Abstract
Landslides are an important type of natural disaster that can cause catastrophic destruction of infrastructure and result in human casualties. An accurate digital surface model (DSM) is essential for analysis of landslide stability and associated risks. This work presents a rapid modeling method of landslide topography by combining high-resolution imagery from an unmanned aerial vehicle (UAV) with structure-from-motion (SfM) photogrammetry. Because of the relatively high efficiency and low costs, the micro-UAV system is suitable for emergency investigation of landslide disaster when compared to geodetic or other remote-sensing techniques, especially for inaccessible landslide area after earthquakes. Based on the micro-UAV images, the use of SfM combines well-established photogrammetric principles with modern computation to rapidly reconstruct a DSM. The relative error determined by comparison with measured data for the UAV–SfM technology is <1%. An unstable slope in Panyu District, Guangzhou City, China, was selected as a case study, in which a small UAV system was deployed for landslide investigation and generation of a highly efficient, cost-effective and user-friendly approach for 3D terrain modeling.
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Abbreviations
- CMVS:
-
Clustering views for multi-view stereo
- DSM:
-
Digital surface model
- GCP:
-
Ground control point
- GPS:
-
Global positioning systems
- LIDAR:
-
Light detection and ranging
- PMVS:
-
Patch-based multi-view stereo
- SfM:
-
Structure-from-motion
- SIFT:
-
Scale-invariant feature transform
- TLS:
-
Terrestrial laser scanning
- UAS:
-
Unmanned aircraft systems
- UAV:
-
Unmanned aerial vehicle
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Acknowledgments
This work was supported by the National Science Fund for Distinguished Young Scholars of China (Grant No. 41625011), the National Key Technologies R&D Program of China (Grant No. 2012BAJ11B04) and Fundamental Research Founds for National University, China University of Geosciences (Wuhan) (Grant No. CUGL170806).
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Yu, M., Huang, Y., Zhou, J. et al. Modeling of landslide topography based on micro-unmanned aerial vehicle photography and structure-from-motion. Environ Earth Sci 76, 520 (2017). https://doi.org/10.1007/s12665-017-6860-x
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DOI: https://doi.org/10.1007/s12665-017-6860-x