Figure 1— Location of study area in central Louisiana.

nonforest land, and water were delineated by digitiz-
ing boundaries directly onto the mosaics displayed on
acomputer monitor. The interpretation polygons were
used to create a grid format file. The interpretation
classes were combined into either forest or nonforest
categories. Forest regeneration areas were categorized
as forest, and urban areas with tree cover were cat-
egorized as nonforest areas.

Sample Point Interpretations (Dot Counts).—
The locations of all SO-FIA permanent forest inven-
tory and intensification plots were marked on the
aerial photos. A photo interpreter decided whether each
plot was forested or not, and this decision was validated
by field personnel as they visited each plot. The num-
bers of correct and incorrect interpretations were used

to adjust the percentage of forest area calculations.

A mylar overlay with a five-by-five matrix of dots
was used to sample the area around each permanent
plot marked on the photos (fig. 3). Locations under
each dot were interpreted as forest or nonforest. The
proportion of dots falling on forest land was used to
estimate percentage of forest area at the parish level.
Information obtained in validation visits to field plots
was used to derive correction factors employed to up-
date the area estimates to the date of the current sur-
vey and to correct photo interpretation errors.

AVHRR Data.—The AVHRR data were extracted
from land classifications used to create the 1993 Re-
source Planning Act (RPA) Forest Type Group map.
These classifications were based on nine biweekly com-
posites obtained between March 1 and November 21,

1991. For ea. week period. five channels of AVHRR.
1991. For each 2-week period, five channels of AVHRR


Field personnel must visit each permanent and in-
tensification plot to determine the true condition of
the land cover. No other resource data are collected
on intensification plots, so the cost of establishing and
using those plots is high in relation to the extent of
data acquired. Reliable estimates of forest area from
AVHRR classifications would eliminate the need for
expensive visits to intensification plots. These AVHRR
forest area estimates could be used routinely in forest
inventory work at regional to national scales. These
area estimates could also be produced for use in sta-
tistical prediction of changes in forest resources be-
tween field survey years.

This research has demonstrated that AVHRR clas-
sifications can be the basis for parish- or county-level
percentage of forest estimates that do not differ sig-
nificantly from those obtained by detailed photo in-
terpretation. However, additional investigation is
needed to identify and correct problems associated
with classification of young forest regeneration areas
on the basis of AVHRR data. Extension of this work to
comparisons for multiple random counties or single
aerial photographic interpretations could provide fur-
ther justification for the application of AVHRR data
for routine forest area estimation.

LITERATURE CITED

Congalton, Russell G. 1991. A review of assessing the
accuracy of classifications of remotely sensed data.
Remote Sensing of Environment. 37: 35-46.

Eidenshink, J.C.; Burgan, R.E.; Haas, R.H. 1991.
Monitoring fire fuel conditions by using time-series
composites of Advanced Very High Resolution Ra-
diometer data. In: Proceedings of the resource tech-
nology international symposium on advanced
technology in natural resource management; 1990
November; Washington, DC. Bethesda, MD: Ameri-
can Society for Photogrammetry and Remote Sens-
ing: 68-82.

Evans, David L.; Zhu, Zhiliang; Eggen-McIntosh, Su-
san [and others]. 1992. Mapping Mexico's forest
lands with advanced very high resolution radiom-
eter. Res. Note SO—367. New Orleans, LA: U.S. De-
partment of Agriculture, Forest Service, Southern
Forest Experiment Station. 4 p.

Fleiss, J.L. 1981. Statistical methods for rates and
proportions. 2d ed. New York: John Wiley & Sons,
Inc. 321 p.

Iverson, L.R.; Cook, E.A.; Graham, R.L. 1989. A tech-
nique for extrapolating and validating forest cover
across large regions: calibrating AVHRR data with
TM data. International Journal of Remote Sensing.
10(11): 1805-1812.

Kelly, John F. 1990. The use of remote sensing for
updating extensive forest inventories. In: Proceed-
ings of the 3d Forest Service remote sensing appli-
cations conference; 1990 April 9-13; Tucson, AZ.
Bethesda, MD: American Society for Photogramme-
try and Remote Sensing: 360-366.

Rosenfield, George H.; Fitzpatrick-Lins, Katherine.
1986. A coefficient of agreement as a measure of
thematic classification accuracy. Photogrammetric
Engineering and Remote Sensing. 52(2): 223-227.

