United States

Department of

Agriculture

Forest Service

Southern

Research Paper

SRS-24
The Author:

William N. Cooke, Research Forester, U.S. Department ofAgriculture,

Forest Service, Southern Research Station, Forest Inventory and

Analysis, Starkville, MS 39759.

July 2001

Southern Research Station

P.O. Box 2680

Asheville, NC 28802
illia . Cooke

Abstract Evaluation of Procedures

The U.S. Depattment of Agriculture, Forest Setvice, Southern Research

Station, appointed a remote-sensing team to develop an image-processing

methodology for mapping forest lands over large geographic areas. The

team has presented a repeatable methodology, which is based on regression

modeling of Advanced Vety High Resolution Radiometer and Landsat

Thematic Mapper data. It is a methodology that Forest lnventoty and

Analysis survey personnel can implement in any region or area. The term

repeatable implies objectivity. Studies in the contenninous United States,

Central America and Mexico, and west Texas and Oklahoma have

provided valuable insights that address the subjective nature ofsome ofthe

steps taken in mapping large forest areas. The team has identified seven

such steps. They have reduced or eliminated subjectivity in four ofthe

steps and identitied two steps in which ob.jectivity can be enhanced.

Keywords: AVHRR, ecoregions, FIA, Landsat, regression modcling,

remote sensing.

Introduction

Lillesand and Kiefer (1987) defined remote sensing as the

science and art of obtaining information about an object,

area, or phenomenon. Their use of the term art raises

questions about the reliability and repeatability of remote

sensing. For various purposes, including compliance with

the Forest and Rangeland Renewable Resources Planning

Act (RPA), periodic updates of forest inventories of the

United States are necessary. To help ensure those updates

are accomplished, the Southern Research Station Forest

Inventory and Analysis (FIA) remote-sensing team is

developing methodologies that remove some of the

subjectivity, or art, from the modeling process.

Zhu and Evans ( 1994) modeled percent forest canopy for the

conterminous United States from Advanced Very High

Resolution Radiometer (AVHRR) data that were based on

calibration of Landsat Thematic Mapper (TM) data. In

Mexico and Central America, Lannom and others ( 1995)

made an effort to repeat and improve on their model.

Current research focuses on application and additional

refinement of this model in the semiarid and arid regions of

Texas and Oklahoma. The evolution of a procedure to

analyze AVHRR data for large-area analyses is described in

this publication.

Phase I-Initial Modeling Efforts for RPA

Zhu and Evans ( 1994) used AVHRR and TM data to model

percent forest cover. The resulting maps displayed

approximate percent forest cover per AVHRR pixel. Those

pixels that had forest cover percentages above a certain

minimum threshold value were classified by forest type.

Generally, to be classified as forest, an area’s within-pixel

forest-cover had to be 20 percent or greater. Data for United

States forest types and percent cover are now available on

CD-ROM at the USDA Forest Service FIA office in

Starkville, MS, and on the World Wide Web at http:il

www.srsfia.usfs. msstate.edu. The team has distributed

about 200 of the CD-ROM’s to organizations and

individuals and over 25,000 of the hard-copy map Forest

l)pe Groups of the United States (fig. 1). The team

compared, by State, forest-area percentages derived from

FIA survey data in the 1993 RPA with the AVHRR foresttype group map. They determined that forest area from the

AVHRR data in the conterminous United States had been

overestimated by 1.95 percent when compared to FIA survey

data. Comparisons are illustrated in table 1 (Zhu 1994).

The modeling efforts used to create the Forest Type Groups

qf the United States map relied on multiple regression

procedures. Regression models that were used to predict

percent forest canopy were developed in part by classifying

Landsat TM scenes to forest types, then aggregating those

types into forest and nonforest classes. Landsat TM scenes

were chosen on the basis of their location within

physiographic regions. Zhu used a combination of

Hammond’s Classes ofLnndSwf&x~ Form and Fenneman’s

Physical Divisions of the United States to locate Landsat

TM scenes (Hammond 1964, Fenneman and Johnson 1946).

