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

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

戴可人^{1, 2,},
铁永波^3, ,,
许强^1, ,,
冯也^2,,
卓冠晨^2,,
史先琳^2,

1.
地质灾害防治与地质环境保护国家重点实验室(成都理工大学) 成都 610059
2.
成都理工大学地球科学学院成都 610059
3.
中国地质调查局成都地质调查中心成都 610081

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
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出版历程
- 收稿日期: 2020-03-01
- 修回日期: 2020-06-01
- 网络出版日期: 2020-06-01

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)

DAI Keren^{1, 2
,},
TIE Yongbo^{3
, ,},
XU Qiang^{1
, ,},
FENG Ye^2
,,
ZHUO Guanchen^2
,,
SHI Xianlin^2
,

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)

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.com; XU Qiang, xq@cdut.edu.cn

摘要

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。
- 雷达遥感 /
- 时间序列InSAR /
- 高山峡谷区 /
- 滑坡灾害隐患早期识别 /
- 雅砻江
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.
- Radar remote sensing /
- Time series InSAR /
- Alpine-canyon terrain /
- Early identification of potential landslides /
- Yalong River

HTML全文

图 1 研究区域

Figure 1. Study area

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图 2 SAR数据集时空基线图

Figure 2. Spatial and temporal baselines of SAR datasets

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图 3 SBAS-InSAR时序分析流程图

Figure 3. Flowchart of SBAS-InSAR time series analysis

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图 4 InSAR年均平均速率监测结果

Figure 4. Mean velocity map derived from InSAR

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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])

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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

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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

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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

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图 7 潜在地质灾害点验证与对比

Figure 7. Verification and comparison on potential landslide geohazards

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图 10 雅砻江日衣村-木灰村段

Figure 10. Yalong River Riyi village-Muhui village section

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图 1 Study area

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图 2 Spatial and temporal baselines of SAR datasets

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图 3 Flowchart of SBAS-InSAR time series analysis

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图 4 Mean velocity map derived from InSAR

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图 5 Early identification results of potential landslide geohazardsin Luri village

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

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图 7 Verification and comparison on potential landslide geohazards

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

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

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图 10 Yalong River Riyi village-Muhui village section

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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

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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	低	村落与雅砻江	高
2	日阿	101°7′ 2″ E 29°32′ 28″ N	2200*900	95	57	2462～3140	低	村落与雅砻江	高
3	日衣	101°7′ 41″ E 29°30′ 29″ N	2100*500	66	57	2452～2896	低	村落与雅砻江	高
4	木恩	101°0′ 37″ E 29°20′ 33″ N	500*800	57	58	2295～2853	低	雅砻江	中
5	中铺子村	101°12′ 30″ E 28°35′ 04″ N	1320*700	73	60	1900～3000	中	村落与雅砻江	高
6	麻撒村	101°14′ 21″ E 28°29′ 07″ N	360*400	46	55	1900～2500	低	村落与雅砻江	高
7	阳山村	101°27′ 32″ E 28°04′ 18″ N	710*1000	53	62	1762～2350	低	雅砻江支流	中
8	独家村	101°30′ 07″ E 28°03′ 31″ N	630*700	60	55	1600～2200	低	雅砻江	中

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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	高
2	日阿	古滑坡	可见小型古滑坡	村落与雅砻江	高
3	日衣	古滑坡	可见古滑坡体	村落与雅砻江	高
4	木恩	古滑坡	可见古滑坡体	雅砻江	中
5	中铺子村	新发现	可见古滑坡体	村落与雅砻江	高
6	麻撒村	新发现	可见古滑坡体	村落与雅砻江	高
7	阳山村	新发现	无明显特征	雅砻江支流	中
8	独家村	新发现	可见历史崩塌	雅砻江	中

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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

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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

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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

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