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Land Subsidence Monitoring and Dynamic Prediction of Reclaimed Islands with Multi-Temporal InSAR Techniques in Xiamen and Zhangzhou Cities, China.
- Source :
-
Remote Sensing . Jun2022, Vol. 14 Issue 12, pN.PAG-N.PAG. 19p. - Publication Year :
- 2022
-
Abstract
- Artificial islands and land reclamation are one of the most important ways to expand urban space in coastal cities. Long-term consolidation of reclaimed material and compaction of marine sediments can cause ground subsidence, which may threaten the buildings and infrastructure on the reclaimed lands. Therefore, it is crucial to monitor the land subsidence and predict the future deformation trend to mitigate the damage and take measures for the land reclamation and any infrastructure. In this paper, a total of 125 SAR images acquired by the C-band Sentinel-1A satellite between June 2017 and September 2021 are collected. The small baseline subsets (SBAS) SAR interferometry (InSAR) method is first conducted to detect the land deformation in Xiamen and Zhangzhou cities of Fujian Province, China, and the distributed scatterers (DS)-InSAR method is used to recover the complete deformation history of some typical areas including Xiamen Airport in Dadeng Island and Shuangyu Island. Then, the sequential estimation and the geotechnical model are jointly applied to demonstrate the current and future evolution of land subsidence of the constructed roads on Shuangyu Island. The results show that the maximum cumulative deformation reaches 425 mm of Xiamen Xiang'an Airport and 626 mm of Shuangyu Island, and the maximum deformation is predicted to be as large as 1.1 m by 2026 of Shuangyu Island. This research will provide important guidelines for the design and construction of Xiamen Xiang'an Airport and Shuangyu Island to prevent and control land subsidence. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 14
- Issue :
- 12
- Database :
- Academic Search Index
- Journal :
- Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 157823851
- Full Text :
- https://doi.org/10.3390/rs14122930