PS-InSAR-based monitoring and analysis of surface subsidence in Shanghai

doi:10.6046/zrzyyg.2021291

Abstract
Figures/Tables
References
Related Articles
Metrics

Download: PDF(3457 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Urban surface subsidence has increasingly severe impacts on human life, making it particularly important to study the methods for effectively monitoring surface subsidence. To monitor the surface subsidence in Shanghai, this study processed 24 scenes of 2019—2020 Sentinel-1A data covering the city using the PS-InSAR technique. After treatment using the permanent scatterer interferometry technique, the residual phase correction was performed using SRTM1 DEM, and the surface subsidence results of the two years were extracted. The analysis of the subsidence rate and cumulative subsidence amplitude in the monitoring results shows that the urban area mainly shows uneven surface subsidence with multiple subsidence funnels, some of which correspond to the historical subsidence data. As shown by time-series surface subsidence data of seldomly selected ground characteristic points, the surface subsidence at these points basically had the same deformation amplitude at different times and highly consistent changing trends, verifying the reliability of the PS-InSAR monitoring method. The results of this study will provide data and decision-making bases for geologic disaster prevention and control in Shanghai.

Keywords surface subsidence InSAR PS-InSAR Shanghai

ZTFLH:

TP79

Issue Date: 21 September 2022

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	Zhihua ZHANG
	Changtao HU
	Zhen ZHANG
	Shuwen YANG

Cite this article:

Zhihua ZHANG,Changtao HU,Zhen ZHANG, et al. PS-InSAR-based monitoring and analysis of surface subsidence in Shanghai[J]. Remote Sensing for Natural Resources, 2022, 34(3): 106-111.

URL:

https://www.gtzyyg.com/EN/10.6046/zrzyyg.2021291 OR https://www.gtzyyg.com/EN/Y2022/V34/I3/106

Fig.1 Geographical location of the study area

Fig.2 PS-InSAR technology flow

Fig.3 Connection diagram

Fig.4 Intensity data

Fig.5 Surface sedimentation rate and feature points in urban areas of Shanghai

Fig.6 Settlement sequence of feature points

Fig.7 Time-series cumulative surface sedimentation

Fig.8 Location of feature point and settling funnel

Tab.1 Summary of geological information of Shanghai area(m)

