Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 55-59, 2016
https://doi.org/10.5194/isprs-archives-XLI-B7-55-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
17 Jun 2016
MONITORING GROUND SUBSIDENCE IN AREAS COVERED BY DENSE VEGETATION USING TERRASAR-X IMAGES: A CASE STUDY OF HANGZHOU
H. A. Wu1, Y. H. Zhang1, G. F. Luo2, Y. K. Kang1, and Y. M. Zhu2 1Chinese Academy of Surveying and Mapping, 100830 Haidian District, Beijing, China
2Zhejiang Academy of Surveying and Mapping, 310012 Xihu District, Hangzhou, China
Keywords: Ground subsidence, InSAR, high resolution, TerraSAR-X, Hangzhou Abstract. Hangzhou, the capital of Zhejiang province has suffered serious ground subsidence during the past several decades, due to long term over-exploration of groundwater. In this paper, the time series InSAR technique using high resolution SAR images is investigated for the generation of subsidence maps over Hangzhou region. 29 TerraSAR-X images acquired from May 2012 to Sep 2015 are used. The results show that serious subsidence has mainly taken place in suburban area, including Yuhang district, Xiaoshan district and Binjiang district. 4 subsidence centers are discovered, namely Tangqi town in Yuhang with an average subsiding velocity of -29.6 mm/year, Xintang (-30.7 mm/year) in Xiaoshan, Zhujiaqiao town (-25.6mm/year) in Xiaoshan, and Miaohouwang town (-30.1mm/year) in Binjiang. The urban area is stable and ground rebound even take place in some places. The results are compared with 19 levelling measurements. The RMS error between them is 2.9 mm/year, which demonstrates that the high resolution TerraSAR-X images has good accuracy for subsidence monitoring in the southeast of China, covered by dense vegetation.
Conference paper (PDF, 1249 KB)


Citation: Wu, H. A., Zhang, Y. H., Luo, G. F., Kang, Y. K., and Zhu, Y. M.: MONITORING GROUND SUBSIDENCE IN AREAS COVERED BY DENSE VEGETATION USING TERRASAR-X IMAGES: A CASE STUDY OF HANGZHOU, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 55-59, https://doi.org/10.5194/isprs-archives-XLI-B7-55-2016, 2016.

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