Detection and Monitoring of Potential Landslides Along Minjiang River Valley in Maoxian County, Sichuan Using Radar Remote Sensing

LI Menghua; ZHANG Lu; DONG Jie; CAI Jiehua; LIAO Mingsheng

doi:10.13203/j.whugis20210367

Volume 46 Issue 10

Nov. 2021

Turn off MathJax

Article Contents

Abstract

References

Geomatics and Information Science of Wuhan University > 2021 > 46(10): 1529-1537. > DOI: 10.13203/j.whugis20210367

LI Menghua, ZHANG Lu, DONG Jie, CAI Jiehua, LIAO Mingsheng. Detection and Monitoring of Potential Landslides Along Minjiang River Valley in Maoxian County, Sichuan Using Radar Remote Sensing[J]. Geomatics and Information Science of Wuhan University, 2021, 46(10): 1529-1537. DOI: 10.13203/j.whugis20210367

Citation:

PDF (67641 KB)

Detection and Monitoring of Potential Landslides Along Minjiang River Valley in Maoxian County, Sichuan Using Radar Remote Sensing

1.
Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China
2.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
3.
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China

Funds:

The National Natural Science Foundation of China 41774006

The National Natural Science Foundation of China 41904001

the Provincial Key Research and Development Program of Sichuan Ministry of Science and Technology 2019YFS0074

the Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing 21R02

More Information

Author Bio:
LI Menghua, PhD, specializes in geohazard monitoring using radar remote sensing. E-mail: menghuali@kust.edu.cn
Corresponding author:
ZHANG Lu, PhD, professor. E-mail: luzhang@whu.edu.cn
Received Date: June 30, 2021
Published Date: October 04, 2021

Graphical Abstract

Abstract

Abstract

Objectives Affected by geological and topographic conditions as well as tectonic activities, landslide disasters in Maoxian County, Sichuan Province happen frequently and constitute a significant threat to the safety of human life and civil infrastructure. Therefore, it is necessary to identify and monitor potential landslide-prone areas effectively.
Methods We applied the time-series interferometric synthetic aperture radar (InSAR) analysis technique to process both ascending and descending Sentinel-1 data stacks from November 2014 to June 2017 to identify and monitor potential landslides in the valley along Minjiang River in Maoxian County. We conducted detailed and in-depth analyses of the critical areas identified. Meanwhile, we analyzed and discussed the sensitivity of InSAR line of sight (LOS) deformation measurements retrieved from SAR data stacks acquired in different orbits.
Results More than 20 potential landslide spots from Goukou Township to Shidaguan Township along the valley of Minjiang River in Maoxian County have been detected. The accuracy of deformation detection is validated by field survey.
Conclusions The results show that the time-series InSAR method can effectively identify and monitor landslide hazards in alpine and valley areas. We suggest that the difference in LOS deformation measurement sensitivity on different slopes should be considered when analyzing the deformation patterns.
- radar remote sensing,
- time-series InSAR technology,
- detection of potential landslide hazard,
- deformation sensitivity,
- Sentinel-1

FullText(HTML)

References (20)