Rosson, James F,, Jr.; Miller, Patrick E.; Vissage, John
8. 1991. Forest statistics for southwest Louisiana
parishes—1991. Resour. Bull. SO-161. New Or-
leans, LA: U.S. Department of Agriculture, Forest
Service, Southern Forest Experiment Station. 34 p.

Rudis, Victor A. 1989. Multiple value forest survey in
the Midsouth States. In: State-of-the-art methodol-
ogy of forest inventory: Proceedings of a symposium,
1989 July 30-August 5; Syracuse, NY. Gen. Tech.
Rep. PNW-263. Portland, OR: U.S. Department of
Agriculture, Forest Service, Pacific Northwest Re-
search Station: 495-504.

Story, Michael; Congalton, Russell G. 1986. Accuracy
assessment: a user’s perspective. Photogrammetric
Engineering and Remote Sensing. 52(3): 397-399.

Teuber, K.B. 1990. Use of AVHRR imagery for large-
scale forest inventories. Forest Ecology and Man-
agement. 33/34: 621-631.

U.S. Department of Agriculture, Forest Service. 1991.
Louisiana forest survey inventory work plan.
Starkville, MS: U.S. Department of Agriculture, For-
est Service, Southern Forest Experiment Station, For-
est Inventory and Analysis Research Work Unit. 59 p.

Zhu, Zhiliang. 1992. Advanced Very High Resolution
Radiometer data to update forest area change for
Midsouth States. Res. Pap. SO-270. New Orleans,
LA: U.S. Department of Agriculture, Forest Service,
Southern Forest Experiment Station. 11 p.

Zhu, Zhiliang. 1994. Forest density mapping in the
lower 48 States: a regression procedure. Res. Pap.
SO-280. New Orleans, LA: U.S. Department of Ag-
riculture, Forest Service, Southern Forest Experi-
ment Station. ii p.

Zhu, Zhiliang; Evans, David L. 1992. Mapping
Midsouth forest distributions. Journal of Forestry.
90(12): 27-30.


Example of FIA Plot Layout

+3 miles

a a a t
+ + @ FIA permanent
plot locations
5 tl ci] Lt @ Intensification
plot locations
= = a a
4 4
a a a a
i 6 miles {

Example of Mylar Overlay Dot Count Layout

|---9-inch photo—

eee ee ee ewe

oe eee ee eee

oe oe ee . M FIA permanent plot locations

eee ee oe oe

oe ee © ee ew ew # Intensification plot
locations

ee oe ee oe ew we ow

oe eee ee eee * Dot count point

oe eee ce eee

eee ee ww wwe

© oe © © 0 6 of

Figure 3.— Schematic figure of FIA permanent and intensification plot locations and the mylar overlay used to sample the
forest area around the permanent plots.


Digital Photo Mosaic

ee i

@ee Forest
. Regeneration areas
MB ONonforest

Figure 2.— Digital photo mosaic, enlarged area, and example interpretation for Grant Parish.


Table 2.— Error matrices for the comparison of the photo interpre-
tation data with the Advanced Very High Resolution
Radiometer (AVHRR) data

Photo interpretation class

Parish AVHRR class Forest Nonforest
Grant
Forest 1, 264 152
Nonforest 140 161
La Salle
Forest 1, 301 146
Nonforest 124 141
Natchitoches
Forest 1, 963 251
Nonforest 195 898

data sets. Both the producer’s and user’s agreement
measures for the nonforest category for Grant and La
Salle are lower than the same measures for Natchi-
toches Parish (table 3), but it was expected that inclu-
sion of the off-diagonal elements of the error matrix
in the calculation of the KHAT statistic would pro-
duce this result. The overall agreement percentage and
the KHAT statistic were highest for Natchitoches Par-
ish (table 3).

The physical size and spatial distribution of
nonforest areas in each parish influenced the mea-
sures of agreement. Most nonforest areas in Grant and
La Salle Parishes were small and discontinuous,
whereas Natchitoches Parish had many large, unbro-
ken agricultural fields. The low resolution of the
AVHRR data hindered identification of nonforest ar-
eas in Grant and La Salle Parishes, and thus reduced
the measures of agreement for these two parishes.

