36/26
37/26
92/07/21
85/07/02
74/07/23
75/07/10
LM85306417040xO
LM85048817110xO
LM8173017025500
LM8216917053500
336
-------
Path 34 Row 27
90/09/18
84/09/17
36/27 75/09/15
37/27 74/09/16
Path 34 Row 28
36/28
37/28
90/09/18
84/09/17
73/07/10
72/09/08
LM85239217011xO
LM85020017113xO
LM8178417011500
LM8178517065500
LM85239217014xO
LM85020017115xO
LM8135217120500
LM8104717173500
Path 34 Row 29
36/29
37/29
91/09/05
84/09/17
75/09/01
72/09/08
LM85274417060xO
LM85020017122xO
LM8222216595500
LM8104717175500
Path 34 Row 30
91/09/21
84/09/17
36/30 75/09/01
37/30 72/09/08
LM85276017063xO
LM85020017124xO
LM8222217002500
LM8104717182500
337
-------
Path. 35 Row 26
37/26
38/26
92/08/13
86/07/28
75/07/10
73/07/12
LM85308717095xO
LM85087917093xO
LM8216917053500
LM8135417224500
Path 35 Row 27
37/27
38/27
92/08/13
86/08/29
74/09/16
73/07/12
LM85308717101xO
LM85091117084xO
LM8178517065500
LM8135417230500
Path 35 Row 28
37/28
38/28
Path 35 Row 29
37/29
38/29
92/08/13
86/08/29
72/09/08
74/07/25
>
92/08/29
86/08/29
72/09/08
74/09/17
LM85308717104xO
LM85091117091xO
LM8104717173500
LM8173217150500
LM85310317104xO
LM85091117093xO
LM8104717175500
LM8178617132500
338
-------
Path 35 Row 30
92/09/30
86/10/16
37/30 72/09/08
38/30 74/09/17
LM85313517103xO
LM85095917082xO
LM8104717182500
LM8178617135500
Path 36 Row 26
91/08/18
84/08/14
38/26 73/07/12
39/26 73/09/05
LM85272617170xO
LM85016617231xO
LM8135417224500
LM8140917273500
Path 36 Row 27
91/08/18
84/08/14
38/27 73/07/12
39/27 73/09/05
Path 36 Row 28
91/08/18
86/08/20
38/28 74/07/25
39/28 72/08/05
LM85272617172xO
LM85016617233xO
LM8135417230500
LM8140917280500
LM85272617174xO
LM85090217154xO
LM8173217150500
LM8101317285500
339
-------
Path 37 Row 26
40/26
91/07/24
84/07/20
75/08/09
LM85270117230xO
LM85014117283xO
LM8511217085500
Second WRS-1 not required?
Path 37 Row 27
40/27
92/07/26
86/08/27
73/09/06
LM85306917225xO
LM85090917211xO
LM8141017334500
Second WRS-1 not required?
Path 37 Row 28
39/28
40/28
Path 38 Row 26
41/26
91/07/24
86/08/27
72/08/05
73/08/01
91/07/15
84/07/19
72/08/07
LM85270117235xO
LM85090917213xO
LM8101317285500
LM8137417344500
LM85269217290xO
LM84073417305xO
LM8101517392500
Second WRS-1 not required?
340
-------
Path 39 Row 26
42/26
92/08/09
85/07/21
74/07/29
LM85308317344xO
LM85050717415xO
LM8173617370500
Second WRS-1 not required?
Path 40 Row 26
43/26
92/07/31
85/07/28
72/08/27
LM85307417405xO
LM85051417480xO
LM8103517511500
Second WRS-1 not required?
341
-------
These lists of triplicates may change in number and
characteristics over time. This may be due to data being
•unavailable, or magnetic tapes may have failed over time in
storage, or for a variety of other reasons.
342
-------
Questions related to the characteristics of the triplicates
may be directed to User Services at the EROS Data Center. The
phone number and address are provided in the overview on page 2.
343
-------
Appendix V:
Interagency Agreement (IAG) Documentation
344
-------
United States Environmental Protection Agency
Washington, DC 2O460
interagency Agreement/
Amendment
Part 1 - General Information
6. Name and Address of EPA Organization
US Environmental Protection Agency
EMSL-LV, AMD/AMS
944 E.Harmon Ave., P.O. Box 93478
Las Vegas, NV 89193-3478
8. Project Title
1. EPA IAG Identification Number
DW14934750-OI-4
2. Other Agency IAG ID Number (if known)
3. Type of Action
Augmentation INCR.
7. Name and Address of Other Agency
Interior Department of the
U.S. Geological Survey
EROS Data Center
Sioux Falls, SD 57198
4. Funding Location by
Region
XI
5. Program Office
Abbreviation
EROS Data Center: AVHRR Data/Product Support
9. EPA Project Officer (Name, Address, Telephone Number)
Lunetta, Ross S.
EMSL-LV, AMD/AMS
Las Vegas, NV 89193-3478
(702) 798-2175
11. Project Period
06/01/90 to 09/30/94
13. Scope of Work (Attach additional sheets, as needed)"
SEE ATTACHED REVISED SOW
10. Other Agency Project Officer (Name, Address, Telephone Number) ~
Lauer, Donald T., Department of the Interior
U.S. Geological Survey, EROS Data Center
Science and Applications Branch
Sioux Falls, SD 57198
(605) 594-6511
12. Budget Period
10/01/91 to 09/30/93
EPA GRANTS SPECIALIST FOR THIS IAG
IS FELICIA TARVER, 202-260-4392
14. Statutory Authority for Both Transfer of Funds and Project Activities
Economy Act 1932 Amended (31 USC 1535)
Clean Air Act As Amended Sec. 327 (b) (6)
15. Other Agency Type
Federal
Previous Amount
Amount This Action
16. EPA Amount
17. EPA In-Kind Amount
18. Other Agency Amount
19. Other Agency In-Kind Amount
20. Total Project Cost
Fiscal Information
Program Elemenl
Doc. Control No.
Obligation/Deobligation Amt
10,000
683/40107
3CC926J011
EPA Form 1610-1 (Rev. 10-88) Previous editions are obsolete.
345
Page 1 of 5
-------
13. Scope of Work
The U.S. Geological Survey, EROS Data Center (EDC) and U.S.
Environmental Protection Agency (EMSL-LV) , will jointly participate
in the production and evaluation of Advanced Very High Resolution
Radiometer (AVHRR)- derived vegetation greeness maps and subsequent
land cover mapping efforts for the 1992 growing season. Also, EDC and
EMSL-LV will work jointly on the Landsat MSS North American Landscape
Characterization Pathfinder (NALC) - Pathfinder Interagency Global
Change project.
Since 1990, EDC has been providing EMSL-LV digital products of the
AVHRR-derived data from the Conterminous U.S. and Alaska greeness mapping
program. This program is continuing in 1992 with the production of
greeness products of 1992 and 1989 AVHRR data from the Conterminous U.S.
and 1992 AVHRR data of Alaska. EDC and the Canada Center for Remote
Sensing have begun developing prototype greeness data sets of North
America. EDC will provide EMSL-LV all interim and final products of
North America as they become available.
EDC- will continue to refine the AVHRR-derived land characterization
data base of the Conterminous U.S. and provide EMSL-LV with improved land
cover data sets. EDC will begin development of the North American land
characterization data base and provide EMSL-LV with data as they become
available.
Cooperation between EMSL-LV and EDC on the NALC-Pathfinder project
is anticipated to continue well into the decade. The areas of NALC :
collaboration include: 1) Landsat MSS 1992 data acquisition for North
America, 2) cloud-free MSS composite data processing, 3) the assembly ;
of multiple date MSS coregistered data sets, 4) data index and archiving, \
and 5) the development of MSS land cover and change detection data analysis
techniques. EDC will collaborate on all of the above tasks with EMSL-LV. •
However, EDC will concentrate on the assemblage of MSS multi-date scenes ;
coregistered data sets, image compositing, data index, and archiving,;; and ;
data distribution. , , :>: :'.< I
f
I Budget (FY92/3) :
... • '. »
The following is the total FY92/3 budget under this IAG. This is the
final budget distribution that reflect changes resulting from the release
of Landsat MSS data distribution rights from EOSAT Corporation.
Funding Elements
FY92
FY93
Personnel (1992 AVHRR Support)
Personnel (NALC Image Compositing) •
NALC Travel Support
EPA NALC Graphics/Data Support
NALC MSS Triplicate Assembly
NALC MSS Data Acq. Brokerage Assistance ......
NALC MSS Data Acquisition
Brazil MSS Data Acquisition (OEPER)
EPA Contribution Access to SPOT(pan.)
Interagency Acquisition (OEPER/EMAP) . .
EPA Contribution to EROS Brazilian Retrieval
of Landsat MSS from decaying HDDT's (OEPER)..
Purchase of approx. 375 Landsat for Selected
Locations in S.E. Asia by David Skole (OEPER)
EDC Procurement of Mexico & C. Am. Topo Maps .
$
$
$
$
39. OK
39. OK
9. OK
19. OK
$ 390.OK
$ 147.5K
$ 152.OK
$ 16.6K
-0-
-0-
-0-
-0-
-0-
-0-
-0-
-0-
$ 15. OK $ 10. OK
$ 20.OK -0-
$ 30.OK -0-
$ 5.225K -0-
346
TOTAL FUNDING
$ 882.325K $ 10.OK
-------
Part II - Approved Budget
EPA IAG Identification Number
22. Budget Categories
ttemization of
This Action
Itemization of Total Project
Estimated Cost to Date
(c)
JOL
(f)Prc
io,nnn
(i)
917.32.
$
(k) Total
(EPA Share IQQ.00%) (Other Agency Share
Q_
0.00%)
:d, purchased,
10.000
(Identify all equipment costing $1,000 or more)
or rented with EPA funds?
Yes
No
24. Are any of these funds being used on extramural agreements? (See hem 22f)
Type of Extramural Agreement
X
Yes
No
Contractor/Recipient Name (if known)
Grant
Cooperative Agreement
Total Extramural Amount Under This Project
10,000
Procurement (Includes Small Purchase Order)
Percent Funded by EPA (if known)
100.00
25.
Funds-Out Agreement
Part III - Funding Methods and Billing Instructions
Disbursement Agreement
jr Repayment
'(Note: EPA Agency Location Code (ALC) - 68010727)
SF1081 °(SF 108°and
Monthly
Quarterly
Upon Completion of Work
Advance
26.
Funds-ln Agreement
Reimbursement Agreement
Repayment
Advance
Allocation Transfer-In
Other Agency's LAG Identification Number
Other Agency's Billing Address (Include Agency Location Code
or Station Symbol Number)
EPA Form 161O-1 (Rev. 10-88)
EPA Program Office Allowance Holder/Responsibility Center Number
Other Agency's Billing Instructions and Frequency
347
Page 2 of 5
-------
Part IV - Acceptance Conditions
EPA IAG Identification Number
DW14934750-01-4
27, Ganotal Conditions
The other agency covenants and agrees that It will expedftiously initiate and complete the project for which funds
nave been awarded under this agreement.
,58, Sp«e«al Conditions (Attach additional shoots if needed)
See Attachment A: DBE Special Condition for Interagency Agreement
Part V - Offer and Acceptance
Note: 1) For Funds-out actions, the agreement/amendment must be signed by the other agency official in duplicate
and one original returned to the Grants Administration Division for Headquarters agreements or to the
appropriate EPA Regional IAG administration office within 3 calendar weeks after receipt or within any
extension of time as may be granted by EPA. The agreement/amendment must be forwarded to the
address cited in Item 29 after acceptance signature.
Receipt of a written refusal or failure to return the properly executed document within the prescribed time
may result in the withdrawal of the offer by EPA. Any change to the agreement/amendment by the other
agency subsequent to the document being signed by the EPA Action Official, which the Action Official
determines to materially alter the agreement/amendment, shall void the agreement/amendment.
2) For Funds-in actions, the other agency will initiate the action and forward two original
agreements/amendments to the appropriate EPA program office for signatu're. The
agreements/amendments will then be forwarded to the appropnate EPA IAG administration office for
acceptance signature on behalf of the EPA. One original copy will be returned to the other agency after
acceptance.
EPA tAG Administration Office (for administrative assistance)
EPA Program Office (for technical assistance)
29, Organization/Address
U.S. EPA
Grants Information and Analysis Branch
Grants Administration Division (PM-216F)
401 M. Street, SW
Washington, DC 20460
30. Organization/Address
U.S. EPA, EMSL-LV, AMD/AMS
P.O. Box 93478
Las Vegas, NV 89193-3478
Certification
All signers certify, that the statements made on this form and all attachments thereto are true, accurate, and
complete. Signers acknowledge that any knowingly false or misleading statement may be punishable by fine or
imprisonment or both under applicable law.
Decision Official on Behalf of the Environmental Protection Agency Program Office
31 Signature
Typed Name and Title
Wayne N. Marchant
Director, EMSL-LV
Date
Action Official on Behalf of the Environmental Protection Agency
32. Signature
Typed Name and Title
W. Scott McMoran, Chief
Grants Info. & Analysis Branch
Date
Authorizing Official on Behalf of the Other Agency
33, Signature
Typed Name and "Title
Allen H. Watkins, Chief
National Mapping Division
Date
EPA Form 1610-1 (Rev. 10-88)
348
Page 3 of 5
-------
DW14934750-01-4
DEE Interagency Agreements Special Conditions
EPA's policy requires at least 8% of its Federal funding for
prime and subcontracts be awarded to businesses or other
organizations owned or controlled by socially and economically
disadvantaged individuals.
