SBAS-InSAR-based long time-series deformation monitoring and landslide hazard identification in the Baihetan reservoir area
YU Bing1,2,3(), ZHANG Chunyu1, WANG Jinri1, LIU Guoxiang4, DAI Keren5, MA Deying1
1. School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China 2. Key Laboratory of Remote Sensing and Mapping Information Technology for Oil and Gas Fields in the Petroleum and Chemical Industry, Chengdu 610500, China 3. Institute of Petroleum and Natural Gas Spatial Information Engineering, Southwest Petroleum University, Chengdu 610500, China 4. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China 5. School of Earth Science, Chengdu University of Technology, Chengdu 610059, China
The reservoir area of the Baihetan hydropower station (also referred to as the Baihetan reservoir area) suffers from frequent geologic hazards. However, there is a lack of monitoring studies on the central area and lower reaches of the hydropower station. Based on the ascending and descending synthetic aperture Radar (SAR) images from the Sentinel-1A satellite, this study performed deformation monitoring and landslide hazard identification in the Baishitan-Yezhutang section of the Baihetan reservoir area using the small baseline subset-interferometric synthetic aperture Radar (SBAS-InSAR) method supported by the generic atmospheric correction online service for InSAR (GACOS). Moreover, this study conducted cross-validation of deformation data from ascending and descending SAR images for low-slope zones. It investigated the spatial distribution of landslide hazards and the movement patterns of typical hazard sites in the study area. Finally, it examined the impacts of factors influencing geologic hazards on the distribution of these hazard sites. The results indicate that the deformation data from ascending and descending SAR images for low-slope zones can be used for cross-validation. Based on the deformation detection results from time-series InSAR and the optical images from Google Earth, 16 landslide hazards were identified, including 14 slow-moving landslides and two significant deformation hazards induced by human engineering activities. Integrating the data of ascending and descending SAR images validated the reliability of deformation results and also enhanced the effectiveness of landslide hazard identification. The analysis of the movement patterns at typical hazard sites indicates a correlation between deformation acceleration and seasonal rainfall. The statistical analysis of factors influencing geologic hazards in the study area reveals that the formation of hazard sites is driven by multiple factors, with varying dominant factors and degrees of influence across different hazards.
于冰, 张椿雨, 王金日, 刘国祥, 戴可人, 马德英. 基于SBAS-InSAR的白鹤滩库区长时序形变监测与滑坡隐患识别[J]. 自然资源遥感, 2025, 37(6): 156-168.
YU Bing, ZHANG Chunyu, WANG Jinri, LIU Guoxiang, DAI Keren, MA Deying. SBAS-InSAR-based long time-series deformation monitoring and landslide hazard identification in the Baihetan reservoir area. Remote Sensing for Natural Resources, 2025, 37(6): 156-168.
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2025, Vol. 37 
), 张椿雨1, 王金日1, 刘国祥4, 戴可人5, 马德英1
