4.3 Article

Monitoring mining-induced subsidence by integrating differential radar interferometry and persistent scatterer techniques

期刊

EUROPEAN JOURNAL OF REMOTE SENSING
卷 54, 期 -, 页码 18-30

出版社

TAYLOR & FRANCIS LTD
DOI: 10.1080/22797254.2020.1759455

关键词

Persistent scatterer interferometry; differential interferometry; DInSAR fusion; splines; subsidence; mining

资金

  1. Operational Programme Smart Growth 2014-2020, Priority IV: Increasing the research potential, Action 4.2: Development of modern research infrastructure of the science sector [POIR.04.02.00-14-A003/16]
  2. European Regional Development Fund

向作者/读者索取更多资源

The study proposed a method to fuse the results of PSInSAR and DInSAR, reducing atmospheric errors and detecting deformation rates more accurately in measuring mining subsidence, especially in fast-moving subsidence areas.
Surface subsidence is a dominant component of the displacement vector triggered by underground mining. Over the last few decades, Differential Interferometry Synthetic Aperture Radar (DInSAR) has been used to efficiently monitor this phenomenon with great spatial and temporal coverage. More advanced multi-temporal DInSAR (MTInSAR) algorithms have been proposed to overcome some of the limitations of conventional DInSAR. However, advanced MTInSAR approaches are also not perfect in terms of measuring mining subsidence (e.g., temporal decorrelation, ambiguity, nonlinearity). For this reason, we propose a fusion of the Persistent Scatterer Interferometry (PSInSAR) and DInSAR results. By combining these complementary techniques, the atmospheric errors in PSInSAR data are reduced and larger deformation rates could have been detected more accurately (thanks to DInSAR) than by an approach solely based on PS-InSAR. This allows to measure areas with fast-moving subsidence (1 m/year) due to ongoing underground coal exploitation. Data from ascending and descending orbits of Sentinel-1A\B were used to obtain the vertical deformation component. The resulting integrated vertical deformation map was compared with the results from levelling benchmarks. The Root Mean Square Error (RMSE) calculated based on this comparison was 22 mm. Moreover, the maximal vertical cumulative subsidence detected in the study area was 1.05 m/year.

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