United States

I Department of

Agriculture

@TS’~

iTI

Qw@

U S Forest Service

 Southern Forest

Research Note

SO-367

Experiment Station February 1992

Mapping Mexico’s Forest Lands with Advanced Very High

Resolution Radiometer

David L. Evans, Zhiliang Zhu, Susan Eggen-McIntosh, Pedro Garcia Mayoral, and

Jose Luis Ornelas de Anda

SUMMARY

Data from the Advanced Very High Resolution

Radiometer (AVHRR) were used in a program sponsored by the U.S. Department of Agriculture, Forest Service, and the United Nations Food and Agriculture Organization to help scientists from Mexico generate

forest-cover maps of that country. Two near-cloud-free

composite images were generated for December and

March 1990 from AVHRR Local Area Coverage data

sets. A supervised training and classification technique

was used to process data in nine physiographic regions

of the country. Regional classifications were combined

to produce the final map. Classification accuracy for 60

test polygons was 78 percent correct for the polygons

and 84 percent correct as evaluated by pixels in selected

polygons. More detailed physiographic or ecologic partitioning of the country was cited as a way to improve

results of the classification technique used in this study.

INTRODUCTION

Data collected from the Advanced Very High Resolution Radiometer (AVHRR) by polar-orbiting satellites of

the National Oceanic and Atmospheric Administration

(NOAA) have been used for continental (LeCompte

1989) and even global (Hastings and others 1989)

vegetation characterization. Much interest has been

placed on use of the Normalized Difference Vegetation

Index (NDVI) for depiction of vegetation vigor(green’-

ness). Malingreau and Tucker (1987) discussed uses of

AVHRR data for monitoring deforestation in the Amazon

River Basin. Other studies have demonstrated uses of

AVHRR data for land-cover (Kerber and Schutt 1986)

and fire-fuels mapping (McKinley and others 1985).

Application of AVHRR data for forest mapping is growing in scope and interest. Teuber (1988) demonstrated

forest-area estimation over a three-State area with

AVHRR data. This work was extended by Zhu and

Teuber (1991) to include detailed comparisons of changes in forest area derived from two dates of AVHRR and

forest inventory surveys of Alabama. lverson and others

(1989) used data from the Landsat TM (thematic mapper) to calibrate percentage of forest cover in AVHRR

pixels (picture elements) of the Southeastern United

States.

Support is growing for forest monitoring programs in

developing countries. The United Nations Food and

Agriculture Organization (FAO) is keenly interested in

making assessments of changes in tropical forest

resources by use of AVHRR imagery. The synoptic

coverage and multispectral characteristics of the data

make them ideal for large-area mapping and monitoring.

Coincident with FAO’s forest assessment program,

Mexico expressed interest in establishing a periodic

forest inventory to keep track of and thereby better

manage the country’s forest resources. The common interests of FAO and Mexico led them to approach the

U.S. Department of Agriculture, Forest Service, about

providing AVHRR analysis training to Mexican scientists.

Support for this training was provided by the International Forestry Division under the Tropical Forestry Program

of the Forest Service.

Southern Forest Experiment Station/l-10210 U.S. Postal Services Bldg., 701 Loyola Avenue, New Orleans, La. 70113

Forest Service, U.S. Department of Agriculture.

Serving Alabama, Arkansas, Louisiana, Mississippi, E. Oklahoma, Tennessee, E. Texas, Puerto Rico, U.S. Virgin Islands

1
The Southern Forest Experiment Station, Forest Inventory and Analysis unit (SOFIA) organized and completed two sessions of instruction in remote sensing with

emphasis on AVHRR digital data analysis. Basic instruction on remote sensing principles was provided by the

Mississippi State University School of Forest Resources.

Hands-on training in AVHRR data processing was

provided by SOFIA, which also gave instructional background on forest inventory techniques and data organization. Work described here focuses on the detailed

analysis procedures used during these sessions to

generate a forest-cover map of Mexico from AVHRR

data.

METHODS

Multidate composites of NOAA-i 1 satellite AVHRR

data were produced to cover Mexico with a minimum of

cloud cover. The primary data set consisted of a threedate composite developed from December 1990 data. A

three-date composite of March 1990 data was also used.

