Use of Remote Sensing Data and GIS to Produce a Landslide Susceptibility Map of a Landslide Prone Area Using a Weight of Evidence Model

Preparation of landslide susceptibility maps is important for engineering geologists and geomorphologists. However, due to complex nature of landslides, producing a reliable susceptibility map is not easy. In this paper, the weights-of-evidence model (a Bayesian probability model) was applied to the task of evaluating landslide susceptibility using GIS. Using landslide location and a spatial database containing information such as topography, soil, landcover, geology, and lineament, the weights-of-evidence model was applied to calculate each relevant factor’s rating for the studied area. In the topographic database the factors were slope, aspect, distance to road, and curvature; in the soil database they were soil texture, soil material, and topographic type; lithology was derived from the geological database; land-cover information extracted from Landsat TM satellite imagery; and lineament data derived from SPOT 5 satellite imagery. Tests of conditional independence were performed for the selection of factors, allowing 33 combinations of factors to be analyzed. For the analysis of mapping landslide susceptibility, the contrast values, W+ and W-, of each factor’s rating were overlaid spatially. The results of the analysis were validated using the previous actual landslides locations in the study area. The combination of slope, curvature, topography, distance to road and distance to drainage, showed the best results. The results can be used for hazard prevention and land-use planning.