Integration of Sentinel-1 and ALOS/PALSAR-2 SAR datasets for mapping active landslides along the Jinsha River corridor, China

被引:0
|
作者
Liu, Xiaojie [1 ]
Zhao, Chaoying [1 ]
Zhang, Qin [1 ]
Lu, Zhong [2 ]
Li, Zhenhong [1 ,3 ]
Yang, Chengsheng [1 ]
Zhu, Wu [1 ]
Liu-Zeng, Jing [4 ]
Chen, Liquan [1 ]
Liu, Chuanjin [5 ]
机构
[1] Changan Univ, Sch Geol Engn & Geomat, Xian, Shaanxi, Peoples R China
[2] Southern Methodist Univ, Roy M Huffington Dept Earth Sci, Dallas, TX USA
[3] Newcastle Univ, Sch Engn, COMET, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England
[4] Tianjin Univ, Inst Surface Earth Syst Sci, Tianjin, Peoples R China
[5] China Earthquake Adm, Monitoring & Applicat Ctr 2, Xian, Shaanxi, Peoples R China
关键词
Jinsha River; Geohazards; Landslide mapping; Sentinel-1; ALOS/PALSAR-2; SAR interferometry; RESERVOIR AREA; INVENTORY; INTERFEROMETRY; STABILITY;
D O I
10.1016/j.enggeo.2021.106033
中图分类号
P5 [地质学];
学科分类号
0709 ; 081803 ;
摘要
Landslide hazards along the Jinsha River corridor pose serious threats to the lives and property of local residents and can affect the safety of hydropower facilities because of the large size, number, strong activity, and disaster chain characteristics that occur following such events (such as a landslide creating a dammed lake which fails and leads to flooding), thus attracting widespread attention in both China and the rest of the world. As there is currently no complete landslide inventory map that covers the entire Jinsha River corridor, in this study the Sentinel-1 and ALOS/PALSAR-2 datasets were employed to detect and map active landslides along the entire Jinsha River corridor. Complex geomorphological conditions, such as the humid climate, dense vegetation, and steep terrain, pose great challenges to conventional InSAR-based landslide mapping methods, which can lead to a high probability of mis-judgements and omissions of landslides. Therefore, we propose a new InSAR-based procedure that can be used to conduct large-area landslide mapping through the integration of surface deformation and geomorphological features. More than 360 SAR images covering the Jinsha River corridor were processed and more than 900 active landslides were detected and mapped over the entire Jinsha River corridor for the first time. In particular, several large-scale landslides with a length and/or width of >1 km were found. Our results show that the landslides over the Jinsha River corridor are mainly located in three high earthquake-prone areas and reservoir areas, and that the landslides are mainly distributed at elevations of 1500-2000 m a.s.l. and have slope angles of 15-25 degrees. Moreover, the deformation time series results indicate that the heavy rainfall in the summer and the rapid decline of water level in the Jinsha River might be two significant factors that accelerate the deformation of active landslides and reactivate unstable slopes. The findings in this research can be directly applied to landslide hazard mitigation and prevention along the entire Jinsha River corridor. In particular, the proposed procedure can be used for the efficient and systematic mapping of active landslides in other regions with similarly complex geomorphological conditions.
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页数:15
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  • [1] Integration of Sentinel-1 and ALOS/PALSAR-2 SAR datasets for mapping active landslides along the Jinsha River corridor, China
    Liu, Xiaojie
    Zhao, Chaoying
    Zhang, Qin
    Lu, Zhong
    Li, Zhenhong
    Yang, Chengsheng
    Zhu, Wu
    Liu-Zeng, Jing
    Chen, Liquan
    Liu, Chuanjin
    [J]. Engineering Geology, 2021, 284
  • [2] COMBINING POLARIMETRIC SENTINEL-1 AND ALOS-2/PALSAR-2 IMAGERY FOR MAPPING OF FLOODED VEGETATION
    Plank, Simon
    Juessi, Martin
    Martinis, Sandro
    Twele, Andre
    [J]. 2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2017, : 5705 - 5708
  • [3] Landslide Inventory in the Downstream of the Niulanjiang River with ALOS PALSAR and Sentinel-1 Datasets
    Wang, Ziyun
    Xu, Jinhu
    Shi, Xuguo
    Wang, Jianing
    Zhang, Wei
    Zhang, Bao
    [J]. REMOTE SENSING, 2022, 14 (12)
  • [4] Mapping of flooded vegetation by means of polarimetric Sentinel-1 and ALOS-2/PALSAR-2 imagery
    Plank, Simon
    Juessi, Martin
    Martinis, Sandro
    Twele, Andre
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2017, 38 (13) : 3831 - 3850
  • [5] Measuring precursory movements of the recent Xinmo landslide in Mao County, China with Sentinel-1 and ALOS-2 PALSAR-2 datasets
    Jie Dong
    Lu Zhang
    Menghua Li
    Yanghai Yu
    Mingsheng Liao
    Jianya Gong
    Heng Luo
    [J]. Landslides, 2018, 15 : 135 - 144
  • [6] Deformation of the Baige Landslide, Tibet, China, Revealed Through the Integration of Cross-Platform ALOS/PALSAR-1 and ALOS/PALSAR-2 SAR Observations
    Liu, Xiaojie
    Zhao, Chaoying
    Zhang, Qin
    Lu, Zhong
    Li, Zhenhong
    [J]. GEOPHYSICAL RESEARCH LETTERS, 2020, 47 (03)
  • [7] Detection of Active Landslides in Southwest China using Sentinel-1 and ALOS-2 Data
    Zhang, Teng
    Xie, Shuai
    Fan, Jinghui
    Huang, Bo
    Wang, Qun
    Yuan, Weilin
    Zhao, Hongli
    Chen, JianPing
    Li, Hongzhou
    Liu, Guang
    Tong, Liqiang
    Sousa, Joaquim J.
    [J]. INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS / INTERNATIONAL CONFERENCE ON PROJECT MANAGEMENT / INTERNATIONAL CONFERENCE ON HEALTH AND SOCIAL CARE INFORMATION SYSTEMS AND TECHNOLOGIES 2020 (CENTERIS/PROJMAN/HCIST 2020), 2021, 181 : 1138 - 1145
  • [8] Assessment of radarsat-1, ALOS PALSAR and sentinel-1 SAR satellite images for geological lineament mapping
    Chakouri, Mohcine
    El Harti, Abderrazak
    Lhissou, Rachid
    El Hachimi, Jaouad
    Jellouli, Amine
    Adiri, Zakaria
    [J]. GEOCARTO INTERNATIONAL, 2022, 37 (27) : 15530 - 15547
  • [9] ALOS-2 PALSAR-2 ScanSAR and Sentinel-1 data for timely tropical forest disturbance mapping: A case study for Sumatra, Indonesia
    Balling, Johannes
    Slagter, Bart
    van der Woude, Sietse
    Herold, Martin
    Reiche, Johannes
    [J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2024, 132
  • [10] Measuring precursory movements of the recent Xinmo landslide in Mao County, China with Sentinel-1-and ALOS-2 PALSAR-2 datasets
    Dong, Jie
    Zhang, Lu
    Li, Menghua
    Yu, Yanghai
    Liao, Mingsheng
    Gong, Jianya
    Luo, Heng
    [J]. LANDSLIDES, 2018, 15 (01) : 135 - 144