Identification of Landslide Risks in the Subtropical Hilly Regions of Southern China Using Integrated Multi-Source Synthetic Aperture Radar Interferometry and Machine Learning

The subtropical hilly regions of southern China are characterized by dense vegetation and highly concealed landslides, making it difficult for traditional, single-source remote sensing methods to meet disaster prevention needs. The core scientific contribution of this study is the development of a hierarchical, progressive hazard identification framework that bridges the gap between InSAR deformation detection and landslide risk identification. This study focuses on Mayang County, Hunan Province, China, and combines time-series InSAR data from C-band Sentinel-1 and L-band ALOS-2 with a random forest (RF) algorithm to construct an early-stage identification model for landslide risks. All SAR data were processed under controlled baseline conditions (perpendicular baseline <150 m; polarization: VV for Sentinel-1, HH for ALOS-2). By screening highly reliable deformation points through dual-source cross-validation and integrating nine evaluation factors including slope, we established a two-layer coupled identification model combining InSAR deformation and susceptibility indices at the slope unit scale. The results showed that the dual-source InSAR approach achieved an identification accuracy of 71% (precision 68%, recall 65%, F1-score 0.66, Cohen’s κ 0.62), significantly outperforming single-source methods (62% for Sentinel-1 alone and 58% for ALOS-2 alone); the AUC was 0.815 under spatial block cross-validation, with an out-of-bag error of 16.8%. The dual-source InSAR approach identified a total of 59 potential hazard sites, 83.1% of which were located in medium- to high-risk zones. Following field surveys and LiDAR verification, 35 of these were confirmed as active landslide sites, demonstrating identification accuracy significantly superior to that of a single data source. The multi-source coupling framework proposed in this study effectively overcomes the decoherence issues associated with single-source SAR data in subtropical vegetated areas, providing reliable technical support for the early identification of landslides in humid hilly regions of southern China.