Rockfall Inventory Research Articles

A Remote-Sensing-Based Method Using Rockfall Inventories for Hazard Mapping at the Community Scale in the Arequipa Region of Peru

Small communities in the Arequipa region of Peru are susceptible to rockfall hazards, which impact their lives and livelihoods. To mitigate rockfall hazards, it is first necessary to understand their locations and characteristics, which can be compiled into an inventory used in the creation of rockfall hazard rating maps. However, the only rockfall inventory available for Arequipa contains limited data of large, discrete events, which is insufficient for characterizing rockfall hazards at the community scale. A more comprehensive inventory would result in a more accurate rockfall hazard rating map—a significant resource for hazard mitigation and development planning. This study addresses this need through a remote method for rockfall hazard characterization at a community scale. Three communities located in geographically diverse areas of Arequipa were chosen for hazard inventory and characterization, with a fourth being used for validation of the method. Rockfall inventories of source zones and rockfall locations were developed using high-resolution aerial imagery, followed by field confirmation, and then predictions of runout distances using empirical models. These models closely matched the actual runout distance distribution, with all three sites having an R2 value of 0.98 or above. A semi-automated method using a GIS-based model was developed that characterizes the generation and transport of rockfall. The generation component criteria consisted of source zone height, slope angle, and rockmass structural condition. Transport was characterized by rockfall runout distance, estimated rockfall trajectory paths, and hazard ratings of corresponding source zones. The representative runout distance inventory model of the validation site matched that of a nearby site with an R2 of 0.98, despite inventorying less than a third of the number of rockfalls. This methodology improves upon current approaches and could be tested in other regions with similar climatic and geomorphic settings. These maps and methodology could be used by local and regional government agencies to warn residents of rockfall hazards, inform zoning regulations, and prioritize mitigation efforts.

Rockfall mapping and susceptibility evaluation based on UAV high-resolution imagery and support vector machine method

Abstract This study aims to develop a comprehensive method for automated rockfall mapping and susceptibility assessment using unmanned aerial vehicle (UAV) tilt photography and the support vector machine (SVM) algorithm. By employing Jinzhong Town in the mountainous forest landscapes of Southwest China as a case study, we leverage photogrammetry principles and computer vision algorithms to generate high-precision, high-resolution digital surface models (DSMs), and digital orthophoto maps through a UAV remote sensing system. The rockfall inventory is accurately and automatically mapped using the object-based classification method and SVM algorithm. The automated rockfall identification method achieves a 93% accuracy with a Kappa coefficient of 0.7967. Statistical analyses of spatial distribution characteristics reveal a significant correlation between rockfall volume and area with a correlation coefficient (R²) of 0.92316 under logarithmic coordinates. In addition, a power function relationship describes the link between rockfall volume and slope height (R² = 0.87), while the relationship with sliding distance is characterized by a weaker linear correlation (R² = 0.65). Rockfall runout distance also shows a significant linear correlation with slope height (R² = 0.79) but exhibits a less-pronounced association with rockfall volume (R² = 0.58). The SVM model employed to assess rockfall susceptibility indicates high accuracy (area under the curve = 0.896), affirming its efficacy in rockfall susceptibility assessment. Our findings underscore the utility of UAV remote sensing for rockfall information extraction and susceptibility evaluation, particularly in challenging mountainous forest environments characterized by intricate topography and geological complexities.

Effect of Rockfall Spatial Representation on the Accuracy and Reliability of Susceptibility Models (The Case of the Haouz Dorsale Calcaire, Morocco)

