Please wait a minute...
 
Remote Sensing for Natural Resources    2021, Vol. 33 Issue (4) : 26-33     DOI: 10.6046/zrzyyg.2020379
|
Land subsidence monitoring based on differential interferometry using time series of high-resolution TerraSAR-X images and monitoring precision verification
YU Bing1,2,3,4(), TAN Qingxue1, LIU Guoxiang5, LIU Fuzhen1, ZHOU Zhiwei4, HE Zhiyong1
1. School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China
2. Institute of Petroleum and Natural Gas Spatial Information Engineering, Southwest Petroleum University, Chengdu 610500, China
3. State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500, China
4. State Key Laboratory of Geodesy and Earth's Dynamics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China
5. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
Download: PDF(4974 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

Urban land subsidence is a kind of slowly developing geological disaster and has sustained negative impacts on the social economy and human life. Therefore, it is of great significance to carry out effective and wide-area urban subsidence monitoring. With 34 high-resolution TerraSAR-X SAR images obtained from April 07, 2009 to December 14, 2010 as data sources, the land subsidence in Tianjin City was monitored using the differential interferometry of time series based on interferometric point target analysis (IPTA) in this study. Then the monitoring precision was verified using the precise leveling data, and a verification method of subsidence time series based on least-squares fitting was adopted. Finally, subsidence analysis and interpretation were carried out based on the verification results. Compared to the leveling data, the root mean square errors of the subsidence rates obtained using IPTA and that using the least squares-fitting of time series were 3.15 mm/a and -3.25 mm/a, respectively. According to the analysis of subsidence results, the overall subsidence of the study area is significantly uneven, the maximum subsidence rate is -128.41 mm/a, and the spatial-temporal distribution of the land subsidence correlates highly with surface cover types and groundwater exploitation.
Keywords TerraSAR-X      time series differential interferometry      land subsidence monitoring and analysis      precision verification     
ZTFLH:  P2  
Issue Date: 23 December 2021
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Bing YU
Qingxue TAN
Guoxiang LIU
Fuzhen LIU
Zhiwei ZHOU
Zhiyong HE
Cite this article:   
Bing YU,Qingxue TAN,Guoxiang LIU, et al. Land subsidence monitoring based on differential interferometry using time series of high-resolution TerraSAR-X images and monitoring precision verification[J]. Remote Sensing for Natural Resources, 2021, 33(4): 26-33.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2020379     OR     https://www.gtzyyg.com/EN/Y2021/V33/I4/26
Fig.1  Study area
序
号		 影像获取
日期		 垂直基
线/m		 时间基
线/d		 序
号		 影像获取
日期		 垂直基
线/m		 时间基
线/d
1 20090407 67.95 -220 18 20091124 46.37 11
2 20090418 -23.40 -209 19 20091205 126.49 22
3 20090429 13.05 -198 20 20091227 133.29 44
4 20090510 30.73 -187 21 20100129 -7.20 77
5 20090521 64.55 -176 22 20100220 -154.94 99
6 20090623 -75.82 -143 23 20100303 -151.00 110
7 20090704 -17.13 -132 24 20100314 -104.42 121
8 20090715 -33.04 -121 25 20100325 9.39 132
9 20090726 -112.48 -110 26 20100405 -93.11 143
10 20090806 137.93 -99 27 20100416 -126.12 154
11 20090828 -101.72 -77 28 20100427 -36.25 165
12 20090908 36.81 -66 29 20100621 18.66 220
13 20090919 -64.36 -55 30 20100702 -78.20 231
14 20091011 -38.69 -33 31 20100804 81.35 264
15 20091022 -65.56 -22 32 20100906 1.45 297
16 20091102 119.21 -11 33 20101111 -22.89 363
17 20091113 主影像 34 20101214 -92.45 396
Tab.1  Interferometric pairs and spatiotemporal baselines
Fig.2  Flow chart of IPTA data processing
Fig.3  Distribution of coherent point subsidence rate and leveling points
Fig.4  Comparison of subsidence between leveling points and cohevent points
Fig.5  Result of fitted subsidence rate verification
Fig.6  Result of subsidence rate
Fig.7  Subsidence profile of AB and CD
Fig.8  Subsidence history
Fig.9  Subsidence rate and land use category analysis of the two selected areas
Fig.10  Subsidence rate of three highways
[1] Solari L, Ciampalini A, Raspini F, et al. Combined use of C-and X-band SAR data for subsidence monitoring in an urban area[J]. Geosciences, 2017,7(21):1-17.
doi: 10.3390/geosciences7010001 url: http://www.mdpi.com/2076-3263/7/1/1
[2] 于冰. 高分辨率相干散射体雷达干涉建模及形变信息提取方法[D]. 成都:西南交通大学, 2015.
[2] Yu B. High resolution coherent scatterer Radar interferometric modeling and deformation information extraction method[D]. Chengdu:Southwest Jiaotong University, 2015.
[3] 段晓峰, 许学工, 王若柏. 天津沿海地区地面沉降及其影响因素[J]. 北京大学学报(自然科学版), 2014,50(6):1071-1076.
[3] Duan X F, Xu X G, Wang R B. Land subsidence and its influencing factors in Tianjin coastal area[J]. Journal of Peking University(Natural Science Edition), 2014,50(6):1071-1076.
[4] 张洪涛. 城市地质工作——国家经济建设和社会发展的重要支撑(代序)[J]. 地质通报, 2003(8):549-550.
[4] Zhang H T. Urban geological work:An important support for national economic construction and social development[J]. Geological Bulletin, 2003(8):549-550.
[5] 曹群, 陈蓓蓓, 宫辉力, 等. 基于SBAS和IPTA技术的京津冀地区地面沉降监测[J]. 南京大学学报(自然科学), 2019,55(3):381-391.
