Nowadays the use of remote monitoring sensors is a standard practice in landslide characterization and monitoring. In the last decades, technologies such as LiDAR, terrestrial and satellite SAR interferometry (InSAR) and photogrammetry demonstrated a great potential for rock slope assessment while limited studies and applications are still available for ArcSAR Interferometry, Gigapixel imaging and Acoustic sensing. Taking advantage of the facilities located at the Poggio Baldi Landslide Natural Laboratory, an intensive monitoring campaign was carried out on May 2019 using simultaneously the HYDRA-G ArcSAR for radar monitoring, the Gigapan robotic system equipped with a DSLR camera for photo-monitoring purposes and the DUO Smart Noise Monitor for acoustic measurements. The aim of this study was to evaluate the potential of each monitoring sensor and to investigate the ongoing gravitational processes at the Poggio Baldi landslide. Analysis of multi-temporal Gigapixel-images revealed the occurrence of 84 failures of various sizes between 14–17 May 2019. This allowed us to understand the short-term evolution of the rock cliff that is characterized by several impulsive rockfall events and continuous debris production. Radar displacement maps revealed a constant movement of the debris talus at the toe of the main rock scarp, while acoustic records proved the capability of this technique to identify rockfall events as well as their spectral content in a narrow range of frequencies between 200 Hz to 1000 Hz. This work demonstrates the great potential of the combined use of a variety of remote sensors to achieve high spatial and temporal resolution data in the field of landslide characterization and monitoring.
The increased accessibility of drone technology and structure from motion 3D scene reconstruction have transformed the approach for mapping inaccessible slopes undergoing active rockfalls and generating virtual outcrop models (VOM). The Poggio Baldi landslide (Central Italy) and its natural laboratory offers the possibility to monitor and characterise the slope to define a workflow for rockfall hazard analysis. In this study, the analysis of multitemporal VOM (2016–2019) informed a rockfall trajectory analysis that was carried out with a physical-characteristic-based GIS model. The rockfall scenarios were reconstructed and then tested based on the remote sensing observations of the rock mass characteristics of both the main scarp and the rockfall fragment inventory deposited on the slope. The highest concentration of trajectory endpoints occurred at the very top of the debris talus, which was constrained by a narrow channel, while longer horizontal travel distances were allowed on the lower portion of the slope. To further improve the understanding of the Poggio Baldi landslide, a time-independent rockfall hazard analysis aiming to define the potential runout associated with several rock block volumetric classes is a critical component to any subsequent risk analysis in similar mountainous settings featuring marly–arenaceous multilayer sedimentary successions and reactivated main landslide scarps.
<p>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.</p><p>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.</p><p>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.</p>
<p>Landslides in urban areas are conceived as phenomena capable of tearing the physical structure as well as the networks of socio-economic, cultural, material and immaterial relations that make up the life of cities. Landslide hazard analysis is usually mandatory for proper land use planning and management. Nevertheless, in some cases (e.g., municipality of Rome in Italy) regulatory plans lack detailed thematic mapping of geohazard-related data. In Italy, the safety of urban areas has become a very important issue in the last decade, therefore projects of national interest have been funded for the mitigation of geological risks.</p><p>Shallow landslides are common mass movements in urban areas. They can be triggered by earthquakes, heavy rains or induced by proximity to specific urban assets, like road cuts or retaining walls. Reliable quantification of landslide hazardous areas is often associated with the existence of static specific predisposing factors, such as local terrain variables, land use, lithology, proximity to roads and streams as well as dynamic factors related to trigger (e.g., antecedent rainfalls). Predictive multivariate statistical analysis, among which Machine Learning (ML) models, takes as input several predisposing and conditioning factors that may reveal patterns with the spatial and temporal distribution of different types of landslides. Therefore, ancillary landslide databases are the key-data to investigate the distribution, types, pattern, recurrence, and statistics of slope failures and consequently to determine the overall landslide hazard. However, the amount and quality of available data may be inadequate to build accurate large-scale predictive models. Open-source landslide inventories are often incomplete in spatial and temporal terms, with heterogeneous geometries, thus generating a data sparse environment consisting of a variety of low-accuracy datasets that need to be integrated and cross-validated to gain reliability.&#160;</p><p>In this study, the adoption of a combined approach based on GIS tools and Machine Learning techniques allowed to estimate landslide susceptibility based on both real and synthetic Landslide Initiation Points (LIPs). Open-source landslide inventories have been collected, cross-validated, and integrated in a unique database, thus creating a richer data product that contains the strengths but overcomes the weakness of each contributing dataset. As the number of LIPs was too low to train reliable ML models, we developed a methodology based on the features of occurred landslides in order to derive synthetic LIPs to boost the original database by three times. This approach has been applied to the Metropolitan area of Rome (Lazio, Central Italy), where rainfall-induced shallow landslides have been widely overlooked.</p><p>The final database with LIPs and predisposing factors has been used to create and validate different ML models and the most accurate one was then deployed to estimate landslide susceptibility for the whole area of the municipality of Rome with a resolution of 5 meters. The obtained results were then compared with pre-existing, regional, national, and European scale susceptibility maps to assess their reliability in case more detailed studies are not available. Eventually, rainfall probability curves were estimated to evaluate the temporal dependence of rainfall-induced shallow landslides.</p>
<p>In the last decades, technologies such as LiDAR, terrestrial and satellite SAR interferometry (InSAR) and photogrammetry demonstrated a great potential for rock slope assessment. However, studies and applications are still limited for ArcSAR Interferometry, Gigapixel imaging, Acoustic sensing and PhotoMonitoring. With an aim to explore deeper potentials of all above mentioned techniques in monitoring rockfalls and the related debris talus, a permanent natural monitoring site was founded in Poggio Baldi landslide (Central Italy) with various remote monitoring instruments. In detail, the annual volume lost from the cliff is about 3x103 m3 due to frequent rockfalls (up to 84 in three days). Officially inaugurated in October 2021, the permanent Natural Laboratory of Poggio Baldi is completely energy independent and remotely controlled, thus allowing a continuous and efficient monitoring of the rock slope. It is equipped with optical tools (multi resolution cameras), 3D modelling tools (LiDAR and drone photogrammetry), radar tools (linear and arc GB-InSAR, and doppler radar), acoustic tools, seismic tools (sound level meter and geophone) and a weather station. Thanks to the Department of Earth Sciences of the Sapienza University of Rome and NHAZCA SRL for the foundation, contribution, and continuous management of the site. The Poggio Baldi natural laboratory is now continuously monitoring the mass movement activities in a failed slope in Poggio Baldi. The goal is to understand the relationship between rockfalls, predisposing and triggering factors such as thermal, seismic, and meteorological stress that can provide critical information for setting up early warning systems. The acquired data are frequently analysed to assess and improve the prevailing facilities. Additionally, various tools, techniques and methodologies are being developed and implemented at the site to further enhance the capabilities of the monitoring activity. The laboratory is open to host third-party companies and research agencies for testing experimental instruments related to rock and slope deformation and associated risks.</p>
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