As a recipient of monies under this IAG, the Department
of the Interior must ensure to the fullest extent
possible that at least 8% of funds for prime or
subcontracts and subgrants for services are made
available to businesses owned or controlled by socially
and economically disadvantaged individuals, women-owned
businesses, and Historically Black Colleges and
Universities. (DBE)
The Department of Interior must submit a report to EPA showing
the total extramural funds awarded and the amount and
percentage of extramural funds awarded to DBEs by November 15,
1992. Reports should be submitted to:
Office of Small and Disadvantaged
Business Utilization (A-149C)
U.S. Environmental Protection Agency
401 M Street, S.W.
Washington, D.C. 20460
349
-------
Page 1 of 3
Untied State* Environmental Protection Agency
Washington DC 20460
.Q. Interagency Agreement /
EPA Amendment
Part 1 - General Information
6, Nam* and Addre»» of EPA Organization
EMSL-LV, AMD/AMS
944 E. HARMON AVE.
P.O. BOX 93478
LAS VEGAS, NV 89193-3478
1 . EPA / IAG Identification Number
DW1 4935881 -01-0
2. Other Agency ID Number
3. Type of Action
NEW
7. Name and Address of Other Agency
INTERIOR DEPARTMENT OF THE
USGS, EROS DATA CENTER
MUNDT FEDERAL BUILDING
SIOUX FALLS, SD 571 98
4. Funding Location
by Region
XI
5. Program Office
Abbreviation
EMSL-LV
8. Proiwt TMle EROS DATA CENTER AVHRR/NALC/PRODUCT SUPPORT
9. EPA Project Officer (Name, Address, Telephone Number)
ROSS S. LUNETTA
EMSL-LV. AMD/AMS
P.O. BOX 93478
LAS VEGAS, NV 89193-3478
(702)798-2175
11. Project Period
10/01/921009/30/94
10. Other Agency Project Officer (Name, Address, Telephone Number)
DONALD T. LAUER, DOI, USGS
EROS DATA CENTER, MUNDT FEDERAL BLDG.
SCIENCE & APPLICATIONS BRANCH
SIOUX FALLS, SD 57198
(605)594-6511
1 2. Budget Period
10/01/921009/30/94
13. Scope of Work (Attach additional sheets, »» needed)
See Attached SOW
THE EPA GRANTS SPECIALIST FOR THIS
IAG IS FELICIA TARVER, 202-260-4392
CGMIl!
VERi
14. Statutory Authority for Both Transfer of Funds and Project Activities 1 5. Other Agency Type
ECONOMY ACT OF 1 932 AS AMENDED FEDERAL
FUNDS
1 6. EPA Amount
17. EPA In-Kind Amount
18. Other Agency Amount
19. Other Agency In-Kind Amount
20. Total Project Cost
PREVIOUS AMOUNT
0
0
0
0
0
AMOUNT THIS ACTION
416,000
0
0
0
416,000
21. Fiscal Information
Program Element
CNRA1E
FY
93
Appropriation
683/40107
Doc. Control No.
JRS006
Account Number
3CNR26J012
AMENDED TOTAL
Object Class
25.71
Obligalion/Deoblig. Amt.
416,000
350
-------
DW14935881-01-0
13. Scope of Work
The U.S. Geological Survey, EROS Data Center (EDC) and U.S.
Environmental Protection Agency {EMSL-LV), will jointly participate
in the production and evaluation of Advanced Very High Resolution
Radiometer (AVHRR)- derived vegetation greenness maps for the 1993
growing season and subsequent land cover mapping efforts for the
1992 and 1993 growing seasons. Also, EDC and EMSL-LV will work
jointly on the Landsat MSS North American Landscape Characterization
Pathfinder (NALC) - Pathfinder Interagency Global Change project..
Since 1990, EDC has been providing EMSL-LV digital products
of the AVHRR-derived data from the Conterminous U.S. and Alaska
greenness mapping program. This program is continuing in 1993 with
the production of greenness products of 1992 and 1993 AVHRR data
from the Conterminous U.S. and Alaska. EDC and the Canada Center
for Remote Sensing have developed prototype greenness data sets of
North America. The production of greeness data sets for North
America will continue for the 1993 growing season. EDC will provide
EMSL-LV all interim and final products of North America as they
become available.
EDC will continue to refine the AVHRR-derived land
characterization data base of the Conterminous U.S. and provide
EMSL-LV wxth improved land cover data sets. EDC will continue
development of the North American land characterization 1992 data
base and provide EMSL-LV with data as they become available.
Cooperation between EMSL-LV and EDC on the NALC-Pathfinder
project is anticipated to continue well into the decade. The areas
of NALC collaboration include: l) cloud-free MSS -composite data
processing, 2) the assembly of multiple date MSS' coregistered data
sets, 3) data index and archiving, and 4) the development of MSS
land cover and change detection data analysis techniques. EDC will
collaborate on all of the above tasks with EMSL-LV. However, EDC
will concentrate on the assemblage of MSS multi-date scenes
coregistered data.sets, image compbsiting, data index and archiving
and data distribution.
Budget (FY93)
The following is the total FY93 buJ.~ ' under this IAG.
Funding Element
Total FY93 Funds
1993 AVHRR Support
Personnel (Image Compositing)
NALC Travel Support
EPA Graphics/Data Support
MSS Multidate Scene Coregistration
Pecora Symposium EPA Sponsorship
$
$
$
$
$
$
45. OK
45. OK
10. OK
15. OK
291. OK
10. OK
TOTAL FY92 FUNDING
$ 416.OK
351
-------
Paga 2 of 3
Part II - Approved Budget
22. Budget Categories
(*) Personnel
(b) Fringe Benefit*
(c) Travel
(d) Equipment
{«} Supplies
(0 Procurement / Assistance
(g) Construction
(h) other Graphics Support
(I) Total Direct Charges
(1) Indirect Costs: Rate 0.00% Base $ 0.
(k) Total:
(EPA Share: 1 00.00%) (Other Agency Share 0.00%)
Itemization of
This Action
381,000
0
10,000
0
0
10,000
0
15,-ODO
416,000
0
416,000
EPA IAG Identification Number
DW1 4935881 -01-0
Hemizatlon of Total Project
Estimated Cost to Date
381,000
0
1 0,000
0
0
10,000
0
15,000
416,000
0
416,000
23. Is Equipment authorized to be furnished by EPA or leased, purchased, or rented with EPA funds?
(Identify all equipment costing S10OO or more.)
Yes
No
24. Are any of these funds being used on extramural agreements? (See Item 221.) [x"l Yes
No
Type of extramural agreement
Grant I I Cooperative Agreement
Procurement (includes Small Purchase Order)
Contractor / Recipient Name (if known)
TBD
(Pecora Symposium)
Total Extramural Amount under this Project
10,000
Percent Funded by EPA (if known)
100.00
Part III - Funding Methods and Billing Instructions
25.
|X] Funds-Out Agreement (Note: EPA Agency Location Code .(ALC) - 68010727)
|XJ Disbursement Agreement
IX | Repayment Request for rep^ tent of actual costs must be itemized on SF-1080 and submitted to the Financial Management
Office, Cincinnati, OH 45268:
I I Monthly |X | Quarterly | | Upon Completion of Work
Only available for'use by Federal agencies on working capital fund or with appropriate justification of need for
C"] Advance this type of payment method. Unexpended funds at completion of work will be returned to EPA. Quarterly cost
reports will be forwarded to the Financial Management Center, EPA, Cincinnati, OH 45268.
Used to transfer obligational authority or transfer of function between Federal agencies. Must receive prior
I—I Allocation approval by the Office of the Comptroller, Budget Division, Budget Formulation and Control Branch, EPA Hdqtrs.
I I Tr.nsfcr nut Forward appropriate reports to the Financial Reports and Analysis Branch, Financial Management Division, PM-
226F, EPA, Washington, DC 20460.
26.
D
Funds-ln Agreement
I _ | Reimbursement Agreement
B
Repayment
Advance
I _ I
Allocation Transfer-In
Other Agency's IAG Identification Number
EPA Program Office Allowance Holder/Resp. Center No.
26J
Other Agency's Billing Address (Include ALC or Station Symbol Number)
Other Agency's Billing Instruction and Frequency
352
-------
Part IV - Acceptance Conditions
EPA IAG Identification Number
DW14935881-01-0
27. General Conditions
The other agency covenants and agrees that it will expeditiously initiate and complete the project for which funds
have been awarded under this agreement.
28. Special Conditions (Attach additional sheets if needed)
.See Attached: DBE Special Condition for Interagency Agreement
Part V - Offer and Acceptance
Note: 1) For Funds-out actions, the agreement/amendment must be signed by the other agency official in duplicate
and one original returned to the Grants Administration !?:---:- ?-. ' idquarters agreements or to the
appropriate EPA Regional IAG administration office within 3 calendar weeks after receipt or within any
extension of time as may be granted by EPA. The agreement/amendment must be forwarded to the
address cited in Item 29 after acceptance signature.
Receipt of a written refusal or failure to return the properly executed document within the prescribed time
may result in the withdrawal of the offer by EPA. Any change to the agreement/amendment by the other
agency subsequent to the document being signed by the EPA Action Official, which the Action Official
determines to materially alter the agreement/amendment, shall void the agreement/amendment.
2) For Funds-in actions, the other agency will initiate the action and forward two original
agreements/amendments to the appropriate EPA program office for signature. The
agreements/amendments will then be forwarded to the appropriate EPA IAG administration office for
, acceptance signature on behalf of the EPA. One original copy will be returned to the other agency after
acceptance. y y
EPA IAG Administration Office (for administrative assistance)
.EPA Program Office (for technical assistance)
29. Organization/Addres
U.S. EPA
Grants Information and Analysis Branch
Grants Administration Division (PM-216F)
401 M Street, SW
Washington, DC 20460
•••'.c .«t' , £c ,.p •'-, X-T-^ -~ - ,.„ ^r-,—-^.;
30. Organization/Address
U.S. EPA, EMSL-LV, AMD/AMS
P.O. Box 93478
Las Vegas, NV 89193-3478
Certification
All signers certify that the statements made on this form and all attachments thereto are true, accurate, and
complete. Signers acknowledge that any knowingly false or misleading statement may be punishable by fine or
imprisonment or both under applicable law.
Decision Official on Behalf of the Environmental Protection Agency Program Office
31 Signature
Typed Name and Title
Wayne N. Marchant
Director, EMSL-LV
Date
32. Sigr-.ature
Action Official on Behalf of the Environmental Protection Agency
/*--.-/.'/> A
Typed Name and Title
W. Scott McMoran, Chief
Grants Info. & Analysis Branch
Date
/
Authorizing Official on Behalf of the Other Agency
;3. SignalL/e
Typed Name and Title
Allen H. Watkins, Chief
National Mapping Division
Date
EPA Form 1610-1 (Rev. 10-88)
353
Page 3 of 5
-------
DW14935881-01-0
DBE Interagency Agreements Special Conditions
EPA's policy requires at least 8% of its Federal funding for
prime and subcontracts be awarded to businesses or other
organizations owned or controlled by socially and economically
disadvantaged individuals.
As a recipient of monies under this IAG, the Department
of the Interior must ensure to the fullest extent
possible that at least 8% of funds for prime or
subcontracts and subgrants for services are made
available to businesses owned or controlled by socially
and economically disadvantaged individuals, women-owned
businesses, and Historically Black Colleges and
Universities. (DBE)
The Department of the Interior must submit a report to EPA
showing the total extramural funds awarded and the amount and
percentage of extramural funds awarded to DBEs by November 15,
1993. Reports should be submitted to:
Office of Small and Disadvantaged
Business Utilization (A-149C)
U.S. Environmental Protection Agency
401 M Street, S.W.
Washington, D.C. 20460
354
-------
EPA-EOSAT 1992 LANDSAT MSB DATA ACQUISITION AGREEMENT
To satisfy EPA's requirement to have MSS data acquired, and
permit EPA the flexibility to purchase multiple scenes of cloud-
prone locations, EPA proposes the following conditions for 1992
MSS data collection:
o The EPA's 1992 Landsat MSS data purchases will be
accomplished through the EROS Data Center (EDC) Federal Government
Brokerage Agreement. The EPA path / row and acquisition windows
are being provided to EDC for transmittal to EOSAT.
o EOSAT will utilize its best efforts to provide MSS data
collects to meet EPA requirements. This includes negotiating with
foreign receiving stations to provide MSS data for EPA's projects.
o EOSAT agrees to collect Landsat MSS data on a continual basis
for the conterminous U.S., Mexico, and portions of Central America
and the Caribbean that are within the collection range of the
Norman, Oklahoma receiving station, starting from the time the
Norman station becomes operational, until the conclusion of the
1992 growing season.
o EPA agrees to purchase a minimum of one MSS scene at 30% or
less cloud cover for each of the EPA specified path / rows
collected by the Norman receiving station during the 1992 growing
season. Cloud cover determinations will be based on the percent
cloud cover over land areas. Cloud cover will be determined by the
visual inspection of image film products at EROS Data Center (EDC)
by EPA or EPA's designated representative at EDC. Cirrus clouds
will be included in the percent cloud cover determinations.
o For those path / row scenes requested by EPA that require
TDRSS acquisitions, EPA agrees to purchase all MSS data having 30%
or less cloud cover, collected within EPA specified acquisition
windows. EPA has the option to close the TDRSS acquisition windows
for specific path / rows by notifying EOSAT at least seven days
prior to the next scheduled acquisition. Notification will be via
FAX by EPA or its EDC designate.
o For the EPA specified path / rows, EPA may purchase any MSS
scene meeting the cloud cover specification of 30% or less at
$1,000.00 per initial scene and $500.00 per scene for all
subsequent scenes of the same path/row. If for a given path / row
there is no scene meeting the 30% cloud cover specification, EPA
may, at its option, purchase any MSS scenes collected within EPA's
specified acquisition window at $500.00 per scene.
o EPA intends to purchase approximately $ 520,000.00 of Landsat
MSS data by December 31, 1992. All data purchases are subject to
the availability of government funds and the availability of MSS
data for the EPA .specified path / rows and acquisition windows.
355
-------
Appendix VI:
Mexico Worksheet for Acquisition and Determining Temporal
Windows for Acquisition of Remotely Sensed Imagery
356
-------
Mexico Worksheet
(by Path/ Row)
January 31, 1992
Path/Re
24/47
24/48
24/49
25/46
25/47
25/48
25/49
26/43
26/44
26/45
26/46
26/47
26/48
27/42
27/43
27/44
27/45
27/46
27/47
27/48
28/41.
28/42
28/43
28/44
28/45
28/46
28/47
29/40
29/41
29/42
29/43
29/44
29/45
29/46
29/47
30/40
30/41
30/42
30/43
30/44-
30/45
30/46
31/40
Phenological Window
Jul-Sep
N/A
N/A
N/A
N/A
Jul-Sep
N/A
N/A
Jun-Aug
N/A
Jun-Aug
Jun-Aug
Jun-Aug
N/A
N/A
Jun-Aug
Jul-Sep
Jul-Sep
Jun-Aug
Jun-Aug
N/A
N/A
N/A
N/A
Jun-Aug
Jun-Aug
Jun-Aug
N/A
N/A
N/A
N/A
Jul-Sep
Jul-Sep
Jul-Sep
Jul-Sep
N/A
N/A
N/A
N/A
N/A
N/A
N/A
Jul-Sep
357
Cloud Window
Mar-May
Feb-Apr
Jan-Mar
N/A
Feb-Apr
Feb-Apr
Feb-Apr
N/A
Jan-Mar
N/A
Feb-Apr
Jan-Mar
Jan-Mar
Jun-Aug
N/A
N/A
Jan-Mar
Jan-Mar
Jan-Mar
Jan-Mar
Jun-Sep
N/A
Apr-Jun
Mar-May
Feb-Apr
Mar-May
Feb-Apr
N/A
N/A
Jun-Aug
Apr-Jun
Mar-May
Mar-May
Mar-May
Feb-Apr
N/A
Apr-Jun
Apr-Jun
Apr-Jun
Mar-May
Feb-Apr .
