GIS-based landslide susceptibility mapping using logistical regression method with LiDAR data in nature slopes

Landslides susceptibility maps are important information for agricultural planning and infrastructure developments. The study area is selected in Mizunami City which is well known for active mass movement, including soil slides, debris slides, rock slides and debris flows. In order to identify the potential hazardous areas related to landslides and to mitigate the risk level for human beings, logistical regression method was used to create the map of landslide susceptibility. In this study, landslide-related factors such as topographical elevation, slope angle, slope aspect, profile curvature, plan curvature, flow direction, flow accumulation, flow length, distance to rivers, distance to highways, distance to faults ,stream power index(SPI), topographical wetness index (TWI), all with 2m×2m pixels or cells were employed in the landslide susceptibility analysis. The weights of causative factor were measured and designated by logistical regression model. Observed landslide points were used to evaluate the effectiveness of the model.