The monitoring and analysis of the coastal lowland subsidence in the southern Hangzhou Bay with an advanced time-series InSAR method

被引:0
|
作者
Peng Han
Xiaoxia Yang
Lin Bai
Qishi Sun
机构
[1] Administrative Center for China’s Agenda 21,College of Earth Science
[2] Chengdu University of Technology,Key Laboratory of Geoscience Spatial Information Technology of Ministry of Land and Resources
[3] Chengdu University of Technology,College of Earth Sciences
[4] University of Chinese Academy of Sciences,undefined
来源
Acta Oceanologica Sinica | 2017年 / 36卷
关键词
coastal areas; land subsidence; DSInSAR; PSInSAR; leveling observation; Hangzhou Bay in China;
D O I
暂无
中图分类号
学科分类号
摘要
Time-series InSAR analysis (e.g., permanent scatterers (PSInSAR)) has been proven as an effective technology in monitoring ground deformation over urban areas. However, it is a big challenge to apply this technology in coastal regions due to the lack of man-made targets. An distributed scatterers interferometric synthetic aperture radar (DSInSAR) is developed to solve the problem of insufficient samples and low reliability in monitoring coastal lowland subsidence, by applying a spatially adaptive filter and an eigendecomposition algorithm to estimating the optimal phase of statistically homogeneous distributed scatterers (DSs). Twenty-four scenes of COSMO-SkyMed images acquired between 2013 and 2015 are used to retrieve the land subsidence over the Shangyu District on south coast of the Hangzhou Bay, Zhejiang Province, China. The spatial pattern of the land subsidence obtained by the PS-InSAR and the DSInSAR coincides with each other, but the density of the DSs is three point five times higher than the permanent scatterers (PSs). Validated by precise levelling data over the same period, the DSInSAR method achieves an accuracy of ±5.0 mm/a which is superior to the PS-InSAR with ±5.5 mm/a. The land subsidence in the Shangyu District is mainly distributed in the urban areas, industrial towns and land reclamation zones, with a maximum subsidence rate–30.2 mm/a. The analysis of geological data, field investigation and historical reclamation data indicates that human activities and natural compaction of reclamation material are major causes of the detected land subsidence. The results demonstrate that the DSInSAR method has a great potential in monitoring the coastal lowland subsidence and can be used to further investigate subsidence-related environmental issues in coastal regions.
引用
收藏
页码:110 / 118
页数:8
相关论文
共 50 条
  • [1] The monitoring and analysis of the coastal lowland subsidence in the southern Hangzhou Bay with an advanced time-series InSAR method
    Han Peng
    Yang Xiaoxia
    Bai Lin
    Sun Qishi
    ACTA OCEANOLOGICA SINICA, 2017, 36 (07) : 110 - 118
  • [2] The monitoring and analysis of the coastal lowland subsidence in the southern Hangzhou Bay with an advanced time-series InSAR method
    HAN Peng
    YANG Xiaoxia
    BAI Lin
    SUN Qishi
    ActaOceanologicaSinica, 2017, 36 (07) : 110 - 118
  • [3] PS InSAR time-series analysis for monitoring ground subsidence
    Liu, B.
    Luo, Y.
    Zhang, J. F.
    Gong, L. X.
    Jiang, W. L.
    ROCK STRESS AND EARTHQUAKES, 2010, : 819 - 822
  • [4] MONITORING THE COASTAL SUBSIDENCE AREAS AND CRITICAL INFRASTRUCTURES ALONG SOUTHEAST KOREA USING SEQUENTIAL TIME-SERIES INSAR ANALYSIS
    Krishnan, Palanisamy Vadivel Suresh
    Kim, Duk-jin
    Lee, Seungwoo
    Song, Juyoung
    Cho, Yang-Ki
    IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2023, : 4131 - 4134
  • [5] Surface Deformation Monitoring and Subsidence Mechanism Analysis in Beijing based on Time-series InSAR
    Wang, Jinghui
    Luo, Ziyan
    Zhou, Lv
    Li, Xinyi
    Wang, Cheng
    Qin, Dongming
    ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 10 (01): : 233 - 239
  • [6] Surface Subsidence Monitoring in Kunming City with Time-Series InSAR and GNSS
    Zhu, Shasha
    Zuo, Xiaoqing
    Shi, Ke
    Li, Yongfa
    Guo, Shipeng
    Li, Chen
    APPLIED SCIENCES-BASEL, 2022, 12 (24):
  • [7] InSAR time-series analysis of land subsidence in Bangkok, Thailand
    Aobpaet, Anuphao
    Cuenca, Miguel Caro
    Hooper, Andrew
    Trisirisatayawong, Itthi
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2013, 34 (08) : 2969 - 2982
  • [8] Monitoring and Cause Analysis of Land Subsidence along the Yangtze River Utilizing Time-Series InSAR
    Chen, Yuanyuan
    Guo, Lin
    Xu, Jia
    Yang, Qiang
    Wang, Hao
    Zhu, Chenwei
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2024, 13 (07)
  • [9] Monitoring Land Subsidence along the Subways in Shanghai on the Basis of Time-Series InSAR
    Zhang, Jinhua
    Ke, Changqing
    Shen, Xiaoyi
    Lin, Jinxin
    Wang, Ru
    REMOTE SENSING, 2023, 15 (04)
  • [10] InSAR Time Series Analysis of Natural and Anthropogenic Coastal Plain Subsidence: The Case of Sibari (Southern Italy)
    Cianflone, Giuseppe
    Tolomei, Cristiano
    Brunori, Carlo Alberto
    Dominici, Rocco
    REMOTE SENSING, 2015, 7 (12): : 16004 - 16023