Table 3.— Percentage of overall, producer’s, and user’s agreement
and KHAT statistic (estimate of agreement from Kappa
analysis) for comparisons of the Advanced Very High
Resolution Radiometer (AVHRR) classification to the
photo interpretation

Agreement
Parish Overall Producer’s User’s KHAT statistic
- Percent -
Grant 83.0 42.1
Forest, 90.0 39.3
Nonforest 51.4 53.5
La Salle 84.2 41.7
Forest 91.3 89.9
Nonforest 49.1 53.2
Natchitoches 86.5 69.9
Forest 91.0 88.7
Nonforest 78.2 82.8

Forest regeneration areas of any age or tree size
were included in the forest category in the photo in-
terpretation data. Young regeneration areas are spec-
trally similar to some agricultural fields because both
have little vegetative cover and large proportions of
bare soil. It is likely that some areas in early stages of
forest regeneration were misidentified as nonforested
areas during the classification of AVHRR data. This
would have reduced agreement between the AVHRR
classifications and the photo interpretations. Because
the AVHRR classification method did not separate for-
est areas by age class, the extent of this problem could
not be determined.

Forest Percentage Estimates.—Listed in table 4
are the estimates of forest percentage for each parish
by data source.

Table 4.— Estimates of percentage of forest at the parish level and
confidence intervals by data source

Photo Dot AVHRR
Parish interpretation count classification

Grant 81.8+0 78.4 + 1.5* 82.5+0
La Salle 83.2+0 82.04 2.1 84.540
Natchitoches 65.3+0 67.64 1.0 66.940

*Confidence intervals for the dot count estimates are from Rosson
and others (1991).

Normal Tests Between Two Proportions.—The
normal test between two proportions indicated that
there were no significant differences (at the 95-per-
cent confidence level) between forest area estimates
obtained by photo interpretation and forest area esti-
mates obtained by the AVHRR classification method
for all three parishes. Significant differences were
detected between the photo interpretation and dot
count estimates for Grant and Natchitoches Parishes.
Because the photo interpretation data are total enu-
merations, forest area estimates based on them are
probably as accurate as or more accurate than esti-
mates based on the dot counts. However, the current
dot count method utilizes field validation visits to
update the forest area estimate to the date of the field
work. These estimate adjustments could have caused
the photo interpretation and dot count estimates to
be significantly different in areas where the forest
cover was altered by harvesting operations. The
AVHRR and dot count estimates did not differ signifi-
cantly for La Salle and Natchitoches Parishes.

SUMMARY AND CONCLUSIONS

The present method of estimating forest area by the
dot count method is expensive and time consuming.


United States
Department of
Agriculture

Forest Service

Southern Forest
Experiment Station

New Orleans,
Louisiana

Research Paper
$O-292
September 1995

cs

Comparison of AVHRR
Classification and Aerial
Photography Interpretation for
Estimation of Forest Area

Keith B. Lannom, David L. Evans, and Zhiliang Zhu


United States
Department of Agriculture

Forest Service

Southern Forest
Experiment Station

701 Loyola Ave., Rm.T-10210
New Orleans, LA 70113-1920

OFFICIAL BUSINESS

Penalty for Private Use $300 (



Lannom, Keith B.; Evans, David L.; Zhu, Zhiliang. 1995. Comparison of
AVHRR classification and aerial photography interpretation for
estimation of forest area. Res. Pap. SO-292. New Orleans, LA: U.S.
Department of Agriculture, Forest Service, Southern Forest Experiment
Station. 8 p.

Forest area was estimated using AVHRR data and dot count procedures.

Keywords: Dot counts, forest area estimates, photo-interpreted data.

The United States Department of Agriculture (USDA) prohibits discrimination in
its programs on the basis of race, color, national origin, sex, religion, age, disability,
political beliefs and marital or familial status. (Not all prohibited bases apply to all
programs.) Persons with disabilities who require alternative means of communication
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USDA is an equai employment opportunity employer.


data were entered: the visible, near-infrared, mid-in-
frared, and thermal channels, and a Normalized Dif-
ference Vegetation Index (NDVI) channel. The lower
48 States were stratified into 15 physiographic regions
based upon ecoregions, land surface forms, soils, po-
tential vegetation, and land physiography (Zhu 1994),
Percentage of forest cover of each physiographic re-
gion was modeled using a process adapted from
Iverson and others (1989). Regression procedures were
used to predict percentage of forest cover within the
AVHRR pixels. Results of this modeling were used to
identify forest land for further spectral classification.
Spectral classifications of forest type groups in each
region were performed on areas with more than 25
percent forest cover. The spectral channels used for
classification were those that had the most significant
loadings in the regression models. Percentage of for-
est cover data were used as an ancillary data layer in
the RPA forest type group classification.

