合成孔径雷达干涉测量（Interferometric Synthetic Aperture Radar, InSAR）技术虽然已经在滑坡隐患识别和监测中得到了广泛应用，但是整合InSAR数据处理、滑坡识别、三维形变监测，尤其是威胁对象分析的流程并不多。本文整合InSAR数据处理、滑坡识别和多维形变监测的流程，并将其应用于青海省化隆县。利用时序InSAR技术处理了2021年1月至2023年6月成像的升降轨Sentinel-1影像，本文获取了化隆县的InSAR地表平均形变速率。结合InSAR结果、C指数与GoogleEarth影像共识别出334个滑坡隐患，其中升轨影像识别出233个，降轨影像识别出265个，164个滑坡隐患可以被升降轨影像同时识别到，滑坡隐患总面积约95.56km2。54个滑坡隐患威胁到建筑物、11个滑坡隐患威胁到黄河/水库、两个滑坡隐患威胁到国道、12个滑坡隐患威胁到省道。识别出的滑坡隐患坡度分布在5°到40°之间，识别到的近南北走向的滑坡隐患明显少于其它坡向的滑坡隐患。利用坡向平行流动模型（Aspect Parallel Flow Model, APFM）计算了面积最大的初麻乡安具乎村滑坡的三维形变序列，并从理论上说明了APFM在滑坡呈近南北走向时，解算得到的三维形变场受观测值误差影响较大。分析发现安具乎村滑坡的水平形变远大于垂直形变，最大水平累积形变超过1米。该滑坡威胁到两个村庄、一条省道和部分种植区，还需要进行持续性监测。本研究将为化隆县地质灾害防治提供参考，同时为类似滑坡隐患识别和三维形变监测提供技术支持。

Objectives: Landslide detection and deformation monitoring are critical for geological hazard prevention and risk mitigation. This study proposes an integrated framework for landslide detection and three-dimensional (3D) deformation monitoring using Interferometric Synthetic Aperture (InSAR). The proposed framework comprises three key components: (1) Time-series InSAR data processing, (a) landslide detection based on InSAR results, optical images and C-index, (3) 3D deformation monitoring. We apply the proposed framework to Hualong County in Qinghai province, a landslide prone area. Methods: Initially, we employ Multi-temporal InSAR (MT-InSAR) to analyze both the ascending and descending Sentinel-1 satellite images acquired between Jan. 2021 and Jun. 2023, encompassing Hualong County in Qinghai Province. Next, deformation rate maps are generated and cross-validated with field measurements from Global Navigation Satellite System (GNSS) station. Landslide identification is performed through the integration of InSAR-derived deformation signals, high-resolution Google Earth? imagery, and C-index of each potential deformation areas. Subsequently, we apply the Aspect Parallel Flow Model (APFM) to calculate the 3D displacement field of representative landslides. Results: The deformation results derived from both InSAR and on-site equipment exhibit strong agreement, and the standard deviation of the obtained average deformation rates is 5 mm/a for both ascending and descending images, showing high reliability of the obtained InSAR deformation results. Through the integration of InSAR deformation rate maps and Google EarthTM imagery, we detected a total of 334 landslides. Among these, 233 landslides were discernible using ascending data, 265 with descending data, and 164 were detectable with both ascending and descending datasets. The total area of the detected landslides is about 95.56 km2. The slope gradients of the detected landslides range between 5° and 40°, with 184 landslides posing direct threats to infrastructure (e.g., buildings, roads) and natural features (e.g., rivers). There is a notably lower count of landslides detected in the near north-south direction compared to other orientations, suggesting that InSAR might exhibit reduced sensitivity to deformations associated with landslides occurring along this axis. Theoretically, it has been demonstrated that observational errors can notably influence the 3D displacement field obtained from APFM, particularly in the context of landslides occurring in a nearly north-south direction. Utilizing APFM, the 3D deformation time series of the Anjuhu landslide in Chuma Township, which encompasses the largest area, was calculated. Analysis reveals that the horizontal deformation significantly outweighed the vertical deformation, with the maximum cumulative horizontal displacement surpassing 1 meter. The landslide presents a threat to two villages, a provincial road, and agricultural areas, necessitating ongoing monitoring. Conclusions: This study demonstrates the effectiveness of InSAR for regional-scale landslide detection and 3D deformation monitoring while also highlighting its limitations, particularly in areas with unfavorable slope orientations. The results illustrate both the benefits and limitations of InSAR in landslide monitoring, offering practical examples that can guide county-level efforts in landslide identification and 3D deformation monitoring, while also providing technical support.