Full text: Proceedings of the Symposium on Global and Environmental Monitoring (Part 1)

H.S. Teotia 1,5 , W.C. Kennard 2 , D.L. Civco 3 and K.A. Ulbricht 4,5 
Laboratory for Remote Sensing, Department of Naturai Resources 
Management and Engineering, The University of Connecticut, 
Storrs, CT 06269-4067 USA 
A portion of a SPOT HRV image data acquired on 5 May 1987 centered 
around Santa Luzia and its environs in Paraiba, Brazil, was processed 
to extract earth resources information. Digital analysis 
supplemented with selective ground truth data gathered by field 
surveys in 1988 was applied to the SPOT multispectral data to derive 
land cover and other natural resources information. The data were 
processed with ERDAS (Earth Resources Data Analysis System) software 
operating on a high performance microcomputer. Training areas were 
selected for 15 categories of land use and land cover. A supervised 
classification was conducted and the digital land use and land cover 
map was corrected geometrically and georeferenced to Universal Trans 
verse Mercator coordinates. Accuracy was assessed by digitizing 
ground truth test polygons and performing a pixel-by-pixel 
comparison. Percentage agreement with ground truth data as well as 
errors of omission and commission were recorded and analyzed. 
By recoding the land use and land cover classes, six geometrically 
corrected maps as follows were prepared: 1) land use and land cover, 
2) soil associations, 3) land capability, 4) slope classes, 5) land 
irrigability, and 6) agro-technical limitations. The first four 
resource information layers (maps) were weighted for their relative 
importance to land development and irrigation potential and combined 
in a logical model to produce a land suitability classification map. 
It was found that water, low density urban and barren rocky land, 
mixed cultivated and pasture, alluvial cultivated and eroded land, 
and forest classes produced the best results in terms of high 
percentage agreement with ground truth data (80.0 to 100.0%) and 
relatively low percentage commissions (zero to 13.5%). These classes 
were easily discriminated from other classes with a high overall 
classification accuracy. Other classes such as cotton sparse 
caatinga forest and rocky land, cotton cultivated and fallow alluvial 
land with dense shrubs/trees could also be distinguished but with 
1,. Professor, Federal University of Paraiba, Campina Grande, 
Paraiba, Brasil ' 
2 and 3. Director and Vice Director, respectively, Laboratory for 
Remote Sensing 
4, Staff Scientist, German Aerospace Research Establishment, DLR - 
Optoelektronik , 8031 Oberpfaffenhofen, FRG 
5. Visiting Professor at the University of Connecticut when this 
paper was prepared. 

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