Model development required that the team choose

calibration windows common to both the Landsat TM and

AVHRR data sets. These windows were considered to be

representative of land-cover conditions within the

physiographic regions. Advanced Very High Resolution

Radiometer channels, channel transformations, and

temporally separated channels became the independent

Forest and lgeland Renewable

from the A riced Very Nigh

ST” FIAPF AVPF Bias’

AI. 67.65 67.86 0.21

A K 35.35 28.05 -7.30

AZ 26.94 25.41 -1.53

A R 53.60 51.07 -2.53

C A 37.33 39.63 2.30

CO 32.14 32.71 .57

CT 58.66 58.19 - .47

DE 31.10 34.38 3.28

FL 7.89 4.41 -3.48

G A 65.12 69.27 4.15

HI 42.52 39.88 -2.64

I D 40.82 43.94 3.12

IL 11.99 12.30 .31

IN 19.34 21.16 1.82

I A 5.73 3.04 -2.69

K S 2.60 1.13 -1.47

KY 50.00 47.72 -2.28

L A 49.72 47.95 -1.77

ME 88.76 90.04 1.28

M D 42.89 43.38 0.49

MA 63.86 64.14 .28

M I 50.20 55.00 4.80

M N 32.81 35.93 3.12

M S 56.62 59.18 2.56

M O 31.77 30.05 -1.72 L

M T

NE

NV

N H

N J

N

NY

N C

N C

O H

O K

O R

P A

R I

S C

S D

T N

T X

UT

VT

VA

W A

W V

W I

W Y

24.17 24.75 0.58

1.47 1.15 - .32

12.72 12.94 .22

86.78 88.75 1.97

42.27 43.38 1.11

19.69 25.18 5.49

61.92 64.08 2.16

61.83 64.20 2.37

1.05 0.98 - .07

30.00 31.91 1.91

12.04 15.35 3.31

45.57 46.61 1.04

59.16 60.60 1.44

59.94 59.15 - .79

63.60 66.9 1 3.3 1

3.48 3.60 .12

5 1.60 50.11 -1.49

11.45 11.73 .28

30.87 29.57 -1.30

76.66 74.78 -1.88

62.57 61.50 -1.07

48.07 48.90 .83

78.68 75.99 -2.69

44.63 41.38 -3.25

16.04 18.22 2.18

Mean 41.03 41.35 1.95

Standard deviation 22.76 22.92 1.47

Minimum 1.05 .98 .07

Maximum 88.76 90.04 7.32

ST” FIAPF AVPF Bias”

(predictor) variables. The dependent (predicted) variable was

the percent forest canopy within the calibration window that

was derived from the 1,andsat TM classification.

The selection of independent variables was based on an allpossible-combinations approach. Advanced Very High

Resolution Radiometer channels and channel transformations

constituted the pool of possible independent variables. The

process of evaluating potential independent variables

included a simple correlation analysis with the forest

percentage variable, a test of colinearity among the AVHRR

bands, and linear models for all possible combinations of the

AVHRR bands. Combination models were tested, and

models were chosen on the basis of highest R’ among

different levels of independent variable combinations.

Figure 2 illustrates essential steps of the modeling

methodology. Statements in brackets, e.g., <Partitioning

System>, denote steps at which subjective decision making

must occur before the analyst proceeds to the next step.

Within the methodology illustrated in figure 2, subjectivity is

present at several steps, each of which will require closer
a AVHRR data

Bailey’s Omernik’s

TM data

TM data classifications (50 classes)

Recode to forest/nonforest

AVHRR data <Where, how many, size>

Calibration wind&

Y (% Forest) = b, + b,x, + b,x, + + *

Xl = channel,, time,

X2 = channel,, time,

.

.

