高山峡谷区广泛分布的潜在滑坡严重威胁人民生命财产安全和城镇可持续发展，因此预测和量化滑坡灾害风险成为了防灾减灾的重点和难点，其中如何预测潜在滑坡的位置和规模更是滑坡灾害定量评价、风险防控和防灾减灾决策的瓶颈问题。本项目拟在滑坡灾害极为严重的白龙江中游，研究区域尺度潜在滑坡规模预测方法。首先，通过滑坡发育特征统计分析，建立历史滑坡的物源区发育特征-体积多元统计模型；然后利用InSAR技术监测区域高精度地表形变，识别潜在滑坡物源区位置和范围，提取潜在滑坡物源区发育特征参数，进一步结合物源区发育特征-体积多元统计模型，进行区域尺度潜在滑坡体积预测；最后根据潜在滑坡变形和地形地貌特征，分析潜在滑坡时空演化规律。项目研究成果可以推进滑坡灾害风险定量评价研究，为区域滑坡灾害防灾减灾和城镇发展规划提供科学依据，为类似滑坡高发区灾害预防和管理提供方法参考。

In mountainous area, potential landslides have the characteristics of large quantity and wide spreading, posing significant risk to local lives, properties and sustainable development. However, it is still a challenge to forecast the location and magnitude of potential landslides accurately at regional scale, which is crucial to quantify the landslide risk and mitigate landslide disasters. This project is designed to address this issue by proposing a new method to provide an assessment of potential landslide magnitude in the middle reachs of Bailong River. To do this, the characteristics of historical landslides will be analyzed to construct a multivariate statistical model linking landslide characteristics to volume. Then we will detect regional ground deformation using InSAR techniques and identify the potential landslide based on remote sensing interpretation, geomorphological analysis and field survey. By extracting the characteristics of the source area of potential landslides and inputting the constructed multivariate statistical model, we could assess the area and volume of potential landslide at regional scale. Finally, the spatial and temporal evolution of potential landslides will be analyzed based on deformation and geomorphological interpretation. In turn this project enables construction of a method that forecasts the magnitude of potential landslide. This research provides significant scientific insights to quantify the magnitude of potential landslides and thus contribute essential information towards landslide risk management. The built method could also provide technical support for the landslide prevention and mitigation in similar areas under high landslide risks.