Rockfalls can cause loss of life and material damage. In Northern Morocco, rockfalls and rock avalanche-deposits are frequent, especially in the Dorsale Calcaire morpho-structural unit, which is mostly formed by Jurassic limestone and dolostone formations. In this study, we focus exclusively on its northern segment, conventionally known as “the Haouz subunit”. First, a rockfall inventory was conducted. Then, two datasets were prepared: one covering exclusively the source area and the other representing the entirety of the mass movements (source + propagation area). Two algorithms were then used to build rockfall susceptibility models (RSMs). The first one (Logistic Regression: LR) yielded the most unreliable results, where the RSM derived from the source area dataset significantly outperformed the one based on the entirety of the rockfall affected area, despite the lack of significant visual differences between both models. However, the RSMs produced using Artificial Neural Networks (ANNs) were more or less similar in terms of accuracy, despite the source area model being more conservative. This result is unexpected given the fact that previous studies proved the robustness of the LR algorithm and the sensitivity of ANN models. However, we believe that the non-linear correlation between the spatial distribution of the rockfall propagation area and that of the conditioning factors used to compute the models explains why modeling rockfalls in particular differs from other types of landslides.

A scenario-based approach for immediate post-earthquake rockfall impact assessment

Different approaches exist to describe the seismic triggering of rockfalls. Statistical approaches rely on the analysis of local terrain properties and their empirical correlation with observed rockfalls. Conversely, deterministic, or physically based approaches, rely on the modeling of individual trajectories of boulders set in motion by seismic shaking. They require different data and allow various interpretations and applications of their results. Here, we present a new method for earthquake-triggered rockfall scenario assessment adopting ground shaking estimates, produced in near real-time by a seismological monitoring network. Its key inputs are the locations of likely initiation points of rockfall trajectories, namely, rockfall sources, obtained by statistical analysis of digital topography. In the model, ground shaking maps corresponding to a specific earthquake suppress the probability of activation of sources at locations with low ground shaking while enhancing that in areas close to the epicenter. Rockfall trajectories are calculated from the probabilistic source map by three-dimensional kinematic modeling using the software STONE. We apply the method to the 1976 MI = 6.5 Friuli earthquake, for which an inventory of seismically-triggered rockfalls exists. We suggest that using peak ground acceleration as a modulating parameter to suppress/enhance rockfall source probability, the model reasonably reproduces observations. Results allow a preliminary impact evaluation before field observations become available. We suggest that the framework may be suitable for rapid rockfall impact assessment as soon as ground-shaking estimates (empirical or numerical models) are available after a seismic event.

Rockfall Intensity under Seismic and Aseismic Conditions: The Case of Lefkada Island, Greece

Rockfall analysis is a multiparametric procedure with many uncertainties and the outputs are largely dependent on some critical engineering geological parameters involved in the used simulation model. In this paper, three completely different limestone rock sequences, named Pantokratoras, Vigla, and Paxos limestones along the western coastal slopes of Lefkada island, in Greece, are examined regarding their rockfall susceptibility as expressed by produced kinetic energy, under aseismic and seismic conditions. A rockfall inventory was prepared through detailed field measurements after the extensive rockfalls which were caused by the strong earthquake of November 2015, while engineering geological surveys were systematically conducted on the limestone rock masses. Two case scenarios were adopted for the rockfall simulations: one without the horizontal initial velocity of the detached rock boulder and the other with an estimated value obtained from the peak ground velocity (PGV) of the main seismic shock. Two-dimensional rockfall simulations were performed in selected cross-sections for each rock mass, and spatial distribution maps of the intensity (kinetic energy) were generated. A comparison of the maps has shown a strong maximum variation in the intensity levels among the three rock masses mainly due to the differential size of the detached boulders because of the inherent engineering geological characteristics of the rock masses. The results show that the earthquake ground velocity generally leads to a fluctuating change in the intensity values due to the trajectory shape and increases the rockfall magnitude as the main triggering factor.