Mar-May
Mar-May
Date of'86 Coverage
N/A
N/A
Mar/Apr
N/A
Mar/Apr
Mar/Apr
Mar/Apr/May
N/A
N/A
N/A
May
Mar/Apr
Mar/Apr
N/A
Mar
Mar
Mar
Mar
Mar
Mar
Apr
Apr
Apr
Apr
Apr
Apr
Apr
Mar
Mar
Mar
Mar
Mar
Mar
Mar
Mar
Mar
Mar
Mar
Mar
Mar
Mar
Mar
N/A
-------
31/41
31/42
31/43
31/44
'32/39
*32/40
•32/41
•32/42
"32/43
33/39
33/40
33/41
33/42
3a'43
33/44
34/39
34/40
34/41
34/42
34/43
34/44
35/39
35/40
35/41
35/42
35/43
36/39
36/40
36/41
36/42
•Jul-Sep
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
Jul-Sep
Jul-Sep
N/A
Jul-Sep
Jul-Sep
N/A
N/A
N/A
Jul-Sep
Jul-Sep
N7A
N/A
N/A
A«g-0ct
Aug-Oct
N/A
N/A
N/A
N/A
N/A
N/A
Apr-Jun
Apr-Jun
Jan-Mar
Nov-Jan
Fob-Apr
Fob-Apr
Fcb-Apr
Feb-Apr
No v-Fob
Apr-Jun
Apr-Jun
Feb-Apr
Apr-Jun
Apr-Jun
Mar-May
Mar-May
Mar-May
Mar-May
Mar-May
Mar-May
Mar-May
Apr-Jun
Apr-Jun
Apr-Jun
Apr-Jun
Apr-Jun
Apr-Jun
Apr-Jun
Apr-Jun
May-Jul
Mar
Mar
Mar
Mai-
Mar
Mar
Mar
Mar
Mar
Mar
Mar
Mar
Mar
Mar
Mar
Mar
N/A
N/A
N/A
Apr
Apr
Mar
Mar
Mar
Mar
Apr
A p r
Apt-
Apr
Apr
* Scene exists for March 1991, with less than 10 % clouds.
358
-------
O
rH
O
CJ
rH
W
O
d
a
Oj
rH
O
a
be
a
CD
Q
bJO
03
a
CD
02
a
CO
-------
PI' •'
CD
co
a
CD
co
.S 8
o be
a d
8 g
rH TH
be 2
S S
•rH O
CO JH
rj O
CO B
o> d
•rH £1
13 H
3*8
^ «
^3 .3
•8£
§ .2
PI s
•rt cr1
§ s
CJ CD
"S r£
§1
cd
a
cfl
•M
cd
ft oj
rH
CD
CD
CO
o
cd
«5 .-S
-23
§ ^
03 2
CJ rM
.cd ^
CD
CJ
™ CD
S3
CS
CO fH
JH * ^
0 >>
•-C ^
S d1
•rH H
b O
g 4^
rZ ^
S S
2 S
^rS
§ ^
EH CD
cd
o
rH
CJ
CD
rH
cd
CO
rO
cd
•rH
rH
Cd 4J
> o
•5-1
CJ P
•rH rH
r£3 P.
£ s
CD r-H
ti r^
•rH CJ
S '£
^ b
CD cd
•8 ft
•-d cd
CO
CO
a ^
I 8
cd co
,2 03
PH -4.3
O
CD
cd
cd
cr
13
rt
cd
>
•rH
•4J
•rH
d
-4J
a
•rH
fl rH
O CD
co £3
CD H
r^H ^
S'S
a
o
'rH
CO
§1
r3 »
S ^
CD
^^ -4-3
! ** -
"^^^" t^^^j
a o
CD
?-» r
rH r
d •
O "co
CO
o
cd
36Q
-------
CJ
cd
o
rH
PH
PH
CD
CJ
fl
cd
•rH
rH
cd
>
f-j
CO
•rH
4-3
cd
Qd
r—H
CO
a
cd
.
cd
rH
O
PH
8
CD
4-3
CD
O
be
a
•rH
An understand
rj
M
O •
_ ^^j
• ^^
CO
CO
•rH
a
o
4_3
13
•rH
CJ
d
rH
CO
•rH
4-3
CO
CD
gion of inter
relating to a re;
planning.
CD
•"^
2
rj
0
CO
CD
r—H
rQ
cd
• »»4
V pll 1^
rH
cd
^
rH
cd
r-H
CJ
• rH
rH
cd
PH
cd
o
CD
The significanc
rA
QO
CD
Id
rH
v^s
CO
a
o
•rH
*CO
•rH
CJ
cd
CD
O
4-3
incorporated in
O'
/i\
vU
CO
^^
• rH
CJ
PH
CO
cd
CO
• rH
m
CO
Q
s — '
a
CD
.CO
CO
rH
0
PH
A Decision Sup
rH
CD
cd
^>i
£
0
• rH
CO
•rH
Q
CD
>4>
•^^^
CD
^
r—H
O
a
•rH
0
.s designed t
application too]
CO
CO
v^
CD
CJ
O
rH
_j
rH
0
•rH
r— <
O
CO
CD
interactively in
CD
Id
• £H
PH
O
rH
PH
PH
rrt
"•w
CD
r~{
-^
be
a
• rH
"cd
rH
O
PH
rH
O
CJ
a
•rH
CO
Q
r—H
cd
rH
o
^^
ft
a
CD
r^^i
^^^i
0
•rH
"cd
p,
•V
^
o
rH
PH
rS
3
0
^
CD"
rH
CD
4-3
«_4
rH
•rH
CD
•rH
•rH
^
•rH
a
cd
-j_j
parameters, wii
•
CO
^CD
ho
CD
4-3
cd
rH
4-3
CO
rH
O
•rH
4-3
•rH
CO
•rH
cr
CJ
cd
t» »\
be
— »
r determinin
a framework fo:
361
-------
None
Jone
None
Phenological "Seasons"
MSS Cloud Cover
Temporal Continuity
(1986 Existing MSS Coverage)
Other Considerations
(Spatial Continuity, Priorities)
362
-------
Appendix VII:
NALC / GCRP Coordination / Collaboration Documents
363
-------
NALC / 6CRF Coordination / Collaboration
Mexico
NALC Task:
Delivery of standard and modified NALC products focused on humid
tropical forested regions, in particular Mexico
GCRP Task:
Characterize changing land cover and land use and forest types in
Southeastern Mexico
GCRP Contact:
Point of contact is Michael A. Cairns, EPA-ERL-C
Phones: Cairns, 430-4777 or 503-754-4777 FAX -4799
GCRP Goals:
A goal of ERL-C is assess terrestrial biosphere management options
as they influence land cover, carbon pools and feedback to
atmospheric conditions. To assist these efforts it is desirable to
supply good land cover and land use (LC/LU) images and tabular
data for areas of interest. These data will be useful in
calculations of carbon pool, change in the pool or carbon flux, and
for model calibration and verification activities. The current and
past condition, and change in these carbon pools are significant
information for the charge of evaluating natural and human-induced
atmospheric conditions and their affects on ecosystems. This
effort can support the understanding of feedbacks of change in land
surface characteristics on atmospheric conditions.
Estimates of total land cover and change in cover will be useful in
calculations of carbon pool, change in the pool or carbon flux, and
for model calibration and verification activities. Quantification
of current and past condition, and change in these carbon pools is
significant in evaluating natural and human-induced influences on
forests. This effort also supports the understanding of feedbacks
from change in terrestrial landscapes on atmospheric conditions, as
well as having utility in planning of substitutes for fossil fuels.
Use of remote sensor data and the objectives of North American
Landscape Characterization (NALC) can supply the LC/LU data and
other products. LC/LU data can be linked with management
information to obtain variables and develop quantities for land use
descriptors including: total forest area, commercial forests,
agroforest systems, pastures, total area of agricultural lands,
urban areas, park and preserves, and other land cover/land uses.
Early work on data sets have been slated for the Southern and
Southeastern portions of Mexico. Coordination with ERL-C has
prioritized the states of Chiapas, Quintana Roo, Campeche, and
Tabasco for early data acquisition and analyses.
364
-------
NALC Applications (Standard Products):
The standard products that apply to this work element would be land
cover/land use images and tabular data, and evaluations of change.
NALC Applications (Modified Products):
NALC data products and analyses will provide assistance with
characterizing and quantifying certain variables. These may
include optimization of images for detection of land use conditions
such as agroforestry, natural regeneration, and increase
sensitivity to forest species composition. Analysis of change
induced by fire, pestilence, and weather is necessary, and the
management practices subseguent to the event would be of interest.
General land use practices may be derived from NALC remote sensor
data.
Scheduling for Deliveries of Standard Products:
Current NALC scheduling calls for the above products to be
delivered in calendar years 1995 for Mexico, the Caribbean,
Chesapeake Bay Watershed, and Central America. Alaska and Western
US areas will be available in 1996. In 1997 the midwest and Great
Plains and Eastern-Southern US will be available.
Potential Schedule for Modified NALC Products:
The above issues may be addressed in preliminary tests of the NALC
program. The levels of sensitivity can be evaluated, and suggested
modified products could be generated in support of GCRP. The
timeframe would be the same from the standard products.
Proposed Coordination Action/Schedule:
Much of this effort will involve production of data sets and
standard and modified products by Cooperators. Hence, it would be
desirable to collaborate on Cooperator tasks to insure that the
products of interest are addressed early in the delivery
timeframes, and that modified products of interest to GCRP will
generated. In the case of the Mexico effort, it would be desirable
to meet with the Cooperators soon to develop plans. Potential
cooperators include the Institute of Geography at the National
Autonomous University of Mexico.
365
-------
NALC / GCRP Coordination / Collaboration
Forests
NALC Task:
Delivery of standard and modified NALC products focused on forested
regions of the US
GCRP Task:
Potential of global forested and agroforested lands to sequester
and conserve carbon
GCRP Contact:
Points of contacts are Robert Dixon and Jack Winjum, EPA-ERL-C
Phones: Dixon, 430-4772 or 503-754-4772 FAX -4799
GCRP Goals:
The goal of ERL-C is to evaluate global forest management practices
and to determine the potential for management options for
conservation of carbon and sequestration in tropical, temperate and
boreal forests. These efforts can be facilitated by supplying
good land cover and land use (LC/LU) images and tabular data for
areas of interest. These efforts would include evaluations of
change in land cover due to natural phenomena and through human
activities. These data can be combined with information on
management practices, and be used to study the forest related
management options in a given region.
Estimates of land cover and change in cover will be useful in
calculations of carbon pool, change in the pool or carbon flux, and
for model calibration and verification activities. Quantification
of current and past condition, and change in these carbon pools is
significant information for evaluating natural and human-induced
influences on forests. This effort also supports the understanding
of feedbacks from change in terrestrial landscapes on atmospheric
conditions, as well as having utility in planning of substitutes
for fossil fuels.
Us© of remote sensor data and the objectives of North American
Landscape Characterization (NALC) can supply the LC/LU data and
other products. Early work on data sets has been slated for
portions of the Pacific Northwest, Chesapeake Bay and for a study
area in Alaska. These efforts will address: a) capability to
detect land cover classes of interest, and b) ability to detect
change in land cover and quantify that change at the scale of
interest. „__
3oo
-------
Later needs for satellite derived data include the greater US
Pacific Northwest region with emphasis on forested areas, the
Canadian Pacific Northwest regions, and the trans-boreal portions
of Canada and Alaska.
NALC Applications (Standard Products):
The standard products that apply to this work element would be land
cover/land use images and tabular data, and evaluations of change.
NALC Applications (Modified Products):
Potential areas of NALC data analysis assistance include
optimization of images for detection of land use conditions such as
agroforestry, natural regeneration, and increase sensitivity to
forest species composition. Analysis of change induced by fire,
pestilence, and weather is necessary, and the management practices
subsequent to the event would be of interest. General land use
practices may be derived from remote sensor data.
Scheduling for Deliveries of Standard Products:
Current NALC scheduling calls for the above products to be
delivered in calendar years 1995 for Mexico, the Caribbean,
Chesapeake Bay Watershed, and Central America. Alaska and Western
US areas will be available in 1996. In 1997 the midwest and Great
Plains and Eastern-Southern US will be available.
Potential Schedule for Modified NALC Products:
The above issues may be addressed in preliminary tests of the NALC
program. The levels of sensitivity can be evaluated, and suggested
modified products could be generated in support of GCRP. The
timeframe would be the same from the standard products.
Proposed Coordination Action/Schedule:
Much of this effort will involve production of data sets and
standard and modified products by Cooperators. Hence, it would be
desirable to collaborate on Cooperator tasks to insure that the
products of interest are addressed early in the delivery
timeframes, and that modified products on interest to GCRP will
generated.
367
-------
NALC / GCRP Coordination / Collaboration
Tropical Forests
NALC Task:
Delivery of standard and modified NALC products focused on
tropical regions
GCRP Task:
Characterize changing land cover and land use and forest types in
humid tropical regions of interest in the NALC area of operations
including Mexico, the Caribbean and Central America
GCRP Contact:
The point of contact is Michael A. Cairns, EPA-ERL-C. A number of
other EPA groups and NASA, and others are interested in the results
of this effort.
Phones: Cairns, 430-4777 or 503-754-4777 FAX -4799
GCRP Goal:
The goal is to determine the quantity of tropical forests and the
rate of conversion to other land uses. These data are valuable for
estimating the current abundance of carbon sequestered in the
regions, and allow better model inputs and calibration and
verification. The determination of change in land cover and land
use (LC/LU) will also assist in the estimation of carbon pool and
the flux or dynamics of the pool.
