Monitoring mining-induced subsidence by integrating differential radar interferometry and persistent scatterer techniques
Kamila Pawłuszek-Filipiak , Andrzej Borkowski
AbstractSurface 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.
|Journal series||European Journal of Remote Sensing, ISSN 2279-7254, (N/A 70 pkt)|
|Publication size in sheets||0.6|
|Keywords in English||Persistent scatterer interferometry, differential interferometry, DInSAR fusion, splines, subsidence, mining|
|ASJC Classification||; ; ;|
|License||Journal (articles only); published final; ; with publication|
|Not used for evaluation||yes|
|Publication indicators||= 0; = 0; : 2018 = 1.094; : 2018 = 1.904 (2) - 2018=2.102 (5)|
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