Machine Learning-Based Rockfalls Detection with 3D Point Clouds, Example in the Montserrat Massif (Spain)

Rock slope monitoring using 3D point cloud data allows the creation of rockfall inventories, provided that an efficient methodology is available to quantify the activity. However, monitoring with high temporal and spatial resolution entails the processing of a great volume of data, which can become a problem for the processing system. The standard methodology for monitoring includes the steps of data capture, point cloud alignment, the measure of differences, clustering differences, and identification of rockfalls. In this article, we propose a new methodology adapted from existing algorithms (multiscale model to model cloud comparison and density-based spatial clustering of applications with noise algorithm) and machine learning techniques to facilitate the identification of rockfalls from compared temporary 3D point clouds, possibly the step with most user interpretation. Point clouds are processed to generate 33 new features related to the rock cliff differences, predominant differences, or orientation for classification with 11 machine learning models, combined with 2 undersampling and 13 oversampling methods. The proposed methodology is divided into two software packages: point cloud monitoring and cluster classification. The prediction model applied in two study cases in the Montserrat conglomeratic massif (Barcelona, Spain) reveal that a reduction of 98% in the initial number of clusters is sufficient to identify the totality of rockfalls in the first case study. The second case study requires a 96% reduction to identify 90% of the rockfalls, suggesting that the homogeneity of the rockfall characteristics is a key factor for the correct prediction of the machine learning models.

Modified “Rockfall Hazard Rating System for Pakistan (RHRSP)”: An Application for Hazard and Risk Assessment along the Karakoram Highway, Northwest Pakistan

Rockfall is a natural mountain hazard posing a severe threat to people, infrastructure, and vehicles along the transportation corridors. In this research, the standard Rockfall Hazard Rating System (RHRS) is slightly modified for the mountainous terrains of Pakistan through the quantification of animal activity along the highways. In the modified Rockfall Hazard and Rating System for Pakistan (RHRSP), animal activity is scored based on permanent and random animal tracks, shallow and higher altitudes, and shoulder width. The model is applied along the Karakoram Highway (KKH), which traverses a suture between Besham and Dasu (≈78 km), for Rockfall hazard and risk assessment mapping. An inventory of rockfalls, topples, and debris slides is compiled at 30 stations. Results show that rockfalls are mostly of the wedge and topple type failures. Fifty-seven percent of the area falls under the very-high to high hazard zone, 18% under moderate hazard, and 25% covers the low to very low hazard zone. Sixty-seven percent of the stretch is at very-high to high risk, distributed from Dubair to Dasu. The main reason for this risk is associated with narrow road width and limited shoulder width for vehicles. The RHRSP model is also applicable for other highways with the same geological and morphological settings.

Deciphering meteorological influencing factors for Alpine rockfalls: a case study in Aosta Valley

This study exploited the historical rockfall inventory and the meteorological stations database of Mont Cervin and Mont Emilius Mountain Communities (Aosta Valley, northern Italy) to decipher relationships between climate processes, typical of mountain environments and rockfall phenomena. The period from 1990 to 2018 was selected as reference to perform the analysis. Climate processes were translated into four climate indices, namely short-term rainfall (STR), effective water inputs (EWI, including both rainfall and snow melting), wet and dry episodes (WD) and freeze-thaw cycles (FT). The comparison between climate indices values at each rockfall occurrence and the statistical distributions describing the whole indices dataset allowed to define not ordinary climatic conditions for each index and their influence on rockfall occurrence. Most of the events analysed (>95% out of 136) occurred in correspondence of the defined not ordinary climatic conditions for one or for a combination of the indices. The relationships between rockfalls and climate showed a seasonality. In spring, most of the events resulted to be connected to FT (70%) while in autumn to EWI (49%). The relative seasonal importance of WD reached its maximum in summer with 23% of the events related to this index alone. Based on these results, different strategies to define empirical critical thresholds for each climate index were explored, in order to make them valid for the whole study area. A preliminary exploratory analysis of the influence of high temperatures and temperature gradients was carried out for some summertime rockfalls, not correlated to the other investigated indices. The presented approach is exportable in neighbouring regions, given the availability of a dated rockfall dataset, and could be adapted to include different processes.

