Broad-scale Spatial Prediction of Areas at Risk from Dryland Salinity

This paper presents a method for predicting areas at risk from dryland salinity using information derived from multi-temporal Landsat Thematic Mapper satellite images combined with landform data derived from high-quality digital elevation models. The method aims to reproduce expert opinion about the future extent of salinity by using decision trees to determine the relationship between salinity risk and variables that describe various aspects of the landscape. Feature selection procedures are used to determine the optimal subset of variables for predicting risk areas. Preliminary studies were conducted in five subcatchments and the model was spatially extrapolated over 30 000 km to produce maps of those areas expected to become saline under current management practices.