Integrated Methodology for Potential Landslide Identification in Highly Vegetation-Covered Areas
It is normally difficult to identify the ground deformation of potential landslides in highly vegetation-covered areas in terms of field investigation or remote sensing interpretation. In order to explore a methodology to effectively identify potential landslides in highly vegetation-covered areas,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/6/1518 |
| _version_ | 1797609184864239616 |
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| author | Liangxuan Yan Quanbing Gong Fei Wang Lixia Chen Deying Li Kunlong Yin |
| author_facet | Liangxuan Yan Quanbing Gong Fei Wang Lixia Chen Deying Li Kunlong Yin |
| author_sort | Liangxuan Yan |
| collection | DOAJ |
| description | It is normally difficult to identify the ground deformation of potential landslides in highly vegetation-covered areas in terms of field investigation or remote sensing interpretation. In order to explore a methodology to effectively identify potential landslides in highly vegetation-covered areas, this paper established an integrated identification method, including sliding prone area identification based on regional geological environment analysis, target area identification of potential landslides in terms of comprehensive remote sensing methods, and landslide recognition through engineering geological survey. The Miaoyuan catchment in Quzhou City, Zhejiang Province, southeastern China, was taken as an example to validate the identification methods. Particularly, the Shangfang landslide was successfully studied in terms of comprehensive methods, such as geophysical survey, drilling, mineral and chemical composition analysis, and microstructure scanning of the sliding zone. In order to assess the landslide risk, the potential runout of the Shangfang landslide was evaluated in a quantitative simulation. This paper suggests a methodology to identify potential landslides from a large area to a specific slope covered by dense vegetation. |
| first_indexed | 2024-03-11T05:57:59Z |
| format | Article |
| id | doaj.art-21dae5b4fc25423fadba404804b4bf75 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T05:57:59Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-21dae5b4fc25423fadba404804b4bf752023-11-17T13:38:12ZengMDPI AGRemote Sensing2072-42922023-03-01156151810.3390/rs15061518Integrated Methodology for Potential Landslide Identification in Highly Vegetation-Covered AreasLiangxuan Yan0Quanbing Gong1Fei Wang2Lixia Chen3Deying Li4Kunlong Yin5Faculty of Engineering, China University of Geosciences, Wuhan 430074, ChinaFaculty of Engineering, China University of Geosciences, Wuhan 430074, ChinaZhejiang Academy of Geology, Hangzhou 310000, ChinaSchool of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, ChinaFaculty of Engineering, China University of Geosciences, Wuhan 430074, ChinaFaculty of Engineering, China University of Geosciences, Wuhan 430074, ChinaIt is normally difficult to identify the ground deformation of potential landslides in highly vegetation-covered areas in terms of field investigation or remote sensing interpretation. In order to explore a methodology to effectively identify potential landslides in highly vegetation-covered areas, this paper established an integrated identification method, including sliding prone area identification based on regional geological environment analysis, target area identification of potential landslides in terms of comprehensive remote sensing methods, and landslide recognition through engineering geological survey. The Miaoyuan catchment in Quzhou City, Zhejiang Province, southeastern China, was taken as an example to validate the identification methods. Particularly, the Shangfang landslide was successfully studied in terms of comprehensive methods, such as geophysical survey, drilling, mineral and chemical composition analysis, and microstructure scanning of the sliding zone. In order to assess the landslide risk, the potential runout of the Shangfang landslide was evaluated in a quantitative simulation. This paper suggests a methodology to identify potential landslides from a large area to a specific slope covered by dense vegetation.https://www.mdpi.com/2072-4292/15/6/1518landslide identificationpotential landslidehigh vegetation coveragesoutheast China |
| spellingShingle | Liangxuan Yan Quanbing Gong Fei Wang Lixia Chen Deying Li Kunlong Yin Integrated Methodology for Potential Landslide Identification in Highly Vegetation-Covered Areas Remote Sensing landslide identification potential landslide high vegetation coverage southeast China |
| title | Integrated Methodology for Potential Landslide Identification in Highly Vegetation-Covered Areas |
| title_full | Integrated Methodology for Potential Landslide Identification in Highly Vegetation-Covered Areas |
| title_fullStr | Integrated Methodology for Potential Landslide Identification in Highly Vegetation-Covered Areas |
| title_full_unstemmed | Integrated Methodology for Potential Landslide Identification in Highly Vegetation-Covered Areas |
| title_short | Integrated Methodology for Potential Landslide Identification in Highly Vegetation-Covered Areas |
| title_sort | integrated methodology for potential landslide identification in highly vegetation covered areas |
| topic | landslide identification potential landslide high vegetation coverage southeast China |
| url | https://www.mdpi.com/2072-4292/15/6/1518 |
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