All satellite data were processed in the ERDAS image

processing software system on Sun Microsystems

workstations. The final map was prepared by using

ARC/INFO geographic information systems (GIS)

software.

Field information was supplied in the form of

1 :l ,OOO,OOO vegetation maps, Landsat TM prints, aerial

photographs, and other ancillary maps. The required expertise was provided by the scientists from Mexico who

worked on the project.

Image Cornpositing Process

All subject images for each composite were visually

compared for overall image quality. A hierarchical approach to image combination and overlay was used to

develop each composite. The best images occupied the

greatest area in each composite. Only areas within 35

‘Mention of company or brand names of products or services is

solely for the reader’s information and does not constitute official

endorsement by the USDA Forest Service.

degrees of nadir were used from each image to avoid atmospheric and geomet’ric attenuation of data at extreme

scan angles.

Clouds were masked out with zero values in the image

data sets. The poorest quality data set represented the

bottom layer for each composite. Images were overlaid

on each other, so new data then filled in the zero areas

of the previous layers. This process was continued until

all images had been combined into the final composites.

Each composite was subjected to low-pass filtering to

smooth obvious transitions between file segments from

the separate images used in the cornpositing processes.

An outline of Mexico was digitized in ERDAS and used

to extract the image data of the land area in the country.

The composites each consisted of five standard AVHRR

channels: (1) 580 to 680 nm, (2) 730 to 1,100 nm, (3)

3,500 to 3,900 nm, (4) 10,300 to 11,300 nm, and (5)

11,500 to 12,500 nm, plus an NDVI channel. (NDVI is

expressed as the difference between channels 2 and 1

divided by their sum and is an indicator for vegetated

land cover.)

Classification

The composite data set for December 1990 was

divided into nine physiographic/ecologic regions for

Mexico. This was done to partition some of the spectral

variance expected to occur between similar cover types

in the separate regions. Each region was processed independently; then all regions were recombined to

produce a general vegetation map of the country.

A supervised classification approach was used to

analyze the data. Ten to 15 training areas were selected

from each region to represent the cover types defined for

the project: (1) temperate forest, (2) tropical high and

medium forest, (3) tropical low (dry) forest, (4)

scrublands, (5) desert, (6) irrigated agriculture lands, (7)

other unforested lands, (8) water, and (9) clouds. All

cover types did not occur within each region and therefore could not be sampled from each region. Sixty test

polygons (for accuracy assessments) were selected

concurrently during the training process. The test

polygons were not used for spectral training. Spectrally

nonhomogeneous samples (large channel variance)

were discarded and replaced as the training phase

proceeded.

David L. Evans and Zhiliang Zhu are research foresters and Susan Eggen-McIntosh is forester at the Forestry Sciences Laboratory, U.S.

Department of Agriculture, Forest Service, Southern Forest Experiment Station, Starkville, MS 39759; Pedro Garcia Mayoral is head of the

Department of Cartography, Direction de Politica Forestal, Subdireccion del lnventario National Forestal, Coyoacan D.F., Mexico; and Jose

Luis Ornelas de Anda, is head of the Department of Land Use, Direction de Cartografia Tematica-DGG-INEGI, 20290 Aguascalientes, Ags,

Mexico.

2
The maximum likelihood algorithm in ERDAS was

used to classify each region. All classified regions were

then combined into a single file. A map of the country

was produced for evaluation by using the ARC/INFO

GIS software package. The map was compared to

vegetation maps and other ancillary information to identify problems in the initial classification. Additional training samples were identified for problem areas, and the

data were reclassifed. Other refinements were provided

by using the March 1990 composite data to account for

some expected seasonal variance.

Accuracy Assessment

The 60 test polygons, each 60 to 100 pixels in size,

were used in two accuracy assessment techniques to

evaluate the classification map. One test compared the

predetermined category of each polygon to the majority

tion. Overall percentage of accuracy for this method was

expressed as correct polygons divided by 60, then multiplied by 100.

The second test compared all polygon pixels in the

classification map to those in the test polygons. Thus,

the percentage of accuracy was calculated as total correct pixels divided by total test pixels, multiplied by 100.

RESULTS

Comparison of the map in figure 1 to vegetation maps

and Landsat TM prints indicated a good general correspondence for the cover types that were identified in

the study. The accuracy of this map was 78 percent as

tested by the polygon comparison method. The pixel

comparison method yielded an accuracy of 84 percent.

