Utilizing a single-temporal full polarimetric Gaofen-3 SAR image to map coseismic landslide inventory following the 2017 Mw 7.0 Jiuzhaigou earthquake (China)
On August 8, 2017, a magnitude 7.0 earthquake struck Jiuzhaigou County in Sichuan Province, triggering numerous coseismic landslides. The prompt identification of these landslides is imperative for emergency rescue efforts and post-earthquake hazard assessments. Optical satellite and unmanned aerial...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843224000116 |
| _version_ | 1797303385864536064 |
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| author | Rubing Liang Keren Dai Qiang Xu Saeid Pirasteh Zhenhong Li Tao Li Ningling Wen Jin Deng Xuanmei Fan |
| author_facet | Rubing Liang Keren Dai Qiang Xu Saeid Pirasteh Zhenhong Li Tao Li Ningling Wen Jin Deng Xuanmei Fan |
| author_sort | Rubing Liang |
| collection | DOAJ |
| description | On August 8, 2017, a magnitude 7.0 earthquake struck Jiuzhaigou County in Sichuan Province, triggering numerous coseismic landslides. The prompt identification of these landslides is imperative for emergency rescue efforts and post-earthquake hazard assessments. Optical satellite and unmanned aerial vehicle (UAV) images are often obstructed by cloud cover and fog following earthquakes. In contrast, polarimetric synthetic aperture radar (PolSAR), unaffected by adverse weather conditions, emerges as an indispensable tool. However, the utilization of spaceborne single-temporal SAR for mapping the inventory of coseismic landslides is infrequent and encounters constraints due to several limitations. In this study, we analyzed the amplitude feature and polarimetric decomposition of multiple ground categories in a full PolSAR image, and proposed an automated method to accurately identify coseismic landslides using a single-temporal full PolSAR image. The coseismic landslide inventory following the Jiuzhaigou Earthquake was mapped and validated using high-resolution UAV images. Detailed analysis was conducted to identify error sources leading to omissions and false positives. Additionally, we evaluated various machine learning models to compare their performance with our proposed method. Finally, we conducted a comprehensive discussion on the strengths and weaknesses of different data types (PolSAR, optical satellite, and UAV) for coseismic landslide identification. Our results indicate that the proposed PolSAR-based method achieves high accuracy in coseismic landslide inventory mapping, offering an effective solution for timely post-earthquake emergency responses in complex environments and all weather conditions in the future. |
| first_indexed | 2024-03-07T23:52:00Z |
| format | Article |
| id | doaj.art-67b329da916043ff91804eaa02134d75 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-03-07T23:52:00Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-67b329da916043ff91804eaa02134d752024-02-19T04:13:08ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322024-03-01127103657Utilizing a single-temporal full polarimetric Gaofen-3 SAR image to map coseismic landslide inventory following the 2017 Mw 7.0 Jiuzhaigou earthquake (China)Rubing Liang0Keren Dai1Qiang Xu2Saeid Pirasteh3Zhenhong Li4Tao Li5Ningling Wen6Jin Deng7Xuanmei Fan8State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology), Chengdu 610059, China; College of Earth Science, Chengdu University of Technology, Chengdu 610059, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology), Chengdu 610059, China; College of Earth Science, Chengdu University of Technology, Chengdu 610059, China; College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710064, China; Corresponding author.State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology), Chengdu 610059, ChinaDepartment of Surveying and Geoinformatics, Faculty of Earth Sciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China; Department of Geotechnics and Geomatics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, Tamilnadu, IndiaCollege of Geological Engineering and Geomatics, Chang’an University, Xi’an 710064, China; Key Laboratory of Western China’s Mineral Resource and Geological Engineering, Ministry of Education, Xi’an 710054, ChinaLand Satellite Remote Sensing Application Center, Ministry of Natural Resources of China, Beijing 100048, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology), Chengdu 610059, China; College of Earth Science, Chengdu University of Technology, Chengdu 610059, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology), Chengdu 610059, China; College of Earth Science, Chengdu University of Technology, Chengdu 610059, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Chengdu University of Technology), Chengdu 610059, ChinaOn August 8, 2017, a magnitude 7.0 earthquake struck Jiuzhaigou County in Sichuan Province, triggering numerous coseismic landslides. The prompt identification of these landslides is imperative for emergency rescue efforts and post-earthquake hazard assessments. Optical satellite and unmanned aerial vehicle (UAV) images are often obstructed by cloud cover and fog following earthquakes. In contrast, polarimetric synthetic aperture radar (PolSAR), unaffected by adverse weather conditions, emerges as an indispensable tool. However, the utilization of spaceborne single-temporal SAR for mapping the inventory of coseismic landslides is infrequent and encounters constraints due to several limitations. In this study, we analyzed the amplitude feature and polarimetric decomposition of multiple ground categories in a full PolSAR image, and proposed an automated method to accurately identify coseismic landslides using a single-temporal full PolSAR image. The coseismic landslide inventory following the Jiuzhaigou Earthquake was mapped and validated using high-resolution UAV images. Detailed analysis was conducted to identify error sources leading to omissions and false positives. Additionally, we evaluated various machine learning models to compare their performance with our proposed method. Finally, we conducted a comprehensive discussion on the strengths and weaknesses of different data types (PolSAR, optical satellite, and UAV) for coseismic landslide identification. Our results indicate that the proposed PolSAR-based method achieves high accuracy in coseismic landslide inventory mapping, offering an effective solution for timely post-earthquake emergency responses in complex environments and all weather conditions in the future.http://www.sciencedirect.com/science/article/pii/S1569843224000116Gaofen-3Full Polarimetric SARPolarization AnalysisCoseismic LandslidesJiuzhaigou Earthquake |
| spellingShingle | Rubing Liang Keren Dai Qiang Xu Saeid Pirasteh Zhenhong Li Tao Li Ningling Wen Jin Deng Xuanmei Fan Utilizing a single-temporal full polarimetric Gaofen-3 SAR image to map coseismic landslide inventory following the 2017 Mw 7.0 Jiuzhaigou earthquake (China) International Journal of Applied Earth Observations and Geoinformation Gaofen-3 Full Polarimetric SAR Polarization Analysis Coseismic Landslides Jiuzhaigou Earthquake |
| title | Utilizing a single-temporal full polarimetric Gaofen-3 SAR image to map coseismic landslide inventory following the 2017 Mw 7.0 Jiuzhaigou earthquake (China) |
| title_full | Utilizing a single-temporal full polarimetric Gaofen-3 SAR image to map coseismic landslide inventory following the 2017 Mw 7.0 Jiuzhaigou earthquake (China) |
| title_fullStr | Utilizing a single-temporal full polarimetric Gaofen-3 SAR image to map coseismic landslide inventory following the 2017 Mw 7.0 Jiuzhaigou earthquake (China) |
| title_full_unstemmed | Utilizing a single-temporal full polarimetric Gaofen-3 SAR image to map coseismic landslide inventory following the 2017 Mw 7.0 Jiuzhaigou earthquake (China) |
| title_short | Utilizing a single-temporal full polarimetric Gaofen-3 SAR image to map coseismic landslide inventory following the 2017 Mw 7.0 Jiuzhaigou earthquake (China) |
| title_sort | utilizing a single temporal full polarimetric gaofen 3 sar image to map coseismic landslide inventory following the 2017 mw 7 0 jiuzhaigou earthquake china |
| topic | Gaofen-3 Full Polarimetric SAR Polarization Analysis Coseismic Landslides Jiuzhaigou Earthquake |
| url | http://www.sciencedirect.com/science/article/pii/S1569843224000116 |
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