The Study on Landslide Hazards Based on Multi-Source Data and GMLCM Approach

The southwest region of China is characterized by numerous rugged mountains and valleys, which create favorable conditions for landslide disasters. The landslide-influencing factors show different sensitivities regionally, which induces the occurrence of disasters to different degrees, especially in small sample areas. This study constructs a framework for the identification, analysis, and evaluation of landslide hazards in complex mountainous regions within small sample areas. This study utilizes small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology and high-resolution optical imagery for a comprehensive interpretation to identify landslide hazards. A geodetector is employed to analyze disaster-inducing factors, and machine-learning models such as random forest (RF), gradient boosting decision tree (GBDT), categorical boosting (CatBoost), logistic regression (LR), and stacking ensemble strategies (Stacking) are applied for landslide sensitivity evaluation. GMLCM stands for geodetector–machine-learning-coupled modeling. The results indicate the following: (1) 172 landslide hazards were identified, primarily concentrated along the banks of the Lancang River. (2) A geodetector analysis shows that the key disaster-inducing factors for landslides include a digital elevation model (DEM) (1321–1857 m), rainfall (1181–1290 mm/a), the distance from roads (0–1285 m), and geological rock formation (soft rock formation). (3) Based on the application of the K-means clustering algorithm and the Bayesian optimization algorithm, the GD-CatBoost model shows excellent performance. High-sensitivity zones were predominantly concentrated along the Lancang River, accounting for 24.2% in the study area. The method for identifying landslide hazards and small-sample sensitivity evaluation can provide guidance and insights for landslide monitoring and harnessing in similar geological environments.