Fault controls on spatial variation of fracture density and rock mass strength within the Yarlung Tsangpo Fault damage zone (southeastern Tibet)

Recent technological developments including Unmanned Aerial Vehicles, terrestrial laser scanning, photogrammetry and point cloud analysis software tools greatly enhance our ability to investigate the relationship between faulting and the spatial geometrical and mechanical characteristics of a rock mass controlled by faulting. Using the Yarlung Tsangpo (YLTP) Fault of southeastern Tibet as a case study, we propose the procedures, investigation approaches, evidence and criteria for quantitatively defining the threshold distance for damage zones combining the spatial variations of fracture density, rock mass strength and rockfall inventory in this study. Based on the study, the extent of threshold distance of damage zone of the YLTP Fault is estimated as 5.9 ± 0.6 km, which has been compared with published data from the evidence of thermal effects related to exactly the same fault and shows a good match. Five predominant joint sets were identified in the study area. Influenced by tectonics, the relationship between mean spacing of the joint sets with distance from the fault core shows a strong positive power relationship. It is consistent with the result of the exponential relation between the mean size of fallen blocks and distance from the fault core that is obtained independently. We conclude that fracture density (the joint volumetric count, Jv) and cohesion of the rock mass show power curve relations with distance within the damage zone. The fault first affects the characteristics of rock mass structures, and then the characteristics of the rock structures influence the stability of slope leading to rockfall.

A New Approach for Identification of Potential Rockfall Source Areas Controlled by Rock Mass Strength at a Regional Scale

The identification of rockfall source areas is a fundamental work for rockfall disaster prevention and mitigation. Based on the Culmann model, a pair of important indicators to estimate the state of slope stability is the relief and slope angles. Considering the limit of field survey and the increasing requirements for identification over a large area, a new approach using the relief–slope angle relationship to identify rockfall source areas controlled by rock mass strength at a regional scale is proposed in this paper. Using data from helicopter-based remote sensing imagery, a digital elevation model of 10 m resolution, and field work, historical rockfalls in the Wolong study area of Tibet where frequent rockfalls occur are identified. A clear inverse relationship between the relief and slope angles of historical rockfalls enables us to calculate the rock mass strength of the landscape scale by the Culmann model and the relief–slope angle relationship curve. Other parameters used in our proposed approach are calculated by ArcGIS and statistic tools. By applying our approach, the potential rockfall source areas in the study are identified and further zoned into three susceptibility classes that could be used as a reference for a regional rockfall susceptibility study. Using the space partition of historical rockfall inventory, our prediction result is validated. Most of the rockfall source areas (i.e., 71.92%) identified in the validation area are occupied by historical rockfalls, which proves the good prediction of our approach. The dominant uncertainty in this paper is derived from the process of calculating rock mass strength, defining the specific area for searching potential rockfall source areas, and the resolution of the digital elevation model.

Rock slope failure in the Western Alps: A first comprehensive inventory and spatial analysis

Rock slope failure (RSF) occurs in different contexts but is typically reported either as (i) single-category inventories or (ii) single-site geotechnical monographs. Few studies have sought to evaluate the spatial incidence of all modes of RSF conjointly, and to infer scenarios of regional landscape evolution from observed patterns of cumulative rock slope overstressing. Here we present the results of a systematic inventory of rock avalanches, rockfalls, rockslides, and gravitational rock slope deformations in the Western Alps (France, Italy, Switzerland) conducted using satellite imagery made available in Google Earth as a detection tool, and aided by preliminary ground-truth checks. The inventory totals 1416 montane RSFs, impacting 9.1% of the study area. Underpinned by GIS tools, the study further examines the spatial distribution of RSF with consideration for (i) predisposing factors (typically: lithology, geological structure), and (ii) preparatory factors (geomorphological process regimes that drives a given slope segment to the point of failure). The latter encompass slower variables (e.g., long-term crustal stress regime, cumulative residence time above equilibrium line altitudes) and faster variables (e.g., short-span glacier-related stresses, permafrost thaw, seismicity). RSF density patterns helped to define seven RSF super-hotspots (large diversity of RSF modes, up to 50% of displaced rock masses/unit area), which define the most intensely overstressed areas of the Western Alps. These super-hotspots occur at sites where highly dynamic, thick, warm-based glaciers above the equilibrium line either intersected (middle Maurienne) or followed the strike of (middle Isère) N-S bands of highly susceptible lithologies and structures during the Quaternary. The widespread incidence of rock slope deformation (cumulative area: 1760 km2, i.e., nearly 3 times the total of the other three RSF categories combined) appears further correlated with the low tectonic activity of the orogen and with its areas dominated by an extensional tectonic regime west of the Penninic Frontal Thrust. This contrasts with seismically active orogens, e.g., New Zealand's Southern Alps, where rock slope deformation is scarce compared to rock avalanches and shallow landslides.

