Deformation retrieval in large areas based on multibaseline DInSAR algorithm: a case study in Cangzhou, northern China

Abstract

The DInSAR technique with a multibaseline is becoming popular nowadays to investigate slow urban deformation. In this paper, we focus on deformation retrieval in large areas, including urban and suburban areas. Based on the multibaseline DInSAR algorithm proposed by Mora, three extensions are derived. First, least‐squares adjustment and error‐controlling methods are used to obtain stable deformation velocity and height error estimations. The least‐squares QR factorizaiton algorithm is emphasized to solve large, linear, and sparse functions. Second, a new complex network is presented to limit noise effects on the Delaunay triangular network. Third, by combining complex and Delaunay networks, large‐area deformation is investigated, from centre urban areas to suburban areas. The enhanced algorithm is performed to investigate the subsidence of Cangzhou, Hebei province (northern China) during 1993–1997 by using 9 ERS SLC data. The experimental results show serious subsidence in the region and are validated by levelling data and groundwater wells data. Compared with levelling data, the estimation errors of linear deformation velocity in urban areas are in the range of (−2, 2) mm year⁻¹, and in suburban areas, the errors are in the range of (−26, 15) mm year⁻¹, which is sufficiently feasible to determine the status of subsidence relative to the maximum deformation velocity of about −100 mm year⁻¹. The subsidence centres in urban areas are consistent with the spatial distribution of groundwater wells, which provides evidence that groundwater overexploitation is the main cause of subsidence in Cangzhou. The closure of wells will be a good way to control subsidence in the future.

Acknowledgements

This work is supported by funding from the National Natural Science Foundation of China (NSFC) (Project No. 40501044). The authors would like to thank Heibei Remote Sensing Center and ESA for the ERS data, the 4th Team of Hydrologic and Geologic Engineering, Hebei Survey Bureau, and Cangzhou Municipal Bureau of State Land and Resources for levelling data. The Hebei Bureau of Surveying and Mapping also provided kind help for collection of part of the levelling data. Cangzhou Bureau of Water Resources supplied data on groundwater wells. The DEM used in this work is the result of the SRTM (shuttle Radar Topography Mission) NASA mission.