Geospatial intelligence for landslide susceptibility and risk analysis: Insights from NH31A and east Sikkim Himalaya settlements

Abstract

Slope instability is a serious concern in the Sikkim Himalayas. The town and numerous road segments along National Highway 31A were ravaged by multiple landslides that occurred in the nearby region. A bivariate statistical method known as frequency ratio (FR), information value (IV), and certainty factor (CF) analysis was employed in this work to examine landslide risk assessment (LRA) and landslide susceptibility zonation (LSZ) maps in the Rorachu watershed. This study represents the first comprehensive analysis of landslide risk in the populated areas of East Sikkim and along NH31A, offering a deeper understanding of the risks involved and contributing to the enhancement of local resilience against landslide hazards. A total of 153 different landslide locations were mapped using Google Earth and GIS software; 30% (46) of these locations were used to validate the models, and 70% of these (107) served as training data for the FR, IV, and CF models. The thirteen landslide causative factors (geology, soil, elevations, slope, curvature, drainage density (DD), road density (RD), rainfall, normalized difference vegetation index (NDVI), land use land cover (LULC), topographic position index (TPI), stream power index (SPI), and topographic wetness index (TWI)) were extracted from a spatial database for LSZ mapping. Landslides were most prevalent on slopes (35°–50°), heights (2500–4100 ​m), and rainfall (2000–2500 ​mm and 3000–3300 ​mm). The area under the curves (AUC) for the FR, IV, and CF models are 0.925 (92.50%), 0.846 (84.60%), and 0.868 (86.20%), respectively. The prediction rates are shown by the AUCs for the FR, IV, and CF models, which are 0.828 (82.8%), 0.750 (%), and 0.836 (83.60%), respectively. According to the landslide risk assessment (LRA), the FR (20.75%), IV (40.91%) and CF (18.78%) models showed high risk on Highway 31A, while the FR (9.05%), IV (38.59%) and CF (20.90%) models showed high risk in densely populated areas. These landslide risk and vulnerability maps can be used to develop land use planning strategies that can save lives and are useful for planners and mitigation measures. Special attention should be paid to urbanization, highway construction, and deforestation.