Multi-Source Remote Sensing Data-Driven Susceptibility Mapping of Retrogressive Thaw Slumps in the Yangtze River Source Region

Despite the ecological sensitivity of the Yangtze River Source Region (YRSR), the current research critically lacks a quantified assessment of the spatial occurrence probability of Retrogressive Thaw Slumps (RTSs) in this specific high-altitude terrain. This study aims to bridge this knowledge gap by establishing a robust susceptibility assessment framework to accurately model the spatial distribution and risk levels of RTSs. The innovations of this research include (i) the construction of a complete and up-to-date 2024 RTS inventory for the entire YRSR based on high-resolution optical remote sensing; (ii) the integration of time-series spectral features (e.g., vegetation and moisture trends) alongside static topographic variables to enhance the physical interpretability of machine learning models; and (iii) the application of advanced ensemble learning algorithms combined with SHAP analysis to establish a comprehensive RTS susceptibility zonation. The results reveal a rapid intensification of instability, evidenced by an 83.5% surge in RTS abundance, with the CatBoost model achieving exceptional accuracy (AUC = 0.994), and identifying that specific static topographic factors (particularly elevations between 4693 and 4812 m and north-to-east aspect) and dynamic spectral anomalies (indicated by declining vegetation vigor and increasing surface wetness) are the dominant drivers controlling RTS distribution. This study provides essential baseline data and spatial guidance for ecological conservation and engineering maintenance in the Asian Water Tower, demonstrating a highly effective paradigm for monitoring permafrost hazards under climate warming.