Dynamic Landslide Susceptibility Modeling and Risk Forecasting in the Nilgiris Using Geospatial Approaches

Landslides remain a significant environmental hazard in India's hill regions, particularly in the Nilgiris district of Tamil Nadu, due to its steep terrain, fractured geology, and heavy seasonal rainfall. This study applies the Frequency Ratio (FR) model within a GIS and remote sensing framework to map landslide susceptibility and identify key contributing factors to slope instability. Ten thematic layers were used, including land use/land cover (LULC), NDVI, slope gradient, soil type and depth, geomorphology, aspect, rainfall, lineament density, and lineament proximity—derived from geological databases, DEMs, and satellite imagery. A landslide inventory was analyzed statistically to evaluate each factor’s role in landslide occurrence. Results indicate that slope gradient (9.15%) and LULC (8.37%) are the most influential factors, followed by geomorphology (7.78%), soil type (7.48%), and lineament density (4.50%). A key innovation of this study is the integration of lineament buffer zones to assess the influence of structural discontinuities, often overlooked in regional models. The model's predictive performance was validated using the Area Under the Curve (AUC) method, yielding a value of 0.879, indicating high accuracy. The resulting susceptibility map categorizes the landscape into low, moderate, and high-risk zones, providing a critical tool for regional planning, infrastructure development, and disaster management. This research supports climate-resilient development and sustainable land-use planning in vulnerable hill regions, emphasizing that both natural terrain characteristics and human-induced land alterations significantly contribute to landslide risk.