Use of remote sensor data will supply a number of useful data or
information^ These would include LC/LU, detection of change in
LC/LU, and potential use of remote sensor data as input to habitat
or resource quality models.
Pilot studies and production of standard NALC product will occur on
tropical forest areas within North America. A separate effort in
Brazil will be conducted under the Deforestation Monitoring
program. The regions for early attention will include the states
of Southern or Southeast Mexico, the Atlantic Forest region of
Brazil, the island of Hispaniola, and other Caribbean islands.
Early work on data sets have been slated for the Southern and
Southeastern portions of Mexico. Coordination with ERL-C has
prioritized the states of Chiapas, Quintana Roo, Campeche, and
Tabasco for early data acquisition and analyses.
368
-------
NALC Applications (Standard Products):
The standard products that apply to this work element would be land
cover/land use images and tabular data, and evaluations of change.
Unprocessed data and standard image products may also be useful.
NALC Applications (Modified Products):
NALC data products and analyses will provide assistance with
characterizing and quantifying certain variables. These may
include optimization of images for detection of change in land use
conditions such as forest harvesting and forest regeneration.
Analysis of change induced by fire, pestilence, and weather is
necessary, and the management practices subsequent to the event
would be of interest.
Scheduling for Deliveries of Standard Products:
Current NALC scheduling calls for the above products to be
delivered in calendar years 1995 for Mexico, the Caribbean,
Chesapeake Bay Watershed, and Central America. Alaska and Western
US areas will be available in 1996. In 1997 the midwest and Great
Plains and Eastern-Southern US will be available.
Potential Schedule for Modified NALC Products:
The above issues may be addressed in preliminary tests of the NALC
program. The levels of sensitivity can be evaluated, and suggested
modified products could be generated the data production work by
Cooperators in support of GCRP. The timeframe would be the same
from the standard products.
Proposed Coordination Action/Schedule:
Early work has been slated for this activity in Brazil, Mexico, the
Caribbean, and Central America. It would be desirable to
communicate on a regular basis, to facilitate exchange of
information.
Much of this effort will involve production of data sets and
standard and modified products by Cooperators. Hence, it would be
desirable to collaborate on Cooperator contracting issues to insure
that the products of interest are addressed early in the delivery
timeframes, and that modified products of interest to GCRP
researchers will be generated.
369
-------
NALC / GCRP Coordination / Collaboration
Atmospheric Trace Gases and Land Cover
NALC Task:
Delivery of standard and modified NALC products focused on land
cover and land cover changes for the Southeastern US.
GCRP Task:
Characterize changing agricultural land cover types and how they
may influence atmospheric trace gases
GCRP Contact:
Point of contact is Lee Mulkey, EPA-ERL-A
GCRP Goals:
A goal of ERL-A is assess terrestrial biosphere management options
as they influence land cover, atmospheric trace gases such as
carbon and methane, and feedbacks to atmospheric conditions. To
assist these efforts it is desirable to supply good land cover and
land cover change images and tabular data for areas of interest.
These data will be useful in calculations of carbon pool, change in
the pool or carbon flux, evaluations of sources/sinks of methane
and nitrous oxide, and for model calibration and verification
activities. The current and past condition, and change in these
land cover are significant information for the charge of evaluating
natural and human-induced atmospheric conditions and their affects
on ecosystems. This effort can support the understanding of
feedbacks of change in land surface characteristics on atmospheric
conditions.
Use of remote sensor data and the objectives of North American
Landscape Characterization (NALC) can supply the land cover and
change in land cover data and other products. These data can be
linked with management information to obtain variables and develop
quantities for land use characteristics including: total
agricultural area and change in area, commercial farms and general
crop types, agroforest systems, conservation reserve areas, urban
areas, park and preserves, and other land cover/land uses.
Early work on data sets have been slated for the Southern and
Southeastern portion of the US. Coordination with ERL-A will
prioritize the states of interest for early data acquisition and
analyses.
370
-------
NALC Applications (Standard Products):
The standard products that apply to this work element would be land
cover/land use images and tabular data, and evaluations of change.
NALC Applications (Modified Products):
NALC data products and analyses will provide assistance With
characterizing and quantifying certain variables. These may
include optimization of images for detection of land cover
conditions such as agricultural areas and change in these areas,
conservation research program areas, and wetland areas. Analysis
of change induced by land cover conversion, fire, pestilence, and
weather is necessary, and the land cover change subsequent to the
event would be of interest.
Scheduling for Deliveries of Standard Products:
Current NALC scheduling calls for the above products to be
delivered in calendar year 1997, when the midwest and Great Plains
and Eastern-Southern US will be available.
Potential Schedule for Modified NALC Products:
The above issues may be addressed in preliminary tests of the NALC
program. The levels of sensitivity can be evaluated, and suggested
modified products could be generated in support of GCRP. The
timeframe would be the same from the standard products.
Proposed Coordination Action/Schedule:
Much of this effort will involve production of data sets and
standard and modified products by Cooperators. Hence, it would be
desirable to collaborate on Cooperator tasks to insure that the
products of interest are addressed early in the delivery
timeframes, and that modified products of interest to GCRP will
generated.
371
-------
NALC / GCRP Coordination / Collaboration
Remote Sensor Methods Development for
Identification of Methane Sources
NALC Task:
Delivery of standard and modified NALC products focused on wetland
and agricultural regions of selected areas of the US and North
America
GCRP Task:
Calibration and verification of models of atmospheric trace gases
in particular methane using detail land cover and land cover change
data
GCRP Contact:
Point of contacts are Lee Mulkey and Richard Zepp, EPA-ERL-A
GCRP Goals:
The goal of ERL-A is to evaluate the influence of agricultural
inputs, outputs and wastes that alter atmospheric concentrations of
trace gases, and study how these gas flux influence global climatic
conditions and evaluate feedbacks to climate. These efforts can be
facilitated by supplying good land cover data, NDVI or biomass
images, and land cover change data for areas of interest. These
data could assist in evaluating the role of change in agricultural
or wetland management practices as they influence natural phenomena
such as atmospheric and terrestrial fluxes of methane.
Estimates of land cover and change in cover will be useful in
calculations of methane flux, and for model calibration and
verification activities. Quantification of current and past land
cover condition, and change in these conditions is significant in
evaluating natural and human-induced influences on landscape. This
effort also supports the understanding of feedbacks from change in
terrestrial landscapes to atmospheric conditions, as well as having
utility in planning of alternative management practices.
372
-------
Use of remote sensor data and the objectives of North American
Landscape Characterization (NALC) can supply the land cover and
change in land cover data, and other products. Early work on data
sets has been slated for portions of the US and North America.
These efforts will address: a) capability to detect land cover
classes of interest, and b) ability to detect change in land covers
such as agricultural type and wetlands.
NALC Applications (Standard Products):
The standard products that apply to this work element would be land
cover images, tabular data, and evaluations of land cover change.
NALC Applications (Modified Products):
Potential areas of NALC data analysis assistance include
optimization of images for detection of land cover conditions such
as agricultural crop composition and wetland growth structure
(shrub vs. tree wetlands) and pattern. Analysis of change induced
by fire and weather is necessary, and regeneration subsequent to
the event would be of interest.
Scheduling for Deliveries of Standard Products:
Current NALC scheduling calls for the above products to be
delivered in calendar year 1997 for the midwest and Great Plains
and Eastern-Southern US. .
Potential Schedule for Modified NALC Products:
The above issues may be addressed in preliminary tests of the NALC
program. The levels of sensitivity can be evaluated, and suggested
modified products could be generated in support of GCRP. The
timeframe would be the same from the standard products.
Proposed Coordination Action/Schedule:
Much of this effort will involve production of data sets and
standard and modified products by Cooperators. Hence, it would be
desirable to collaborate on Cooperator tasks to insure that the
products of interest are addressed early in the delivery
timeframes, and that modified products on interest to GCRP will
generated.
373
-------
NALC / GCRP Coordination / Collaboration
Trans-Boreal/ Former Soviet Union
NALC Task:
Delivery of standard and modified NALC products focused on pilot
efforts to characterise land cover / land use in Alaska. Identify
sources to obtain additional remote sensor-derived data on land
cover or NDVI for areas in the former Soviet Union
GCRP TasX:
Characterize the quantity and variety of land cover in the former
Soviet Union :and Trans-Boreal ecosystem, and determine change, and
utilize the data in .modeling of carbon and methane flux
GCRP Contact:
The points of contact are Jeff Lee of ERL-C, and Chris Elvidge of
ORD/DRI. There are a variety of parties involved in the data
generation and in development of products. Their are also a number
of end users of the data. In particular, Ted Vinson of Oregon
State University would be interested in land cover information of
the former Soviet Union. This would assist his data collection and
modeling efforts related to carbon budget.
Phones: Lee, 430-4634 or 503-754-4634 FAX -4799
GCRP Goals:
A number of parties are involved in the Pathfinder activities
related to boreal forests. A goal is to evaluate the carbon budget
of these areas, because they sequester considerable carbon and
potentially can be a large source and/or sink depending on
management and climate conditions. The inventory these land covers
and use results in modeling of carbon pool and flux requires a
remote sensor experiment-
Use of remote sensor data can potentially supply location and
extent of land covers. Use of multiple date images will allow the
evaluation of change in land covers such as deforestation. These
analyses will also supply information on natural events that can
forest morbidity or mortality. Event include fires, certain
diseases, windthrow from tornados or hurricane, volcanic eruption,
and other events.
The Trans-Boreal effort is a continuing one and products will
ultimately proiTide detail valuable to global change analysis
efforts. Initial products include AVHRR bi-weekly composites of
selected areas, and region area composites by year.
374
-------
NALC Applications (Standard Products):
The standard products that apply to this work element would be land
cover/land use images and tabular data, and evaluations of change.
Unprocessed data and standard image products may also be useful.
These products would be used to study the capability of remote
sensor input, and would utilize NALC products from Alaska and
Boreal Canada.
NALC Applications (Modified Products):
NALC data products and analyses will provide assistance with
characterizing and quantifying certain variables. These may
include optimization of images for detection of change in extent of
land covers due to forest harvesting. Analysis of change induced
by fire, pestilence, and weather is necessary, and the management
practices subsequent to the event would be of interest.
Scheduling for Deliveries of Standard Products:
Current NALC scheduling calls for the above products to be
delivered beginning in calendar year 1995 for pilot studies of
interest. Data for Alaska will be available in the 1995 timeframe,
also.
Potential Schedule for Modified NALC Products:
The above issues may be addressed in preliminary tests of the NALC
program. The levels of sensitivity can be evaluated, and suggested
modified products could be generated the data production work by
Cooperators in support of GCRP. The timeframe would be the same
from the standard products.
Products from other Landsat Pathfinder experiments or AVHRR
Pathfinder experiment will become available, and will be obtained
the facilitate the evaluation of conditions in the former Soviet
Union.
Proposed Coordination Action/Schedule:
It would be desirable to coordinate activities with the other
Pathfinder and Trans-Boreal initiative to insure early access to
products of interest.
375
-------
NALC / GCRP Coordination / Collaboration
Rice Growing Areas
NALC Task:
Delivery of standard and modified NALC products focused on rice
growing areas of the US, and identify points of contact to obtain
additional remote sensor-derived data on rice growing areas
GCRP Task:
Characterize the quantity of rice growing lands and determine
change, and utilize the data in modeling of carbon and methane
flux.
GCRP Contact:
The point of contact is Daniel Marks, EPA-ERL-C. Chris Elvidge of
ORD/DRI will coordinate this activity to provide linkages with non-
NALC remote sensing programs.
Phones: Marks, 430-4634 or 503-754-4634=FAX -4799
GCRP Goals:
The goal is to estimate the extent and the change over time of rice
growing lands. The information will assist the evaluation of
carbon pool and flux, and the potential for generation of methane
from cropped anaerobic soils. These and other data can be helpful
in analysis of management activities and may potentially assist in
development and implementation of policies.
The remote sensor data can supply data on the location and extent
of rice lands, and multiple date analyses will yield information on
change. A goal of the program is to evaluate US rice growing
areas, and develop methods to supply information. Later, these
techniques can be used to evaluate other rice growing lands. It is
also a goal of the Landsat Pathfinder program is to develop a
Multispectral Scanner (MSS) data set for Southeast Asia. The
format will be similar to that of the North American Landscape
Characterization (NALC) program. From this effort, it will be
possible to estimate the rice lands of Southeast Asia and provide
tabular and imagery summarizes of the data.
376
-------
NALC Applications (Standard Products):
The standard products that apply to this work element would be land
cover/land use images and tabular data, and evaluations of change.
Unprocessed data and standard image products may also be useful.
NALC Applications (Modified Products):
NALC data products and analyses will provide assistance with
characterizing and quantifying certain variables. These may
include optimization of images for detection of change in extent of
rice land and change in management. Analysis of change induced by
fire, pestilence, and weather is necessary, and the management
practices subsequent to the event would be of interest.
Scheduling for Deliveries of Standard Products:
Current NALC scheduling calls for the above products to be
delivered in calendar years 1996 for the Western US such as rice
growing areas of California. In 1997 the midwest and Great Plains
and Eastern-Southern US will be available, and supply detail on
rice growing areas in Louisiana, Arkansas and other states.
Potential Schedule for Modified NALC Products:
The above issues may be addressed in preliminary tests of the NALC
program. The levels of sensitivity can be evaluated, and suggested
modified products could be generated the data production work by
Cooperators in support of ,GCRP. The timeframe would be the same
from the standard products.
Proposed Coordination Action/Schedule:
Much of this effort will involve production of data sets and
standard and modified products by Cooperators. Hence, it would be
desirable to collaborate on Cooperator contracting issues to insure
that the products of interest are addressed early in the delivery
timeframes, and that modified products of interest to GCRP
researchers will be generated.
