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Recent Ground Subsidence in the North China Plain, China, Revealed by Sentinel-1A Datasets
- Source :
- Remote Sensing, Vol 12, Iss 21, p 3579 (2020)
- Publication Year :
- 2020
- Publisher :
- MDPI AG, 2020.
-
Abstract
- Groundwater resources have been exploited and utilized on a large scale in the North China Plain (NCP) since the 1970s. As a result of extensive groundwater depletion, the NCP has experienced significant land subsidence, which threatens geological stability and infrastructure health and exacerbates the risks of other geohazards. In this study, we employed multi-track Synthetic Aperture Radar (SAR) datasets acquired by the Sentinel-1A (S1A) satellite to detect spatial and temporal distributions of surface deformation in the NCP from 2016 to 2018 based on multi-temporal interferometric synthetic aperture radar (MT-InSAR). The results show that the overall ground displacement ranged from −165.4 mm/yr (subsidence) to 9.9 mm/yr (uplift) with a standard variance of 28.8 mm/yr. During the InSAR monitoring period, the temporal pattern of land subsidence was dominated by a decreasing tendency and the spatial pattern of land subsidence in the coastal plain exhibited an expansion trend. Validation results show that the S1A datasets agree well with levelling data, indicating the reliability of the InSAR results. With groundwater level data, we found that the distribution of subsidence in the NCP is spatially consistent with that of deep groundwater depression cones. A comparison with land use data shows that the agricultural usage of groundwater is the dominant mechanism responsible for land subsidence in the whole study area. Through an integrated analysis of land subsidence distribution characteristics, geological data, and previous research results, we found that other triggering factors, such as active faults, precipitation recharge, urbanization, and oil/gas extraction, have also impacted land subsidence in the NCP to different degrees.
- Subjects :
- land subsidence
Sentinel-1
MT-InSAR
NCP
Science
Subjects
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 12
- Issue :
- 21
- Database :
- Directory of Open Access Journals
- Journal :
- Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.3a822803e3447578465e38d6bfd1da2
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/rs12213579