References

[1]	许强, 李为乐, 董秀军, 等. 四川茂县叠溪镇新磨村滑坡特征与成因机制初步研究[J]. 岩石力学与工程学报, 2017, 36(11): 2 612-2 628 https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201711002.htm Xu Qiang, Li Weile, Dong Xiujun, et al. The Xinmocun Landslide on June 24, 2017 in Maoxian, Sichuan: Characteristics and Failure Mechanism[J]. Chinese Journal of Rock Mechanics and Engineering, 2017, 36(11): 2 612-2 628 https://www.cnki.com.cn/Article/CJFDTOTAL-YSLX201711002.htm
[2]	Fan X, Qiang X, Scaringi G, et al. Failure Mechanism and Kinematics of the Deadly June 24th 2017 Xinmo Landslide, Maoxian, Sichuan, China [J]. Landslides, 2017, 14(3): 2 129-2 146 http://www.researchgate.net/profile/Gianvito_Scaringi/publication/320318336_Failure_mechanism_and_kinematics_of_the_deadly_June_24th_2017_Xinmo_landslide_Maoxian_Sichuan_China/links/59dd8de0a6fdcc276fa36b4f/Failure-mechanism-and-kinematics-of-the-deadly-June-24th-2017-Xinmo-landslide-Maoxian-Sichuan-China.pdf
[3]	Dong J, Zhang L, Li M, et al. Measuring Precursory Movements of the Recent Xinmo Landslide in Mao County, China with Sentinel-1 and ALOS-2 PALSAR-2 Datasets[J]. Landslides, 2018, 15(1): 135-144 doi: 10.1007/s10346-017-0914-8
[4]	李振洪, 宋闯, 余琛, 等. 卫星雷达遥感在滑坡灾害探测和监测中的应用: 挑战与对策J]. 武汉大学学报·信息科学版, 2019, 44(7): 967-979 doi: 10.13203/j.whugis20190098 Li Zhenhong, Song Chuang, Yu Chen, et al. Application of Satellite Radar Remote Sensing to Landslide Detection and Monitoring: Challenges and Solutions[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 967-979 doi: 10.13203/j.whugis20190098
[5]	黄润秋. 岩石高边坡稳定性工程地质分析[M]. 北京: 科学出版社, 2012 Huang Runqiu. Engineering Geology for High Rock Slopes[M]. Beijing: Science Press, 2012
[6]	郭华东. 雷达对地观测理论与应用[M]. 北京: 科学出版社, 2000 Guo Huadong. Theory and Application of Earth Observation by Radar[M]. Beijing: Science Press, 2000
[7]	廖明生, 张路, 史绪国, 等. 滑坡变形雷达遥感监测方法与实践[M]. 北京: 科学出版社, 2017 Liao Mingsheng, Zhang Lu, Shi Xuguo, et al. Methodology and Practice of Landslide Deformation Monitoring with SAR Remote Sensing[M]. Beijing: Science Press, 2017
[8]	刘国祥, 张波, 张瑞, 等. 联合卫星SAR和地基SAR的海螺沟冰川动态变化及次生滑坡灾害监测[J]. 武汉大学学报·信息科学版, 2019, 44(7): 980-995 doi: 10.13203/j.whugis20190077 Liu Guoxiang, Zhang Bo, Zhang Rui, et al. Monitoring Dynamics of Hailuogou Glacier and the Secondary Landslide Disasters Based on Combination of Satellite SAR and Ground-Based SAR[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 980-995 doi: 10.13203/j.whugis20190077
[9]	许强, 董秀军, 李为乐. 基于天-空-地一体化的重大地质灾害隐患早期识别与监测预警[J]. 武汉大学学报·信息科学版, 2019, 44(7): 957-966 doi: 10.13203/j.whugis20190088 Xu Qiang, Dong Xiujun, Li Weile. Integrated Space-Air-Ground Early Detection, Monitoring and Warning System for Potential Catastrophic Geohazards[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 957-966 doi: 10.13203/j.whugis20190088
[10]	Wasowski J, Bovenga F. Investigating Landslides and Unstable Slopes with Satellite Multi-temporal Interferometry: Current Issues and Future Perspectives[J]. Engineering Geology, 2014, 174: 103-138 doi: 10.1016/j.enggeo.2014.03.003
[11]	廖明生, 董杰, 李梦华, 等. 雷达遥感滑坡隐患识别与形变监测[J]. 遥感学报, 2021, 25(1): 332-341 https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB202101023.htm Liao Mingsheng, Dong Jie, Li Menghua, et al. Radar Remote Sensing for Potential Landslides Detection and Deformation Monitoring[J]. National Remote Sensing Bulletin, 2021, 25(1): 332-341 https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB202101023.htm
[12]	Zhao C, Lu Z, Zhang Q. et al. Large-Area Landslide Detection and Monitoring with ALOS/PAL-SAR Imagery Data over Northern California and Southern Oregon, USA[J]. Remote Sensing Environment, 2012, 124: 348-359 doi: 10.1016/j.rse.2012.05.025
[13]	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): 2 202-2 212 http://www.onacademic.com/detail/journal_1000035902697410_073b.html
[14]	Berardino P, Fornaro G, Lanari R, et al. A New Algorithm for Surface Deformation Monitoring Based on Small Baseline Differential SAR Interferograms [J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11): 2 375-2 383 doi: 10.1109/TGRS.2002.803792
[15]	Ferretti A, Fumagalli A, Novali F, et al. A New Algorithm for Processing Interferometric Data-Stacks: SqueeSAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(9): 3 460-3 470 doi: 10.1109/TGRS.2011.2124465
[16]	廖明生, 王腾. 时间序列InSAR技术于应用[M]. 北京: 科学出版社, 2014 Liao Mingsheng, Wang Teng. Time Series InSAR Technology and Its Applications[M]. Beijing: Science Press, 2014
[17]	Minh D, Hanssen R, Rocca F. Radar Interferometry: 20 Years of Development in Time Series Techniques and Future Perspectives[J]. Remote Sensing, 2020, 12(9): 1 364 doi: 10.3390/rs12091364
[18]	Hooper A. A Multitemporal InSAR Method Incorporating both Persistent Scatterer and Small Baseline Approaches[J]. Geophysical Research Letter, 2008, 35(16): 1-5 http://140.115.21.141/Course/SeminarII/abstract2011_1/reference_abstracts/1027-1_abstract_A%20multi-temporal%20InSAR%20%20and%20small%20baseline%20approaches%201.pdf
[19]	van Leijen F J. Persistent Scatterer Interferometry Based on Geodetic Estimation Theory[D]. Delft: Delft University of Technology, 2014
[20]	Dong J, Zhang L, Tang M, et al. Mapping Landslide Surface Displacements with Time Series SAR Interferometry by Combining Persistent and Distributed Scatterers: A Case Study of Jiaju Landslide in Danba, China[J]. Remote Sensing of Environment, 2018, 205: 180-198 http://www.sciencedirect.com/science/article/pii/S0034425717305710

