Dynamic susceptibility assessment is essential for mitigating evolving landslide risks in alpine gorge regions. To address the static limitations and unit mismatch issues in conventional landslide susceptibility assessments in alpine gorge regions, this study proposes a dynamic framework integrating time-series InSAR-derived deformation. Applied to the Xinlong–Kangding section of the Yalong River, annual surface deformation velocities were retrieved using SBAS-InSAR with Sentinel-1 data, identifying 24 active landslide zones (>25 mm/a). The Geodetector model quantified the spatial influence of 18 conditioning factors, highlighting deformation velocity as the second most significant (q = 0.21), following soil type. Incorporating historical landslide data and InSAR deformation zones, slope unit delineation was optimized to construct a refined sample dataset. A Random Forest model was then used to assess the contribution of deformation factors. Results show that integrating InSAR data substantially improved model performance: “Very High” risk landslides increased from 67.21% to 87.01%, the AUC score improved from 0.9530 to 0.9798, and the Kappa coefficient increased from 0.7316 to 0.8870. These results demonstrate the value of InSAR-based dynamic monitoring in enhancing landslide susceptibility mapping, particularly for spatial clustering, classification precision, and model robustness. This approach offers a more efficient dynamic evaluation pathway for dynamic assessment and early warning of landslide hazards in mountainous regions.

The LuTan-1 (LT-1) satellite, launched in 2022, is China’s first L-band full-polarimetric Synthetic Aperture Radar (SAR) constellation, boasting interferometry capabilities. However, given its limited use in subsidence monitoring to date, a comprehensive evaluation of LT-1’s interferometric quality and capabilities is necessary. In this study, we utilized the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique to analyze mining-induced subsidence results near Shenmu City (China) with LT-1 data, revealing nine subsidence areas with a maximum subsidence of −19.6 mm within 32 days. Furthermore, a comparative analysis between LT-1 and Sentinel-1 data was conducted focusing on the aspects of subsidence results, interferometric phase, scattering intensity, and interferometric coherence. Notably, LT-1 detected some subsidence areas larger than those identified by Sentinel-1, attributed to LT-1’s high resolution, which significantly enhances the detectability of deformation gradients. Additionally, the coherence of LT-1 data exceeded that of Sentinel-1 due to LT-1’s L-band long wavelength compared to Sentinel-1’s C-band. This higher coherence facilitated more accurate capturing of differential interferometric phases, particularly in areas with large-gradient subsidence. Moreover, the quality of LT-1’s monitoring results surpassed that of Sentinel-1 in root mean square error (RMSE), standard deviation (SD), and signal-to-noise ratio (SNR). In conclusion, these findings provide valuable insights for future subsidence-monitoring tasks utilizing LT-1 data. Ultimately, the systematic differences between LT-1 and Sentinel-1 satellites confirm that LT-1 is well-suited for detailed and accurate subsidence monitoring in complex environments.

Landslide InSAR monitoring is crucial for understanding the evolutionary mechanisms of geological disasters and enhancing risk prevention and control capabilities. However, for complex terrains and large-scale landslides, satellite-based SAR monitoring faces challenges such as a low observation frequency and limited spatial deformation interpretation capabilities. Additionally, two-dimensional monitoring struggles to comprehensively capture multi-directional movements. Taking the post-disaster monitoring of the landslide in Yunchuan, Sichuan Province, as an example, this study proposes a method for three-dimensional deformation dynamic monitoring by integrating dual-view MIMO ground-based synthetic aperture radar (GB-InSAR) data with high-resolution digital elevation model (DEM) data, successfully reconstructing the three-dimensional displacement fields in the east–west, north–south, and vertical directions. The results show that deformation in the landslide area evolved from slow accumulation to rapid failure, particularly concentrated in the middle and lower regions of the landslide. The average three-dimensional deformation of the main slip zone was approximately 60% greater than that of the original slope, with a maximum deformation of −100 mm. These deformation characteristics are highly consistent with the topographic structure and sliding direction. Field investigations further validated the radar data, with observed surface cracks and accumulation zones consistent with the high-deformation regions identified by the monitoring system. This system provides a solid foundation for geological disaster early warning systems, mechanism research, and risk prevention and control.