Remote Sensing and GIS-based Landslide Susceptibility Analysis and its Cross-validation in Three Test Areas Using a Frequency Ratio Model

The paper presents the results of the cross-validation of a frequency ratio model using remote sensing data and GIS for landslide susceptibility analysis in the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Nine factors which influence landslide occurrence, i. e. slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, and NDVI, were extracted from the spatial database and the frequency ratio of each factor was computed. For all three areas the landslide susceptibility was analysed using the frequency ratios derived not only from the data for the respective area but also using the frequency ratios calculated from each of the other two areas (nine susceptibility maps in all) as a cross-validation of the model. For verification, the results of the analyses were then compared with the field-verified landslide locations. Among the nine cases, the case of Cameron based on the Cameron frequency ratio showed the highest accuracy (83%), and the case of Selangor based on the Penang frequency ratio showed the lowest accuracy (70%). Qualitatively, the model yields reasonable results which can be used for preliminary landslide hazard mapping.