Automated Mapping of Ms 7.0 Jiuzhaigou Earthquake (China) Post-Disaster Landslides Based on High-Resolution UAV Imagery
The Ms 7.0 Jiuzhaigou earthquake that occurred on 8 August 2017 triggered hundreds of landslides in the Jiuzhaigou valley scenic and historic-interest area in Sichuan, China, causing heavy casualties and serious property losses. Quick and accurate mapping of post-disaster landslide distribution is o...
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MDPI AG
2021-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/7/1330 |
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| author | Rubing Liang Keren Dai Xianlin Shi Bin Guo Xiujun Dong Feng Liang Roberto Tomás Ningling Wen Xuanmei Fan |
| author_facet | Rubing Liang Keren Dai Xianlin Shi Bin Guo Xiujun Dong Feng Liang Roberto Tomás Ningling Wen Xuanmei Fan |
| author_sort | Rubing Liang |
| collection | DOAJ |
| description | The Ms 7.0 Jiuzhaigou earthquake that occurred on 8 August 2017 triggered hundreds of landslides in the Jiuzhaigou valley scenic and historic-interest area in Sichuan, China, causing heavy casualties and serious property losses. Quick and accurate mapping of post-disaster landslide distribution is of paramount importance for earthquake emergency rescue and the analysis of post-seismic landslides distribution characteristics. The automatic identification of landslides is mostly based on medium- and low-resolution satellite-borne optical remote-sensing imageries, and the high-accuracy interpretation of earthquake-triggered landslides still relies on time-consuming manual interpretation. This paper describes a methodology based on the use of 1 m high-resolution unmanned aerial vehicle (UAV) imagery acquired after the earthquake, and proposes a support vector machine (SVM) classification method combining the roads and villages mask from pre-seismic remote sensing imagery to accurately and automatically map the landslide inventory. Compared with the results of manual visual interpretation, the automatic recognition accuracy could reach 99.89%, and the Kappa coefficient was higher than 0.9, suggesting that the proposed method and 1 m high-resolution UAV imagery greatly improved the mapping accuracy of the landslide area. We also analyzed the spatial-distribution characteristics of earthquake-triggered landslides with the influenced factors of altitude, slope gradient, slope aspect, and the nearest faults, which provided important support for the further study of post-disaster landslide distribution characteristics, susceptibility prediction, and risk assessment. |
| first_indexed | 2024-03-10T12:44:58Z |
| format | Article |
| id | doaj.art-d8897066b58d4ac7aada16569237d3cc |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T12:44:58Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d8897066b58d4ac7aada16569237d3cc2023-11-21T13:36:31ZengMDPI AGRemote Sensing2072-42922021-03-01137133010.3390/rs13071330Automated Mapping of Ms 7.0 Jiuzhaigou Earthquake (China) Post-Disaster Landslides Based on High-Resolution UAV ImageryRubing Liang0Keren Dai1Xianlin Shi2Bin Guo3Xiujun Dong4Feng Liang5Roberto Tomás6Ningling Wen7Xuanmei Fan8College of Earth Science, Chengdu University of Technology, Chengdu 610059, Sichuan, ChinaCollege of Earth Science, Chengdu University of Technology, Chengdu 610059, Sichuan, ChinaCollege of Earth Science, Chengdu University of Technology, Chengdu 610059, Sichuan, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, Sichuan, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, Sichuan, ChinaCollege of Earth Science, Chengdu University of Technology, Chengdu 610059, Sichuan, ChinaDepartamento de Ingeniería Civil, Escuela Politécnica Superior, Universidad de Alicante, P.O. Box 99, E-03080 Alicante, SpainCollege of Earth Science, Chengdu University of Technology, Chengdu 610059, Sichuan, ChinaState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, Sichuan, ChinaThe Ms 7.0 Jiuzhaigou earthquake that occurred on 8 August 2017 triggered hundreds of landslides in the Jiuzhaigou valley scenic and historic-interest area in Sichuan, China, causing heavy casualties and serious property losses. Quick and accurate mapping of post-disaster landslide distribution is of paramount importance for earthquake emergency rescue and the analysis of post-seismic landslides distribution characteristics. The automatic identification of landslides is mostly based on medium- and low-resolution satellite-borne optical remote-sensing imageries, and the high-accuracy interpretation of earthquake-triggered landslides still relies on time-consuming manual interpretation. This paper describes a methodology based on the use of 1 m high-resolution unmanned aerial vehicle (UAV) imagery acquired after the earthquake, and proposes a support vector machine (SVM) classification method combining the roads and villages mask from pre-seismic remote sensing imagery to accurately and automatically map the landslide inventory. Compared with the results of manual visual interpretation, the automatic recognition accuracy could reach 99.89%, and the Kappa coefficient was higher than 0.9, suggesting that the proposed method and 1 m high-resolution UAV imagery greatly improved the mapping accuracy of the landslide area. We also analyzed the spatial-distribution characteristics of earthquake-triggered landslides with the influenced factors of altitude, slope gradient, slope aspect, and the nearest faults, which provided important support for the further study of post-disaster landslide distribution characteristics, susceptibility prediction, and risk assessment.https://www.mdpi.com/2072-4292/13/7/1330Jiuzhaigou earthquakelandslide mappingunmanned aerial vehicle imagerysupport vector machinelandslide-distribution analysis |
| spellingShingle | Rubing Liang Keren Dai Xianlin Shi Bin Guo Xiujun Dong Feng Liang Roberto Tomás Ningling Wen Xuanmei Fan Automated Mapping of Ms 7.0 Jiuzhaigou Earthquake (China) Post-Disaster Landslides Based on High-Resolution UAV Imagery Remote Sensing Jiuzhaigou earthquake landslide mapping unmanned aerial vehicle imagery support vector machine landslide-distribution analysis |
| title | Automated Mapping of Ms 7.0 Jiuzhaigou Earthquake (China) Post-Disaster Landslides Based on High-Resolution UAV Imagery |
| title_full | Automated Mapping of Ms 7.0 Jiuzhaigou Earthquake (China) Post-Disaster Landslides Based on High-Resolution UAV Imagery |
| title_fullStr | Automated Mapping of Ms 7.0 Jiuzhaigou Earthquake (China) Post-Disaster Landslides Based on High-Resolution UAV Imagery |
| title_full_unstemmed | Automated Mapping of Ms 7.0 Jiuzhaigou Earthquake (China) Post-Disaster Landslides Based on High-Resolution UAV Imagery |
| title_short | Automated Mapping of Ms 7.0 Jiuzhaigou Earthquake (China) Post-Disaster Landslides Based on High-Resolution UAV Imagery |
| title_sort | automated mapping of ms 7 0 jiuzhaigou earthquake china post disaster landslides based on high resolution uav imagery |
| topic | Jiuzhaigou earthquake landslide mapping unmanned aerial vehicle imagery support vector machine landslide-distribution analysis |
| url | https://www.mdpi.com/2072-4292/13/7/1330 |
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