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Integrated Methodology for Potential Landslide Identification in Highly Vegetation-Covered Areas

Authors :
Liangxuan Yan
Quanbing Gong
Fei Wang
Lixia Chen
Deying Li
Kunlong Yin
Source :
Remote Sensing, Vol 15, Iss 6, p 1518 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

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.

Details

Language :
English
ISSN :
20724292
Volume :
15
Issue :
6
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.21dae5b4fc25423fadba404804b4bf75
Document Type :
article
Full Text :
https://doi.org/10.3390/rs15061518