The geohazards associated with strongly deformed and reactivated large-scale ancient landslide are analyzed through a study of the Xiongba ancient landslide. The SBAS-InSAR method, combined with remote sensing interpretation, was used to obtain the Xiongba ancient landslide surface deformation characteristics, on the western bank of the Jinsha River, during the period from October 2017 to June 2020. Two large strong deformation zones were discovered in this study, H₁ and H₂, which were located at the front edge of the Xiongba landslide. The maximum cumulative deformation in the H₁ deformation zone was approximately 204 mm, and the deformation in the H₂ deformation zone was approximately 302 mm. Influenced by the Jinsha River erosion, the Baige landslide-dammed lake-dam breakage-debris (LDLDB) flow/flood hazard chains, which occurred 75 km upstream reaches in October and November 2018, and the erosion of the foot of the Xiongba ancient landslide foot resulted in notably enhanced deformation. The creep rate in the H₁ deformation zone was 14~16 times that before the Baige landslide hazard chains occurred, and the hazard chains caused sliding in the H₂ zone. The Xiongba ancient landslide is undergoing retrogressive reactivation. The Xiongba ancient landslide is currently experiencing continuously creep-sliding, and the deformation rate in some areas is accelerating, which may induce a large-scale reactivation of the Xiongba ancient landslide and an LDLDB hazard chain.

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.

位于四川省丹巴县聂呷乡甲居村的甲居古滑坡主要由甲居滑坡(H01)、聂呷坪滑坡(H02)、小巴旺村滑坡(H03)、聂拉村滑坡(H04)和山顶滑坡(H05)等5个次级滑体组成.受区域构造、强降雨、河流侵蚀、地层岩性等因素影响，甲居古滑坡次级滑体持续发生蠕滑变形，对位于滑体上的村庄、道路和前缘大金河等具有较大危害，2020年遭受50年一遇的强降雨后，古滑坡变形速率有进一步增大的趋势.采用SBAS-InSAR技术，结合遥感解译和现场调查，获取了甲居古滑坡2018年6月至2021年8月的地表变形特征，通过二维形变速率转换获取了甲居古滑坡沿斜坡向(slope)和垂直向(vertical)的形变速率.研究认为，甲居古滑坡沿雷达视线方向(V_LOS)形变速率最大达-179 mm/a，沿斜坡方向的形变速率(V_s)最大为-211 mm/a，沿垂直方向的变形速率(V_v)最大为-67 mm/a.甲居滑坡的北侧区域、聂拉村滑坡的南侧区域和山顶滑坡后缘变形较大，总体上位于强变形-极强变形区.甲居古滑坡的变形机制具有一定差异，其中甲居滑坡以牵引式变形为主，聂拉村滑坡以推挤式变形为主.由于古滑坡地质构造复杂、新构造活动强烈，在强降雨和河流侵蚀作用下极易导致滑坡蠕滑速率加快并进一步失稳，形成堵江溃坝等灾害.建议加强次级滑体的地表变形监测，为流域性地质安全风险防灾减灾提供技术支撑和科学依据.

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.