377
-------
Appendix VIII:
NALC Product Refinement Workshop
Agenda
Participants
Workshop Summary
Letter Reports
Houghton
Lawrence
Salas
378
-------
NORTH AMERICAN LANDSCAPE CHARACTERIZATION (NALC) - PATHFINDER
PRODUCT REFINEMENT WORKSHOP
April 27, 1993
Las Vegas, NV
8:00 AM
8:15 AM
9:00 AM
9:30 AM
10:00
-10:20 AM
10:20 AM
11:00 AM
11:40 AM
- 1:00 PM
1:00
- 1:30 PM
WELCOME
Eugene Meier - U.S. EPA, EMSL-LV
INTRODUCTION & NALC OVERVIEW PRESENTATION
Ross Lunetta - NALC Technical Director
U.S. EPA GCRP Land Cover Data Requirements
Peter Beedlow - ERL/Corvallis Perspective
U.S. EPA GCRP Land Cover Data Requirements
Lee Mulkey - ERL/Athens Perspective
BREAK
Tropical Deforestation - Data Base Products
William Lawrence - University of Maryland
William Salas - University of New Hampshire
Carbon Modeling - Model Input Requirements
Richard Houghton - Woods Hole Research Center
LUNCH
EMSL-LV Remote Sensing Laboratory Tour
DISCUSSION SESSIONS
1:40 PM
NALC MSS LAND COVER PRODUCTS
2:50 PM
NALC MSS LAND COVER CHANGE PRODUCTS
4:00 PM
NALC DATA QUALITY OBJECTIVES
5:00 PM
PARTICIPANTS CLOSING REMARKS
5:30 PM
ADJOURN
379
-------
NORTH AMERICAN LANDSCAPE CHARACTERIZATION (NALC) - PATHFINDER
PRODUCT REFINEMENT WORKSHOP
April 27, 1993
Las Vegas, NV
Workshop Participants:
Name
Peter Beedlow
Richard Houghton
William Keith
William Lawrence
Barbara Levinson
Ross Lunetta
John Lyon
Gene Meier
Lee Mulkey
William Salas
James Sturdevant
Hal Walker
Dorsey Worthy
Affiliation
EPA/ERC-Covallis
Woods Hole Research Center
EPA/OMMSQA-HQ
University of Maryland
EPA/OEPER-HQ
EPA/EMSL-Las Vegas
OSU/EMSL-Las Vegas
EPA/EMSL-Las Vegas
EPA/ERL-Athens
University of New Hampshire
USGS/EROS Data Center
EPA/ERL-Narragansett
EPA/EMSL-Las Vegas
380
-------
GLOBAL CHANGE SCIENCE ISSUES
NALC PRODUCT REFINEMENT WORKSHOP
Las Vegas, NV, April 27, 1993
WORKSHOP SUMMARY
The workshop was organized to address several questions
advanced by the Technical Review Panel. These questions focused on
the use of the NALC data products by the science community.
One question was how to optimize the match between science
issues and the products that NALC will supply in support of these
Global Change Research Program issues. A second question or effort
was how to define products better with help from Agency and other
global change scientists, and thereby to further optimize the
products for science applications.
The major Agency science clients and other global change
scientists presented their research program related to global
climate change, and identified how NALC products will assist these
efforts. The global change scientists have provided their response
to the project and this information is provided here.
Laboratory Science Issues
The Environmental Research Laboratory (ERL) in Corvallis is
concerned with several programs related with global climate change.
In particular, the assessment of forest management practices
envisions .the use of NALC products. The goal is to identify
strategies for the sequestration of carbon in forest land cover
through improved forest management practices. The initial area of
activity is in southern Mexico, with later work in Brazil and
perhaps the Caribbean. The plan is that NALC will provide a land
cover baseline and the land cover change over the last twenty
years.
An additional area of effort is the forest sector carbon
budget. This is focused on the western and eastern US, Mexico,
Brazil and the former Soviet Union. In the US and in the former
Soviet Union good forestry statistics are available for estimation
purposes. In Mexico and in Brazil these statistics are rare, and
in Mexico it is hoped that NALC will help provide the spatial
distribution of land cover types for forest carbon budget
estimates.
381
-------
A third area of interest is the program of modeling the global
redistribution of vegetation due to climate change. These process
models require baseline information on the distribution of
vegetation and NALC data can potentially be used to initiate the
modeling scenarios. NALC data may also be useful for verification
or validation of the results of climate and vegetation distribution
scenarios.
In general, NALC data can be useful to predict the spatial
distribution of vegetation or plant assemblages. In these efforts,
NALC data along with known variances or uncertainties can be used
with carbon density estimates for each land cover type of interest.
The combination of spatial distribution of land cover from NALC and
carbon density of land cover types from field experiments, along
with uncertainties of each estimates, will help allow a
determination of spatial distribution of carbon and a carbon
inventory. Use of NALC land cover change data along with carbon
density data will potentially allow estimates of carbon flux over
the last twenty years.
The Environmental Research Laboratory in Athens suggested
several areas where NALC data may be useful. NALC land cover data
may be useful in their efforts to model the release and
sequestration of carbon and other atmospheric trace gases in soils
(methane, nitrogen compounds). Land cover could be important in
determining the spatial distribution of managed land cover types
and their soils, and help fix these quantities for use as input to
models.
A second area of effort would be in their determinations of
greenhouse gas budgets (GHG) and how they are influenced by land
use changes and the land use management activities. In this case,
the change of land cover types as determined from NALC products
could be useful in better estimating the area that these processes
occur.
The Agency and other global change scientists discussed the
general value of Multispectral Scanner Data (MSS). It is clear
that MSS data are valuable as compared to coarser resolution data,
as based on spatial resolution, spectral resolution, and the
twenty-year digital data archive. In particular, the estimates of
deforestation in Mexico and Central America are a valuable use of.
MSS and NALC data products, due to the lack of organized forestry
statistics.
A number of visiting global change researchers discussed their
work and addressed the potential contributions of NALC products to
science issues. Their inputs are included throughout this
document. In particular, they agreed upon the potential value of
NALC products. NALC products can be used as a baseline and serve
a valuable role as there are no data sets available for these
researchers to use. It will also supply data with known accuracy
382
-------
characteristics, and this is valuable for making better quality
estimates of carbon stocks.
Comments on specific elements of the NALC technical plan and
the data products are presented below.
Land Cover Products
Land cover category products will be valuable for carbon
inventory and flux calculations. The NALC products will supply the
general land cover types. Carbon density estimates for each land
cover type can be developed from field data. Carbon density data
and NALC products on land cover can be used to integrate or
inventory carbon stocks. Land cover types will provide the
mechanism to distribute the carbon and calculate stocks.
A further advantage is the definition of the current condition
or current boundaries. This definition of baseline is important
for monitoring change, for setting initial model conditions and
verifying model results.
In terms of land cover, it is desirable to determine the
antecedent forest types, the timing of the event such as forest
harvesting or a forest fire, and the current condition such as
whether the site was reforested.
From a regional forest carbon budget viewpoint, it would be
desirable to use the NALC land cover woody class to compare forest
source/sink conditions in the US. For example, it may be desirable
to examine the carbon sink conditions in the eastern deciduous
forest and in the southeastern forests, as compared to the carbon
source conditions in the Pacific Northwest.
A number of scientists were supportive of the availability of
"raw" or slightly processed data sets as they come from the EROS
Data Center (EDC). This may be a very useful product and may be
favored by certain application scientists.
The land cover categorization scheme was deemed adequate to
address the needs of carbon cycling researchers. It was emphasized
that the land cover categorization scheme was optimized for
identification from the MSS instrument.
Land Cover Change Products
Two techniques will be used for production of change detection
products. These include images generated from the brightness value
data through differencing of multiple band data (pre-
categorization). The second approach will employ comparisons of
categorized and labeled scenes or a post-categorization effort.
383
-------
The two techniques are to be proposed' due to some of the great
differences found in different image scenes. The first technique
is the easiest to implement and will be used in a number of
locations. The second technique will be useful where there is a
high level of cloud cover, or a great difference in seasonal dates
to be compared.
One valuable result of MSS-based NALC data is the
identification of both carbon sinks (afforestation for example) as
well as carbon sources (deforestation) . NALC will be able to
identify both and provide quantities and spatial distribution.
Global Change Research Program Pilot
It is necessary to develop further the utility of NALC and
related products in Agency global change research experiments. To
develop the NALC products and their applications to science issues
there will be Process Pilot studies. These studies will link the
NALC and related products to field and lab based measurements, and
allow use of results in modeling activities.
The Process Pilot studies will examine measurements of soil
carbon and soil moisture with field and sensor instruments, and
will seek to identify forest or woody land cover type using
combinations of sensors such as NALC MSS and satellite and airborne
radars. The evaluation of radar data would be performed under
EPA'S core remote sensing research program as it is beyond the
current scope of the NALC project.
Data Quality Objectives
It was deemed important to conduct an accuracy assessment of
the land cover categories. The uncertainties of estimates in land
cover, or for that matter in process estimates, are important to
supplying error bounds on calculations that use NALC results. The
accuracy assessments of individual categories are vital in carbon
flux calculations. Large variations in uncertainties will greatly
weaken estimates and broaden those estimates to the point of
diminishing value. Hence, it is important to assign accuracies or
uncertainties to address carbon science issues.
The data quality objectives as presented at the Workshop were
deemed adequate for the proposed uses of the data.
Various science users may require more detail than is
available from the land cover categorization system and the level
of accuracy supplied by NALC. Hopefully, needs that go beyond the
requirements of the NALC project can be met with additional
processing of the pre-categorized or categorized NALC data sets.
384
-------
A Report to the NALC Overview Committee
following the April 27th, 1993 workshop in Las Vegas, NV
R.A. Houghton
April 29, 1993
THE IMPORTANCE OF NALC PRODUCTS TO SCIENTIFIC ISSUES
As part of the Landsat Pathfinder program, the North American Landscape
Characterization (NALC) Project helps prepare the EOS community for handling large
volumes of data, searches the current Landsat archive, accumulates additional current
scenes, and makes products available and useful to the broader user community.
More specifically, the NALC Program will provide information useful for analysis
of the global carbon cycle. In particular, it has the potential to offer three products that
are essential for carbon cycle research:
1. Triplicate (1973, 1986, 1991) Landsat scenes for all of North and Central
America, including the Caribbean (coverage of Canada will be provided independently by
the Canadian Center for Remote Sensing). For the approximately 20-year record of
Landsat, these triplicate products will provide estimates of change in forest area. Change
in the area of forests is currently the single most important datum for determining the
flux of carbon from land use. (As satellite data are used to provide these data globally
over the next years, variation in carbon density (biomass and soil carbon) will become the
factor contributing most uncertainty to estimates of terrestrial carbon flux. Rate of
change in forest area is most important at present, however.)
2. Images of change between the three dates over the 1972-1991 period. Change
in the area of forests is most important and is readily determined from Landsat MSS.
Carbon density is not. However, carbon density may be determined indirectly from
Landsat data if change detection is carried out with pre-classified data. For example,
change detection may be able to identify logging and regrowth if young and old forests
are not first classified as identical. And there may be other changes and variations
observable with Landsat MSS data that could serve to determine carbon densities
indirectly (see Level of Classification, below). Every effort should be made to include not
only changes in forest - nonforest, but changes in the age, structure, and condition of
ecosystems.
3. A base map (1991) for North and Central America against which to
compare future land covers. The value of such a digital product can be appreciated by
considering how much more we would know now about sources and sinks of terrestrial
carbon if we had such images of land cover, globally, over the last 100 years. (AVHRR
would provide such images for less money, but not with a resolution that allows
385
-------
monitoring of deforestation/reforestation) (see below).
Potential uses of the NALC products (base map and 20-year record of changes)
include:
A. Determination of net flux of carbon from changes in land cover (Note: The
net flux of carbon in 1990 is dependent on regrowth of forests logged before the launch
of Landsat. Such forests are currently accumulating carbon, contributing to a carbon sink.
For this reason, sinks are difficult to document with only 20 years of satellite data. The
20-year record of Landsat will overestimate terrestrial emissions because releases of
carbon from logging are greater per unit area than long-term sinks of carbon associated
with regrowth. Over the period of Landsat, deforestation will have released some amount
of carbon. But regrowth of forests on lands harvested or abandoned before 1972 will be
accumulating carbon and will not be 'seen' by Landsat. This bias must be considered by
those who would use the NALC products to determine flux.
B. Determination of the areal extent of major land cover types. A knowledge of
land cover will, in turn, be useful for:
a. assigning carbon densities (biomass and soil carbon) for
determination of North American carbon stocks. (Note: assigning
carbon densities is required for A, above, for at least that part of the
region that shows change);
b. estimating the potential for carbon sequestration. Again, carbon
stocks for each cover type will have to be assigned independently of
the NALC products. NALC will provide the detailed digital product
(the map);
c. assigning greenhouse gas fluxes of CH4, N2O, CO to cover types;
d. assigning climatological parameters (e.g., albedo, roughness, rooting
depth, etc.) to cover types.
e. other?
C, The NALC 1991 base map will be useful in the future to determine areas that
indicate change, either as a result of
a. land-use change, or
b. climate change.
Monitoring programs such as NALC must be continued in the future. Ten-year intervals
may be adequate in temperate and boreal regions; five-year intervals or less may be
required in the tropics.
386
-------
Why North America?
The contribution of North America to the global terrestrial flux of carbon is
thought to be small. Furthermore, the net flux cannot be determined with data provided
by NALC alone (independent data on historical land use and on carbon densities are
required). Nevertheless, the flux from North America is important because the global flux
is the sum of all regions. If we consider only the regions of large source strength, we will
have only part of the flux — most of it, perhaps, but only part. Accuracies in global
estimates are now ±0.5 PgC or better (combustion of fossil fuels releases 6 PgC/yr +_
10% (15%)?). Furthermore, we probably don't know areas of flux well enough to be
certain of identifying the "important" regions. AH regions'are important, or have the
potential to be so in the future. We certainly do not know the regions where a major
carbon sink exists. My suspicion is that we don't know well even the land-use flux from
temperate and boreal regions. What is the role of fire, for example? Has the frequency
of forest fires increased? Will it in the future?
We need to know the flux of carbon from land-use change (including both sources
from deforestation and sinks from regrowth), globally. The need results from the fact that
there are two components to the terrestrial flux of carbon: a component related to
changes in land use (direct human effects), and a component related to other, more
subtle effects (climatic change, elevated CO2, increased availability of fixed nitrogen or
other nutrients or toxins). Changes in these environmental factors affect metabolism and,
hence, the storage of carbon per unit area. Such changes are very difficult to appraise
locally, let alone globally. So far, only the component resulting from direct human effects
can be determined; the other component has never been measured. It has been inferred
indirectly from analyses based on geophysical data — in other words, by difference. The
land-use component (the first one) is important to determine accurately because it helps
constrain the magnitude of the second component. One should measure what can be
measured.
Sources are important even in a region that is thought to be largely a sink.
Together the sources and sinks provide the net flux, and the net flux is required for the
global carbon budget. (Bank accounts are not balanced by considering only deposits.)
