The Jiaju ancient landslide is a giant landslide located upstream of the Dadu River, eastern Tibetan Plateau, with a volume of approx. 7.04 × 10⁸ m³. The Jiaju ancient landslide is complex and comprises five secondary sliding bodies, e.g., the Jiaju landslide (H01), Niexiaping landslide (H02), Xiaobawang landslide (H03), Niela landslide (H04), and Mt.-peak landslide (H05). Affected by regional neotectonic movement, heavy rainfall, river erosion, and lithology, the secondary sliding bodies of the Jiaju ancient landslide are undergoing significantly different creep-sliding deformation, which will cause great damage to villages, roads, and rivers around the sliding bodies. Combined with the SBAS-InSAR method, Sentinel-1A data from June 2018 to August 2021, remote sensing and field surveys, this study obtained the Jiaju ancient landslide deformation characteristics and deformation rate in the line-of-sight direction (V_LOS), slope (V_Slope), and vertical (V_Vertical). It is concluded that the maximum deformation rate of the Jiaju ancient landslide is significant. The maximum of V_LOS, V_Slope, and V_Vertical are −179 mm/a, −211 mm/a, and −67 mm/a, respectively. The Niela landslide (H04), Jiaju landslide (H01), and Mt.-peak landslide (H05) are very large and suffer strong deformation. Among these, the Niela landslide (H04) is in the accelerative deformation stage and at the Warn warning level, and the Jiaju landslide (H01) is in the creep deformation and attention warning level, especially heavy rainfall, which will accelerate landslide deformation and trigger reactivation. Because the geological structure is very complex for the Jiaju ancient landslide and strong neotectonic movement, under heavy rainfall, the secondary landslide creep-sliding rate of the Jiaju ancient landslide is easily accelerated and finally slides in part or as a whole, resulting in river blocking. It is suggested to strengthen the landslide deformation monitoring of the Niela landslide and Jiaju landslide and provide disaster mitigation and prevention support to the government and residents along the Dadu River watershed.

The Upper Jinsha River region on the southeastern Tibetan Plateau is characterized by intense tectonic activity, extreme topographic relief, and widespread large-scale landslides, which pose significant threats to communities, transportation networks and major infrastructure. However, accurate landslide susceptibility assessment in high-relief mountainous terrain remains challenging. Conventional methods rely on point-based sampling and static environmental factors, which fail to capture the spatial heterogeneity of large landslides and adequately represent the influence of continuous surface deformation on landslide evolution, resulting in considerable uncertainty and limited predictive accuracy. To address these issues, this study established a polygon-based landslide inventory comprising 3831 landslides and developed an improved susceptibility assessment framework integrating multi-scale polygon-based sampling with InSAR-derived surface deformation constraints. By coupling the Random Forest (RF) and Optimized Frequency Ratio (OFR) models, quantitative susceptibility assessment and model validation were conducted. The results show that the integrated RF-OFR model achieved the best predictive performance, with an Area Under the Curve (AUC) of 0.906, representing improvements of 6.0%, 4.2%, and 2.7% over the conventional FR, RF-FR, and OFR models, respectively. Fluvial incision, terrain relief, and precipitation were identified as the dominant conditioning factors controlling regional landslide occurrence. High-susceptibility zones are primarily distributed along the Jinsha River and deeply incised tributary valleys, showing strong spatial agreement with active fault zones and persistent surface deformation. Compared with conventional single-scale point sampling, the proposed multi-scale polygon sampling strategy more effectively represents the spatial characteristics of large landslides in high-relief mountain regions, reduces sampling bias and incorporating InSAR-derived deformation information further enhances the identification of actively deforming slopes, demonstrating the value of integrating dynamic surface deformation with conventional environmental factors in landslide susceptibility modeling. The proposed framework provides an effective approach for regional landslide susceptibility assessment and can support hazard identification and risk-informed infrastructure and land use planning in high-relief mountainous regions.