The Monitoring and Analysis of Land Subsidence in Kunming (China) Supported by Time Series InSAR

Bo Xiao, Junsan Zhao, Dongsheng Li (), Zhenfeng Zhao, Wenfei Xi and Dingyi Zhou
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Bo Xiao: Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
Junsan Zhao: Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
Dongsheng Li: International Cooperation Department, Kunming Metallurgy College, Kunming 650033, China
Zhenfeng Zhao: Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
Wenfei Xi: Faculty of Geography, Yunnan Normal University, Kunming 650500, China
Dingyi Zhou: Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China

Sustainability, 2022, vol. 14, issue 19, 1-21

Abstract: As urban construction has been leaping forward recently, large-scale land subsidence has been caused in Kunming due to the special hydrogeological conditions of the city; the subsidence scope has stretched out, and the subsidence rate has been rising year by year. As a consequence, Kunming’s sustainable development has seriously hindered. The PS-InSAR (Persistent Scatterer Interferometric Synthetic Aperture Radar) and the SBAS-InSAR (Small Baseline Subsets Interferometric Synthetic Aperture Radar) technologies were adopted to process the descending Sentinel-1A data stacks from July 2018 to November 2020 to monitor the land subsidence of Kunming, so as to ensure the sustainable development of the city. Moreover, the causes were analyzed. As revealed by the results, (1) the overall subsidence trend of Kunming was large in the south (Dian lakeside), whereas it was relatively small in the north. The significant subsidence areas showed major distributions in Xishan, Guandu and Jining district. The maximal average subsidence rates of PS-InSAR and SBAS-InSAR were −78 mm/a and −88 mm/a, respectively. (2) The ground Subsidence field of Kunming was analyzed, and the correlation coefficient R 2 of the two methods was reported as 0.997. In comparison with the leveling data of the identical period, the root mean square error (RMSE) is 6.5 mm/a and 8.5 mm/a, respectively. (3) Based on the urban subway construction data, geological structure, groundwater extraction data and precipitation, the causes of subsidence were examined. As revealed by the results, under considerable urban subways construction, special geological structures and excessive groundwater extraction, the consolidation and compression of the ground surface could cause the regional large-area subsidence. Accordingly, the monthly average precipitation in Kunming in the identical period was collected for time series analysis, thereby indicating that the land subsidence showed obvious seasonal variations with the precipitation. The results of this study can provide data support and facilitate the decision-making for land subsidence assessment, forecasting and construction planning in Kunming.

Keywords: Sentinel-1A; time series InSAR; land subsidence; attribution analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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