[1]	张静, 冯东向, 綦巍, 等. 基于SBAS-InSAR技术的盘锦地区地面沉降监测[J]. 工程地质学报, 2018, 26(4):999-1007.
[1]	Zhang J, Feng D X, Qi W, et al. Monitoring land subsidence in Panjin region with SBAS-InSAR method[J]. Journal of Engineering, 2018, 26(4):999-1007.
[2]	汪宝存, 朱琳, 潘登, 等. 郑州市地面沉降时空演变规律研究[J]. 国土资源遥感, 2020, 32(3):143-148.doi: 10.6046/gtzyyg.2020.03.19. doi: 10.6046/gtzyyg.2020.03.19
[2]	Wang B C, Zhu L, Pan D, et al. Research on temporal and spatial evolution law of land subsidence in Zhengzhou[J]. Remote Sensing for Land and Resources, 2020, 32(3):143-148.doi: 10.6046/gtzyyg.2020.03.19. doi: 10.6046/gtzyyg.2020.03.19
[3]	Yang Q, Ke Y H, Zhang D Y, et al. Multi-Scale analysis of the relationship between land subsidence and buildings:A case study in an eastern Beijing urban area using the PS-InSAR technique[J]. Remote Sensing, 2018, 10(7):1006. doi: 10.3390/rs10071006 url: http://www.mdpi.com/2072-4292/10/7/1006
[4]	Lyu M Y, Ke Y H, Guo L, et al. Change in regional land subsidence in Beijing after south-to-north water diversion project observed using satellite Radar interferometry[J]. GIScience and Remote Sensing, 2020, 57(1):140-156. doi: 10.1080/15481603.2019.1676973 url: https://www.tandfonline.com/doi/full/10.1080/15481603.2019.1676973
[5]	袁悦. 基于SBAS-InSAR技术的海口地区地面沉降监测及机理分析[D]. 北京: 中国地质大学(北京), 2020.
[5]	Yan Y. Ground subsidence monitoring and mechanism analysis in Haikou area based on SBAS-InSAR technology[D]. Beijing: China University of Geosciences(Beijing), 2020.
[6]	聂运菊, 刘国祥, 石金峰, 等. 基于PSI技术监测上海市2009年-2010年区域地表沉降[J]. 遥感信息, 2013, 28(2):56-61.
[6]	Nie Y J, Liu G X, Shi J F, et al. Ground subsidence of Shanghai from 2009 to 2010 monitored by PSI technique[J]. Remote Sensing Information, 2013, 28(2):56-61.
[7]	Ferretti A, Prati C, Rocca F. Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(5):2202-2212. doi: 10.1109/36.868878 url: http://ieeexplore.ieee.org/document/868878/
[8]	高二涛, 范冬林, 付波霖. 基于PS-InSAR和SBAS技术监测南京市地面沉降[J]. 大地测量与地球动力学, 2019, 39(2):158-163.
[8]	Gao E T, Fan D L, Fu B L. Land subsidence monitoring of Nanjing area based on PS-InSAR and SBAS technology[J]. Geodesy and Geodynamics, 2019, 39(2):158-163.
[9]	麻源源, 左小清, 麻卫峰. 基于PS-InSAR的天津地区沉降监测及分析[J]. 遥感技术与应用, 2019, 34(6):1324-1331.
[9]	Ma Y Y, Zou X Q, Ma W F. Settlement monitoring and analysis of Tianjin area based on PS-InSAR[J]. Remote Sensing Technology and Application, 2019, 34(6):1324-1331.
[10]	刘欣, 商安荣. PS-InSAR和SBAS-InSAR在城市地表沉降监测中的应用对比[J]. 全球定位系统, 2016, 41(2):101-105.
[10]	Liu X, Shang A R. Application contrast of PS-InSAR and SBAS-InSAR in urban surface subsidence monitoring[J]. GNSS World of China, 2016, 41(2):101-105.
[11]	上海市统计局. 上海统计年鉴2020[M]. 北京: 中国统计出版社, 2020.
[11]	Shanghai Municipal Bureau of Statistics. Shanghai statistical yearbook 2020[M]. Beijing: China Statistics Press, 2020.
[12]	Cigna F, Tapete D. Present-day land subsidence rates,surface faulting hazard and risk in Mexico City with 2014—2020 Sentinel-1 IW InSAR[J]. Remote Sensing of Environment, 2021, 253:1-19.
[13]	张兆旭. 基于PS-InSAR技术的高速铁路沉降监测研究[D]. 北京: 中国地质大学(北京), 2016.
[13]	Zhang Z X. Research on settlement monitoring of high speed railway based on PS-InSAR technology[D]. Beijing: China University of Geosciences(Beijing), 2016.
[14]	Dumka R K, SuriBabu D, Malik K, et al. PS-InSAR derived deformation study in the Kachchh,Western India[J]. Applied Computing and Geosciences, 2020, 8:1-8.
[15]	熊佳诚, 聂运菊, 罗跃, 等. 利用双极化Sentinel-1数据监测城市地面沉降——以上海市为例[J]. 测绘通报, 2019(11):98-102,129.
[15]	Xiong J C, Nie Y J, Luo Y, et al. Monitoring urban land subsidence by dual-polarization Sentinel-1 data:A case study of Shanghai[J]. Bulletin of Surveying and Mapping, 2019(11):98-102,129.
[16]	史玉金. 上海地区地面沉降新特征及对重大市政设施影响研究[D]. 上海: 上海交通大学, 2018.
[16]	Shi Y J. Recent characteristics of land subsidence in Shanghai and its effect on performance of key municipal facilities[D]. Shanghai: Shanghai Jiao Tong University, 2018.
[17]	朱菊艳. 沧州地区地面沉降成因机理及沉降量预测研究[D]. 北京: 中国地质大学(北京), 2014.
[17]	Zhu J Y. Research on land subsidence mechanism and prediction of settlement in Cangzhou area[D]. Beijing: China University of Geosciences(Beijing), 2014.