A 6-year lidar survey reveals enhanced rockwall retreat and modified rockfall magnitudes/frequencies in deglaciating cirques

Abstract. Cirque erosion contributes significantly to mountain denudation and is a key element of glaciated mountain topography. Despite long-standing efforts, rates of rockwall retreat and the proportional contributions of low-, mid- and high-magnitude rockfalls have remained poorly constrained. Here, a unique, terrestrial-lidar-derived rockfall inventory (2011–2017) of two glaciated cirques in the Hohe Tauern range, Central Alps, Austria, is analysed. The mean cirque wall retreat rate of 1.9 mm a−1 ranks in the top range of reported values and is mainly driven by enhanced rockfall from the lowermost, freshly deglaciated rockwall sections. Retreat rates are significantly elevated over decades subsequent to glacier downwasting. Elongated cirque morphology and recorded cirque wall retreat rates indicate headward erosion is clearly outpacing lateral erosion, most likely due to the cataclinal backwalls, which are prone to large dip-slope failures. The rockfall magnitude–frequency distribution – the first such distribution derived for deglaciating cirques – follows a distinct negative power law over 4 orders of magnitude. Magnitude–frequency distributions in glacier-proximal and glacier-distal rockwall sections differ significantly due to an increased occurrence of large rockfalls in recently deglaciated areas. In this paper, the second of two companion pieces, we show how recent climate warming shapes glacial landforms, controls spatiotemporal rockfall variation in glacial environments and indicates a transient signal with decadal-scale exhaustion of rockfall activity immediately following deglaciation crucial for future hazard assessments.

Current glacier recession causes significant rockfall increase: the immediate paraglacial response of deglaciating cirque walls

Abstract. In the European Alps, almost half the glacier volume has disappeared over the past 150 years. The loss is reflected in glacier retreat and ice surface lowering even at high altitude. In steep glacial cirques, surface lowering exposes rock to atmospheric conditions probably for the very first time in several millennia. Instability of rockwalls has long been identified as one of the direct consequences of deglaciation, but so far cirque-wide quantification of rockfall at high resolution is missing. Based on terrestrial lidar, a rockfall inventory for the permafrost-affected rockwalls of two rapidly deglaciating cirques in the Central Alps of Austria (Kitzsteinhorn) is established. Over 6 years (2011–2017), 78 rockwall scans were acquired to generate data of high spatial and temporal resolution. Overall, 632 rockfalls were registered, ranging from 0.003 to 879.4 m3, mainly originating from pre-existing structural rock weaknesses. A total of 60 % of the rockfall volume detached from less than 10 vertical metres above the glacier surface, indicating enhanced rockfall activity over tens of years following deglaciation. Debuttressing seems to play a minor effect only. Rather, preconditioning is assumed to start inside the randkluft (void between cirque wall and glacier) where measured sustained freezing and ample supply of liquid water likely cause enhanced physical weathering and high quarrying stresses. Following deglaciation, pronounced thermomechanical strain is induced and an active layer penetrates into the formerly perennially frozen bedrock. These factors likely cause the observed paraglacial rockfall increase close to the glacier surface. This paper, the first of two companion pieces, presents the most extensive dataset of high-alpine rockfall to date and the first systematic documentation of a cirque-wide erosion response of glaciated rockwalls to recent climate warming.

