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
Citation: 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

Integrated Space-Air-Ground Early Detection, Monitoring and Warning System for Potential Catastrophic Geohazards

Funds: 

The National Innovation Research Group Science Fund 41521002

the Major Scientific and Technological Fund of Sichuan Natural Resources Department KJ-2018-21

the Funds of Sichuan Science and Technology Support Plan 2018SZ0339

More Information
  • Author Bio:

    XU Qiang, PhD, professor, specializes in the theories and methods of geological disaster prevention. E-mail: xq@cdut.edu.cn

  • Received Date: March 08, 2019
  • Published Date: July 04, 2019
  • In China, traditional methodology on early detection of natural terrain to landslides is challenging as zones most prone to slope failure are usually inaccessible due to high location and dense vegetation. This can lead to underestimation of potential landslide events to the degree of wrongly identifying unstable areas as stable. This paper provides a solution for these cases by proposing an integrated space-air-ground investigation system that allows for the early detection, real-time prediction, and warning of catastrophic geohazards. Firstly, high-resolution optical images and interferometric synthetic aperture radar (InSAR) data from satellites are employed to obtain a global panorama of a region, highlighting these problematic locations; yet results are detailed enough to provide reliable estimates of deformations at particular points along time spans of days and weeks. As consequence, it makes the compilation of long displacement time-histories feasible, contributing to the understanding of long-term landslide-driving phenomena in regions where it has been underestimated. This is called the general investigation. Then, detailed assessments can be done through the deve-lopment of unmanned aerial vehicles (UAV) for elaborating high-resolution relief maps and photogrammetric representations based on both visual images and light laser detection and ranging (LiDAR) data. The system finally allows for precise tagging of locations that warrant real-time site monitoring of displacements using global navigation satellite system (GNSS) and crack gauges, validating expecting behavior of these critical, but previously hidden hazardous locations. The overall approach makes it possible to establish a four-level comprehensive early warning system, which meets the urgent needs of the country and promotes a practical and operational application of such system in the field of geohazard prevention.
  • [1]
    Fruneau B, Achache J, Delacourt C. Observation and Modelling of the Saint-Étienne-de-Tinée Landslide Using SAR Interferometry[J]. Tectonophysics, 1997, 265(3-4): 181-190
    [2]
    Hilley G E. Dynamics of Slow-Moving Landslides from Permanent Scatterer Analysis[J]. Science, 2004, 304(5 679): 1 952-1 955 doi: 10.1126-science.1098821/
    [3]
    Zhao C, Lu Z, Zhang Q, et al. Large-Area Landslide Detection and Monitoring with ALOS/PALSAR Imagery Data over Northern California and Southern Oregon, USA[J]. Remote Sensing of Environment, 2012, 124: 348-359 doi: 10.1016/j.rse.2012.05.025
    [4]
    Wasowski J, Bovenga F. Investigating Landslides and Unstable Slopes with Satellite Multi-temporal Interferometry: Current Issues and Future Perspectives [J]. Engineering Geology, 2014, 174(8): 103-138 http://cn.bing.com/academic/profile?id=1b81bb577743b04c6ac7cdef3fb043f0&encoded=0&v=paper_preview&mkt=zh-cn
    [5]
    Sun Q, Zhang L, Ding X L, et al. Slope Deformation Prior to Zhouqu, China Landslide from InSAR Time Series Analysis[J]. Remote Sensing of Environment, 2015, 156: 45-57 doi: 10.1016/j.rse.2014.09.029
    [6]
    Dai K, Li Z, Tomás R, et al. Monitoring Activity at the Daguangbao Mega-landslide (China) Using Sentinel-1 TOPS Time Series Interferometry[J]. Remote Sensing of Environment, 2016, 186: 501-513 doi: 10.1016/j.rse.2016.09.009
    [7]
    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 Its Applications [M]. Beijing: Science Press, 2014
    [8]
    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 doi: 10.1109/36.868878
    [9]
    Zhang L, Lu Z, Ding X, et al. Mapping Ground Surface Deformation Using Temporarily Coherent Point SAR Interferometry: Application to Los Angeles Basin[J]. Remote Sensing of Environment, 2012, 117: 429-439 doi: 10.1016/j.rse.2011.10.020
    [10]
    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
    [11]
    Dong J, Liao M, Xu Q, et al. Detection and Displacement Characterization of Landslides Using Multi-temporal Satellite SAR Interferometry: A Case Study of Danba County in the Dadu River Basin[J]. Engineering Geology, 2018, 240: 95-109 doi: 10.1016/j.enggeo.2018.04.015
    [12]
    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 doi: 10.1016/j.rse.2017.11.022
    [13]
    Liu X, Zhao C, Zhang Q, et al. Multi-temporal Loess Landslide Inventory Mapping with C-, X- and L-Band SAR Datasets—A Case Study of Heifangtai Loess Landslides, China[J].Remote Sensing, 2018, 10(11): 1 756 http://cn.bing.com/academic/profile?id=fd4a00f413950e6b3405c7288befae35&encoded=0&v=paper_preview&mkt=zh-cn
    [14]
    Costantini M, Ferretti A, Minati F, et al. Analysis of Surface Deformations over the Whole Italian Territory by Interferometric Processing of ERS, Envisat and COSMO-SkyMed Radar Data[J].Remote Sensing of Environment, 2017, 202: 250-275 doi: 10.1016/j.rse.2017.07.017
    [15]
    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): 17-33 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yslxygcxb201711002

