Military training activities have degraded land conditions, thereby increasing the requirement for extensive land rehabilitation.  The U. S. Army, with its emphasis on ground training and tracked vehicle use, has had a deleterious impact on ecosystem structure and function. Up to thirty percent of all land rehabilitation projects are considered failures due to a lack of knowledge concerning the inherent potential for successful rehabilitation. To provide more strategic and scientific knowledge for the rehabilitation effort, the U.S. Army Engineering Research and Development Center (ERDC) developed the Land Rehabilitation Potential Model (LRPM) that uses several ecologically based site-factors that are known to influence the potential for successful rehabilitation of damaged sites.  The LRPM uses a matrix structure consisting of a combination of twenty-three climatic, edaphic, biotic, stressor, landscape, and management factors that were weighted according to level of influence on successful rehabilitation.  Site specific factor data are used to drive the executable LRPM software to list sites according to their rehabilitation potential. Results from LRPM can provide prioritized schema for strategic investments of land management funding during land rehabilitation processes. Input for LRPM can be prepared using GIS in calculating land rehabilitation potential. Spatial data input for LRPM can be prepared as the secondary data from several different data sources including digital elevation models (DEM), Soil Survey Geographic Database (SSURGO) and National Land Cover Dataset (NLCD). Therefore, GIS is a beneficial tool in use of LRPM because GIS includes spatial data management and variety of map algebraic tools. Advances in the Internet environment have brought opportunities to develop web-based spatial decision support tools utilizing Internet-based GIS capability that incorporate computational models and geo-spatial information. Using the web-based GIS technique, Land Rehabilitation Potential Model (LRPM) can evolve to become a web-based spatial decision support tool, thereby enhancing its accessibility and convenient utilization.