X” = channel,, time,

I

<Partitioning system>

<Location>

<Class labels>

<Independent variables>

<Percent forest threshold>

AVHRR pixels

Forestlnonforest mask

~~~ Classify

AVHRR data

<Class labels>

4
examination: (1) choice of a landscape partitioning system;

(2) location of TM scenes within the partitions; (3) classlabeling process for TM classifications; (4) number, size,

and location of calibration windows; (5) independentvariable selection process; (6) choice of a percent forest

canopy threshold; and (7) class-labeling process for AVHRR

classification.

Choice of a landscape partitioning system-Landscape

divisions may be based on changes in soils, physiography,

climatic zones, elevation, and ecoregions, to name just a few.

Choosing the most applicable landscape-partitioning system

to determine land-cover classes and generate the digital data

products necessary for study is a critical step. Jensen (1986)

notes the general agreement that spectral signatures used in

automated classification procedures become less

representative of actual land-cover conditions when those

statistical signatures are extrapolated over large distances.

Zhu (1994) used geographic data partitioning to reduce

spectral variations among regional physiographic or

ecosystem conditions and to emphasize spectral variations

among local vegetation types. His choice of Fenneman’s

and I-iammond’s combined physiographic regions was purely

subjective. IIe also considered using Powell’s physiographic

regions and Lobeck’s physiographic provinces.

Location of TM scenes with partitions-Because a

statistical signature becomes less representative of a landcover class extrapolated over great distances (Jensen 1986)

the analyst must carefully choose the location of TM scenes

within each partition. Should TM scenes be chosen at

partition boundaries, or should they be near the center of the

area partitioned‘?

Class-labeling for TM classification-The analyst’s

knowledge of the geographic area and his or her access to

reference imagery or photography and ground-verification

information are critical. Class labeling is the assignment of

names to the classes produced during the automated

classification procedure. Reclassification or combinations

of some classes may be necessary. Once labels have been

assigned, classes are combined as forest or nonforest. At

this step, distinctions between land use and land cover

become important. For example, the image analyst’s

automated classification process may not enable

differentiation between a clearcut and agricultural bare-soil

conditions. If an analyst determines that an area will be

replanted or allowed to revert to some natural forest

condition, survey personnel in the field will classify a clearcut as forest land. Conversely, if automated classification

procedures were followed, both would probably be classified

as bare soil.

Number, size, and location of calibration windows-The

modeling process uses high-resolution data (TM) to predict

percent forest canopy in areas for which low-resolution data

(AVHRR) are available. To accomplish that, the analyst

chose calibration windows over geographic areas common to

both data sets. Those data serve as input for the modeling

process, so the analyst chooses windows that are

representative of actual land-cover conditions, as well as the

number size, and location of those windows. If they are too

small, TM and AVHRR coregistration errors may be a

problem; if they are too large, statistical calculations may be

extremely slow and file storage requirements may be

difficult. The same concerns apply to the number of

windows chosen to represent forest and nonforest classes. If

calibration windows are too few, land-cover conditions may

not be adequately sampled; if too many, performance and

storage problems may occur. Therefore, the location of

calibration windows should maximize information content

while minimizing number and location.

Selecting independent variables-Zhu (1994) used an allpossible-combinations approach to independent variable

selection. Although this is the only method guaranteed to

find the best model, it requires fitting 2k -1 models (k = the

number of variables). Its disadvantages are computational

inefficiency and the amount of time the scientist must spend

assessing correlation matrices for all possible independent

variable combinations. Other approaches include forward

selection, backward elimination, and stepwise independent

variable selection. These are automated procedures that

quickly find the best possible combination of independent

variables. One disadvantage of automated variable selection

routines is that they are designed to give one answer without

displaying the results on a large number of subset models

(Myers 1990). It is also possible that the truly best model

may not survive any of the automated procedures. Myers

( 1990) recommends automated independent variable

selection when there is a large number of potential

