Identification and assessment of small landslides in densely vegetated areas based on airborne LiDAR technique

doi:10.6046/zrzyyg.2023101

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Abstract

Landslides may cause the loss of lives and property, and an accurate and complete map showing the spatial distribution of landslides and the determination of landslide susceptibility areas assist in guiding the optimization of the production, living, and ecological spaces. However, landslide investigations are complicated by dense vegetation. LiDAR technology enables the presentation of actual terrain features, thereby achieving landslide identification in densely vegetated areas. This study obtained the LiDAR point cloud data of the study area through ground-imitating flight and then built a digital elevation model (DEM) through data processing. Then, based on mountain shadow analysis, color-enhanced presentation, and 3D scene simulation, the locations and scales of existing landslides in the study area were identified. The field verification revealed an interpretation accuracy of landslides of up to 86.4%. For the assessment of landslide susceptibility areas, this study, with existing landslides as samples, delineated landslide susceptibility areas through remote sensing classification for the first time. Specifically, images were synthesized using the landslide-related elevations, slopes, and surface undulations, and then landslide susceptibility areas were determined using the support vector machine (SVM) classification method. The analysis of the inspection samples reveals a landslide identification accuracy of 81.91%. The results show that the image identification based on high-accuracy LiDAR data and visually enhanced images allows for the delineation of small landslides and that the SVM classification method enables the accurate location of landslide susceptibility areas. This study provides a basis for the future planning and optimization of the production, living, and ecological spaces.

Keywords LiDAR landslide mountain shadow SVM landslide susceptibility area

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TP79

Issue Date: 03 September 2024

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	Gang CHEN
	Shefeng HAO
	Bo JIANG
	Yongxiang YU
	Zengguang CHE
	Hanhu LIU
	Ronghao YANG

Cite this article:

Gang CHEN,Shefeng HAO,Bo JIANG, et al. Identification and assessment of small landslides in densely vegetated areas based on airborne LiDAR technique[J]. Remote Sensing for Natural Resources, 2024, 36(3): 196-205.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023101 OR https://www.gtzyyg.com/EN/Y2024/V36/I3/196

Fig.1 Geographical location of the study area

Fig.2 LiDAR data collection and processing method

Fig.3 Effect of extracting original point clouds and ground points in the study area

Fig.4 Light simulation direction in bidirectional and multi-directional mountain shadow maps

Fig.5 Schematic diagram of the basic idea of support vector machine

Fig.6 Shadow images of mountains under different production methods

Fig.7-1 Color enhanced display effect

Fig.7-2 Color enhanced display effect

Fig.8 Landslide photos during field exploration in the study area

Fig.9 Remote Sensing Interpretation Characteristics of Small Landslides

Fig.10 Distribution of landslide prone areas

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