Urban Land Subsidence Analyzed Through Time-Series InSAR Coupled with Refined Risk Modeling: A Wuhan Case Study

Due to extensive soft soil and high human activities, Wuhan is a hotspot for land subsidence. This study used the time-series InSAR to calculate the spatial and temporal distribution map of subsidence in Wuhan and analyze the causes of subsidence. An improved fuzzy analytic hierarchy process (GD-FAHP) was proposed and integrated with the Entropy Weight Method (EWM) to assess the hazard and vulnerability of land subsidence using multiple evaluation factors, thereby deriving the spatial distribution characteristics of subsidence risk in Wuhan. Results indicated the following: (1) Maximum subsidence rates reached −49 mm/a, with the most severe deformation localized in Hongshan District, exhibiting a cumulative displacement of −135 mm. Comparative validation between InSAR results and leveling was conducted, demonstrating the reliability of InSAR monitoring. (2) Areas with frequent urban construction largely coincided with subsidence locations. In addition, the analysis indicated that rainfall and hydrogeological conditions were also correlated with land subsidence. (3) The proposed risk assessment model effectively identified high-risk areas concentrated in central urban zones, particularly the Hongshan and Wuchang Districts. This research establishes a methodological framework for urban hazard mitigation and provides actionable insights for subsidence risk reduction strategies.

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Publication Details

Journal
ISPRS International Journal of Geo-Information
Published
2025-08-22
DOI
https://doi.org/10.3390/ijgi14090320
Primary Topic
Synthetic Aperture Radar (SAR) Applications and Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Urban Land Subsidence Analyzed Through Time-Series InSAR Coupled with Refined Risk Modeling: A Wuhan Case Study

Q. Chen, Jie Qin, Hongchang He, Lv Zhou et al.
ISPRS International Journal of Geo-Information
Synthetic Aperture Radar (SAR) Applications and Techniques
article

Urban Land Subsidence Analyzed Through Time-Series InSAR Coupled with Refined Risk Modeling: A Wuhan Case Study

Q. Chen, Jie Qin, Hongchang He, Lv Zhou, Fei Yang, Xinyi Li, Hongmin Li, Jie Bai, Lin Liang
article en

Abstract

Due to extensive soft soil and high human activities, Wuhan is a hotspot for land subsidence. This study used the time-series InSAR to calculate the spatial and temporal distribution map of subsidence in Wuhan and analyze the causes of subsidence. An improved fuzzy analytic hierarchy process (GD-FAHP) was proposed and integrated with the Entropy Weight Method (EWM) to assess the hazard and vulnerability of land subsidence using multiple evaluation factors, thereby deriving the spatial distribution characteristics of subsidence risk in Wuhan. Results indicated the following: (1) Maximum subsidence rates reached −49 mm/a, with the most severe deformation localized in Hongshan District, exhibiting a cumulative displacement of −135 mm. Comparative validation between InSAR results and leveling was conducted, demonstrating the reliability of InSAR monitoring. (2) Areas with frequent urban construction largely coincided with subsidence locations. In addition, the analysis indicated that rainfall and hydrogeological conditions were also correlated with land subsidence. (3) The proposed risk assessment model effectively identified high-risk areas concentrated in central urban zones, particularly the Hongshan and Wuchang Districts. This research establishes a methodological framework for urban hazard mitigation and provides actionable insights for subsidence risk reduction strategies.

ISPRS International Journal of Geo-InformationVol. 14(9)
Ministry of Natural Resources (CN), China University of Mining and Technology (CN), Guilin University of Technology (CN), Guangxi Zhuang Autonomous Region Health and Family Planning (CN), Guangxi Veterinary Research Institute (CN), State Key Laboratory of Remote Sensing Science (CN)
Ministry of Natural Resources of the People's Republic of China, National Natural Science Foundation of China, Guangxi Key Research and Development Program
Openalex Percentile: Top 70%
Synthetic Aperture Radar (SAR) Applications and Techniques
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