We need to know the flux associated with global changes in land use to ±0.5 PgC
or better (0.2 PgC?) in order to reduce the uncertainty to a level consistent with other
terms in the global carbon equation (atmosphere, fossil fuels, oceans). Thus, despite the
fact that North and Central America may not be regions of high flux at present, their
contribution to atmospheric carbon must nevertheless be determined. The determination
need not require wall-to-wall coverage with triplicates of Landsat MSS. For
determination of land use change over the last 20 years, wall-to-wall inventories or wall-
to-wall application of change detection might be applied only to hot spots, with some
intensity of sampling used to cover areas not thought to be undergoing change. But while
sampling may be adequate for retrospective estimates of land-use change, it will not
387
-------
provide a baseline map. A map with usefulness for the future will require wall-to-wall
coverage, and I think establishment of such a map is highly justified (see Landsat MSS vs
AVHRR, below).
Level of Classification Required
If the NALC products are to be used to calculate carbon emissions from land-use
change, a level 1 classification is adequate. Forest - nonforest is the change involving the
greatest change in carbon. Types of forest are generally less important.
If the NALC products are to provide a baseline or inventory of cover types for
subsequent assessment of change or subsequent assigning of biomass, then the highest
level of classification possible should be used. However, even the highest levels of
classification (at great expense) would probably be inadequate. What is needed for
carbon cycling is a combination of sensors that will give biomass (and soil carbon). The
techniques are not yet developed, but when or if they are, my guess is that existing land-
cover classifications will not be adequate for assigning variations in carbon stocks. New
classification will be required, based on the new sensors of biomass.
The state of the art classification of 85% accuracy 75% of the time suggests that
pre-classification change detection is better than post-classification, especially if level 1
classification is used. For example, level 1 recognizes woody vegetation, which would
include in the same class both old growth forest and young forests. Post-classification
change detection would show no change after 5-10 years of regrowth following logging.
Pre-classification change detection, on the other hand, would show a change — and the
change is important. It allows aging of the forest and indirect estimation of structure or
biomass.
Pre-classification change detection should be used as an exploratory tool in all
regions, and should be explored in the pilot studies of NALC. Level 1 is probably
appropriate for classification but pre-classification change detection should be used to
determine the level of classification appropriate for cover types that have changed.
Landsat MSS vs. AVHRR
AVHRR is cheapen
AVHRR has high temporal resolution (twice daily).
AVHRR is good for broad categories.
Landsat has high spatial resolution.
Landsat is good for land-use change. AVHRR overestimates deforestation. (Could
MSS be used to determine a correction for AVHRR? Early studies suggest not; in areas
where forest cover is <70%, AVHRR may either over- or under-estimate it.) Landsat
MSS or TM is near the lower limit of resolution for determining short-term changes in
388
-------
forest area.
Implications
In a strict sense, there are at least two scientific issues here, and they might be
most economically addressed with different approaches. First, for determination of the
net flux of carbon between North American ecosystems and the atmosphere, one needs
high resolution Landsat data, but not necessarily wall-to-wall. Stratified sampling would
probably be adequate and would be more economical. On the other hand, for
establishment of a baseline map, LAC AVHRR might be adequate and might be more
economical than Landsat (significantly more?). The NALC program is in a position to
address both issues (20-year change and base map). It is not clear from these two issues,
alone, whether NALC should attempt to address both. There may be other uses of the
products that help determine the appropriate goals for NALC. If the net flux of carbon is
important not only over the last 20 years, but over the next decades, as well, then a good
argument exists for obtaining, now, wall-to-wall coverage with Landsat MSS.
389
-------
Letter Report on NALC Pathfinder
Product Refinement Workshop
US EPA EMSL-LV April 27, 19S3
.submitted by:
William T, Lawrence
University of Maryland
Dept. of Geography
[301] 405-S809/wl33@umail. umd.edu
My immediate impression of the NALC Pathfinder Project is that it
is perceived within EPA as a very carefully designed data product
•with an as yet poorly defined science product and end-user
community. The fault in the product's definition seems more
conceptual than actual. I find the project very much on- track.
It is clear to me that the proposed products of NALC will prove
to be extremely va-luable to the science community, The mapped,
cloud-screened MSS image triplicates are in themselves an
invaluable product. The addition of a straightforward change
classification and lineage attributes make the basic dataset even
more useful. This community-wide value will become apparent once
the product stream is robust enough to allow general announcement
and release of the products to the ex-EPA user community. At
present, it seems that there is still a strong outreach effort to
be undertaken within EPA to generate some excitement about the
NALC product and its wide application. The dual roles of this
project; fulfilling both NASA Pathfinder and EPA Lab-wide needs
makes it much more difficult to build a strong consensus.
However, the initial interactions I observed between NALC, Athens
and Corvallis labs seems to be the very promising beginning of
collaboration that will be of benefit to all involved.
Products/End Uses:
There is a high level of interest in generating products beyond
the triplicates and associated land cover/land cover change maps
within NALC .Pathfinder. Such an effort on the part of NALC would
be an over- commitment of both time and resources, and is best
left for later collaborative, user community or EMSL-LV led
effotts that will surely follow given the tremendous potential
applications of the basic data product . I think it became clear
during the Product Refinement Meeting that, given appropriate
examples and some in-depth familiarization, projects at both
Athens and Corvallis labs could make very profitable use of the
basic suite of NALC data.
Some of the immediate uses of this basic dataset were made very
apparent during the presentations and discussions . I have
outlined a few below.
Soil carbon contents - The brief discussion of work at the
Lawrence - 1
390
-------
Athens lab brought many potential uses of NALC data to mind. The
large scale soil carbon modeling of this group could benefit
greatly from the utilization of a spatial dataset such as NALC.
This is not a trivial application, but a project that would
require some time to carry out. However, the results would not
only test the linkage of statistical and spatial data, but also
help define the accuracy of the current methodologies with their
broad spatial generalization.
Global Carbon Stocks - Both Athens and Corvallis labs are
interested in global carbon stocks. The standard approach uses
non-spatially explicit values for models. The use of spatial
data, such as the NALC product, for distribution of carbon
densities across landscapes present very real difficulties, so
have not been widely used. This is .an area of research that
could profitably engage both NALC and other EPA labs in
development of state-of-the-art science products. Other closely
related modeling efforts that could use the NALC products are the
whole suite of Earth-system modeling projects, assessment of
management practices on carbon cycling, greenhouse gas emission,
and global carbon balance studies.
State-of-the-Art Maps - The NALC land cover products will be
the first ever available for accurate assessment of the spatial
extent and -change in important land use type.s. No such baseline
maps exist at the scale of the NALC. Simple spatial analyses of
the land cover maps can yield statistics on the state of
cultivated lands, deforestation, and even wetland distribution.
Such maps are critical to accurate parameterization of ecosystem
models and assessment of management planning. Applications in
social and political science, and other as yet unidentified
fields are sure to follow the wide distribution of this dataset.
Methods:
Digital Triplicates - The digital triplicates seem extremely
well thought out in terms of the content, mapping, and cloud-
reduction mosaic processing. I am a bit concerned that the types
of distribution, files are proliferating as changes are made in
the data stream, ie. the method of copying mosaics. If a data
layer is made for each of several cloud contaminated images,
rather than the earlier mosaic process, things begin to get very
complicated. A simplification might be the distribution of two
cloud cover exclusive scenes for any one year without masking or
mosaicing. These datasets may get so voluminous that they are
impossible to distribute once classification, lineage, land-cover
and_land cover change layers are added. Distribution of a very
basic land cover and land cover change dataset would be useful.
Many users need only the product, not the entire digital dataset.
Additional information might include 8-bit color composites for
each year simulating visible or near-lR photoproducts.
^supervised Classification. - Care should be taken not to
distribute too many classes. Tens of classes from an initial
classification should be sufficient to describe the basic
Lawrence - 2
391
-------
variability for the collaborators that are doing the labeling and
aggregation, I understand that this process is still in
development , Some image smoothing may also be useful . We find
that our digital classification produces a data product that is
entirely too complicated, so majority filter smoothing and
removal of groups of pixels below a minimum mapping unit is used
to simplify the basic data prior to editing and analysis.
The type of change detection to be used need very careful
attention. Whether to use pre- or post-classification detection
will probably depend on the types of change and land cover-
present, as well as your ability to normalize MSS scenes through
time. If normalization, is successful, then I would suggest the
distribution of the normalized images/ rather than the raw
products -
of Land Cover Types - There was some
discussion of the level at which to distribute the land cover
information. I would let your collaborators classify to the
finest detail that they can, using digital and ancillary
datasets, with later post -process ing to aggregate the data to the
lowest common denominator, which would be based on your
Classification criteria* In any case < level 2 [Ecologically-
oriented Land Cover Classification System] would be unacceptable
for most ecological or Earth-science modeling uses, especially
when concerned with carbon modeling. The finer classifications,
when successful, could be distributed as case studies, or even as
separate data layers. A useful paradigm might be distribution of
the MSS digital triplicates separately from the land cover and
land cover change information.
Results of
The Product Refinement meeting was valuable for me and hopefully
our outside point of view will prove useful to the EPA NALC
Project as well. During the meeting I learned much of the NALC
and its placement within EPA and among other EPA laboratories .
The basic triplicate product of NALC is of exceptional value, as
it makes a uniform set of image products available to EPA and
other researchers, "Hie near-term science product, the land cover
characterization and change maps, will prove to be even more
important to the user community, but clearly are still in a
developmental stage. Much intensive research remains before a
truly operational land cover and/or change product is
forthcoming, but this should be accomplished in the near-term.
The research planned for the Chiapas and Oregon Transect pilot
studies will probably meet these developmental needs and answer
many methodological questions that remain. A very fruitful
collaboration with Athens and Corvallis labs in terms of NALC
dataset development and utilization seems highly likely. These
collaborations will not only enhance the NALC process but also
serve to further the linkage of spatial and statistical datasets
in modeling.
Lawrence - 3
392
-------
Recommendations -
* use pilot studies to test utility of unsupervised
classification and appropriate level of detail for land
cover class identification
* a pilot study in agriculturally-dominated landscapes may be
useful to test land cover classification methodologies [if
such land cover types are not well represented in current
pilot study sites]
*• go into production mode with basic products - triplicates, land
cover change - let later projects develop the more
discipline-specific science products
> build constituency with other EPA labs, using them to help
define future products/collaborative research efforts - this
will be somewhat of an MALC 'outreach' until others catch on
to the utility of this multi-temporal dataset
+ formalize inter-lab NALC data based research in some way - eg,
travel funds, some limited support
*• consider wide distribution of a simple dataset, eg. land cover
classes for three periods,.plus change and perhaps an 8-bit
color composite [visible or near-IR - no MSS digital) as
this will reduce the data volume and suit many applications
»• MSS digital triplicates might be distributed separately from
all land cover/land cover change data, if datasets become
unmanageably large
»- 'radiometric rectification' or some normalization of the
digital data may be useful prior to distribution - research
work on pre-classification change detection will clearly
show if this is a viable option
*• maintain close ties with other Landsat Pathfinder projects,
especially in change detection methodologies - NALC land
cover classification could be applied to other Pathfinder
datasets
^C-MJ
William T. Lawrence
May 10, 1993
Lawrence - 4
393
-------
North American Landscape Characterization (NALC)
Product Refinement Workshop
EMSL-LV April 27, 1993
Review comments by William Salas.
A product refinement workshop for the North American
Landscape Characterization project (NALC) was held at the
Environmental Monitoring Systems Laboratory in Las Vegas on Apri 1
27, 1993. This workshop was extremely informative and useful.
As part of the Landsat Pathfinder program, NALC is making
important contributions in data processing, distribution, and
archiving techniques needed for development of EOS-DIS. In
particular, the use of EDC as the Land DAAC to create triplicate
MSS images that are image to image registered demonstrates how a
"science computing facility" could use a DAAC in the EOS era.
While this point may seem trivial, it is an important test of the
DAAC-SCF infrastructure design of EOS-DIS. NALC's assistance in
completing the MSS 1992 acquisition for complete coverage of
North America is another important contribution in itself. The
now complete 20 year archive of MSS data is an extremely
important data set, in that it provides the capability to study
temporal phenomena over a 20 year period with the same
instrument.
The processing algorithms being developed by NALC for land
cover and land cover change mapping are important but difficult
tasks. Characterization of thematic classes for large areas over
time is extremely difficult using remote sensing data, and as a
result has not been attempted until the recent implementation of
the Landsat Pathfinder projects. Success of these programs will
demonstrate the ability to handle massive, multitemporal, high
spatial resolution data sets. However, in order to succeed
efficient processing algorithms are needed. NALC use of pilot
studies to assess the utility of several candidate processing
algorithms is imperative since the success of the algorithms may
depend on the regional under study. NALC pre-classification
change detection strategy is exciting and should provide a better
product for the science community.
Overall the NALC project has a solid start toward obtaining
their goals of producing "land cover and land cover change data
products at sub-kilometer spatial resolution" for most of North
America and the project will be a success. However, as with any
project, there are a few areas of concern that if addressed could
possibly enhance the success of the project.
The project needs to address how the NALC products may be
used throughout the science community, not just within the EPA. I
realize one of the goals of this meeting was to address this
issue, but, for example, there are many EOS investigators that
would be extremely interested in hearing about the NALC project
and could integrate the NALC products with ongoing EOS research.
394
-------
For instance, there is a group at the University of New Hampshire
that would be extremely interested in providing inputs to NALC
regarding the land cover data sets for the northeast US and how
the data could be used to supplement their efforts to model net
primary production and evapotranspiration in the region. As
another example, NALC data products could be used as test sites
for the North American AVHRR 1-km project. This is an important
exercise in how can high spatial resolution be scaled up for use
in testing the lower. As a result I suggest that another pilot
project be developed to directly link the NALC data set with an
existing global change research project. This would enhance the
visibility and broaden the base of support for NALC, as well as
demonstrate how the data products can be used to address .specific
global change research needs.
The use of a clustering algorithm for an entire scene may
present some-problems. From experience we have scene that
variations in atmospheric conditions within a scene can cause
different land cover types to have the same spectral
characteristics. This occurred often occurred in scenes along or
near coastal areas, due to differences in the atmosphere over
terrestrial and marine areas. As a result, the scene needed to be
broken up into smaller areas to accurately classify land cover
types.
The data analysis tracking techniques are an important part
of a large processing oriented project like NALC and the current
system of creating a bitmap relating the origin of each pixel is
important and properly addressed. However, some thought as to how
to account and track sidelap (7% at equator to 60+% in Alaska) in
the processing stream is needed.
