Landslide Susceptibility Mapping: An Integrated Approach using Geographic Information Value, Remote Sensing, and Weight of Evidence Method

摘要

Landslide events cause significant financial losses, human casualties, and irreversible changes in the natural landscape. In this paper, we have addressed the mapping of landslides zones using the extracted Cartosat-1 Digital Elevation Model (DEM) and a knowledge-based numerical rating system. The main objective of the study is to use the Weight of Evidence (WoE) technique to produce a Landslide Hazards Zonation (LHZ) map and a Landslide Susceptible Map (LSM) to establish a relationship between landslide causality factors and past landslide locations. For the prediction and generation of the LSM, accurate DEM is extracted using the noise-free stereo images of the Cartosat-1 sensor. This research proposed a prediction model for landslide susceptibility mapping based on the combination of a knowledge-based numerical rating system, remote sensing, and the WoE technique. The WoE technique and information value method are used to calculate the weightage and ranking of each landslide causality factor. The sum of the landslide causality factor products calculates the Landslide Susceptible Index (LSI) value for every pixel. The area under concern was classified into five susceptibility classes based on the derived LSI, ranging from very low to scars. The overall prediction and forecast accuracy of the LSM generated using the WoE technique is 92.68%.