Interferometric SAR Time Series Analysis for Ground Subsidence of the Abandoned Mining Area in North Peixian Using Sentinel-1A TOPS Data

  • Xixi Liu
  • Yunjia Wang
  • Shiyong Yan
Research Article


The North Peixian mining area of China has rich coal resources, with total proven reserves of 2.37 billion tons. However, the underground coal mining activities have resulted in ground collapse, which has caused serious harm to the environment and threatened the lives and properties of local residents. In this study, 12 Sentinel-1A terrain observation by progressive scans (TOPS) mode acquisitions between 30 July 2015 and 13 May 2016 over the abandoned mining area in North Peixian were analyzed using the interferometric synthetic aperture radar (InSAR) time series method to detect the ground subsidence, with the maximum ground subsidence reaching 83 mm/a and an average value of about 12.7 mm/a. The subsidence results derived from the Sentinel-1A TOPS mode dataset were proven to be effective in investigating and monitoring the ground subsidence in the North Peixian mining area. Compared to the rapid deformation during the ongoing period of mining excavation, the ground subsides slowly in abandoned mining areas and shows a linear relationship with time over a relatively long period of time. Spatial correlation between the subsidence distribution and land cover was found, in that the magnitude of the subsidence in urban areas was smaller than that in rural areas, which is associated with the controlled coal mining activities under buildings, railways, and water bodies. The results demonstrate that Sentinel-1A TOPS SAR images can be used to effectively and accurately detect and monitor ground subsidence in a mining area, which is critically important when investigating land subsidence in a large-scale mining area.


Sentinel-1A TOPS Subsidence Abandoned mining area 



We would like to thank ESA for providing the Sentinel-1 SAR dataset of the abandoned mining area in North Peixian. We thank NASA for providing the optical remote sensing image of LandSat8. We would also like to thank Andy Hooper, David Bekaert, Karsten Spaans for making the StaMPS Toolbox available. The research work is funded by Fundamental Research Funds for the Central Universities (No. 2015XKMS052), Natural Science Foundation of China (No. 51574221), Fundamental Research Funds for the Central Universities (No. KYLX16_0545).


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Copyright information

© Indian Society of Remote Sensing 2017

Authors and Affiliations

  1. 1.School of Environment Science and Spatial InformaticsChina University of Mining and TechnologyXuzhouPeople’s Republic of China
  2. 2.Key Laboratory of Land Environment and Disaster MonitoringNational Administration of Surveying Mapping and Geoinformation NASGXuzhouPeople’s Republic of China
  3. 3.Jiangsu Provincial Key Laboratory Resources and Environment InformationXuzhouPeople’s Republic of China

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