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Enhanced Landslide Visualization and Trace Identification Using LiDAR-Derived DEM
Jie Lv1, Chengzhuo Lu2, Minjun Ye2
1Department of Earth Science and Technology, City College, Kunming University of Science and Technology, Kunming 650093, China.
Sensors (Basel, Switzerland)
|July 30, 2025
Summary
This study introduces an enhanced airborne LiDAR method for improved landslide micro-topography display and trace recognition in vegetated, complex terrains. The technique accurately extracts landslide traces, outperforming traditional orthophotos.
Area of Science:
- Geosciences
- Remote Sensing
- Geomorphology
Background:
- Traditional remote sensing struggles with micro-topography in vegetated, complex terrain.
- Accurate landslide mapping is crucial for disaster management and risk assessment.
Purpose of the Study:
- To develop an enhanced method for landslide terrain display and trace recognition using airborne LiDAR.
- To improve the accuracy of landslide micro-topography feature extraction in challenging environments.
Main Methods:
- Construction of a high-precision LiDAR-DEM from point cloud data.
- Generation of visualization images (hillshade, slope, openness, Sky View Factor).
- Pixel-level image fusion, fractal modeling, and Mean-Shift algorithm for trace extraction.
Main Results:
- Pixel-level image fusion effectively enhances landslide micro-topography display.
- The combined fractal model and Mean-Shift algorithm accurately extract landslide traces.
- The proposed method shows superior performance compared to orthophoto-based extraction.
Conclusions:
- Airborne LiDAR combined with advanced visualization and image processing offers superior landslide terrain analysis.
- This method provides effective technical support for landslide disaster emergency investigations.
- The findings are valuable for landslide research in vegetated, complex mountainous areas.
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