independent variables.

Choice of percent forest-cover threshold-The model

produces an AVHRR image wherein each pixel represents a

percentage of forest cover. Subsequent automated landcover classifications of AVHRR data forest-type mapping are

made for pixels that exceed some minimum percentage of

forest cover, which the analyst chooses. Comparisons of

forest-area estimates based on Zhu and Evans’ (1994) work

with FIA field-plot data are shown in table 1. Choice of the

5
percent forest-canopy threshold directly affects such

comparisons.

Class-labeling for AV classifications-The concerns

about class labeling for TM classifications (step 3) also

apply to labeling AVHRR classifications. Whereas class

labeling for TM classification efforts can sometimes be

applied at the species level, class labeling for AVHRR

classification efforts generally is applied at the major foresttype group level. The AVHRR class labeling process

requires that the analyst is familiar with ecosystem processes

that operated at the landscape level and also has a basic

understanding of the distribution of forest-type groups

associated with these processes.

hase II-Application of

exico and Central America

ues in

The U.S. Agency for International Development (USAID)

funded a land-cover classification study for Mexico and

Central America, which provided an opportunity to replicate

and improve modeling methodologies. The study resulted in

valuable refinements to the modeling process.

Choice of a landscape partitioning system was subjective;

therefore, no improvement was made in step 1 of the

process.

The location of TM scenes was detemnned primarily by the

availability of cloud-free imagery. In many parts of the

world, persistent cloud cover prevents the acquisition of TM

data in areas that are optimum for modeling purposes.

Wherever possible, cloud-free TM scenes were recorded in

or near the center of ecoregions, which allows the analyst to

reduce problems tied to signature extrapolation. As greater

geographic distances are traversed, land-cover class means

and standard deviations may change. If TM scenes are

acquired near the center of an ecoregion, forest-class means

and standard deviations are more likely to be representative

of the population for that region. However, the analyst

should carefully weigh this conclusion against the

appropriateness of the partitioning system for the forest or

land-cover class. If time and budget constraints prevent the

acquisition of a sufficient number of TM scenes to cover

each ecoregion, they can be obtained between ecoregions.

The class-labeling process for Mexico and Central America

points out a weakness in the methodology. Field visits to

foreign countries are expensive, and reference data are often

inaccurate or unavailable. Whenever possible, the analyst

should closely involve an understudy expert that is familiar

with local vegetation types in the TM class-labeling process.

The analyst can then either classify TM scenes on location

and send results to the SRS remote sensing team for

modeling, or experts can travel to the data processing

location.

The team tested the number, size, and location of calibration

windows, although those tests were inconclusive. Mayaux

and Lambin ( 1995) found that in tropical areas, a minimum

13 by 13 AVHRR pixel window minimizes coregistration

problems. The team developed a new technique that

significantly improved coregistration ofcalibration windows

for the AVHRR and Landsat TM data sets-one that helps

ensure the precise comer pixels. The ability to precisely

locate corner pixels will, in turn, increase the probability of

modeling success if the analyst selects numerous small

calibration windows rather than a few large ones.

The team developed automated routines for importing cell

values from calibration windows into the statistical software.

Once cell values have been exported from the image

processing system in ASCII format, these routines reformat

the data into data frames appropriate to the S-Plus’ statistical

software. Although these routines do not specifically

address an area of subjectivity, they indirectly impact

subjective processes by enabling scientists to allocate more

time to areas of subjectivity.

Stepwise independent-variable selection procedures were

used for this study. Automated variable selection procedures

have both strengths and weaknesses. Speed is an obvious

strength of forward and backward automated selection

procedures. Stepwise selection is a modification of the

forward selection technique that permits reexamination of

the variables incorporated into or dropped from the model in

earlier steps. A partial F-test for each variable is computed

as though it was the most recent variable entered. This

technique is a good compromise between the calculation

inefficiency of the all-possible-combinations approach; and

the forward or backward selection techniques are chosen

strictly on the basis of their computational efficiency.