Verification procedures and accuracy assessments are
difficult to provide for large projects like NALC. The procedures
for spatial sampling outlined during the workshop and in the
technical work plan are good. A measure of variability in
labeling due to differences in individual interpretation should
be addressed.
The concerns provided here are minor and an emphasis on all
the good aspects of the project should be inferred. The project
has a strong understanding of its goals and has presented~a~well
thought out and realizable approach to insure that the goals are
met. It was extremely instructive for me to sit in on this
workshop and I hope my brief comments can be of use to the
project.
395
-------
Appendix IX:
Landsat Pathfinder Initiative and the Canada Centre for Remote
Sensing (CCRS), and Documents from the December, 1992 Meeting with
CCRS.
396
-------
LU
V)
a
a:
O
Q
Z
<
LU
LU
<
CO
cc
O
O
O)
O
05
•o
c
(O
a.
•»-•
(0
(O
•a
c
(0
CN
O
O)
CO
o
JD
E
o
o
a)
Q
397
O)
d
cu
Q
o»
.c
0)
z a:
52
cc
o
o
-------
EPA/EDC/CCRS Meeting
December 9 & 10, 1992
Agenda
December 9. 1992 in Room 301 - 09:00 - 12:00
Introduct ion
Welcome to CCRS
Review Agenda
Pathfinder Overview
North American Land Characterization (NALC)
NALC Data Processing Overview
NALC Standard Data Processing Methods
Discussion
December 9, 1992 in Room 201 - 13:00 - 16:00
Terry Fisher
Florian Guertin
Terry Fisher
Ross Lunetta
Ross Lunetta
James Sturdevant
John Lyon
• Detailed discussion on pathfinder processing methods and
algorithms
• catalog guerying
• radiometric processing
• geometric prcessing
• product definition & distribution
December 10, 1992 in Room 201 - 09:00 - 15:00
This day will be devoted to discussion of topics of mutual
interest including: CCRS Global Change initiatives, change
detection, etc. and possibly a tour of CCRS facilities at Booth
Street.
Suggested areas:
• CREO Optical tape
• GEOSCOPE
• ERS-1 Mosaics / system
• GCNet/Video Disk
398
-------
LANDSAT PATHFINDER INITIATIVES
AT THE
CANADA CENTRE FOR REMOTE SENSING
In February 1993 a project was initiated at the Canada Centre for
Remote Sensing (CCRS) to develop and demonstrate capabilities to
manipulate and post-process LANDSAT Pathfinder data. Of particular
interest is the ability to merge scene-based imagery into seamless mosaics
for large area monitoring purposes. The project, which will be completed
at the end of March, 1995, will include the development of a PC-based,
prototype workstation. As a means to demonstrate this technology and
to increase Canadian scientific community awareness of the potential of
Pathfinder imagery, an example mosaic of the Great Lakes region will be
generated for the 1988 time window. The mosaic will include both U.S.
Pathfinder products and Canadian coverage pre-processed on Canadian
systems. The prototype workstation will expand upon a current capability
for spaceborne SAR image mosaicking and will include (a) radiometric and
geometric normalization, (b) automated seam delineation, (c) selectable
spatial resolution and (d) a simple DBMS to aid in planning processing
strategies and in monitoring and assessing processing status and product
quality.
The present U.S. program does not include plans for the routine
inclusion of Canadian coverage. As an added objective, CCRS will
conduct a technical assessment of Canadian operational products with a
view to their potential as a source of Pathfinder data.
399
-------
CM
ffJ
I
08
en
u
Q
t
Oi
JC
"-P
o
4)
O
T3
C
H-
Z
CC
a
a.
•a
m
CO
§
Q
O
CC
a.
UJ
LU
O
CO
a
a
2
<
LU
z
LU
O
CO
CO
CO
CO
Q
G
LU
O
LU Q
CC UJ
Ufa
UJ O
CC O
o o
LU LU
O O
CO
o
ID
LL
Q
LU
O
y Q
LU O
CC O
O O
LU LU
O CD
UJ 111
u a
O
O
^ w
" O
Q ut
? «
3 E
< a
o "
CO
CC
O
O
400
-------
co
LU
U
DC
UJ
CO
flC
ui
DC
O
O
LU
CC
o
(D
OtO
-1 Q
3» UJ
02
05
UJ o
CO ll
< —i
OQ O
co"-
LLJ LU
Z X
UJ H-
£"-
^o
1°
1<
Si
UJ
_J
LU
CO
CD
^
O
O
SATELLITE ID
SENSOR ID
F APPLICABLE)
LU
SENSOR MOD
<
APPLICABLE)
~^
u.
VIEW ANGLE
UJ
H
Q
ACQUISITION
UJ
UJ
O
CO
X
o
LU
LL.
O
LU
H
ACQUISITION
H
IMAGE QUALI
DC
CLOUD COVE
ORDINATES
O
O
GEOGRAPHIC
EARCHES
CO
SS INVENTOR
O
CC
o
ALL SATELLITE/SENSOR
DC
O
SEARCH ANY
i/l A SINGLE USER REQUEST
^
O
CC
)ATABASES F
L_J
401
CM
0>
O
<^
•a
O)
u
0
Q
o»
I
0
1
u>
•a
5
51
UJ -4U
O
o
55
C7)
CC
O
O
-------
CD
O
a
2
X
O
DC
O
2
CO
CO
LU
O
O
cc
Q.
^
o
o
v£
o
d
o
o
1h
r-
CO
cc
LU
CO
^
LU
O
LU
O
LL.
O
CO
LU
o
cc
LU
LU
<
CO
CO
DC
O
O
tD to
s ®
Total
Scenes
Received
t! -fl>
£ «
co Q
c
"55
IN, s
co ;
JN
rM
i
JN
ico
CO
IN,
t—
"5
CO
CO
•+-» :
CO
W
-o
.C
CO
_J
IN
^
00
T—
o
LO
CD"
o
CM
CO
05
,'t~J
O)
H
^_,
fO
(0
TD
C
10
O :
CT>
CM
O
CM
LO"
CO
CO
CO
v"
a.
~
o
Q.
'3
i^
"*~*
1-
O
CL
CO
LO
O
CO
CO
LO
CO
CO
00
0)
r-
Q.
"•+3
CO
anchrom
CL.
^^
J-
0
Q.
CO
CO
LO
0)
^J*
•MH
CO
CO
00
CO
O)
T~
CO
cc
CO
CO
LU
T—
CO
o
^
T—
*^
CO
r—
CO
en
TOTAL
CD
'O
O)
Q
TD
c
ro
CL
T3
C
03
o
o
< CC
•Z K
< O
o Z.
CO
a:
o
o
402
-------
CO
LU
o
DC
LU
OC
o
LU
z
or
o
o
UJ
DC
CO
UJ
O
DC
UJ
CO
>•
cc
o
H
o
LU
DC
G
LU
Z
o
o
u. Q
o z
LU O
DC P
> O
<3
03 I
l-<
CO QC
LU Jn
£P
-JO <
SJ *- *-
OQ . - CO
g<
H DC
O LU ;?
ii i |— UJ
es E I
Q O h-
uj co
co >•
D tO
LU
UJ <
iZ O
Z H
in "J
CO CC
ii
I UJ
CO
LU
DC
G
o
LU
z
o z
i- O
uj O
CO >-
< o
G iZ
o F;
-------
en
o
LU
0)
o
c
Q
V*
HI
CC
LU
CC
I
LU
2'
'" MWK '
Q
2
<
o
LU
CC
a
O
OQ
O
CC
O
O
O
CO
I- Q
CC LU
O H-
CL O
LU LU
CC -J
— LU
cc co
CO
LU
LU
O
CO
o
111
o
LU
—I
UJ
CO
LL.
O
o
H
o
O LU
>2
CO
LU
LU
O
CO
Q
LU
O
LU
_l
LU
CO
CC
o
LL.
CC
LU
LU
CO
O
CC
GQ
O
O LU
UJ CO
h- < CO
o m LLJ
10 — 2
CC < LLJ
o
0
k.
o
•a
S
CL
CO
CD
Q
Q LL
" o
II
CO
LT
O
O
404
-------
ULf
Q
I
Ul
cc
O
Q I
1
CC
O
O
LU
CC
Q
I
2?
o
r-
00
«
U
0
a
o»
•*:
o
•o
3
2z
_ o
< o
O
O
Z ui
iw Jr
" o
il
CO
CC
o
o
405
-------
APPENDIX X
LAND COVER CATEGORIZATION SYSTEM FOR
USE IN THE NALC-PATHFINDER PROJECT
406
-------
INTRODUCTION
The United States Environmental Protection Agency's (USEPA's) North
American Landscape Characterization (NALC) Pathfinder program requires a
categorization system that can be used to categorize Landsat MSS data across the
entire North American continent. The two main categorization systems in use by
Federal Agencies are Anderson et al. (1976): A Land Use and Land Cover
Classification System for Use with Remote Sensor Data, and Cowardin et al.
(1979): Classification of Wetlands and Deep Water Habitats of the United States.
The NALC categorization system combines features from the Anderson et
al., Cowardin et al., and other systems. It provides the capability to "crosswalk"
between the NALC system and other systems. Crosswalk is a categorization term
referring to the ability to integrate data which has been categorized with qne
system with data that has been categorized using a different system. The NALC
categorization scheme is a hierarchial system with broad categories at the more
general levels and detailed land cover and land use categories at the finer levels.
The resulting system was designed to remain relatively simple at the basic level,
while still defining features most important for assessing carbon stocks and
monitoring changes.
The NALC categorization system has been developed with major input from
the USEPA Environmental Monitoring Systems Laboratory-Las Vegas (EMSL-LV);
the NALC Technical Review Panel; and the NALC Technical Working Group.
Considerable consideration was given to the interagency working group
categorization activities. The Interagency Categorization System was developed
through the input from many sources including the U. S. Geological Survey
(USGS); the U. S. Fish and Wildlife Service - National Wetlands Inventory
(USFWS-NWI); the National Oceanic and Atmospheric Administration (NOAA); the
NOAA - National Marine Fisheries Service (NMFS); the University of Delaware; the
Oak Ridge National Laboratory; the Salisbury State University; and the Florida
Department of Natural Resources.
NALC CATEGORIZATION SYSTEM
The standard NALC categorization product for North America will be a Level
II database product. However, the NALC categorization system (Table 1) currently
contains three levels of information. The third level will have regional and site
specific applications for global change process pilot studies and unique regional
scale applications. A level is a stratum or tier of appropriate effort and technology
required to derive a given level of land cover information. Each of these levels
corresponds to different degrees of information processing, progressing from
407
-------
NALC Pathfinder Categorization System
LEVEL 0
Land
Wotor
Other
LEVEL 1
1.0 Barren or Developed
Land
2.0 Woody
3.0 Herbaceous
4.0 Arid
5.0 Snow/Ice
6.0 Water & Submerged
Land
7.0 Other
LEVEL II
1.1 Exposed Land
1 .2 Developed Land
2.1 Forest
2.2 Scrub/Shrub
3.1 Herbaceous
4.1 Arid Vegetation
4.2 Riparian
5.1 Snow/Ice
6.1 Ocean
6.2 Coastal
6.3 Near-Shore
6.4 Inland
7.1 Cloud
7.2 Shadow
7.3 Missing
7.4 Indeterminable
LEVEL III
'"$**& * m^r^f4yff
., ,,, ,,,, , ,,,.;,,,, , ,r/f
2.1 '.1 0p&ftF2 Mode««& Forest
3,1,$ £>e«$0 F>W«9t
£,&f <}pftff.&<;ft(h/$jw|
2,£3fj$»3l+g! tj^afts Sors^^tub,
3t .1.1 Fastara/SrdsBliand
3.1.2R«iyvf>Ci'Qj*
3*1*3 Arctic TUrtdfa
4.1,1 Alpine Tundra
4<1,2 Arid Fores*
4*14 $$ V^dcWIland w
4.1 ,4 Arid StertifcfaCtd
*(1>S Arid &^$(fto«t
4<1 <$ Arid (?«se«bfld
4.^1 Ariif Sty/amp't'Qfiesit^,
4-5 4 A^rf SwannpBOfHif '
4,2,3, Arid Marahtsn^
4,2:4 tfncf 'Strandland s
S-1 1 $rt»W% v 1 v
§4i ^/s^<^^ ; _"• ,
..'i>-, -•,-, --.-. <<^
f \$Y,*,f •*
- „„', „ &?'**•-
'~ '~f jfe. * V-WM-V o %
^,,«
' - ,»'>"•<« ' £ ;r
S ff fft ff>ts,jf"S '": •':" <"• \-~ 'f .<•
, ,,v~.
, ' ' ','>
408
-------
automated spectral clustering of satellite and aircraft image
data, to manual collection of field measurements.
This categorization system is intended to provide a
framework for categorizing a broad variety of land cover types.
If more specific information is needed about a particular area,
the user can modify the system by adding categories at deeper
levels. Any of the Level III categories can be subdivided
creating Level IV categories.
DEFINITIONS
This section contains the definitions for the categorization
system. Areas are categorized and labelled according to the
resources present on the ground at the time of imagery
acquisition. If a agricultural field is devoid of vegetation and
contains bare soil at the time of image acquisition, the field
would be labelled as barren or developed rather than herbaceous.
The categorization system is divided into three organizational
categories: Land Division, Water Division, and Other. The actual
categories in the NALC database would be the Level I and II
categories.
LAND DIVISION
The Land Division includes all categories other than open water
and other. It is divided into five Level I categories: 1.0
Barren or Developed Land; 2.0 Woody; 3.0 Herbaceous; 4.0 Arid;
and 5.0 Snow and Ice.
1.0 Barren or Developed Land
This category is composed of cleared, burned, or otherwise
barren areas. This includes areas of artificial
anthropogenic cover, with much of the land covered by
artificial materials such as buildings and other man-made
impervious surfaces. This category is subdivided into two
Level II categories: 1.1 Exposed Land; and 1.2 Developed
Land.
1.1 Exposed Land
The Exposed Land category includes naturally occurring
areas that have limited ability to support life and
other areas disturbed by man. In general, the surface
of these areas are covered with soil, sand, or rocks
and contains less than one-third vegetation or water.
Naturally barren areas includes rock outcroppings,
talus slopes, sandy beaches, etc. The exposed areas
may be transitional or permanent. These areas include
409
-------
lands cleared for a variety of purposes, i.e., building
construction, quarries, landfills, gravel pits, or
strip mines. Recently clearcut forests with all the
vegetation removed and bare soil agricultural fields
would be included in this category.
