Time series surface deformation of the upper reaches of Huangdeng hydropower station in Lanping County based on ascending and descending SAR data
YU Wenxuan1(), LI Yimin2(), JI Peikun2, FENG Xianjie1, XIANG Qianying1
1. Institute of International Rivers and Eco-security, Yunnan University,Kunming 650500, China 2. College of Earth Science, Yunnan University, Kunming 650500, China
The interferometric synthetic aperture radar (InSAR) technique is widely applied to surface deformation monitoring, providing all-weather, all-time, and high-precision measurements over large areas. However, due to the limitations of the single deformation observation method, significant uncertainties inevitably arise during the monitoring process, leading to potential misinterpretations. Using the SBAS-InSAR (small baseline subset) two-dimensional solution technique based on ascending and descending SAR data, this study analyzed the surface deformations of the upper reaches of the Huangdeng Hydropower Station from April 2020 to August 2022. A total of 34 scenes of ascending and descending data from the Sentinel-1 satellite were used to derive the two-dimensional deformations of the upper reaches, with six potential landslide hazard sites there being identified. The results indicate that the study area displayed a predominance of horizontal surface deformations, with the highest two-dimensional deformation rates of up to 158 mm/a horizontally and 81 mm/a vertically observed in the Cheyiping area. Additionally, by correlation analysis between the distance from the Lancang River bank, rainfall, and the time-series deformations, this study identified the distribution of two-dimensional deformations in the upper reaches and its seasonal variations.
俞文轩, 李益敏, 计培琨, 冯显杰, 向倩英. 基于升降轨SAR数据的兰坪县黄登水电站上游时间序列地表形变研究[J]. 自然资源遥感, 2024, 36(4): 282-294.
YU Wenxuan, LI Yimin, JI Peikun, FENG Xianjie, XIANG Qianying. Time series surface deformation of the upper reaches of Huangdeng hydropower station in Lanping County based on ascending and descending SAR data. Remote Sensing for Natural Resources, 2024, 36(4): 282-294.
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