Remote Sensing and GIS-based landslide susceptibility mapping using bivariate statistical approach of Chhukha District

Abstract

Chhukha, a southern district of Bhutan remains susceptible to landslides due to excessive temporal rainfall variability and land instability aggravated by anthropogenic factors. This has led to multiple fatalities, substantial financial losses, and damages to infrastructure, farmland, and transportation networks. This study developed the district scale Landslide susceptibility index (LSI) by a bivariate statistical approach called Probabilistic Frequency Ratio (FR) and logistic regression (LR) with the help of a geospatial technology system. A total of 236 historical landslide inventories were identified through field deputation and google earth interpretation with the rationing of 70:30. 70% of the existing landslides were used to train the models, while the remaining 30% of them were used for model validation. The FR model outperformed the LR model with an accuracy of 88.3% and 83.2% respectively. The AUC model verification shows satisfactory agreement to predict landslide susceptibility at the district scale in the Himalayan region. Both models indicated that the central and northern parts of the district account for the least susceptibility, while the southern portion of the Chhukha district accounts for the highest susceptibility to landslides. These authentic findings of the research enable the local government and other decision-making bodies in developing policies, implement innovative measures, and disseminate awareness and preparedness for the consequences of landslide disasters.