The forest type groups were classified by means of
an unsupervised statistical clustering procedure per-
formed on the AVHRR biweekly composite data sets
(Zhu and Evans 1992). Twenty-three forest type groups
were identified by the classification. Before the RPA
map was published, it was reviewed by other Forest
Service FIA units and various other organizations. The
AVHRR classification procedure identified four forest
type groups in the Louisiana study area: longleaf—
slash pine, loblolly—-shortleaf pine, oak—hickory, and
oak—gum-cypress.

The 1:2,000,000 DLG files of the parish boundaries
were used to extract the AVHRR classifications that
matched the photo interpretation data. The classifi-
cations were recoded to forest and nonforest classes.

Classification Comparisons

‘Two types of comparisons were used to evaluate the
utility of AVHRR data to estimate forest area at the
parish level. The first was a test of site-specific
(pixel-to-pixel) agreement between the AVHRR clas-
sifications and the photo interpretation data. To al-
low this direct comparison, the photo interpretation
data were resampled using a nearest neighbor tech-
nique to match the pixel size of the AVHRR data. The
site-specific agreement comparisons were used to
judge the integrity of the AVHRR classifications. The
results were used to calculate an overall agreement of
AVHRR classifications to the photo interpretations.
Because it cannot be assumed that the photo inter-
pretations were 100-percent correct, the term “percent-
age of agreement,” rather than the term “percentage
of accuracy,” is used here.

Congalton (1991) recommended reporting classifi-
cation accuracy (agreement in this case) in the form
of an error matrix. Overall, producer’s and user’s per-

centage of agreements were calculated for the com-
parisons. Producer's agreement is a measure of omis-
sion error and is the number of correct samples divided
by the total number of reference (ground) samples for
a category (Story and Congalton 1986). User’s agree-
ment, or commission error, is the number of correct
samples in a category divided by the total number of
samples in that category as identified from the map
(Story and Congalton 1986).

Another measure of the site-specific agreement, the
KHAT statistic, was computed by means of a Kappa
analysis. Congalton (1991) and Rosenfield and
Fitzpatrick-Lins (1986) review the Kappa analysis
procedure. The KHAT statistic takes into account the
major diagonal and off-diagonal elements of the error
matrix. Including the off-diagonal elements incorpo-
rates the overall, producer’s, and user’s agreement
measures into a single estimate of agreement.

The second method of evaluation employed normal
tests for differences between proportions. These were
used to determine whether there were significant dif-
ferences among estimates of forest area by the dot
count method, the photo interpretation method, and
the AVHRR classification method (Fleiss 1981). The
following equation was used to compare the percent-
age of forest area estimates:

kez pal-a( +a)

2

\wa@(t+4)

where p, (p,) was the proportion estimated from
method 1 (2) and n, (n,) was the total sample size used
in estimation by method 1 (2). The Z statistic was a
test for equality of two proportions. Values of the other
variables in the equation were solved for by:

p= (py)(my) + (pa) (N29)
ny tng
and
q=1-p
RESULTS
Site-Specific Test

The measures of agreement were calculated from
data reported in the error matrices (table 2). The over-
all agreement percentages for the parishes indicate
that the AVHRR classification method was fairly suc-
cessful in separating forest land from nonforest land
(table 3). The data for this table were derived by
pixel-by-pixel cross-comparisons between the input


METHODS
Study Area

The study area is Grant, La Salle, and Natchitoches
Parishes in central Louisiana (fig. 1) and is approxi-
mately 661,500 hectares in size (table 1). The Red
River runs southeast across Natchitoches Parish and
also forms the border between Natchitoches and Grant
Parishes. There are large agricultural areas on both
sides of the Red River. Most of the area’s forest cover
is loblolly—shortleaf pine, but various hardwood spe-
cies occur in the bottomlands. A recent SO-FIA sur-
vey (Rosson and others 1991) found that 44.1 percent
of the 503,000 hectares of forest land in the three par-
ishes is in pine. The rest of the forest cover consists of
oak-pine, oak—hickory, oak-gum-cypress, and elm—
ash-cottonwood types.

Commercial timber harvesting is prevalent through-
out the area. The typical harvesting operation involves
removing all the merchantable trees for utilization.
Part of the Kisatchie National Forest is within Grant
and Natchitoches Parishes. Small clearings occupied
by oil and gas wells are common in northwestern and
southeastern La Salle Parish. The city of Natchitoches
is the only large urban locality in the three-parish site.
The rest of the study area is mainly rural with scat-
tered small towns.

