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Rocky coastlines are characterised by steep cliffs, which frequently experience a variety of natural processes that often exhibit intricate interdependencies, such as rainfall, ice and water run-off, and marine actions. The advent of high temporal and spatial resolution data, that can be acquired through remote sensing and geomatics techniques, has facilitated the safe exploration of otherwise inaccessible areas. The datasets that can be gathered from these techniques, typically combined with data from fieldwork, can subsequently undergo analyses employing/applying machine learning algorithms and/or numerical modeling, in order to identify/discern the predominant influencing factors affecting cliff top erosion. This study focuses on a specific case situated at the Conero promontory of the Adriatic Sea in the Marche region. The research methodology entails several steps. Initially, the morphological, geological and geomechanical characteristics of the areas were determined through unmanned aerial vehicle (UAV) and conventional geological/geomechanical surveys. Subsequently, cliff top retreat was determined within a GIS environment by comparing orthophotos taken in 1978 and 2022 using the DSAS tool (Digital Shoreline Analysis System), highlighting cliff top retreat up to 50 m in some sectors. Further analysis was conducted via the use of two Machine Learning (ML) algorithms, namely Random Forest (RF) and eXtreme Gradient Boosting (XGB). The Mean Decrease in Impurity (MDI) methodology was employed to assess the significance of each factor. Both algorithms yielded congruent results, emphasising that cliff top erosion rates are primarily influenced by slope height. Finally, a validation of the ML algorithm results was conducted using 2D Limit Equilibrium Method (LEM) codes. Ten sections extracted from the sector experiencing the most substantial cliff top retreat, as identified by DSAS, were utilised for 2D LEM analysis. Factor of Safety (FS) values were identified and compared with the cliff height of each section. The results from the 2D LEM analyses corroborated the outputs of the ML algorithms, showing a strong correlation between the slope instability and slope height (R2 of 0.84), with FS decreasing with slope height. [ABSTRACT FROM AUTHOR]

The characterization of landslides located in remote areas poses significant challenges due to the costs of reaching the sites and the lack of reliable subsurface data to constrain geological interpretations. In this paper, the advantages of combining field and remote sensing techniques to investigate the deformation and stability of rock slopes are demonstrated. The characterization of the Fels landslide, a large, slowly deforming rock slope in central Alaska, is described. Historical aerial imagery is used to highlight the relationship between glacier retreat and developing instability. Airborne laser scanning (ALS) and Structure-from-Motion (SfM) datasets are used to investigate the structural geological setting of the landslide, revealing a good agreement between structural discontinuities at the outcrop and slope scales. The magnitude, plunge, and direction of slope surface displacements and their changes over time are studied using a multi-temporal synthetic aperture radar speckle-tracking (SAR ST) dataset. The analyses show an increase in displacement rates (i.e., an acceleration of the movement) between 2010 and 2020. Significant spatial variations of displacement direction and plunge are noted and correlated with the morphology of the failure surface reconstructed using the vector inclination method (VIM). In particular, steeper displacement vectors were reconstructed in the upper slope, compared to the central part, thus suggesting a change in basal surface morphology, which is largely controlled by rock mass foliation. Through this analytical approach, the Fels landslide is shown to be a slow-moving, compound rockslide, the displacement of which is controlled by structural geological features and promoted by glacier retreat. [ABSTRACT FROM AUTHOR]

Cascading landslides are sequences of multiple landslides that commonly involves significant volumes of material and exhibit variable velocity up to several m/s. The impact of these processes is generally significant so that they can claim victims and be responsible of significant losses. Considering the complexity of the process, their mitigation involves the understanding of causeeffect relations between the initial triggering event and subsequent cascading processes as well as the development of methodological framework for their analysis. On this basis, this work aims to analyse the characteristics of the cascading landslide event of Morino-Rendinara, in the L’Aquila province, applying a procedure that, comprising multiple methods, is capable of providing data depicting mechanism and kinematics of the system, anatomy of landslides and prospective susceptibility scenario. [ABSTRACT FROM AUTHOR]

Rockfalls are among the most dangerous natural hazards. The study of these phenomena may be complex in relation to the geology and the rock mass characteristics. Recent advances on the use of remote sensing techniques made the survey of rock slopes easier and faster, increasing the amount and quality of data. At the same time, the improved availability of software for characterizing rock slopes and simulating rockfalls permits a more detailed and precise definition of rockfall hazard areas. In this context, this research highlights the importance of using remote sensing techniques in the study of these phenomena, especially in developing accurate and reliable geological and structural models. The Regional Park of Monte Conero (Ancona, Italy) is used as the case example. The study area has been investigated through conventional geological/structural surveys, UAV photogrammetry and iPad-based LiDAR. The data gathered from surveys have been used to perform rockfall simulations and define potential mitigation measures. Finally, innovative visualization techniques based on the use of Virtual Reality will be introduced for an improved interpretation of geological and structural data and simulation results. [ABSTRACT FROM AUTHOR]

