As GIS technology has become accepted as a planning tool, a wide 
variety of data sources are being integrated into such systems. 
Generally, GIS are used to store large volumes of spatial data 
derived from a variety of sources including remote sensors and to 
efficiently retrieve, manipulate, analyze and display these data 
according to user-defined specifications for entire regions or parts 
of a region. A GIS can facilitate decision making based upon 
analysis of complex spatial interrelationships in a rapid, 
cost-effective manner. Ideally, GIS technology is suited to 
integrate the diverse, multi-disciplinary data required for global 
research studies. Ripple (1987) provided specific examples involving 
water, soil and vegetation resources management applications based on 
the integrated use of GIS and image processing technologies. 
Tomlinson and Boyle (1982) summarized the various categories of 
spatial data handling functions developed during the 1960's and 
1970's for use in natural resources management studies. Kennard et 
a). (1988) are developing a GIS system for land use planning and 
management of the semi-arid regions in northeastern Brazil using 
Landsat TM and SPOT data and Teotia et al_. (1988) conducted optical 
and digital interpretations of SPOT imagery of the area. Steyaert 
(1989) explained that current GIS technology and cartographic data 
represent major untapped resources to meet cross- disciplinary 
research needs of nontraditional GIS users involved with global 
change research. The GIS technology can contribute to several types 
of research including 1) spatial data base management, 2) natural 
resources inventories, 3) thematic mapping, 4) environmental 
monitoring, etc. 
The purpose of our research using remote sensing and GIS technology 
was to apply image processing and pattern recognition techniques to 
SPOT multi spectral image data to derive seven types of earth 
resources information: 
1. Land use and land cover classification (Anderson et^ al_., 1976) 
2. Soil associations (USDA, 1975) 
3. Land capability classification (USDA, 1966) 
4. Slope classes (USDA, 1975 and SUDENE, 1972) 
5. Land irrigability classification (IARI, 1972) 
6. Agro-technical limitations (USDA,1975) 
7. Suitability classification for land development and irrigation 
potential 
Data about the various components such as land use, soil, topography, 
geology, slope and elevation, vegetation and forest cover, climate, 
irrigation and satellite data were gathered from various federal and 
state agencies and institutions. 
PROCEDURES 
Study Area 
The study area chosen for this research is a part of the semi-arid 
region of Paraiba, Brazil, (Santa Luzia and its environs) covering an 
area of approximately 700 knr (Figure 1). The study was carried 
out on terrain which ranges from mountainous to alluvial and low