Definition of Forest Area

For consistency, the aerial photography used to pro-
vide photo interpretation data was interpreted in ac-
cordance with the same guidelines SO-FIA personnel
follow when performing dot counts on aerial photos.
The SO-FIA definition of forest land is:

Land at least 10-percent stocked by forest trees
of any size, or formerly having such tree cover,
and not currently developed for nonforest use. The
minimum area for classification of forest land, or
subclass of forest land, is 0.4 hectares (1 acre).
Roadside, streamside, and shelterbelt strips of
timber must have a width of at least 36.6 meters
(120 feet) to qualify as forest land. Unimproved
roads and trails, streams, and clearings in forest
areas shall be classified as forest if they are less
than 36.6 meters in width (USDA-FS 1991).
According to this definition, any regeneration area
regardless of tree age and tree size is considered to be
forest land. Nonforest land is:
Land that has never supported forests and lands
formerly forested where use for timber manage-
ment is precluded by development for other uses.
Includes areas used for crops, improved pasture,
residential areas, city parks, improved roads of
any width and adjoining clearings, power line
clearings of any width and adjoining clearings
(USDA-FS 1981).

Table 1.— Area by parish and land class (Rosson and others 1991)

Forest land*

All Timber-
Parish land* ‘Total land*

Reserved Non-
timberland’ forest

Thousand hectares

Grant 169.0 134.9 134.9 0.0 34.1
La Salle 165.2 140.7 140.7 0.0 24.5
Natchitoches 327.3 227.4 223.8 3.6 99.9

*Land at least 10-percent stocked by forest trees of any size, or
formerly having such tree cover, and not currently developed for
nonforest use.

*From U.S. Department of Commerce, Bureau of the Census.

‘Forest land that is producing or capable of producing at least
1.4 m° of industrial wood per hectare per year.

§Forest land sufficiently productive to qualify as commercial for-
est land, but withdrawn from timber utilization through statute
or administrative designation.

All areas of water, including rivers, other streams, and
ponds, were classified as nonforest.

Data Sources

Three data sources were used for this research:
photo interpretation data, sample point interpreta-
tions (dot counts), and AVHRR classifications. The
photo interpretation data were used to demonstrate
the spatial accuracy of the AVHRR data. Currently,
SO-FIA uses the dot count data for forest area esti-
mation. The photo interpretation and dot count esti-
mates were derived from the same photos and same
classification guidelines. However, the photo interpre-
tation estimates are based on total enumeration,
whereas the dot count estimates are based on a sam-
pling scheme.

Photo Interpretation Data.—The aerial photo
interpretation served two purposes for this study.
First, it provided a benchmark estimate of percent-
age of forest land within each parish. Second, it served
as reference data for the comparison to the AVHRR
classification.

The photo interpretation data used in the study were
produced by interactive digital interpretation of a
mosaic of video-scanned aerial photos. A video cam-
era system captured National Aerial Photography
Program (NAPP) 1:40,000-scale prints taken in early
1990, digitally, as raster files. All images covering a
parish were rectified to 1:24,000 quad sheets and
joined together to form a single digital image of each
parish (fig. 2). Digital Line Graph (DLG) 1:2,000,000
data from the U.S. Geological Survey were utilized to
exclude image data outside of the parish borders.

Forest land, forest regeneration areas, urban areas
with tree cover (e.g., parks or residential areas),


Comparison of AVHRR Classification and Aerial Photography
Interpretation for Estimation of Forest Area

Keith B. Lannom, David L. Evans, and Zhiliang Zhu

INTRODUCTION

The USDA Forest Service, Southern Forest Experi-
ment Station’s Forest Inventory and Analysis (SO-FIA)
unit inventories forest resources in Alabama, Arkan-
sas, Louisiana, Mississippi, Oklahoma, Tennessee,
Texas, and Puerto Rico continuously on a rotation cycle
of approximately 8 years. Data collected from sample
plots on the ground are compiled into extensive data-
bases and are currently the only source of detailed
regional information on the Midsouth’s forest re-
sources (Rudis 1989).