Natural hazard is recognized to be a threat to human society that is becoming more critical under the perspective of climate change. Italy is one of the most exposed European countries to hydrogeomorphological hazard, as proven by the annual casualties and economic losses. Among the most recent events, the one happened in the Misa river catchment in 2022 is of particular interest for the great amount of rainfall poured in just one night, which led to extensive erosion. The mountainous part of the catchment was hit by several landslides, and the resulting sediments and debris, transported downstream by rivers, increased the amount of damages. This event was interpreted as directly caused by climate change, as it was linked to a tropical storm not common at this latitude. This work proposes an analysis of this exceptional event from a hydrological point of view. The 2022 event was compared with other relevant events from the past, in order to evaluate the peak discharge, the sediment erosion and transport processes in the area for different scenarios. The analysis included the modelling at catchment scale with two physically-based models: SWAT, which provides results on channel processes, and SMART-SED, working at the catchment scale. The two scales of analysis, together with field surveys, gave a better understanding of the evolution of the shallow processes during the events. Finally, this work intends to highlight the relevance of hydrological modelling for the management of hazard in different scenarios at catchment scale. [ABSTRACT FROM AUTHOR]

Italy is famous for its one-of-a-kind landscapes and the many cultural heritage sites characterizing the story of its regions. In central Italy, during the medieval age, some of them were built on the top of high and steep cliffs, often on the top of ancient ruins, to protect urban agglomerations, goods and people. The geographical locations of these centers allowed them to maintain their original conformation over time, but, at the same time, exposed them to a high risk of landslides. In this context, this research aimed to present an integrated and low-cost approach to study the potential landslide phenomena affecting two medieval towns. Field surveys and mapping were carried out through the use of innovative digital mapping tools to create a digital database directly on the field. Data gathered during field surveys were integrated with GIS analyses for an improved interpretation of the geological and geomorphological features. Due to the inaccessibility of the cliffs surrounding the two villages, a more detailed analysis of these areas was performed through the use of unmanned aerial vehicle-based photogrammetry, while advanced differential synthetic aperture radar interferometry (A-DInSAR) interpretation was undertaken to verify the stability of the buildings in proximity to the cliffs and other potential active failures. The results of the study highlighted the similar geometry and structural settings of the two areas. Kinematically, the intersection of three main joint sets tends to detach blocks (sometimes in high volumes) from the cliffs. The A-DInSAR analysis demonstrated the presence of a landslide failure along the northwest side of the Monte San Martino town. The buildings in proximity to the cliffs did not show evidence of movements. More generally, this research gives insights into the pro and cons of different survey and analysis approaches and into the benefits of their procedural integration in space and in time. Overall, the procedure developed here may be applied in similar contexts in order to understand the structural features driving slopes' instabilities and create digital databases of geological/monitoring data. [ABSTRACT FROM AUTHOR]

This work presents the case study of the San Lorenzo road tunnel, a linear infrastructure located in Friuli Venezia Giulia, northern Italy, affected by the so-called Passo della Morte landslide. This slope instability phenomenon has caused several problems since the beginning of the tunnel construction works, like water seepage and concrete lining detachments, increasing the safety risk for road users. Due to these circumstances, numerous studies have been conducted and a monitoring system has been developed since 2014 to control the ongoing situation of the most damaged portions of the road tunnel. Building on the monitoring data, studies and knowledge accumulated over the years, this paper describes the work done to trace back the sum of landslide-induced stresses directly responsible for the current damages on the tunnel. The first steps taken in this direction have included an adequate representation of the crack pattern on the tunnel vault. This has been used both as an initial goal and as a means of validation in the development of a finite element model. From the latter, it will be possible to infer the landslide-induced stress combination mentioned earlier. [ABSTRACT FROM AUTHOR]

The town of San Leo, situated in the northern Apennines of Italy, is subject to slope instability phenomena due to its geological/structural setting. The 3D geological model of the eastern sector of the San Leo rocky plateau has been developed through the integration of data obtained from laser scanning and digital photogrammetric surveys along with core drilling geomechanical analysis. The geological features have been integrated into one single model that enables the visualization of all structural features of the plateau, including superficial and in-depth discontinuities. The model highlights how the area rear of the northeastern scarp, formed by the 2014 landslide event, is highly fractured, whereas the eastern cliff presents lower fracture intensity. Two possible collapse scenarios have been simulated in relation to the fracture analysis that has been carried and emulating the 2014 failure mechanism, that involved a rock volume around 330.000 m3. The analysis of persistence of the fractures involved in these scenarios indicate that such events appear not to be imminent at the present time. [ABSTRACT FROM AUTHOR]

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This article presents a multi-scale and multidisciplinary study of large, late Pleistocene or early Holocene slumps in Eocene sedimentary rocks at Mount Burnaby, just east of Vancouver, British Columbia (BC). Airborne Light Detection and Ranging (LiDAR) and field data were integrated into a Geographic Information System to understand the origin, kinematics, and subsequent history of the landslides. Products derived from the bare-earth LiDAR data include an engineering geomorphology map, shaded relief maps, and several LiDAR slope profiles. To understand the landslides better, we analyzed discontinuities and structural lineaments. The structure of the Eocene rocks underlying Mount Burnaby was compared with trends of local lineaments and the shape of the coastline of Burrard Inlet and Indian Arm as well as trends of regional faults and lineaments identified by previous researchers working in southwest BC. Two main joint systems likely played a key role in conditioning the north slope of Mount Burnaby for failure. The landslides likely happened during or soon after deglaciation of the area at the end of the Pleistocene on the steep north face of Mount Burnaby after a 200-m fall in relative sea level caused by glacio-isostatic uplift of the crust. [ABSTRACT FROM AUTHOR]