Surface Deformation of Xiamen, China Measured by Time-Series InSAR

被引:1
|
作者
He, Yuanrong [1 ,2 ]
Qian, Zhiheng [1 ]
Chen, Bingning [1 ]
Yang, Weijie [1 ]
Hao, Panlin [1 ]
机构
[1] Xiamen Univ Technol, Big Data Inst Digital Nat Disaster Monitoring Fuji, Xiamen 361024, Peoples R China
[2] Hunan Key Lab Remote Sensing Monitoring Ecol Envir, Changsha 410004, Peoples R China
关键词
land subsidence; Xiamen; PS-InSAR; SBAS-InSAR; cause analysis; LAND SUBSIDENCE; PERMANENT SCATTERERS; ERROR;
D O I
10.3390/s24165329
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Due to its unique geographical location and rapid urbanization, Xiamen is particularly susceptible to geological disasters. This study employs 80 Sentinel-1A SAR images covering Xiamen spanning from May 2017 to December 2023 for comprehensive dynamic monitoring of the land subsidence. PS-InSAR and SBAS-InSAR techniques were utilized to derive the surface deformation field and time series separately, followed by a comparative analysis of their results. SBAS-InSAR was finally chosen in this study for its higher coherence. Based on its results, we conducted cause analysis and obtained the following findings. (1) The most substantial subsidence occurred in Maluan Bay and Dadeng Island, where the maximum subsidence rate was 24 mm/yr and the maximum cumulative subsidence reached 250 mm over the course of the study. Additionally, regions exhibiting subsidence rates ranging from 10 to 30 mm/yr included Yuanhai Terminal, Maluan Bay, Xitang, Guanxun, Jiuxi entrance, Yangtang, the southeastern part of Dadeng Island, and Yundang Lake. (2) Geological structure, groundwater extraction, reclamation and engineering construction all have impacts on land subsidence. The land subsidence of fault belts and seismic focus areas was significant, and the area above the clay layer settled significantly. Both direct and indirect analysis can prove that as the amount of groundwater extraction increases, the amount of land subsidence increases. Significant subsidence is prone to occur after the initial land reclamation, during the consolidation period of the old fill materials, and after land compaction. The construction changes the soil structure, and the appearance of new buildings increases the risk of subsidence.
引用
收藏
页数:24
相关论文
共 50 条
  • [21] Gravity-driven deformation of Tenerife measured by InSAR time series analysis
    Fernandez, J.
    Tizzani, P.
    Manzo, M.
    Borgia, A.
    Gonzalez, P. J.
    Marti, J.
    Pepe, A.
    Camacho, A. G.
    Casu, F.
    Berardino, P.
    Prieto, J. F.
    Lanari, R.
    GEOPHYSICAL RESEARCH LETTERS, 2009, 36
  • [22] Present-Day Surface Deformation of Sicily Derived From Sentinel-1 InSAR Time-Series
    Henriquet, Maxime
    Peyret, Michel
    Dominguez, Stephane
    Barreca, Giovanni
    Monaco, Carmelo
    Mazzotti, Stephane
    JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH, 2022, 127 (03)
  • [23] Time-Series InSAR Technology for Ascending and Descending Orbital Images to Monitor Surface Deformation of the Metro Network in Chengdu
    Hu, Bo
    Li, Zhicong
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2021, 14 (14) : 12583 - 12597
  • [24] SURFACE DEFORMATION OF HIGH-SPEED RAILWAY BETWEEN CHANGCHUN AND HARBIN BASED ON TIME-SERIES INSAR TECHINQUE
    Meng, Zhiguo
    Shu, Chuanzeng
    Wu, Qiong
    Wang, Yongzhi
    Yang, Ying
    Fu, Zhe
    IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2020, : 1023 - 1025
  • [25] Ground Deformation Revealed by Sentinel-1 MSBAS-InSAR Time-Series over Karamay Oilfield, China
    Yang, Chengsheng
    Zhang, Dongxiao
    Zhao, Chaoying
    Han, Bingquan
    Sun, Ruiqi
    Du, Jiantao
    Chen, Liquan
    REMOTE SENSING, 2019, 11 (17)
  • [26] Combining InSAR and Time-Series Clustering to Reveal Deformation Patterns of the Heifangtai Loess Terrace
    Xu, Hao
    Shu, Bao
    Zhang, Qin
    Xiong, Guohua
    Wang, Li
    REMOTE SENSING, 2025, 17 (03)
  • [27] Saline-Soil Deformation Extraction Based on an Improved Time-Series InSAR Approach
    Xiang, Wei
    Zhang, Rui
    Liu, Guoxiang
    Wang, Xiaowen
    Mao, Wenfei
    Zhang, Bo
    Fu, Yin
    Wu, Tingting
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2021, 10 (03)
  • [28] Deformation monitoring of photovoltaic power stations and substations in Yangquan using time-series InSAR
    Jia, Jianjun
    Yang, Haifei
    Gong, Hao
    Ke, Xianbing
    Guo, Xiao
    Bie, Shiguang
    Wu, Nian
    2020 ASIA CONFERENCE ON GEOLOGICAL RESEARCH AND ENVIRONMENTAL TECHNOLOGY, 2021, 632
  • [29] Prediction of InSAR deformation time-series using improved LSTM deep learning model
    Soni, Rupika
    Alam, Mohammad Soyeb
    Vishwakarma, Gajendra K.
    SCIENTIFIC REPORTS, 2025, 15 (01):
  • [30] Time-Series Prediction Approaches to Forecasting Deformation in Sentinel-1 InSAR Data
    Hill, P.
    Biggs, J.
    Ponce-Lopez, V.
    Bull, D.
    JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH, 2021, 126 (03)