1.2 Developed Land
This category includes areas in which 70% or more of
the land surface is covered by structures and other
man-made impervious features. This corresponds with
those areas termed "Urban or Built-up Land" by Anderson
et al. (1976) who states:
Included in this category are cities; towns;
villages; strip developments along highways;
transportation, power, and communications
facilities; and areas such as those occupied by
mills, shopping centers, industrial and commercial
complexes, and institutions that may, in some
instances, be isolated from urban areas.
2.0 Woody
The category Woody refers to areas dominated by plant
species that have an aerial stem which persists for more
than one season, and a cambium layer for periodic growth in
diameter (Harlow and Harrar, 1969). Succulents such as
cacti would be included in the Arid category. The woody
vegetation category requires a crown closure of 25% or
greater. This category includes scrub/shrubs and trees and
is subdivided into two Level II categories: 2.1 Forest; and
2.2 Scrub/Shrub.
2.1 Forest
The forest category contains single stemmed woody
vegetation (trees). Harlow and Harrar (1969) defines a
tree as a "woody plant which at maturity is 6 meters or
more in height, with a single trunk, unbranched for at
least several feet above the ground, and having a more
or less definite crown". This category is subdivided
into three Level III categories: 2.1.1 Open Forest;
2.1.2 Moderate Forest; and 2.1.3 Dense Forest
2.1.1 Open Forest
Corresponds to communities dominated by trees with
a mean potential height usually under 15 meters a
canopy which is usually open, and is singularly
layered. The crown closure is between 25 and 50
410
-------
percent. These areas typically have a grassland
understory. Savannas are an ecotone between woody
and herbaceous.
2.1.2 Moderate Forest
Corresponds to forests having a canopy closure of
greater than or equal to 50%, but less than 75%.
2.1.3 Dense Forest
Corresponds to forests having a canopy closure of
greater than or equal to 75%. -
2.2 Scrub/Shrub
The Scrub/Shrub category contains woody vegetation
which at maturity are usually less than 6 meters in
height. The crown closure is greater than 25%.
Scrub/Shrub are smaller than trees and unusually
exhibit several erect, spreading, or prostrate stems
and a general bush appearance This category is
subdivided into three Level III categories: 2.2.1 Open
Scrub/Shrub; 2.2.2 Moderate Scrub/Shrub; and 2.2.3
Dense Scrub/Shrub.
2.2.1 Open Scrub/Shrub
Corresponds to all Scrub/Shrub, having a canopy
closure of greater or equal to 25%, but less than
50%.
2.2.2 Moderate Scrub/Shrub
Corresponds to all Scrub/Shrub, having a canopy
closure of greater than 50%, but less than 75%.
2.2.3 Dense Scrub/Shrub
Corresponds to all Scrub/Shrub, having a canopy
closure of greater than or equal to 75%.
3.0 Herbaceous
This category includes areas dominated by herbaceous cover.
This category encompasses natural and managed areas of
herbaceous cover, including lawns, natural grasslands,
cultivated (agricultural) fields, pastures, or herbaceous
wetlands (marshes). Land used for food production such as
corn, soybeans, wheat, etc., are included in this category.
411
-------
Some grasslands may have been seeded to introduce or
domesticate plant species. Other grasslands are
successional in an area such as areas that have been burned
by fire or agricultural fields are left fallow. This
category is not divided at the Level II category.
Herbaceous is subdivided into three Level III categories.
3.1 Herbaceous
This category is subdivided into:
3.1.1 Pasture/Grassland
Pasture/Grassland encompasses those areas that
have both managed and natural grassland cover.
This category includes small grains such as oats
and wheat, hayfields, pastures, lawns, herbaceous
road right-of-ways, herbaceous fields, or natural
prairie areas.
3.1.2 Row Crop
Row crop refers to areas cultivated for
agricultural purposes. Examples include fields
used to grow soybeans, corn, and potatoes. Areas
that are used for agricultural purposes that are
temporarily unvegetated due to cropping or tilling
practices would be categorized as barren or
developed land.
3.1.3 Arctic Tundra
The Arctic Tundra category exists in an
environment so cold that moisture is unavailable
for most of the year, precluding the establishment
of trees, and in which the maximum vegetation
development is of herbaceous root perennials,
lichens and mosses, with grasses poorly
represented or at least not dominant (Brown et
al., 19.79). Tundra categorized in this category
consists of arctic and near arctic areas north of
60 degrees latitude.
4.0 Arid
An Arid region is characterized by dryness, variously
defined as rainfall insufficient for plant life or for crops
without irrigation; receives less than 25 cm of annual
rainfall; or has a higher evaporation rate than
precipitation rate (Dictionary of Geological Terms, 1976).
In alpine settings above 5000 feet, the precipitation may be
412
-------
between 25 and 75 cm. The areas with higher amounts of
precipitation support forest communities. Land cover types
included in this category have a soil regime which is dry
more than half the time when not frozen, and is never moist
for more than 90 consecutive days (Buol et al., 1989). This
category is divided into two Level II categories: 4.1 Arid
Vegetation; and 4.2 Riparian.
4.1 Arid Vegetation
The vegetation categories within the arid represent
upland communities which derive moisture primarily from
available precipitation, rather than from surface
water. Vegetation in these categories would often be
considered the background or regional vegetation. The
category is subdivided into six Level III categories:
4.1.1 Alpine Tundra; 4.1.2 Arid Forest; 4.1.3 Arid
Woodland; 4.1.4 Arid Scrubland; 4.1.5 Arid Grassland;
and 4.1.6 Arid Desertland.
4.1.1 Alpine Tundra
Plant communities existing in an environment so
cold that moisture is unavailable during most of
the year, which precludes the establishment of
trees. The climax vegetation consists of
herbaceous root perennials, shrubs, lichens, and
mosses with grasses poorly represented. Areas
categorized as 4.1.1 differ from tundras
categorized as 3.1.3 by lying at high altitudes
above treeline and in an alpine setting.
4.1.2 Arid Forest
Arid Forest areas are typically open forests
dominated by coniferous trees over 6 meters in
height. The understory may consist of grasses or
other mesophytic plants, or there may be no
understory vegetation. These forest areas differ
from woody forested areas in that the annual
precipitation is less than 75 centimeters and is
an alpine setting. If found below 5000 feet the
annual rainfall is less than 25 centimeters.
4.1.3 Arid Woodland
Arid Woodlands are those areas which typically
contain trees under 15 meters in height which are
widely spaced with singular canopies.
4.1.4 Arid Scrubland
413
-------
Communities dominated by either sclerophyllic or
microphyllic shrubs which generally do not exceed
6 meters in height. The canopy can be closed, or
open with a perennial understory interspersed
between shrubs. Areas with more than 25 percent
of the ground area covered by shrubs fall into
this, category.
4.1.5 Arid Grasslands
Arid Grasslands include those areas in which the
dominant vegetation consists of grasses and other
herbaceous plants. There may be some shrubs
scattered within this category but less than 25%.
In some cases the differentiating factor between
this category and the woodland is the spacing of
the trees. Areas with more than 75 percent of the
ground cover containing grass fall into this
category.
4.1.6 Arid Desertland
Vegetation in this category can be either annual
or perennial. The areas between plants usually
consists of rock or sand with virtually no
intervening vegetation.
4.2 Riparian
It is understood that riparian communities exist within
all types of landscapes. These communities are
wetlands associated with a stream, or river. The
defining factor in this category is that the average
annual precipitation is less than 25 centimeters. Four
Level III categories lie within this category: 4.2.1
Arid Swamp Forest; 4.2.2 Arid Swampscrub; 4.2.3 Arid
Marshland; and 4.2.4 Arid Strandland.
4.2.1 Arid Swamp Forest
This community has an overstory of trees over 6
meters high. There is often a shrub and
herbaceous understory within this category.
Cottonwoods (Populus spp.) and sycamores (Platanus
spp.) are typical dominants within this category.
4.2.2 Arid Swampscrub
The dominant vegetation within this category
consists of low trees or shrubs below 6 meters in
height. Willows (Salix spp.,) and alder (Alnus
spp.) are common dominants within this category.
414
f
,; i
-------
4.2.3 Arid Marshland
Wetland communities in which the principle plants
are herbaceous emergents which have their basal
portions annually, periodically, or continually
submerged. Examples of dominant vegetation in
this category would be cattails (Typha spp.), reed
(Phragmites spp.), and bulrush (Scirpus spp.). At
the edge of these communities there is often some
intermingling of nearby upland species. In
addition to the emergent plants, aquatic bed
vegetation such as pondweed (Potamogeton spp.) are
often present. These areas are often associated
with fairly extensive bodies of standing water.
4.2.4 Arid Strandland
The dynamic nature of these communities precludes
the establishment of perennial vegetation,
although annuals occasionally are found. The
surface of these areas often consists of sand or
cobbles. Examples of these communities include
desert washes, and beach areas within an arid
context. Beach and river channel communities
subject to infrequent but periodic submersion,
wind driven waves and/or spray are also included.
Plants are separated by significant areas devoid
of perennial vegetation1 (Brown et al., 1979).
5.0 Snow/Ice
This category includes areas that are covered with snow or
ice. Anderson et al. (1976) defines snow and ice in the
following way:
Certain lands are covered either seasonally or have a
perennial cover of either snow or ice. A combination
of environmental factors cause some of these areas to
survive the summer melting season, while in other areas
the snow and ice melt to reveal another category or
type of land cover.
!Strand communities are situated in harsh physical
environments that produce their characteristic physiognomy.
Accordingly, strandland is' treated as the wetland equivalent of
desert land. While occurring in the usual sense on beaches and
other seacoast habitats, freshwater (or interior) strands also
occur in river channels, along lake margins, and below reservoir
high water lines (Brown et al., 1979).
415
-------
This category is not subdivided into Level II categories,
rather it is subdivided at Level III.
5.1 Snow/Ice
This category is subdivided into two Level III
categories.
5.1.1 Snow
This category includes all areas which are covered
with snow.
5.1.1 Ice/Glacier
This category refers to those areas that are
permanently covered with ice as well as glaciers.
Ice and superficial snow persists throughout the
year and flows downhill under its own weight.
WATER
The Water Division contains all areas that are covered either
permanently or semi-permanently covered by water as the primary
substrate. There is only one Level 1 category: 6.0 Water and
Submerged Land.
6.0 Water and Submerged Land
This category contains areas representing standing and deep
water, either natural or man-made. The single feature of
this category is that the soil or substrate is saturated
with and covered by water. Man-made areas of water would
include reservoirs, impoundments, diked areas, ponds, and
canals. The Water and Submerged Land category is subdivided
into three Level II categories: 6.1 Ocean; 6.2 Coastal; 6.3
Near-Shore; and 6.4 Inland.
6.1 Ocean
This category contains those marine waters which extend
seaward from the land and coastal region. Waters with
relatively high salinities also exist in this category.
The category does not include bays or estuaries that
extend inland, for these are included under Coastal.
Examples are: the Atlantic Ocean, Pacific Ocean, Gulf
of Mexico, and the Arctic Ocean.
6.2 Coastal
Coastal waters include bays, estuaries, and lagoons
416
-------
which lie between terrestrial and marine environments.
Sheltered water bodies behind barrier beaches, and
tidal mudflats are examples of this type of
environment. The water regime in these areas include
marine and estuarine. Examples of these areas would be
Chesapeake Bay, Puget Sound, or San Francisco Bay.
6.3 Near-Shore
Includes the zone which lies between the normal high
water mark and the extent of the emergent and floating
vegetation into the open water.
6.4 Inland
This category includes ponds, lakes, reservoirs,
streams, and rivers that are not considered coastal or
ocean. The water is most often fresh but may be
brackish or highly saline (Great Salt Lake).
OTHER
The categories included in this division appear both over Land
and Water. There is only one Level 1 category under the
division: 7.0 Other.
7.0 Other
The Other category contains unidentified resources or
anomalous data which can not be interpreted to fit into any
of the land cover categories or water above. This category
is divided into:
7.1 Cloud
Clouds that are present in the imagery that obscure
land cover categories that exist underneath them. This
area also contains hazed areas where the land cover
categories are obscured.
7.2 Shadow
Shadows obscure the land cover categories underneath
them. The shadows may be caused by clouds or terrain.
7.3 Missing
This category includes any imagery that may be missing
due to data drop out, striping of data, or other
anomalies in the data set.
417
-------
7.4 Indeterminable
This category includes those areas that cannot be
identified with remote sensing sources due to
conditions such as fire. These categories could be
spectral clusters that contain only a few pixels, or
may be large clusters that contain a wide variety of
. land cover resources.
418
-------
REFERENCES
Anderson, J.R., Hardy, E.E., Roach, J.T., and R.E. Witman, 1976,
A Land Use and Land Cover Classification System for Use with
Remote sensor Data. U.S. Geological Survey Professional Paper
964, Washington, D.C., 28 pp.
Brown, D.E., 1982, Biotic Communities of the American Southwest-
United States and Mexico. Vol. 4, Numbers 1-4, University of
-Arizona, Superior, AZ, 342 pp.
Boul, S.W., Hole, F.D., and R.J. McCracken, 1989, Soil Genesis
and Classification. Iowa State University Press, Ames, IA 446
pp.
Cowardin, L.M., Carter, V., Golet, F.C., and E.T. LaRoe, 1979, A
Classification of Wetlands and Deepwater Habitats of the United
States, Office of Biological Services, Fish and Wildlife Service,
U.S. Department of the Interior, Washington, D.C., 103 pp.
Dictionary of Geological Termsr 1976, Anchor Books, Anchor
Press/Doubleday, Garden City, N.Y., 472 pp.
Harlow, W.M., and E.S. Harrar, 1969, Textbook of Dendrology;
Covering the Important Forest Trees of the United States and
Canada. McGraw-Hill Book Company, New York, N.Y. 511 p.
419
•6-U.S. GOVERNMENT PRINTING OFFICE: 1994 - 550-001/80329
-------
-------
-------
EPA/600/R-
CD
00
in
Official Bus
Penalty for
$300
"05"
3-8
a. »
CD
(/>
CD
o o m c
5- CD 3 3
ttfl
l?ig
~_ m 3 ??
i|S w
^n O ^^
sii
00 CD O
if
3J >
CD (a
W CD
CO 3
c5 ^
rr
a
o
3.
o
------- |