The analyst arbitrarily chose the percent forest-canopy

threshold values. Comparisons of the percent forest area

map (fig. 3) with forest-cover maps (where available)

formed the basis for the threshold value choice. As a result,

the analyst did not reduce subjectivity at this stage in the

modeling process.

Labeling of the AVHRR classifications followed conventions

established for major forest-type groups in Mexico and

Central America. The requirement that forest-type groups fit

6

categories determined npriori is a major complication at this

stage. Fitting the classes to predetermined categories

implies that these categories (major forest-type groups)

actually exist on the landscape at the AVHRR data resolution

(l-km pixels).

The modeling process is now complete, and the project

manuscript “Forest Mapping of Central America and Mexico

with AVHRR Data,” has been accepted for publication by

Geo Carta International. The forest-type maps for Mexico

and Central America have been completed and are available

from the USDA Forest Service, Southern Research Station,

10 1 -A G.T. Thames Drive, Starkville, MS 39759.

Phase III-Application of Modeling Techniques in the

Semiarid and Arid Regions of West Texas and West

Oklahoma

The SRS remote sensing team further refined the modeling

process in west Texas and Oklahoma, incorporating

methodological improvements made in earlier studies. The

team’s primary focus in this study concerned the choice of a

landscape partitioning system, a choice that impacts the

entire methodology. Zhu ( 1994) used geographic data

partitioning to reduce spectral variations among regional

physiographic or ecosystem conditions and to emphasize

spectral variations among local vegetation types. Spectral

variations among different regional conditions are

minimized if the landscape partitioning system accurately

reflects forest-cover conditions.

The team has completed quantitative tests of the general

effectiveness of partitioning and has presented its findings at

the Sixth Forest Service Biennial Conference of Remote

Sensing (Cooke 1996). Calculation of Jeffries-Matusita

(J-M) distances for woody and nonwoody vegetation

spectral classes was the basis of the quantitative tests. The

J-M test measures the distance (separation) between the

means and variances of spectral classes to determine their

statistical “uniqueness.” The team tested pixels representing

all woody and nonwoody areas within each adjacent pair of

landscape partitions. In that complete enumeration of the

pixel population, the team found that landscape partitioning

using Bailey’s ecoregions improved the distinction between

woody and nonwoody vegetation. Figure 4 illustrates

Bailey’s ecoregions and shows the locations of TM scenes

used in regression modeling. Ecosystems 3 15B and 3 15C

are illustrated by red text in figure 4 as representative of

partitioning effectiveness in two adjacent ecoregions.

Random statistical sampling tests of the variability of the

J-M divergence test results confirm the results of the

partitioning tests based on the completely enumerated

population (Cooke 1997). Figure 5 depicts the improvement

partitioning makes in woody and nonwoody spectral class

separability. Figure 6 displays results of the 1 -percent

random statistical tests of variability in the J-M distance test

for Bailey’s ecoregions 3 15B and 32 1 A.

Bailey’s ecoregions also were found to be more useful than

Omernik’s system in separating mesquite (fig. 7). These

results reveal the importance of Bailey’s inclusion of a

climatic variable, which may account for the east-west

climatic gradient that impacts plant-community distribution

in Texas (Norwine and Greegor 1983).

To complete labeling for TM classifications, the team

acquired land-cover information from several sources,

including extensive natural color video data and National

Aerial Photography Program (NAPP) stereo pairs within

each TM scene. The Texas Parks and Wildlife Department

supplied hard-copy maps and digital Geographic

Information System (GIS) files of the “Vegetation Types of

Texas.” The team is still seeking analysts who have

extensive knowledge of the plant communities and

ecosystem processes in the study area.

Stepwise variable selection procedures were used, and tests

of number, size, and location of calibration windows were

performed. These tests substantiated the work of Mayaux

and Lambin ( 1995), which indicates only marginal

improvements in models using windows larger than 13 by 13

pixels.

Choice of a percent forest-canopy threshold remains a major

concern in west Texas and west Oklahoma. Initial efforts

have concentrated on differentiating woody from nonwoody

vegetation. It is likely that field crews will be needed for

determining which woody vegetation classes represent forest

as defined by the Forest Service.

Phase IV--Future Modeling Improvements

Data for inventoried areas, which have been published in