[1]	LIU Xiaojie, ZHAO Chaoying, LI Bin, WANG Wenda, ZHANG Qin, GAO Yang, CHEN Liquan, WANG Baohang, HAO Junming, YANG Xiaohui. Identification and Dynamic Deformation Monitoring of Active Landslides in Jishishan Earthquake Area （Gansu, China） Using InSAR Technology[J]. Geomatics and Information Science of Wuhan University, 2025, 50(2): 297-312. DOI: 10.13203/j.whugis20240054
[2]	MA Shenglong, ZHOU Yu, SHEN Xuzhang. Analysis of Le Teil Earthquake in France and Its Correlation with Le Teil Quarry Extraction Using Sentinel-1 and Topographic Data[J]. Geomatics and Information Science of Wuhan University, 2024, 49(7): 1190-1200. DOI: 10.13203/j.whugis20210248
[3]	ZHAO Feng, ZHANG Leixin, WANG Teng, WANG Yunjia, YAN Shiyong, FAN Hongdong. Polarimetric Persistent Scatterer Interferometry for Urban Ground Deformation Monitoring with Sentinel-1 Dual Polarimetric Data[J]. Geomatics and Information Science of Wuhan University, 2022, 47(9): 1507-1514. DOI: 10.13203/j.whugis20210496
[4]	DAI Keren, ZHANG Lele, SONG Chuang, LI Zhenhong, ZHUO Guanchen, XU Qiang. Quantitative Analysis of Sentinel-1 Imagery Geometric Distortion and Suitability Along Qinghai-Tibet Plateau Traffic Corridor[J]. Geomatics and Information Science of Wuhan University, 2021, 46(10): 1450-1460. DOI: 10.13203/j.whugis20210130
[5]	WU Wenhao, LI Tao, LONG Sichun, ZHOU Zhiwei. Coregistration of Sentinel-1 TOPS Data for Interferometric Processing Using Real-Time Orbit[J]. Geomatics and Information Science of Wuhan University, 2019, 44(5): 745-750. DOI: 10.13203/j.whugis20170098
[6]	ZHANG Xiaobo, ZHAO Xuesheng, GE Daqing, LIU Bin. Motion Characteristics of the South Inilchek Glacier Derived from New C-Band SAR Satellite[J]. Geomatics and Information Science of Wuhan University, 2019, 44(3): 429-435. DOI: 10.13203/j.whugis20160538
[7]	LI Fei, WANG Zhenling, ZHANG Yu, ZHANG Shengkai, ZHU Tingting. Amery Ice Shelf Frontal Position Automatic Detection from Sentinel-1 SAR Imagery[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 2012-2022. DOI: 10.13203/j.whugis20180171
[8]	SHEN Zhaoqing, SHU Ning, TAO Jianbin. An Algorithm of Weighted “1 V m” SVM Multi-classification for Hyperspectral Remote Sensing Image with NPA[J]. Geomatics and Information Science of Wuhan University, 2009, 34(12): 1444-1447.
[9]	WANG Kongzheng. Time-Varying Parameter PGM (1, 1) Deformation Prediction Model and Its Applications[J]. Geomatics and Information Science of Wuhan University, 2005, 30(5): 456-459.
[10]	Zhang Zuxun, Gu Tianxiang, Hu Zhigui. The Use of SODAMS for Generating DEM to Drive the Wild Avioplan OR-1 for Orthophoto Production[J]. Geomatics and Information Science of Wuhan University, 1986, 11(3): 20-29.

