高山峡谷区滑坡灾害隐患InSAR早期识别——以雅砻江中段为例

戴可人 铁永波 许强 冯也 卓冠晨 史先琳

戴可人, 铁永波, 许强, 等. 高山峡谷区滑坡灾害隐患InSAR早期识别——以雅砻江中段为例[J]. 雷达学报, 2020, 9(3): 554–568. doi: 10.12000/JR20012
引用本文: 戴可人, 铁永波, 许强, 等. 高山峡谷区滑坡灾害隐患InSAR早期识别——以雅砻江中段为例[J]. 雷达学报, 2020, 9(3): 554–568. doi: 10.12000/JR20012
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
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

高山峡谷区滑坡灾害隐患InSAR早期识别——以雅砻江中段为例

doi: 10.12000/JR20012
基金项目: 中国地质调查局公益性地质调查项目(DD20190640),国家自然科学基金(41801391),四川省科技计划重大研发项目(2019YFS0074),四川省科技计划项目(2019YJ0404),地质灾害防治与地质环境保护国家重点实验室自主研究课题(SKLGP2018Z019)
详细信息
    作者简介:

    戴可人(1989–),男,四川成都人,教授,博士研究生导师。成都理工大学珠峰计划引进人才,地质灾害防治与地质环境保护国家重点实验室固定研究人员。主要研究方向包括合成孔径雷达干涉测量高山峡谷区滑坡灾害早期识别与监测预警,遥感滑坡灾害评估与编目等。近五年在IEEE GRSM,RSE,INT J APPL EARTH OBS等国际遥感期刊发表论文10余篇。E-mail: daikeren17@cdut.edu.cn

    铁永波(1979–),男,云南人,中国地质调查局成都地质调查中心教授级高级工程师,博士研究生导师,第十二批四川省学术带头人后备人选,研究方向为地质灾害形成机理与风险评价。E-mail: tyb038@qq.com

    许 强(1968–),男,四川南江人,博士,二级教授,博士研究生导师,现任成都理工大学副校长,地质灾害防治与地质环境保护国家重点实验室常务副主任,国家杰出青年基金获得者,教育部长江学者特聘教授,国家杰出专业技术人才,全国五一劳动奖章获得者,国务院特殊津贴专家。专长于地质灾害成因机理、早期识别、监测预警与应急处置,作为核心成员完成的科研成果获国家科技进步一等奖2项,省部级科技进步奖一等奖6项。E-mail: xq@cdut.edu.cn

    冯 也(1992–),男,四川遂宁人,成都理工大学地球科学学院硕士研究生。主要研究方向为InSAR数据处理及应用。E-mail: 2018050049@stu.cdut.edu.cn

    卓冠晨(1995–),男,福建邵武人,成都理工大学地球科学学院硕士研究生。主要从事基于合成孔径雷达干涉测量的地表形变监测研究。E-mail: zhuoguanchen_RS@foxmail.com

    史先琳(1980–),女,四川成都人,博士,副教授,硕士研究生导师。四川省测绘地理信息学会教育专委会委员,获四川省高等教育教学成果二等奖,四川省测绘科技进步奖三等奖。主要研究方向包括遥感地质与空间信息智能服务。E-mail: shixianlin06@cdut.edu.cn

    通讯作者:

    铁永波 tyb2009@qq.com

    许强 xq@cdut.edu.cn

  • 责任主编:张路 Corresponding Editor: ZHANG Lu
  • 中图分类号: TN95

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)

