Land Subsidence Monitoring by Joint Estimation of Multi-platform Time Series InSAR Observations

YANG Mengshi; LIAO Mingsheng; SHI Xuguo; ZHANG Lu

doi:10.13203/j.whugis20140924

Volume 42 Issue 6

Jun. 2017

Turn off MathJax

Article Contents

Abstract

References

Geomatics and Information Science of Wuhan University > 2017 > 42(6): 797-802. > DOI: 10.13203/j.whugis20140924

YANG Mengshi, LIAO Mingsheng, SHI Xuguo, ZHANG Lu. Land Subsidence Monitoring by Joint Estimation of Multi-platform Time Series InSAR Observations[J]. Geomatics and Information Science of Wuhan University, 2017, 42(6): 797-802. DOI: 10.13203/j.whugis20140924

Citation:

PDF (4186 KB)

Land Subsidence Monitoring by Joint Estimation of Multi-platform Time Series InSAR Observations

1.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2.
Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China

Funds:

The National Natural Science Foundation of China 61331016

the Key Projects of Natural Science Foundation of Hubei Province 2014CFA047

the National Program on Key Basic Research Project of China 2013CB733205

More Information

Author Bio:
YANG Mengshi, PhD, specializes in SAR data processing and analysis. E-mail:yangms@whu.edu.cn
Received Date: July 05, 2015
Published Date: June 04, 2017

Graphical Abstract

Abstract

Abstract

A method for estimating the land subsidence velocity field was proposed by combining m ulti-platform InSARdata set. The precise estimation of subsidence could be achieved by combing redun-dant observations.However, the key problems to be solved in integration of different SAR datasetsincluded different imaging geo metries and inconsistent spatial/tem poral resolutions.We discussed allthese problems in detail and given one solution. Our method then was applied to detect unbiased long-term velocity field in Shanghai, China with 18 Terra SAR-X、16 ENVISAT ASARand 20 ALOS PAL-SAR datasets. Firstly, large-scale velocity maps from three SAR data stacks were extracted whichshow similar deformation patterns and different nu merical ranges. Then, weighted least squares ad-justment was used to derive unbiased velocity field. The experimental results were validated with lev-eling data. The experiment results show that combing multi-platform InSA R data achieved precise es-timation of deformation without any prioriinformation.
- multi-platform,
- joint analysis,
- time series InSAR analysis,
- least square

FullText(HTML)

References (10)

References

[1]	廖明生, 王腾.时间序列InSAR技术与应用[M].北京:科学出版社, 2014 Liao Mingsheng, Wang Teng. Time Series InSAR Technology and Application[M]. Beijing: Science Press, 2014
[2]	李德仁, 廖明生, 王艳.永久散射体雷达干涉测量技术[J].武汉大学学报·信息科学版. 2004, 29(08): 664-668 http://ch.whu.edu.cn/CN/abstract/abstract4443.shtml Li Deren, Liao Mingsheng, Wang Yan. Progress of Permanent Scatterer Interferometry[J]. Geomatics and Information Science of Wuhan University, 2004, 29(08): 664-668 http://ch.whu.edu.cn/CN/abstract/abstract4443.shtml
[3]	Hooper A, Segall P, Zebker H. Persistent Scatterer Interferometric Synthetic Aperture Radar for Crustal Deformation Analysis, with Application to Volcán Alcedo, Galápagos[J]. Journal of Geophysical Research, 2007, DOI: 10.1029/2006JB004763
[4]	Ferretti A, Prati C, Rocca F. Permanent Scatterers in SAR Interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing. 2001, 39(1): 8-20 doi: 10.1109/36.898661
[5]	Amelung F, Galloway D L, Bell J W, et al. Sensing the Ups and Downs of Las Vegas: InSAR Reveals Structural Control of Land Subsidence and Aquifer-System Deformation[J]. Geology, 1999, 27(6): 483-486 doi: 10.1130/0091-7613(1999)027<0483:STUADO>2.3.CO;2
[6]	Gernhardt S, Bamler R. Deformation Monitoring of Single Buildings Using Meter-Resolution SAR Data in PSI[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 73(SI): 68-79 http://adsabs.harvard.edu/abs/2012JPRS...73...68G
[7]	胡俊, 李志伟, 朱建军, 等.融合升降轨SAR干涉相位和幅度信息揭示地表三维形变场的研究[J].中国科学:地球科学, 2010, 40(3): 307-318 http://www.cnki.com.cn/Article/CJFDTOTAL-JDXK201003005.htm Hu Jun, Li Zhiwei, Zhu Jianjun, et al. Inferring Three-dimensional Surface Displacement Field by Combining SAR Interferometric Phase and Amplitude Information of Ascending and Descending Orbits[J]. Science China, Earth Sciences, 2010, 40(3): 307-318 http://www.cnki.com.cn/Article/CJFDTOTAL-JDXK201003005.htm
[8]	裴媛媛, 廖明生, 王寒梅.时间序列SAR影像监测堤坝形变研究[J].武汉大学学报·信息科学版, 2013, 38(3): 266-269 http://ch.whu.edu.cn/CN/abstract/abstract51.shtml Pei Yuanyuan, Liao Mingsheng, Wang Hanmei. Monitoring Levee Deformation with Repeat-Track Space-Borne SAR Images[J]. Geomatics and Information Science of Wuhan University. 2013, 38(3): 266-269 http://ch.whu.edu.cn/CN/abstract/abstract51.shtml
[9]	崔希璋, 於宗俦, 陶本藻, 等.广义测量平差[M].武汉:武汉大学出版社, 2009 Cui Xizhang, Yu Zongchou, Tao Benzao, et al. Generalized Surveying Adjustment[M]. Wuhan: Wuhan University Press, 2009
[10]	Perissin D, Rocca F. High-Accuracy Urban DEM Using Permanent Scatterers[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(11): 3 338-3 347 doi: 10.1109/TGRS.2006.877754