[1]	JIANG Decai, ZHENG Xiangxiang, WANG Ning, XIAO Chunlei, ZHU Zhenzhou. Application of the time-series InSAR technology in the identification of geological hazards in the Pearl River Delta region[J]. Remote Sensing for Natural Resources, 2023, 35(3): 292-301.
[2]	LIN Jiahui, LIU Guang, FAN Jinghui, ZHAO Hongli, BAI Shibiao, PAN Hongyu. Extracting information about mining subsidence by combining an improved U-Net model and D-InSAR[J]. Remote Sensing for Natural Resources, 2023, 35(3): 145-152.
[3]	GAO Chen, MA Dong, QU Man, QIAN Jianguo, YIN Haiquan, HOU Xiaozhen. Exploring the anomaly mechanism of borehole strain at the Huailai seismic station based on PS-InSAR[J]. Remote Sensing for Natural Resources, 2023, 35(3): 153-159.
[4]	LIU Hui, XU Xinyue, CHEN Mi, CHEN Fulong, DING Ruili, LIU Fei. Time-series InSAR-based dynamic remote sensing monitoring of the Great Wall of the Ming Dynasty in Qinhuangdao[J]. Remote Sensing for Natural Resources, 2023, 35(2): 202-211.
[5]	PAN Jianping, FU Zhanbao, DENG Fujiang, CAI Zhuoyan, ZHAO Ruiqi, CUI Wei. A time-series InSAR-based analysis of surface deformation of hydro-fluctuation belts and the effects of hydrological elements[J]. Remote Sensing for Natural Resources, 2023, 35(2): 212-219.
[6]	HU Xiaoqiang, YANG Shuwen, YAN Heng, XUE Qing, ZHANG Naixin. Time-series InSAR-based monitoring and analysis of surface deformation in the Axi mining area, Xinjiang[J]. Remote Sensing for Natural Resources, 2023, 35(1): 171-179.
[7]	LI Zhu, FAN Hongdong, GAO Yantao, XU Yaozong. DS-InSAR-based monitoring and analysis of a long time series of surface deformation in the fire area of the Wuda coal field[J]. Remote Sensing for Natural Resources, 2022, 34(3): 138-145.
[8]	MA Xuefei, ZHANG Shuangcheng, HUI Wenhua, XU Qiang. InSAR-based large-scale detection and monitoring of the surface deformation in Linfen mining areas, Shanxi Province[J]. Remote Sensing for Natural Resources, 2022, 34(3): 146-153.
[9]	DONG Jihong, MA Zhigang, LIANG Jingtao, LIU Bin, ZHAO Cong, ZENG Shuai, YAN Shengwu, MA Xiaobo. A comparative study of the identification of hidden landslide hazards based on time series InSAR techniques[J]. Remote Sensing for Natural Resources, 2022, 34(3): 73-81.
[10]	LUO Xuewei, XIANG Xiqiong, LYU Yadong. PS correction of InSAR time series deformation monitoring for a certain collapse in Longli County[J]. Remote Sensing for Natural Resources, 2022, 34(3): 82-87.
[11]	YANG Xianhua, WEI Peng, LYU Jun, HAN Lei, SHI Haolin, LIU Zhi. Application of mining collapse recognition technology based on multi-source remote sensing[J]. Remote Sensing for Natural Resources, 2022, 34(2): 162-167.
[12]	XU Zixing, JI Min, ZHANG Guo, CHEN Zhenwei. Method for dynamic prediction of mining subsidence based on the SBAS-InSAR technology and the logistic model[J]. Remote Sensing for Natural Resources, 2022, 34(2): 20-29.
[13]	YANG Wang, HE Yi, ZHANG Lifeng, WANG Wenhui, CHEN Youdong, CHEN Yi. InSAR monitoring of 3D surface deformation in Jinchuan mining area, Gansu Province[J]. Remote Sensing for Natural Resources, 2022, 34(1): 177-188.
[14]	LI Mengmeng, FAN Xueting, CHEN Chao, LI Qiannan, YANG Jin. Monitoring and interpretation of land subsidence in mining areas in Xuzhou City during 2016—2018[J]. Remote Sensing for Natural Resources, 2021, 33(4): 43-54.
[15]	SHI Min, GONG Huili, CHEN Beibei, GAO Mingliang, ZHANG Shunkang. Monitoring of land subsidence in Beijing-Tianjin-Hebei plain during 2016—2018 based on InSAR and Sentinel-1A data[J]. Remote Sensing for Natural Resources, 2021, 33(4): 55-63.

Viewed

Full text

Abstract

Cited

Shared

Discussed