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)
More Information
    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.comXU Qiang, xq@cdut.edu.cn
  • 摘要: 我国西部山区滑坡灾害频发,具有强隐蔽性、高突发性、强破坏性等特点,对灾害隐患点进行早期识别是最为有效的防灾减灾措施。西部山区多为高山峡谷区域且范围辽阔,人不易至甚至人不能至,传统的人工排查早期识别方法较难实施。合成孔径雷达干涉测量技术(InSAR)作为新兴雷达遥感测量手段,可以高效准确地对高山峡谷区域进行滑坡灾害隐患早期识别。该文基于欧洲空间局(ESA)的哨兵一号(Sentinel-1)SAR遥感数据,利用时间序列InSAR技术对雅砻江流域雅江县-木里县段的高山峡谷区域进行了滑坡灾害隐患广域早期识别,成功探测到8处隐患区域。并结合滑坡隐患历史资料与光学影像遥感解译对识别结果进行了验证与分析,对灾害点风险等级进行了评定。并探讨了几何畸变因素对高山峡谷区域InSAR技术滑坡灾害隐患广域早期识别的影响。该案例可为当地的防灾减灾提供有力的数据与技术支持,并为高山峡谷区的滑坡灾害隐患早期识别提供思路与参考。

     

  • 图  1  研究区域

    Figure  1.  Study area

    图  2  SAR数据集时空基线图

    Figure  2.  Spatial and temporal baselines of SAR datasets

    图  3  SBAS-InSAR时序分析流程图

    Figure  3.  Flowchart of SBAS-InSAR time series analysis

    图  4  InSAR年均平均速率监测结果

    Figure  4.  Mean velocity map derived from InSAR

    图  8  卫星入射角(LOS)观测方向与沿坡向形变关系(修改自文献[22])

    Figure  8.  Relationships between the Line Of Sight (LOS) and the downslope displacements for different slope orientations (adapted from Ref. [22])

    图  9  雅砻江流域Sentinel-1数据几何畸变分布图

    Figure  9.  Geometric distortion of Sentinel-1 datasets along Yalong River

    图  5  鲁日村滑坡灾害隐患识别结果

    Figure  5.  Early identification results of potential landslide geohazards in Luri village

    图  6  中铺子村滑坡灾害隐患识别结果

    Figure  6.  Early identification results of potential landslide geohazards in Zhongpuzi village

    图  7  潜在地质灾害点验证与对比

    Figure  7.  Verification and comparison on potential landslide geohazards

    图  10  雅砻江日衣村-木灰村段

    Figure  10.  Yalong River Riyi village-Muhui village section

    图  1  Study area

    图  2  Spatial and temporal baselines of SAR datasets

    图  3  Flowchart of SBAS-InSAR time series analysis

    图  4  Mean velocity map derived from InSAR

    图  5  Early identification results of potential landslide geohazardsin Luri village

    图  6  Early identification results of potential landslide geohazards in Zhongpuzi village

    图  7  Verification and comparison on potential landslide geohazards

    图  8  Relationships between the Line Of Sight (LOS) and the downslope displacements for different slope orientations (adapted from Ref. [22])

    图  9  Geometric distortion of Sentinel-1 datasets along Yalong River

    图  10  Yalong River Riyi village-Muhui village section

    表  1  Sentinel-1卫星SAR影像数据主要参数

    Table  1.   Main parameters of Sentinel-1 SAR datasets

    主要参数 Sentinel-1卫星数据
    轨道方向 升轨
    波段 C
    雷达波长(cm) 5.6
    空间分辨率(m) 5×20
    重访周期(d) 12
    入射角(°) 36.8
    下载: 导出CSV

    表  2  雅砻江潜在灾害点早期识别结果列表

    Table  2.   Early identification results on potential disaster sites along Yalong River

    编号 滑坡名 经纬度 变形范围(m) 最大形变速率
    (mm/year)
    平均坡
    度(%)
    高程范围(m) 植被覆
    盖情况
    威胁对象 风险
    等级
    1 鲁日村 101°7′ 35″ E 29°34′ 43″ N 1800*1000 76 30 2463~3085 村落与雅砻江
    2 日阿 101°7′ 2″ E 29°32′ 28″ N 2200*900 95 57 2462~3140 村落与雅砻江
    3 日衣 101°7′ 41″ E 29°30′ 29″ N 2100*500 66 57 2452~2896 村落与雅砻江
    4 木恩 101°0′ 37″ E 29°20′ 33″ N 500*800 57 58 2295~2853 雅砻江
    5 中铺子村 101°12′ 30″ E 28°35′ 04″ N 1320*700 73 60 1900~3000 村落与雅砻江
    6 麻撒村 101°14′ 21″ E 28°29′ 07″ N 360*400 46 55 1900~2500 村落与雅砻江
    7 阳山村 101°27′ 32″ E 28°04′ 18″ N 710*1000 53 62 1762~2350 雅砻江支流
    8 独家村 101°30′ 07″ E 28°03′ 31″ N 630*700 60 55 1600~2200 雅砻江
    下载: 导出CSV

