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Identification of rocky ledge on steep, high slopes based on UAV photogrammetry

Author

Listed:
  • Xuan-hao Wang

    (Wuhan University)

  • Wei Cui

    (Tianjin University)

  • Gui-ke Zhang

    (Yalong River Hydropower Development Company, LTD)

  • Hong Yang

    (Yalong River Hydropower Development Company, LTD)

Abstract

The rocky ledge on steep, high slopes easily tends to instability under the action of gravity, earthquake, excavation unloading, etc., which threatens the safety of the constructors and water conservancy and hydropower engineering. The early investigation of rocky ledge is of great significance. Due to large areas of slopes and inconvenient traffic, manual investigation is time-consuming and dangerous. A rapid identification method for rock ledge is proposed based on unmanned aerial vehicle (UAV) photogrammetry. This method consists of generating the point cloud model of the slope by UAV photogrammetry, segmenting the point cloud model by kernel density estimation, clustering the point cloud by the density-based spatial clustering of applications with noise, and classifying the point clusters representing rocky ledges by the geometric feature. The slope near the Lianghekou hydropower station is used to study. The results show that the method can rapidly identify the rocky ledge on the whole slope scale, which reduces the risk and improves efficiency.

Suggested Citation

  • Xuan-hao Wang & Wei Cui & Gui-ke Zhang & Hong Yang, 2023. "Identification of rocky ledge on steep, high slopes based on UAV photogrammetry," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 3201-3224, April.
    • Handle: RePEc:spr:nathaz:v:116:y:2023:i:3:d:10.1007_s11069-022-05803-z
    DOI: 10.1007/s11069-022-05803-z
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    References listed on IDEAS

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    1. Mirko Francioni & Riccardo Salvini & Doug Stead & John Coggan, 2018. "Improvements in the integration of remote sensing and rock slope modelling," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 90(2), pages 975-1004, January.
    2. Siyuan Ma & Jiangbo Wei & Chong Xu & Xiaoyi Shao & Shiyang Xu & Shaofeng Chai & Yulong Cui, 2020. "UAV survey and numerical modeling of loess landslides: an example from Zaoling, southern Shanxi Province, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(1), pages 1125-1140, October.
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