Volume 9 Issue 3
Jun.  2020
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ZHANG Dalin, YI Wei, and KONG Lingjiang. Optimal joint allocation of multijammer resources for jamming netted radar system[J]. Journal of Radars, 2021, 10(4): 595–606. doi: 10.12000/JR21071
Citation: DAI Keren, TIE Yongbo, XU Qiang, et al. Early identification of potential landslide geohazards in alpine-canyon terrain based on SAR interferometry—a case study of the middle section of yalong river[J]. Journal of Radars, 2020, 9(3): 554–568. doi: 10.12000/JR20012
shu

Early Identification of Potential Landslide Geohazards in Alpine-canyon Terrain Based on SAR Interferometry—a Case Study of the Middle Section of Yalong River (in English)

DOI: 10.12000/JR20012
  • 1.

    State Key Laboratory of Geohazard Prevention and Geoenviroment Protection, Chengdu University of Technology, Chengdu 610059, China

  • 2.

    College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China

  • 3.

    China Geological Survey Chengdu geological survey center, Chengdu 610081, China

Funds:  The Public Geological Survey Project of China Geological Survey (DD20190640), The National Natural Science Foundation of China (41801391), The Provincial Key R&D Program of the Sichuan Ministry of Science and Technology (2019YFS0074), Sichuan Science and Technology Plan Project (2019YJ0404), State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project (SKLGP2018Z019)
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  • Author Bio:

    DAI Keren was born in Sichuan, China in 1989. He is currently a professor and doctoral supervisor in Chengdu University of Technology and permanent researchers of the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection. His main research interests include the early identification of landslide hazards in alpine-valley area by synthetic aperture radar interferometry and early warning, remote sensing landslide disaster assessment and cataloging, etc. In recent five years, he has published more than 10 papers in international remote sensing journals such as IEEE GRSM, RSE, INT J APPL EARTH OBS, etc. E-mail: daikeren17@cdut.edu.cn

    TIE Yongbo was born in Yunnan, China in 1979. He is currently a professor of engineer of Chengdu Geological Survey Center of China Geological Survey, doctoral supervisor, and candidate of the 12th academic leader in Sichuan province. His research direction is the formation mechanism and risk assessment of geological disasters. E-mail: tyb038@qq.com

    XU Qiang was born in Sichuan, China in 1968. He is currently a professor and doctoral supervisor, vice President of Chengdu University of Technology, Executive Vice Director of State Key Laboratory of Geohazard Prevention and Geoenvironment Protecion, winner of National Outstanding Youth Fund, Distinguished Professor of Cheungkong Scholars program of the Ministry of Education, National Outstanding professional Technology Talent, national May 1st labor Medal winner, the State Council special allowance experts. Specializing in the mechanism of geological hazards, early identification, monitoring and early warning and emergency response, the scientific research achievements completed as a core member won two national science and technology progress award, provincial and ministerial science and technology progress award, six prizes.E-mail: xq@cdut.edu.cn

    FENG Ye was born in Sichuan, China in 1992. He is now postgraduate student in Chengdu University of Technology. His main research fields are InSAR data processing and application. E-mail: 2018050049@stu.cdut.edu.cn

    ZHUO Guanchen was born in Fujian, China in 1995. He is now postgraduate student in Chengdu University of Technology. He is mainly engaged in surface deformation monitoring research based on synthetic aperture radar interferometry. E-mail: zhuoguanchenRS@foxmail.com

    SHI Xianlin was born in Sichuan, China in 1980. She is now Ph.D., associate professor, tutor for postgraduate students. Member of the Education Committee of the Society of Surveying, Mapping and Geographic Information of Sichuan province, won the second prize of Higher Education Teaching Achievement of Sichuan Province and the third prize of The Progress Award of Surveying, Mapping and Technology of Sichuan Province. Her research interests include remote sensing geology and space information intelligence services. E-mail: shixianlin06@cedut.edu.cn