    表  3  雅砻江流域InSAR早期识别验证与对比

    Table  3.   Verification and comparison on early identification results from InSAR along Yalong River

    编号 滑坡名 历史记录 地貌地形特征 威胁对象 危险等级
    1 鲁日 古滑坡 可见古滑坡体 村落与雅砻江
    2 日阿 古滑坡 可见小型古滑坡 村落与雅砻江
    3 日衣 古滑坡 可见古滑坡体 村落与雅砻江
    4 木恩 古滑坡 可见古滑坡体 雅砻江
    5 中铺子村 新发现 可见古滑坡体 村落与雅砻江
    6 麻撒村 新发现 可见古滑坡体 村落与雅砻江
    7 阳山村 新发现 无明显特征 雅砻江支流
    8 独家村 新发现 可见历史崩塌 雅砻江
    下载: 导出CSV

    表  1  Main parameters of Sentinel-1 SAR datasets

    The main parameters Sentinel-1 SAR data
    Orbital direction Ascending
    Bands C
    Radar wavelength (cm) 5.6
    Spatial resolution (m) 5×20
    Revisit cycle (d) 12
    Incident angle (°) 36.8
    下载: 导出CSV

    表  2  Early identification results on potential disaster sites alongYalongRiver

    Number Landslide Latitude and
    longitude
    Deformation range (m) Maximum deformation rate (mm/year) Average slope (%) Elevation range (m) Vegetation coverage Threat object Risk level
    1 Luri ${\rm{101^\circ7'35''E\;29^\circ34'43''N} }$ 1800*1000 76 30 2463~3085 low Villages and Yalong River high
    2 Ri’a ${\rm{101^\circ7'2''E\;29^\circ32'28''N} }$ 2200*900 95 57 2462~3140 low Villages and Yalong River high
    3 Riyi ${\rm{101^\circ7'41''E\;29^\circ30'29''N} }$ 2100*500 66 57 2452~2896 low Villages and Yalong River high
    4 Muen ${\rm{101^\circ0'37''E\;29^\circ20'33''N} }$ 500*800 57 58 2295~2853 low Yalong River middle
    5 Zhongpuzi ${\rm{101^\circ12'30''E\;28^\circ35'04''N } }$ 1320*700 73 60 1900~3000 middle Villages and Yalong River high
    6 Masa ${\rm{101^\circ14'21''E\;28^\circ29'07''N} }$ 360*400 46 55 1900~2500 low Villages and Yalong River high
    7 Yangshan ${\rm{101^\circ27'32''E\;28^\circ04'18''N} }$ 710*1000 53 62 1762~2350 low Yalong River tributary middle
    8 Dujia ${\rm{101^\circ30'07''E\;28^\circ03'31''N} }$ 630*700 60 55 1600~2200 low Yalong River middle
    下载: 导出CSV

    表  3  Verification and comparison on early identification results from InSAR along Yalong River

    Number Landslide History record Topographic features Threat object Risk level
    1 Luri ancient landslide Visible ancient landslide Villages and Yalong River high
    2 Ri’a ancient landslide Visible small ancient landslide Villages and Yalong River high
    3 Riyi ancient landslide Visible ancient landslide Villages and Yalong River high
    4 Muen ancient landslide Visible ancient landslide Yalong River middle
    5 Zhongpuzi new discovery Visible ancient landslide Villages and Yalong River high
    6 Masa new discovery Visible ancient landslide Villages and Yalong River high
    7 Yangshan new discovery No obvious features Yalong River tributary middle
    8 Dujia new discovery Visible history collapse Yalong River middle
    下载: 导出CSV
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  • 收稿日期:  2020-03-01
  • 修回日期:  2020-06-01
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