Landslide Susceptibility Mapping Using Satellite Images and GIS-Based Statistical Approaches in Part of Kullu District, Himachal Pradesh, India

Among the various natural geological phenomenon affecting the Indian peninsula, landslides have had huge damaging effects on human life and infrastructure, thereby requiring researchers to delve into more in-depth landslide-related studies with a focus on finding effective methods to better delineate landslide-prone areas as well as better understand the various underlying causative factors. The district of Kullu, situated in the Indian state of Himachal Pradesh has been the witness to some of these disastrous events, possibly owing to a recent surge in tourism, erratic climatic variation, and un-scientific construction. This study is an attempt to evaluate the applicability and effectiveness of four probabilistic approaches; Frequency Ratio (FR), Shannon Entropy (SE), Information Value (IV), and Weight-of-Evidence (WoE) in a Geographic Information System (GIS) environment for producing landslide susceptibility maps, to aid local authorities, in finding better-suited land use planning and risk-reduction strategies. The nine selected causative factors: slope, aspect, land use/land cover, elevation, curvature, distance to faults/lineaments, distance to roads, distance to drainage, and lithology along with an updated landslide inventory, compiled from past incidence maps from the Geological Survey of India (GSI) and through visual interpretation of google earth imageries (2001–2019) formed the input for this research work. Three different metrics namely, landslide density index (LDI), relative landslide density index (Rindex), and area under curve (AUC) were then used to validate and compare the resulting landslide susceptibility maps. The highest model fitness and predictive ability were demonstrated by the frequency ratio and shannon entropy approaches for this geographical extent.