  • Corresponding author: TIE Yongbo, tyb2009@qq.com; XU Qiang, xq@cdut.edu.cn
  • Received Date: 2020-03-01
  • Rev Recd Date: 2020-06-01
    • Available Online: 2020-06-19
  • Publish Date: 2020-06-01
    Abstract
  • Landslide disasters occur frequently in the western mountainous regions of China and are characterized by high concealment, suddenness, and strong destructiveness. Early identification of potential disaster hazards is the most effective prevention and mitigation measure. The western mountainous areas mostly have a wide range of alpine-canyon terrain, which is hard or even impossible to reach. Moreover, traditional early identification methods, such as manual inspection, are difficult to implement in these areas. As an emerging radar remote-sensing method, Interferometric Synthetic Aperture Radar (InSAR) can efficiently and accurately identify the hidden dangers of landslides. Based on the synthetic aperture radar data of the European Space Agency’s Sentinel-1, this study used time series InSAR technology to identify the potential landslide hazards in the alpine-canyon terrain along the Yajiang-Muli County of the Yalong River; eight potential geohazards were detected. On the basis of the historical data of landslide hazards and the interpretation of optical remote sensing data, the results of early identification were verified and analyzed, and the danger level of the disaster points was evaluated. The influence of geometric distortion in InSAR technology on the early identification of potential landslides in alpine-canyon terrain was also discussed. This case study can provide powerful data and technical support for local disaster prevention and mitigation and provide ideas and references for the early identification of the hidden dangers of landslides in mountain-valley areas.

     