The higher accuracy by the pixel method was probably

due to correct pixel classifications within otherwrse incorcategory derived for each polygon from the classifica- rect polygons.

Temperate Forest

Tropical High and Medium Forest

D Tropical Dry Forest

Scrub Vegetation

0 Pasture, Urban, and Agriculture

. , m Irrigated Agriculture

Kilometers

Figure 1 . - General forest classification of Mexico from Advanced Very High Resolution (AVHRR) data (preliminary

version).

3
Many obvious classification errors were identified as

problems of inadequate sampling in the first training

phase of the project. These errors were corrected by additional sampling, particularly near the edges of the

physiographic regions where spectral variation in

vegetation was expected to occur. All scientists involved

in the project agreed that some confusion would be

eliminated by partitioning the country into more distinct

zones of ecologic diversity before training and classification begin.

FUTURE CONSIDERATIONS

The current map will undergo additional review in

Mexico and then will be refined based on additional field

verification and Landsat data. Additional data sets will

further define seasonal and zonal variations in the wide

range of ecologic settings that exist in Mexico. Scientists

from both Mexico and SOFIA anticipate that remote

sensing products such as the classification map will help

form the basis for the nationwide forest inventory

programs of Mexico.

LITERATURE CITED

Hastings, David; Matson, Michael; Horvitz, Andrew H.

1989. Monitoring vegetation: first of a two-part series.

Photogrammetric Engineering and Remote Sensing.

55(l): 40-41.

Iverson, Lewis R.; Cook, Elizabeth A.; Graham, Robin L.

1989. Estimating forest cover over southeastern

United States using TM-calibrated AVHRR data. In:

Global natural resource monitoring and assessments:

preparing for the 21st century; proceedings of the international conference and workshop; 1989 September 24-30; Venice, Italy. Bethesda, MD: American

Society of Photogrammetry and Remote Sensing:

1252-l 262.

Kerber, A.G.; Schutt, J.B. 1986. Utility of AVHRR channels 3 and 4 in land-cover mapping. Photogrammetric

Engineering and Remote Sensing. 52(12): 1877-

1883.

LeCompte, Douglas. 1989. Using AVHRR for warning of

famine in Africa: second of two-part series.

Photogrammetric Engineering and Remote Sensing.

52(2): 168-l 69.

Malingreau, J.P.; Tucker, C.J. 1987. The contribution of

AVHRR data for measuring and understanding global

processes: large-scale deforestation in the Amazon

Basin. In: Remote sensing: understanding the Earth

as a system: Proceedings of the 1987 international

geoscience and remote sensing symposium (IGARSS

‘87); 1987 May 18-21; Ann Arbor, MI. New York, NY:

Institute of Electrical and Electronics Engineers: 443-

448.

McKinley, Randy A.; Chine, Edward P.; Werth, Lee F.

1985. Operational fire fuels mapping with NOAAAVHRR data. In: Remote sensing in forest and range

management: Proceedings of the Pecora 10 symposium; 1985 August 20-22; Fort Collins, CO. Bethesda, MD: American Society of Photogrammetry and

Remote Sensing: 295-304.

Teuber, Kurt B. 1988. Large-scale forest area estimation

using advanced very high resolution radiometer

(AVHRR) data. In: Remote sensing for resource inventory, planning, and monitoring: Proceedings of the

2nd Forest Service remote sensing applications conference; 1988 April 11-15; Slidell, LA. Bethesda, MD:

American Society of Photogrammetry and Remote

Sensing: 145-l 52.

Zhu, Zhiliang; Teuber, Kurt B. 1991. AVHRR data for

forest area change in Alabama. In: Proceedings, 1991

ACSM-ASPRS annual convention; 1991 March 25-

29; Baltimore, MD. Bethesda, MD: American Society

of Photogrammetry and Remote Sensing: 516-522.

Persons of any race, color, national origin, sex, age,

religion, or with any handicapping condition are welcome to use and enjoy all facilities, programs, and services of the USDA. Discrimination in any form is strictly against agency policy, and should be reported to the

Secretary of Agriculture, Washington, DC 20250.

4