Abstract
Rockfalls are one of the most dominant geological hazards in mountainous rocky regions with the potential to turn catastrophic if they occur in an anthropogenic environment. Therefore, the identification of potential rockfall locations is of high importance. Susceptibility is the magnitude that describes these locations and its qualitative and quantitative assessment is necessary for the timely treatment of potential events. Quantitative susceptibility assessment can be conducted using either data-driven methods such as bivariate and multivariate statistics as well as artificial neural networks or numerical methods such as static and dynamic models. In both approaches mathematical assumptions have to be made concerning the predisposing factors distribution and so there is an inherent need to achieve the higher possible confidence level in the input data. Such high-resolution data can be acquired using light detection and ranging (LiDAR) scanners. In the current study, LiDAR technology was implemented, and the data processing technique is analyzed step by step providing the reader with a view of the whole procedure. The results produced by the current methodology are validated and interpreted according to in situ measurements and observations based on unmanned aerial vehicle imagery. Post data processing, joint orientation, joint spacing and potential block volumes were extracted considering both persistent and non-persistent joints. The proposed methodology provides the creation of detailed high-resolution spatial distribution maps of the previously mentioned parameters, considering the variability of their values along the slope. The results can be used in a space-resolved susceptibility assessment providing higher-resolution input data for the subsequent susceptibility analysis.
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Farmakis, I., Marinos, V., Papathanassiou, G. et al. Automated 3D Jointed Rock Mass Structural Analysis and Characterization Using LiDAR Terrestrial Laser Scanner for Rockfall Susceptibility Assessment: Perissa Area Case (Santorini). Geotech Geol Eng 38, 3007–3024 (2020). https://doi.org/10.1007/s10706-020-01203-x
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DOI: https://doi.org/10.1007/s10706-020-01203-x