Rockfall hazard assessment of the Poggio Baldi landslide by combining 3D and 2D multitemporal remote sensing data
- 1Sapienza University of Rome, Earth Science Department, Piazzale Aldo Moro, 5, 00185 Rome, Italy
- 2Camborne School of Mines, University of Exeter, Penryn, Cornwall TR10 9EZ, UK
- 3NHAZCA s.r.l., Spin-off Sapienza University of Rome, Via Vittorio Bachelet, 12, 00185 Rome, Italy
Rockfalls could be catastrophic for their inherent characteristics such as limited precursor deformation, unforeseeable movement, and extreme velocity. Potential damages in a rockfall event are mostly associated with blocks reaching vulnerable elements during their descent down the slope. The block volumes involved in a rockfall as well as detachment locations, trajectories and velocity along a slope are the parameters that directly determine the intensity of a rockfall hazard. Therefore, there is a dire need to develop effective evaluation strategies for rockfall phenomena through efficient monitoring and analysis techniques. Recent years have witnessed significant developments in the monitoring, analytical and physical methods for the study of rockfall phenomena. Improvements in the use of laser scanning, and drone photogrammetry have allowed to exploit high-resolution virtual outcrop models (VOMs) and derive accurate information about slope evolution. Rock falls are strictly related to fracture patterns pervading the rock mass. Hence, kinematic analyses can quantify the susceptibility to failure of a rock block. Moreover, discontinuity extraction represents the key data to investigate the spatial distribution of fractures and consequently to determine the potential rock block volumes. The trajectories of the rock fragments depend on the slope geometry and the characteristics of the propagation zone, local asperities, and the mechanical attributes of the exposed bedrock and soil cover.
The present study concerns the evaluation of rockfall activity, susceptibility, and hazard modelling of the Poggio Baldi landslide (Central Italy). The Poggio Baldi landslide is affected by frequent rockfalls, and it is being monitored for several years with multiple remote sensing instruments. It is home to a permanent natural monitoring laboratory managed by the Department of Earth Sciences of the Sapienza University of Rome and NHAZCA SRL. Over the years, many surveys and investigations have been carried out using modern remote sensing techniques to capture active gravitational processes.
Here, we introduce a new approach combining 3D and 2D VOM to assess rockfall activity and the associated hazard. Most active rockfall source sectors were found using 3D change detection on multitemporal VOMs, thus suggesting the state of activity of the rock scarp. In these sectors, we thoroughly surveyed the discontinuity sets and their patterns, such as spacing and persistence by integrating data from UAV-based photogrammetric point clouds and orthoimages. These data were then used to calculate the volume of the typical rock blocks characterizing each area. Moreover, we implemented a GIS-based modified kinematic method to assess the failure susceptibility of the rock scarp using slope morphometry and discontinuity orientations. Finally, to simulate the potential runout of falling blocks from the most active and susceptible areas of the slope, rockfall trajectory simulations were performed on a physical characteristics-based GIS model. The results of kinematic susceptibility and rockfall runout were then statistically assessed by comparing them with real depletion and accumulation areas derived by the multitemporal VOMs with a time span of 3 years. Through this approach, it was possible to perform detailed rockfall hazard simulations for each source area using specific structural/geomechanical data.
How to cite: Mastrantoni, G., Kundu, J., Santicchia, G., Cosentino, A., Robiati, C., and Mazzanti, P.: Rockfall hazard assessment of the Poggio Baldi landslide by combining 3D and 2D multitemporal remote sensing data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16048, https://doi.org/10.5194/egusphere-egu23-16048, 2023.