Cited By

Cited by

Periodical cited type(13)

1.	戴佩玉，李世忠，季顺平，任妮. 一种基于域自适应泛化增强的云检测方法. 武汉大学学报(信息科学版). 2025(01): 110-119 .
2.	徐梓川，龚晓峰. 基于语义指导和自适应卷积的遥感云检测算法. 电子测量技术. 2024(01): 136-143 .
3.	李长萍，于大海，郭豪，李杰，胡滨. 复杂场景下多重损失的步态识别系统的实现. 软件. 2023(05): 11-18 .
4.	刘广进，王光辉，毕卫华，刘慧杰，杨化超. 基于DenseNet与注意力机制的遥感影像云检测算法. 自然资源遥感. 2022(02): 88-96 .
5.	黄学飞，梁昌远，郭杰. 融合注意力机制的LandSat8遥感影像云检测算法. 电脑知识与技术. 2022(23): 63-65 .
6.	黄宇，潘励. 基于显著性图像与纹理特征的遥感影像云检测. 测绘地理信息. 2021(02): 16-19 .
7.	王亚利，都伟冰，王双亭. 高斯混合模型自动阈值法遥感冰川信息提取. 遥感学报. 2021(07): 1434-1444 .
8.	王明，刘正佳，陈元琰. 基于Sentinel-2波段/产品的图像云检测效果对比研究. 遥感技术与应用. 2020(05): 1167-1177 .
9.	刘云峰，杨珍，韩骁，付俊. 国产高分辨率卫星影像云检测方法分析. 测绘通报. 2020(11): 66-70 .
10.	徐冬宇，厉小润，赵辽英，舒锐，唐琪佳. 基于光谱分析和动态分形维数的高光谱遥感图像云检测. 激光与光电子学进展. 2019(10): 93-101 .
11.	余长慧，于海威，张文，孟令奎. 神经网络支持下的Sentinel-2卫星影像自动云检测. 测绘通报. 2019(08): 39-43 .
12.	董志鹏，王密，李德仁，王艳丽，张致齐. 利用对象光谱与纹理实现高分辨率遥感影像云检测方法. 测绘学报. 2018(07): 996-1006 .
13.	张波，刘鹤飞，王坤. 异分布混合模型及其参数的贝叶斯估计. 曲靖师范学院学报. 2018(06): 1-4 .

Other cited types(14)

Get Citation

PDF

XML

Article views (1136) PDF downloads (220) Cited by(27)

Detection and Monitoring of Potential Landslides Along Minjiang River Valley in Maoxian County, Sichuan Using Radar Remote Sensing

Abstract

References

Related Articles

Cited by

Periodical cited type(13)

Other cited types(14)

Catalog

Related

Detection and Monitoring of Potential Landslides Along Minjiang River Valley in Maoxian County, Sichuan Using Radar Remote Sensing

Abstract

References

Related Articles

Cited by

Periodical cited type(13)

Other cited types(14)

Catalog

Related

Export File

Citation

Format

Content