Pixel-level landslide stability mapping based on three-dimensional time-series InSAR monitoring and dynamics factors extraction

Conventional methods for evaluating landslide stability based on physical-mechanical properties and deformation often fail to accurately capture engineering geological conditions and spatiotemporal kinematic evolution across entire slopes. In this study, we propose a pixel-level landslide stability mapping method that uses three-dimensional time-series interferometric synthetic aperture radar (InSAR) monitoring and dynamic factor extraction. The method was applied to two reactivated landslides, Xiongba and Sela, in Gongjue County, Tibet. Using 212 Sentinel-1 ascending and descending orbit images (acquired between 2017 and 2021), we derived line-of-sight (LOS) deformation velocities using the Small Baseline Subset (SBAS) InSAR technique. Subsequently, these velocities were decomposed into full three-dimensional displacements based on a surface-parallel flow assumption and Helmert variance component estimation. A dual-parameter stability criterion combining the displacement vector angle and rate was established and compared with the traditional tangential angle method by integrating kinematics with elastoplastic principles. The results show that the new criterion effectively reduces sensitivity to deformation fluctuations and reveals dominant elastic deformation in both landslides, controlled by Poisson’s ratio and stress proportionality. The Xiongba source area, platform, and front edges correspond to the plastic, initial, and elastic stages, respectively, while the Sela landslide exhibits elastic, plastic, and sliding stages, both of which contain secondary deformation zones. Meteorological data analysis identified the Jinsha River’s scouring erosion and seasonal heavy rainfall as the primary external factors triggering landslide reactivation. Additionally, the 2018 Baige landslide-dam burst disaster chain further worsened the Xiongba landslide deformation. These findings offer valuable insights for future disaster prevention efforts.