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    Created with Highcharts 5.0.7Chart context menuAccess Area Distribution其他: 17.3 %其他: 17.3 %其他: 0.9 %其他: 0.9 %China: 0.3 %China: 0.3 %Hanoi: 0.0 %Hanoi: 0.0 %India: 0.0 %India: 0.0 %Kao-sung: 0.0 %Kao-sung: 0.0 %Korea Republic of: 0.3 %Korea Republic of: 0.3 %Viet Nam: 0.1 %Viet Nam: 0.1 %[]: 0.8 %[]: 0.8 %上海: 0.7 %上海: 0.7 %上饶: 0.1 %上饶: 0.1 %东京: 0.1 %东京: 0.1 %东莞: 0.0 %东莞: 0.0 %丹东: 0.0 %丹东: 0.0 %丽水: 0.0 %丽水: 0.0 %京畿道: 0.5 %京畿道: 0.5 %佛山: 0.1 %佛山: 0.1 %保定: 0.0 %保定: 0.0 %信阳: 0.1 %信阳: 0.1 %元朗新墟: 0.0 %元朗新墟: 0.0 %克孜勒苏: 0.0 %克孜勒苏: 0.0 %包头: 0.0 %包头: 0.0 %北京: 12.2 %北京: 12.2 %北海: 0.1 %北海: 0.1 %华盛顿州: 0.0 %华盛顿州: 0.0 %南京: 1.7 %南京: 1.7 %南宁: 0.1 %南宁: 0.1 %南平: 0.0 %南平: 0.0 %南昌: 0.1 %南昌: 0.1 %南通: 0.1 %南通: 0.1 %厦门: 0.0 %厦门: 0.0 %台北: 0.1 %台北: 0.1 %台州: 0.0 %台州: 0.0 %合肥: 0.1 %合肥: 0.1 %呼和浩特: 0.1 %呼和浩特: 0.1 %哈尔滨: 0.0 %哈尔滨: 0.0 %商丘: 0.1 %商丘: 0.1 %圣彼得堡: 0.2 %圣彼得堡: 0.2 %大庆: 0.1 %大庆: 0.1 %大连: 0.1 %大连: 0.1 %天津: 0.0 %天津: 0.0 %威尔明顿: 0.1 %威尔明顿: 0.1 %官坑: 0.1 %官坑: 0.1 %宝鸡: 0.0 %宝鸡: 0.0 %宣城: 0.1 %宣城: 0.1 %宿迁: 0.1 %宿迁: 0.1 %岳阳: 0.0 %岳阳: 0.0 %崇左: 0.0 %崇左: 0.0 %巴中: 0.3 %巴中: 0.3 %巴中市巴州区: 0.0 %巴中市巴州区: 0.0 %常州: 0.1 %常州: 0.1 %广州: 0.6 %广州: 0.6 %张家口: 0.8 %张家口: 0.8 %张家口市: 0.0 %张家口市: 0.0 %徐州: 0.1 %徐州: 0.1 %恩施: 0.0 %恩施: 0.0 %成都: 1.0 %成都: 1.0 %成都市新都区: 0.0 %成都市新都区: 0.0 %新乡: 0.4 %新乡: 0.4 %无锡: 0.4 %无锡: 0.4 %昆明: 0.0 %昆明: 0.0 %昭通: 0.1 %昭通: 0.1 %杭州: 1.3 %杭州: 1.3 %株洲: 0.0 %株洲: 0.0 %武汉: 0.7 %武汉: 0.7 %汕头: 0.0 %汕头: 0.0 %沈阳: 0.0 %沈阳: 0.0 %沧州: 0.1 %沧州: 0.1 %泸州: 0.0 %泸州: 0.0 %洛阳: 0.3 %洛阳: 0.3 %济南: 0.2 %济南: 0.2 %深圳: 0.4 %深圳: 0.4 %温州: 0.0 %温州: 0.0 %湖州: 0.3 %湖州: 0.3 %湘潭: 0.0 %湘潭: 0.0 %漯河: 0.1 %漯河: 0.1 %潍坊: 0.0 %潍坊: 0.0 %玉林: 0.3 %玉林: 0.3 %益山: 0.3 %益山: 0.3 %石家庄: 0.4 %石家庄: 0.4 %红河: 0.3 %红河: 0.3 %纽约: 0.1 %纽约: 0.1 %绍兴: 0.2 %绍兴: 0.2 %美国伊利诺斯芝加哥: 0.0 %美国伊利诺斯芝加哥: 0.0 %美国新泽西锡考克斯: 0.3 %美国新泽西锡考克斯: 0.3 %芒廷维尤: 14.5 %芒廷维尤: 14.5 %芝加哥: 0.2 %芝加哥: 0.2 %苏州: 0.1 %苏州: 0.1 %苏州市: 0.1 %苏州市: 0.1 %衡水: 0.2 %衡水: 0.2 %西宁: 33.2 %西宁: 33.2 %西安: 1.0 %西安: 1.0 %西安市: 0.0 %西安市: 0.0 %西藏林芝: 0.1 %西藏林芝: 0.1 %贵港: 0.2 %贵港: 0.2 %运城: 0.4 %运城: 0.4 %连云港: 0.0 %连云港: 0.0 %郑州: 1.5 %郑州: 1.5 %金华: 0.0 %金华: 0.0 %长沙: 0.2 %长沙: 0.2 %雅加达: 0.2 %雅加达: 0.2 %青岛: 0.1 %青岛: 0.1 %驻马店: 0.0 %驻马店: 0.0 %鹰潭: 0.1 %鹰潭: 0.1 %其他其他ChinaHanoiIndiaKao-sungKorea Republic ofViet Nam[]上海上饶东京东莞丹东丽水京畿道佛山保定信阳元朗新墟克孜勒苏包头北京北海华盛顿州南京南宁南平南昌南通厦门台北台州合肥呼和浩特哈尔滨商丘圣彼得堡大庆大连天津威尔明顿官坑宝鸡宣城宿迁岳阳崇左巴中巴中市巴州区常州广州张家口张家口市徐州恩施成都成都市新都区新乡无锡昆明昭通杭州株洲武汉汕头沈阳沧州泸州洛阳济南深圳温州湖州湘潭漯河潍坊玉林益山石家庄红河纽约绍兴美国伊利诺斯芝加哥美国新泽西锡考克斯芒廷维尤芝加哥苏州苏州市衡水西宁西安西安市西藏林芝贵港运城连云港郑州金华长沙雅加达青岛驻马店鹰潭

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      沈阳化工大学材料科学与工程学院 沈阳 110142

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