Surface Deformation-Constrained Landslide Susceptibility Mapping Based on Multi-Source Data Integration

Landslide susceptibility mapping (LSM) is fundamental to disaster prevention and spatial risk management in mountainous regions. However, traditional LSM methods typically rely on static geo-environmental indicators and subjective classification thresholds, resulting in limited temporal adaptability and reduced interpretability. To address these limitations, this study proposed a surface deformation-constrained LSM framework that integrated SBAS-InSAR-derived ground deformation data with machine learning models to enhance the accuracy and temporal relevance of susceptibility results. Using the Wangmo County area in Guizhou Province, China as a case study, multi-source data, including remote sensing imagery, geo-environmental factors, and field investigations, were used to construct and validate four machine learning models. The Random Forest (RF) and Back Propagation Neural Network (BPNN) models exhibited superior performance and were further enhanced through Shapley value analysis to improve interpretability. Surface deformation was extracted from 31 Sentinel-1A images using SBAS-InSAR technology, and a Pearson correlation-based optimization approach was applied to align susceptibility classifications with ground deformation patterns. The improved LSM showed significantly improved temporal relevance compared to traditional LSM methods and better performance in identifying landslide activity during specific periods, as verified by two representative cases. The proposed framework significantly improved the temporal sensitivity and scientific robustness of LSM, providing a promising tool for dynamic landslide risk monitoring and decision-making in complex terrains.