Article ; Online: Extraction of landslide morphology based on Topographic Profile along the Direction of Slope Movement using UAV images
Geomatics, Natural Hazards & Risk, Vol 14, Iss
2023 Volume 1
Abstract: AbstractThe landslide morphology is quickly and accurately extracted from Unmanned Air Vehicle (UAV) images. It is of great significance for emergency rescue and quantitative evaluation of landslide disasters. However, due to the complexity of landslide ... ...
Abstract | AbstractThe landslide morphology is quickly and accurately extracted from Unmanned Air Vehicle (UAV) images. It is of great significance for emergency rescue and quantitative evaluation of landslide disasters. However, due to the complexity of landslide morphology, choosing the reasonable extraction thresholds is a challenging issue. A threshold selection method of Topographic Profile along the Direction of Slope Movement (TP-DSM) was proposed. Firstly, a hierarchical extraction rule sets for landslide morphology was constructed by integrating multi-feature information such as spectral, texture, geometry, topography and space of UAV images. Second, TP-DSM was proposed to select the optimal elevation thresholds for classifying different landslide morphology. Finally, the thresholds were introduced into the rule sets to achieve effective extraction of landslide morphology. This study uses Digital Orthophoto Map (DOM) and Digital Elevation Model (DEM) generated by UAV images as data sources, and the landslide in Luquan County, Yunnan Province, China as the Study area, the results show that the overall accuracy (OA) of landslide morphology extraction was 89.58%, and the Kappa coefficient was 0.88, which is effective and more consistent with the reality. The proposed method can also be applied to other potential locations. |
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Keywords | UAV images ; multi-feature information ; TP-DSM ; landslide morphology ; Environmental technology. Sanitary engineering ; TD1-1066 ; Environmental sciences ; GE1-350 ; Risk in industry. Risk management ; HD61 |
Subject code | 550 |
Language | English |
Publishing date | 2023-12-01T00:00:00Z |
Publisher | Taylor & Francis Group |
Document type | Article ; Online |
Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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