[5] Cao Q, Chen B B, Gong H L, et al. Land subsidence monitoring in Beijing Tianjin Hebei region based on SBAS and IPTA technology[J]. Journal of Nanjing University(Natural Science), 2019,55(3):381-391.
[6] 董克刚, 周俊, 于强, 等. 天津市地面沉降的特征及其危害[J]. 地质灾害与环境保护, 2007(1):67-70.
[6] Dong K G, Zhou J, Yu Q, et al. Characteristics and hazards of land subsidence in Tianjin[J]. Geological Hazards and Environmental Protection, 2007(1):67-70.
[7] 于海若, 宫辉力, 陈蓓蓓, 等. 京津冀地区地面沉降研究进展与思考[J]. 测绘科学, 2020,45(4):125-133,141.
[7] Yu H R, Gong H L, Chen B B, et al. Land subsidence research progress and thinking in Beijing Tianji Hebei region[J]. Surveying and Mapping Science, 2020,45(4):125-133,141.
[8] 袁悦, 贾丽云, 张绪教, 等. 基于SBAS-InSAR技术的海口地区地面沉降监测及机理分析[J]. 地理与地理信息科学, 2020,36(5):56-64.
[8] Yuan Y, Jia L Y, Zhang X J, et al. Land subsidence monitoring and mechanism analysis in Haikou area based on SBAS-InSAR technology[J]. Geography and Geographic Information Science, 2020,36(5):56-64.
[9] 廖明生, 卢丽君, 王艳, 等. 基于点目标分析的InSAR技术检测地表微小形变的研究[J]. 城市地质, 2006(2):38-41.
[9] Liao M S, Lu L J, Wang Y, et al. Study on InSAR technology for detecting surface micro deformation based on point target analysis[J]. Urban Geology, 2006(2):38-41.
[10] 丁荣荣, 徐佳, 林晓彬, 等. 基于高分辨率TerraSAR-X影像的PSInSAR地表形变监测[J]. 国土资源遥感, 2015,27(4):158-164.doi: 10.6046/gtzyyg.2015.04.24.
doi: 10.6046/gtzyyg.2015.04.24
[10] Ding R R, Xu J, Lin X B, et al. Surface deformation monitoring by PSInSAR based on high resolution TerraSAR-X image[J]. Remote Sensing of Land and Resources, 2015,27(4):158-164.doi: 10.6046/gtzyyg.2015.04.24.
doi: 10.6046/gtzyyg.2015.04.24
[11] Caló F, Notti D, Galve J P, et al. DInSAR-based detection of land subsidence and correlation with groundwater depletion in Konya Plain,Turkey[J]. Remote Sensing, 2017,9(1):1-25.
[12] Alam M S, Kumar D, Chatterjee R S, et al. Assessment of land surface subsidence due to underground metal mining using integrated spaceborne repeat-pass differential interferometric synthetic aperture Radar (DInSAR) technique and ground based observations[J]. Journal of the Indian Society of Remote Sensing, 2018,46(10):1569-1580.
doi: 10.1007/s12524-018-0810-2 url: https://doi.org/10.1007/s12524-018-0810-2
[13] Vidal-Páez P, Derauw D, Fernandez-Sarría A, et al. Monitoring of mining subsidence in a sector of central chile through the processing of a time series of Sentinel 1 images using differential interferometry techniques (DInSAR)[J]. Proceedings, 2019,19(1):1-4.
doi: 10.3390/proceedings2019019001 url: https://www.mdpi.com/2504-3900/19/1/1
[14] Pawluszek-Filipiak K, Borkowski A. Integration of DInSAR and SBAS techniques to determine mining-related deformations using Sentinel-1 data:The case study of Rydułtowy mine in Poland[J]. Remote Sensing, 2020,12(2):1-23.
doi: 10.3390/rs12010001 url: https://www.mdpi.com/2072-4292/12/1/1
[15] 常素娟. 天津市公共租赁房住区生态环境设计研究[D]. 天津:河北工业大学, 2015.
[15] Chang S J. Study on ecological environment design of public rental housing in Tianjin[D]. Tianjin:Hebei University of Technology, 2015.
[16] Yi L X, Zhang F, Xu H, et al. Land subsidence in Tianjin,China[J]. Environmental Earth Sciences, 2011,62(6):1151-1161.
doi: 10.1007/s12665-010-0604-5 url: http://link.springer.com/10.1007/s12665-010-0604-5
[17] 刘晓杰, 赵超英, 康亚, 等. 茂县滑坡形变的Sentinel-1数据分析[J]. 测绘科学, 2019,44(4):55-59,71.
[17] Liu X J, Zhao C Y, Kang Y, et al. Sentinel-1 data analysis of landslide deformation in Maoxian County[J]. Surveying and Mapping Science, 2019,44(4):55-59,71.
[1] LI Xingyou, ZHANG Fei, WANG Zheng. Present situation and development trend in building remote sensing monitoring models of soil salinization[J]. Remote Sensing for Natural Resources, 2022, 34(4): 11-21.
[2] Qun WANG, Jinghui FAN, Wei ZHOU, Weilin YUAN, Liqiang TONG, Zhaocheng GUO. Research on the DEM-assisted offset tracking technique applied to glaciers movement monitoring[J]. Remote Sensing for Land & Resources, 2018, 30(3): 167-173.
[3] DING Rongrong, XU Jia, LIN Xiaobin, XU Kang. Monitoring of surface subsidence using PSInSAR with TerraSAR-X high resolution data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(4): 158-164.
[4] ZHAO Xinggang, LIU Lin, QIAN Jing. Classification of Arctic sea ice with TerraSAR-X polarimetric data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(3): 130-134.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech