Research on monitoring methods of landslide surface deformation based on laser point cloud

Jun Liu; Jiale Li; Qiuling Wang; Xiaobo Zhang; Feng Li

doi:10.1117/12.2668113

23 February 2023 Research on monitoring methods of landslide surface deformation based on laser point cloud

Jun Liu, Jiale Li, Qiuling Wang, Xiaobo Zhang, Feng Li

Proceedings Volume 12551, Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022); 1255119 (2023) https://doi.org/10.1117/12.2668113
Event: Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022), 2022, Changchun, China

Abstract

In order to meet the needs of landslide stability evaluation, we introduced the 3D laser scanning technology into the landslide physical model test slope surface deformation monitoring to obtain the deformation of the physical model of landslide under the action of external rain factor using the DoD method, the C2C method and the M3C2 method. The results show that: 1) All three methods can obtain the overall deformation and displacement of the landslide surface, avoiding the limitations of traditional point-based monitoring methods; 2) the DoD method requires the prior construction of the DEM using point clouds, and the process is more complicated than the C2C and M3C2 methods. The DoD method can only obtain the deformation in the vertical direction, which does not reflect the true 3D deformation of the landslide surface; 3) The C2C and M3C2 methods directly compare the point clouds without constructing the DEM, which has the advantages of fast and efficient. But the distances calculated by the C2C method do not necessarily represent the true deformations occurring on the surface. The M3C2 method computes the 3D distance between the reference and comparison point clouds using the point cloud normal vectors of the best planes constructed at multiple scales, which further optimizes the assertion criterion of the C2C method for extracting approximate homonymous points and can distinguish the collapse and accumulation zones of landslides based on the difference in color values, making the monitoring results more reliable. The deformation information of the landslide surface extracted from the laser point cloud provides a reliable information and scientific basis for the correct assessment of landslide stability.

Citation Download Citation

Jun Liu, Jiale Li, Qiuling Wang, Xiaobo Zhang, and Feng Li "Research on monitoring methods of landslide surface deformation based on laser point cloud", Proc. SPIE 12551, Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022), 1255119 (23 February 2023); https://doi.org/10.1117/12.2668113

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KEYWORDS

Landslide (networking)

Point clouds

Deformation

3D modeling

3D scanning

Clouds

Data modeling

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