China Aero Geophysical Survey and Remote Sensing Center for Natural ResourcesHost
地质出版社Publish
2022 Vol. 34, No. 3
Article Contents

MA Xuefei, ZHANG Shuangcheng, HUI Wenhua, XU Qiang. 2022. InSAR-based large-scale detection and monitoring of the surface deformation in Linfen mining areas, Shanxi Province. Remote Sensing for Natural Resources, 34(3): 146-153. doi: 10.6046/zrzyyg.2021289
Citation: MA Xuefei, ZHANG Shuangcheng, HUI Wenhua, XU Qiang. 2022. InSAR-based large-scale detection and monitoring of the surface deformation in Linfen mining areas, Shanxi Province. Remote Sensing for Natural Resources, 34(3): 146-153. doi: 10.6046/zrzyyg.2021289

InSAR-based large-scale detection and monitoring of the surface deformation in Linfen mining areas, Shanxi Province

  • The ground subsidence caused by continuous mining in mining areas will seriously destroy the environment. There is an urgent need to quickly identify the locations and surface deformation of large-scope mining areas in the mining area monitoring. Given this, this study carried out large-scale detection and monitoring of the subsidence of mining areas in Linfen City using the synthetic aperture Radar interferometry (InSAR) technique. Firstly, by processing and analyzing 12 scenes of Sentinel 1A ascending data using the differential interferometric synthetic aperture Radar (D-InSAR) technique, this study conducted large-scale detection of subsidence disasters in mining areas in the study area. Then, this study processed 432 scenes of Sentinel 1A ascending data from different orbits using the small baseline subset InSAR (SBAS-InSAR) and monitored the obtained key areas. The results of this study show that there are a total of 105 subsidence areas in Linfen City, all of which are located in the mountains on both sides of the faulted Linfen basin. Further time-series deformation monitoring of key subsidence areas shows that many subsidence areas are continuously deforming, with high deformation amplitude and the deformation rate up to a maximum of -381 mm/a, and have caused huge damage to the ecological environment and infrastructure on the surface. The mining points near the subsidence area were identified according to optical images, thus verifying the reliability of the large-scale detection and monitoring method based on the InSAR technology. The results of this study will provide an important basis for the prevention and control of subsidence disasters in the mining areas of Linfen.
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