The SO-FIA collects forest inventory data in per-
manent plots in a 3- by 3-mile grid and in intensifica-
tion plots in a 6- by 6-mile grid. Data on tree species,
tree volumes, occurrence of human activities, proxim-
ity to water, and evidence of forest management prac-
tices are gathered on all forested permanent plots. The
only information gathered on nonforested permanent
plots is the land use (agriculture, pasture, water, ur-
ban, etc.). All plots are visited by field crews during
each inventory cycle. Intensification plots increase the
field sampling intensity, thereby improving the aerial
photo estimates of forest area at the county or parish
level. Intensification plots are determined to be for-
ested or nonforested and provide no other information.
Percentage of forest cover (as a proportion of total land
area) is determined by classifying points (dot counting)
on aerial photographs on a forest or nonforest basis
(Kelly 1990). Data from the permanent and intensifica-
tion field plots are used to adjust the forest area esti-
mates derived from aerial photo dot counts to the date
of each survey and to correct any interpretation errors.

The SO-FIA is evaluating modeling techniques for
predicting changes in forest resources for periods be-
tween survey cycles. An important element of update
modeling is the estimation of forest cover. Manage-
ment activity by forest industry changes the forest
cover of the Midsouth constantly. Satellite imagery will
provide more timely information concerning these re-
source issues.

Scientists at SO-FIA have developed techniques for
mapping forest cover from satellite data. An impor-
tant source of information is National Oceanic and
Atmospheric Administration weather satellite data
obtained from the Advanced Very High Resolution
Radiometer (AVHRR). Local Area Coverage (LAC) data
provide information on the spectral reflectance and
thermal emittance of the Earth’s surface in 1-square-
kilometer picture elements (pixels). The AVHRR data
can be collected daily and are useful for vegetation
monitoring and mapping of large areas.

Early AVHRR research at SO-FIA focused mainly
on uses of single-date imagery in the making of forest
assessments (Teuber 1990, Zhu 1992). In subsequent
research, single-date classifications were compared to
reveal changes in distributions of forest cover (Zhu
1992). The availability of multidate composites of ra-
diometry data for large areas (Eidenshink and others
1991) facilitated research in more detailed forest map-
ping and monitoring. Recently, studies such as those
by Evans and others (1992), Iverson and others (1989),
and Zhu and Evans (1992) have demonstrated that
AVHRR analysis can provide detailed information
about forest lands.

The research reported here was conducted to deter-
mine whether AVHRR classifications could be used to
estimate forest area at the county or parish level.
Iverson and others (1989) found a high correlation
between AVHRR-derived and Forest Service
county-level estimates of percentage of forest cover. If
forest area estimates based on AVHRR classification
are not significantly different from those based on
photo interpretation, then AVHRR classifications
might be a replacement for the present method of es-
timating forest area. Replacing dot count estimates
with AVHRR forest area estimates would provide two
benefits. First, changes in forest area between field
cycles could be monitored without extensive field veri-
fication of photo interpretations. Second, field visits
to intensification plots used to calibrate dot count es-
timates of forest area would no longer be necessary.

Keith B. Lannom is a research forester at the Forest Sciences Laboratory, U.S, Department of Agriculture, Forest Service, Southern Forest
Experiment Station, Starkville, MS 39759; David L. Evans is a supervisory research forester at the Forest Sciences Laboratory, U.S.
Department of Agriculture, Forest Service, Southern Forest Experiment Station, Starkville, MS 39759; and Zhiliang Zhu is a senior
scientist with Hughes STX Corporation, EROS Data Center, Sioux Falls, SD 57198.


SUMMARY

The USDA Forest Service Southern Forest Experiment Station’s Forest In-
ventory and Analysis (SO-FIA) unit uses a dot count method to estimate the
percentage of forest area in counties or parishes from aerial photographs.
Ground verification is used to adjust the photo interpretation estimates. This
verification is expensive and time consuming. The research reported here was
designed to determine whether Advanced Very High Resolution Radiometer
(AVHRR) data could be used to estimate forest area at the county or parish
level. For this study, AVHRR data for three parishes in central Louisiana were
extracted from a 1991 AVHRR forest type map of the United States. Photo
interpretation data were obtained from a digital mosaic of aerial photography
of the parishes. Forest area estimates obtained by means of photo interpreta-
tion did not differ significantly from those obtained by analyzing AVHRR data.
Thus, forest area estimation based on AVHRR data could replace forest area
estimation based on the dot count method. Extension of testing to multiple,
randomly selected counties or to individual aerial photographs is recommended.