    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): 17-33 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yslxygcxb201711002
    [16]
    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(6): 2 129-2 146 doi: 10.1007/s10346-017-0907-7
    [17]
    许强, 郑光, 李为乐, 等. 2018年10月和11月金沙江白格两次滑坡-堰塞堵江事件分析研究[J].工程地质学报, 2018, 26(6): 1 534-1 551 http://d.old.wanfangdata.com.cn/Periodical/gcdzxb201806017

    Xu Qiang, Zheng Guang, Li Weile, et al. Study on Successive Landslide Damming Events of Jinsha River in Baige Village on October 11 and November 3, 2018[J]. Journal of Engineering Geology, 2018, 26(6): 1 534-1 551 http://d.old.wanfangdata.com.cn/Periodical/gcdzxb201806017
    [18]
    Intrieri E, Raspini F, Fumagalli A, et al. The Maoxian Landslide as Seen from Space: Detecting Precursors of Failure with Sentinel-1 Data[J].Landslides, 2018, 15(1): 123-133 doi: 10.1007/s10346-017-0915-7
    [19]
    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
    [20]
    许强, 汤明高, 黄润秋.大型滑坡监测预警与应急处置[M].北京:科学出版社, 2015

    Xu Qiang, Tang Minggao, Huang Runqiu. Monitoring, Early Warning and Emergency Disposal of Large Landslides[M]. Beijing: Science Press, 2015
  • Related Articles

    [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]LIU Bin, ZHANG Li, GE Daqing, LI Man, ZHOU Xiaolong, GUO Zhaocheng, SHI Pengqing, ZHANG Ling, JIN Dingjian, WAN Xiangxing, WANG Yu, WANG Yan. Application of InSAR Monitoring Large Deformation of Landslides Using Lutan-1 Constellation[J]. Geomatics and Information Science of Wuhan University, 2024, 49(10): 1753-1762. DOI: 10.13203/j.whugis20230478
    [3]YANG Chengsheng, LI Xiaoyang, ZHANG Qin, WEI Yunjie, LI Zufeng, ZHU Sainan. Monitoring and Analysis of Post-Earthquake Landslide in Sindhupalchowk District, Nepal Based on InSAR Technology[J]. Geomatics and Information Science of Wuhan University, 2023, 48(10): 1684-1696. DOI: 10.13203/j.whugis20230258
    [4]YANG Mengshi, LIAO Mingsheng, CHANG Ling, HANSSEN Ramon F.. Interpretation of Multi-epoch InSAR Deformation for Urban Scenes: A Problem Analysis and Literature Review[J]. Geomatics and Information Science of Wuhan University, 2023, 48(10): 1643-1660. DOI: 10.13203/j.whugis20230289
    [5]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
    [6]ZHANG Shuangcheng, SI Jinzhao, XU Yongfu, NIU Yufen, FAN Qianyou, ZHU Wu, AN Peng, GUO Yanhui. Time-Series InSAR for Stability Monitoring of Ankang Airport with Expansive Soil[J]. Geomatics and Information Science of Wuhan University, 2021, 46(10): 1519-1528. DOI: 10.13203/j.whugis20210223
    [7]SHI Xuguo, ZHANG Lu, XU Qiang, ZHAO Kuanyao, DONG Jie, JIANG Houjun, LIAO Mingsheng. Monitoring Slope Displacements of Loess Terrace Using Time Series InSAR Analysis Technique[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 1027-1034. DOI: 10.13203/j.whugis20190056
    [8]ZHAO Chaoying, LIU Xiaojie, ZHANG Qin, PENG Jianbing, XU Qiang. Research on Loess Landslide Identification, Monitoring and Failure Mode with InSAR Technique in Heifangtai, Gansu[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 996-1007. DOI: 10.13203/j.whugis20190072
    [9]XU Xiaolan, LI Fei. Application of InSAR and DInSAR to Zhongba Earthquake[J]. Geomatics and Information Science of Wuhan University, 2008, 33(7): 727-729.
    [10]LI Zhenhong, LIU Jingnan, XU Caijun. Error Analysis in InSAR Data Processing[J]. Geomatics and Information Science of Wuhan University, 2004, 29(1): 72-76.

Catalog

    Article views (6378) PDF downloads (2041) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return