Cited By

Cited by

Periodical cited type(13)

1.	陈月，王磊，池深深，王羽，戚鑫鑫，朱尚军. 基于SBAS-InSAR和CNN-GRU模型的采动村庄地表沉降监测预计. 金属矿山. 2025(02): 138-144 .
2.	何毅，姚圣，陈毅，闫浩文，张立峰. ConvLSTM神经网络的时序InSAR地面沉降时空预测. 武汉大学学报(信息科学版). 2025(03): 483-496 .
3.	倪尔瑞，张建新，邱明剑，权力奥，朱晓峻. 基于SBAS-InSAR技术的淮北市地表沉降监测分析. 北京测绘. 2024(03): 312-317 .
4.	吴启琛，于瑞鹏，王丽，赵乙泽，范开放. 利用Sentinel-1的山东枣庄高新区地面沉降监测与分析. 地理空间信息. 2024(06): 80-83 .
5.	杨芳，丁仁军，李勇发. 基于SBAS-InSAR技术的金沙江流域典型滑坡时空演化特征分析. 测绘通报. 2024(11): 102-107 .
6.	祝杰，李瑜，师宏波，刘洋洋，韩宇飞，邵银星，王坦. 鹤岗煤矿区地面沉降时空特征InSAR时间序列监测研究. 中国地震. 2023(03): 596-608 .
7.	柴龙飞，魏路，张震. 基于SBAS-InSAR的安徽省宿州市埇桥区2019—2022年地面沉降监测及影响因素分析研究. 安徽地质. 2023(04): 348-352 .
8.	祝杰，韩宇飞，王坦，李瑜，王阅兵，师宏波，刘洋洋，樊俊屹，邵银星. 2017年九寨沟M_S7.0地震同震地表三维形变场解算研究. 中国地震. 2022(02): 348-359 .
9.	吴毅彬，葛红斌，刘光庆，刘海旺. 基于MT-InSAR技术的厦门新机场填海区沉降监测. 工程勘察. 2021(02): 57-61 .
10.	翟振起. 基于InSAR沉降监测技术的城市供水管线安全监测系统开发. 水利科学与寒区工程. 2021(01): 103-106 .
11.	廖明生，王茹，杨梦诗，王楠，秦晓琼，杨天亮. 城市目标动态监测中的时序InSAR分析方法及应用. 雷达学报. 2020(03): 409-424 .
12.	熊寻安，王明洲，龚春龙. MT-InSAR技术监测水库土石坝表面变形研究. 测绘地理信息. 2019(05): 78-81 .
13.	王茹，杨天亮，杨梦诗，廖明生，林金鑫，张路. PS-InSAR技术对上海高架路的沉降监测与归因分析. 武汉大学学报(信息科学版). 2018(12): 2050-2057 .

Other cited types(4)

Get Citation

PDF

XML

Article views (1538) PDF downloads (392) Cited by(17)

Land Subsidence Monitoring by Joint Estimation of Multi-platform Time Series InSAR Observations

Abstract

References

Cited by

Periodical cited type(13)

Other cited types(4)

Catalog

Related

Land Subsidence Monitoring by Joint Estimation of Multi-platform Time Series InSAR Observations

Abstract

References

Cited by

Periodical cited type(13)

Other cited types(4)

Catalog

Related

Export File

Citation

Format

Content