FIA reports and the RPA, are in agreement in most States

(table 1) and indicate extremely high agreement for the areas

inventoried by field crews. For prediction of forest area in

regions of the United States where FIA field inventory data

exist, correction of regression models by field data may be

possible. However, analysts should use caution in

developing correction factors; those comparing field data

with remotely sensed data should only be used when the data

are gathered at the same time. Ideally, the data should be

collected in the same calendar year. Forest Inventory

Analysis survey cycles should be taken into consideration

8
12oc

1000

8

5

z

0 800

m

.z3

f 600

.g

5

2

400

200

Figure 4---Bailey’s landscape partitions used to test partitioning effectiveness. Boxed areas

represent Landsat TM scenes used for regression modeling.

-+- 3 I SB woody vs. 32 I A nonwoody (partitioned)

-Q-- 32lA woody vs. 3 1SB nonwoody (partitioned)

+ 3 15B, 32lA woody vs. nonwoody (unpartitioned)

0 1 2 3 4 5 6

AVHRR Spectral Channels

Figure 5 Mean Jeffries-Matusita separability of Bailey’s adjacent

partitioned ecoregions 3 I5B and 32 I A versus unpartitioned ccolegions

3 ISB and 321A combined. AVHRR data.

1200 -I----

1000 j

400 i/

Partitioned Data

woody 32 I A non-woody 3 1 SB

/

iUnpartitioned Data

woody / non-woody 32 1 A and 3 1%

0 1 2 3 4 5 6

Channels

Figure 6 Means and confdence intervals for I percent mndom tests of’

woody vegetation within Bailey’s ecoregions 3 I SB ve~jus nonwoody

vegetation within Bailey’s ecoregion 32 I A (c( = 0.05).

9
-Mean Separability, North/South Adjacency, Bailey’s Emregions

rMean Separability. East/West Adjacency, Bailey’s Ecoregions

?f?Mean Separability, North/South Adjacency, Omernlk’s Ecoregions

~Mean Separability, East/West Adjacency, Omernik’s Ecoregions

/

300 ’

I 3 4 5

Spectral Channels

Dataset 1 (April 1 6 - April 29, 1993)

Figure 7 Mean Jcffries-Matusita sepanbility of Bailey’s ecoregions

versus Omcmik’s ccoregions.

when the described modeling techniques are used for

predicting forest area. Advanced Very High Resolution

Kadiometer and TM imagery should be contemporaneous

with field inventories.

If data are collected at nearly the same time. correction

factors can be used to make midcycle updates of Sorest area

using the modeling methodology alone. This would ensure

the best possible midcycle estimates of forest area using the

AVHRR and TM modeling process. Recalculation of

correction factors should be done during the next combined

FIA field survey and remote sensing modeling study. If the

Southern Annual Forest Inventory System (SAFIS) were

implemented in all 13 Southern States, the S-year

completion cycle for each State would further facilitate

calculation of those correction factors.

roc~~ures~~~~Accuracy assessment

of classifications of AVHRR data for forest area and forest

type employs the technique of cross validation. Calibration

windows of the TM imagery that are used for modeling are

considered as samples drawn without replacement.

Windows from other regions of the TM data, which were not

used for modeling, are used to test the model’s performance.

R-squared values indicate the performance of a model over

an ecoregion and indirectly indicate the accuracy of the

modeling results. Other sources of data may also be used

for accuracy comparisons.

To test classification accuracy in estimating forest area,

Lannom and others ( 1995) compared interpreted aerial

photography with AVHRR classifications. Estimates made

by photo interpreters did not differ significantly from those

made through analysis of AVHRR data within calibration

windows to a 1000-m cell size. Grayscale images are

created from the AVHRR and TM data within the calibration

windows. Visual and quantitative comparisons of the

grayscale images’ frequency distributions arc indicative of

classifjcation accuracy. Replication of this procedure in

areas for which both AVHRR and TM imagery arc

available--~-but not within the calibration windows+ ~~requires

a combination of cross validation and grayscale

comparisons.

Because the pixel size of AVHRR data is so large and the

spectral mixing of land-cover classes within each 1000-m

pixel may be extensive, field verification of those data is

extremely difficult. However, field verification of TM

classifications, aerial photography, and video imagery is

important in validating the results of TM reference data.

Accurately located FIA plot data should be used to verify

Landsat TM classifications and ancillary data sources that

refine the TM classifications. Unfortunately, AVHRRYT’M

modeling efforts are hampered in west Texas and west

Oklahoma by the absence of a field-based forest inventory.

No discussion of error or confidence intervals for estimates

of forest area and type derived from this methodology is

presented in this paper, nor does it imply that the removal of

subjective decisionmaking will improve the accuracy of

estimates. The methodology does, however, reduce

subjectivity at four of seven major decision-making points;

and reduction of subjectivity is possible at two others.

Although reduced subjectivity does not necessarily improve

the accuracy of data, a greater degree of repeatability

ensures a more consistent product and facilitates

comparisons across geographic regions and among data sets.

Large-area mapping projects based on modeling TM and

AVHRR data would benefit from the expertise of remote

sensing specialists who are familiar with the landscape and

ecology of an entire FL4 region.

10
Accuracy assessment of AVHRR classifications is difficult

using traditional field methods. Nonetheless, field

measurements should be used to verify Landsat TM

classifications and the ancillary data used in refining those

classifications.

The first phase of sampling design is based on the forest1

nonforest interpretation of recent aerial photography of

temporary plots that represent about 230 acres (Kelly 199 1 ),

These areas are compatible, and the AVHRR data can be

used to classify the area between temporary plots. The

AVHRR data are available for about $32 per year; and

temporal data composites enable the monitoring of

phenological changes in land-cover classes over any given

time.

Forest area estimation procedures are not consistent among

FIA regions. Valid comparisons of the results of this

methodology for different geographic areas are

compromised by this inconsistency.

Finally, what does or does not constitute forest land is an

issue that cannot be resolved by remote sensing. Although

data so gathered can yield information about forest canopy

spectral conditions, determining forest area on multiple-use

lands and within urban, semi-arid, or arid areas is

problematic.

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confcrencc; I996 April 29 May 3; Denver, CO. Bethesda, MD:

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quantitatively testing the applicability and implementation oflandscapc

partitioning systems with advanced very high-I-csolution mdiornetel

satellite data Starkville, MS: Mississippi State University. X9 p. Ph.D.

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Geological Survey. Physical divisions ofthe United States. 1:7,000,000;

Polyconic projection.

Hammond, Edwin N. 1964. Classes of land sui?‘dce form in the fortyeight States, U.S.A. Annals of the Association of American

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Environment. 53: I 16.

Myers, Raymond H. 1990. Classical and modern regression with

applications. 2dcd. Boston, MA: PWS-Kent Publishing Company.

48X p.

Norwine, .I.; Greegor, D.H. 1983. Vegetation classification based on

advanced very high resolution radiometer(AVHRR) satellite imagery.

1.3: 69 X7.

Zhu, Z. 1994. Forest density mapping in the lower 4X States: a regression

procedure. Rcs. Pap. SO-280. New Orleans: U.S. Depdrtlncnt of

Agriculture, Forest Service, Southern Forest Experiment Station.

January 1994. I I p.

Zho, Z.; Evans, D.L. 1994. U.S. forest types and predicted percent fol-est

cover from AVHRR data. Photogramctric Engineering and Remote

Sensing. 60(S): 525 53 I.

1 1

Cooke, William H. 2001. Development of a methodology for predicting forest area for largearca resource monitoring. Res. Pap. SRS-24. Asheville, NC: U.S. Department of

agriculture, Forest Service, Southern Research Station. 11p,

The U.S. Department of Agriculture, Forest Service, Southcm Research Station, appointed a

remote-sensing team to develop an image-processing methodology for mapping forest lands

over large geographic areds. The team has presented a repeatable methodology, which is based

on regression modeling ofAdvanced Very High Resolution Radiometer (AVHRR) and Landsat

Thematic Mapper (TM) data. It is a methodology that Forest inventory and Analysis (FIA)

survey personnel can implement in any region or area. The term repeatable implies objectivity.

Studies in the conterminous United States, Central America and Mexico, and west Texas and

Oklahoma have provided valuable insights that address the subjective nature ofsome ofthe

steps taken in mapping large forest areas. The team has identified seven such steps. They

have reduced or eliminated subjectivity in four of the steps and identified two steps in which

objectivity can be enhanced.

Keywords: AVHRR, ecoregions, FIA, Landsat, regression modeling, remote sensing.
The Forest Service, United States Department of

Agriculture (USDA), is dedicated to the principle of

multiple use mar~agelnent of the Nation’s forest resources

for sustained yields of wood, water, forage, wildlife, and recreation.

Through forestry research, cooperation with the States and private

forest owners, and management of the National Forests and National

Grasslands, it strives -as directed by Congress-to provide

increasingly greater service to a growing Nation

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U.S. Government Prnitng Office: 2001-665-023/00043