An assessment of landslide susceptibility in the Faifa area, Saudi Arabia, using remote sensing and GIS techniques

Abstract. An integrated approach was adopted over Faifa Mountain and its surroundings, in Saudi Arabia, to identify landslide types, distribution, and controlling factors, and to generate landslide susceptibility maps. Given the inaccessibility of the area, we relied on remote sensing observations and GIS-based applications to enable spatial analysis of data and extrapolation of limited field observations. Susceptibility maps depicting debris flows within ephemeral valleys (Type I) and landslides caused by failure along fracture planes (Type II) were generated. Type I susceptibility maps were generated applying linear relationships between normalized difference vegetation index (NDVI) and threshold slope values (30°), both of which were extracted over known debris flow locations. For Type II susceptibility maps, landslides were predicted if fracture planes had strike values similar to (within 20°) those of the slope face strike and dip angles exceeding the friction, but not the slope angles. Comparisons between predicted and observed debris flows yielded success rates of 82% (ephemeral valleys); unverified predictions are interpreted as future locations of debris flows. Our approach could serve as a replicable model for many areas worldwide, in areas where field measurements are difficult to obtain and/or are cost prohibitive.