Application of Small Baseline Set Time-Series InSAR Technique in Landslide Disaster Monitoring in Southern Hilly Mining Area

Mountainous open-pit mines are highly susceptible to landslides, yet quantitative risk assessment remains a challenge. This study aims to develop and validate a quantitative landslide risk assessment model by integrating multi-source data to enhance hazard identification in these complex environments. Taking the Dexing Copper Mine as a case study, we used Small Baseline Subset InSAR (SBAS-InSAR) to derive surface deformation rates. This deformation data was integrated with geological and topographical factors within a Geographic Information System (GIS), using an information value model combined with weighting from the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) to generate a comprehensive landslide risk map. The results show that 3860 potential landslide points were identified, with deformation rates ranging from −338.74 to 80.61 mm/a. High and very high-risk zones were primarily concentrated around the Fujiawu and Zhujiawu dump sites, and the model’s performance was validated with a high degree of accuracy, achieving an Area Under the Curve (AUC) value of 0.871. This study demonstrates that the integration of multi-source data provides a robust and effective approach for quantitative landslide risk assessment in mountainous mining areas. The proposed framework can serve as a valuable tool for targeted disaster prevention and management.