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Spatial–temporal evolution patterns of land subsidence with different situation of space utilization

Author

Listed:
  • Beibei Chen
  • Huili Gong
  • Xiaojuan Li
  • Kunchao Lei
  • Mingliang Gao
  • Chaofan Zhou
  • Yinghai Ke

Abstract

Long-term over-exploitation of underground water, static and dynamic load is increasing year by year, which influenced the occurrence and development of regional land subsidence in Beijing, China. We used Envisat advanced synthetic aperture radar data acquired from 2003 to 2009 and PSI (persistent scatterers for SAR interferometry) and small baseline technology to estimate regional land subsidence information in Beijing, China. In different situation of space utilization, we chose five typical settlement areas according to classified information of land-use, multi-spectral remote sensing images and geological data. We analyzed the time-series evolution characteristics of uneven subsidence by GIS spatial analysis. The comparative analysis results suggest that for five typical settlement areas, the complex situations of space utilization affect the trend of uneven subsidence, the simpler space utilization situation (relatively fewer transport lines, construction), the smaller settlement differences and the smaller trend of the uneven subsidence. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Beibei Chen & Huili Gong & Xiaojuan Li & Kunchao Lei & Mingliang Gao & Chaofan Zhou & Yinghai Ke, 2015. "Spatial–temporal evolution patterns of land subsidence with different situation of space utilization," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 77(3), pages 1765-1783, July.
    • Handle: RePEc:spr:nathaz:v:77:y:2015:i:3:p:1765-1783
    DOI: 10.1007/s11069-015-1674-1
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    Cited by:

    1. Fengkai Li & Guolin Liu & Qiuxiang Tao & Min Zhai, 2023. "Land subsidence prediction model based on its influencing factors and machine learning methods," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 3015-3041, April.
    2. Yongyong Li & Huili Gong & Lin Zhu & Xiaojuan Li & Rong Wang & Gaoxuan Guo, 2017. "Characterizing land displacement in complex hydrogeological and geological settings: a case study in the Beijing Plain, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 87(1), pages 323-343, May.
    3. Keren Dai & Xianlin Shi & Jisong Gou & Leyin Hu & Mi Chen & Liang Zhao & Xiujun Dong & Zhenhong Li, 2020. "Diagnosing Subsidence Geohazard at Beijing Capital International Airport, from High-Resolution SAR Interferometry," Sustainability, MDPI, vol. 12(6), pages 1-16, March.

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