Decoding Complex Erosion Responses for the Mitigation of Coastal Rockfall Hazards Using Repeat Terrestrial LiDAR

A key factor limiting our understanding of rock slope behavior and associated geohazards is the interaction between internal and external system controls on the nature, rates, and timing of rockfall activity. We use high-resolution, monthly terrestrial light detection and ranging (LiDAR) surveys over a 2 year monitoring period to quantify rockfall patterns across a 0.6 km-long (15.3 × 103 m2) section of a limestone rock cliff on the northeast coast of England, where uncertainty in rates of change threaten the effective planning and operational management of a key coastal cliff top road. Internal system controls, such as cliff material characteristics and foreshore geometry, dictate rockfall characteristics and background patterns of activity and demonstrate that layer-specific analyses of rockfall inventories and sequencing patterns are essential to better understand the timing and nature of rockfall risks. The influence of external environmental controls, notably storm activity, is also evaluated, and increased storminess corresponds to detectable rises in both total and mean rockfall volume and the volumetric contribution of large (>10 m3) rockfalls at the cliff top during these periods. Transient convergence of the cumulative magnitude–frequency power law scaling exponent (ɑ) during high magnitude events signals a uniform erosion response across the wider cliff system that applies to all lithologies. The tracking of rockfall distribution metrics from repeat terrestrial LiDAR in this way demonstrably improves the ability to identify, monitor, and forecast short-term variations in rockfall hazards, and, as such, provides a powerful new approach for mitigating the threats and impacts of coastal erosion.

Emergent characteristics of rockfall inventories captured at a regional scale

AbstractHigh‐resolution rockfall inventories captured at a regional scale are scarce. This is partly owing to difficulties in measuring the range of possible rockfall volumes with sufficient accuracy and completeness, and at a scale exceeding the influence of localized controls. This paucity of data restricts our ability to abstract patterns of erosion, identify long‐term changes in behaviour and assess how rockfalls respond to changes in rock mass structural and environmental conditions. We have addressed this by developing a workflow that is tailored to monitoring rockfalls and the resulting cliff retreat continuously (in space), in three‐dimensional (3D) and over large spatial scales (>104 m). We tested our approach by analysing rockfall activity along 20.5 km of coastal cliffs in North Yorkshire (UK), in what we understand to be the first multi‐temporal detection of rockfalls at a regional scale. We show that rockfall magnitude–frequency relationships, which often underpin predictive models of erosion, are highly sensitive to the spatial extent of monitoring. Variations in rockfall shape with volume also imply a systemic shift in the underlying mechanisms of detachment with scale, leading us to question the validity of applying a single probabilistic model to the full range of rockfalls observed here. Finally, our data emphasize the importance of cliff retreat as an episodic process. Going forwards, there will a pressing need to understand and model the erosional response of such coastlines to rising global sea levels as well as projected changes to winds, tides, wave climates, precipitation and storm events. The methodologies and data presented here are fundamental to achieving this, marking a step‐change in our ability to understand the competing effects of different processes in determining the magnitude and frequency of rockfall activity and ultimately meaning that we are better placed to investigate relationships between process and form/erosion at critical, regional scales. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd

The Implications of M3C2 Projection Diameter on 3D Semi-Automated Rockfall Extraction from Sequential Terrestrial Laser Scanning Point Clouds

Rockfall inventories are essential to quantify a rockfall activity and characterize the hazard. Terrestrial laser scanning and advancements in processing algorithms have resulted in three-dimensional (3D) semi-automatic extraction of rockfall events, permitting detailed observations of evolving rock masses. Currently, multiscale model-to-model cloud comparison (M3C2) is the most widely used distance computation method used in the geosciences to evaluate 3D changing features, considering the time-sequential spatial information contained in point clouds. M3C2 operates by computing distances using points that are captured within a projected search cylinder, which is locally oriented. In this work, we evaluated the effect of M3C2 projection diameter on the extraction of 3D rockfalls and the resulting implications on rockfall volume and shape. Six rockfall inventories were developed for a highly active rock slope, each utilizing a different projection diameter which ranged from two to ten times the point spacing. The results indicate that the greatest amount of change is extracted using an M3C2 projection diameter equal to, or slightly larger than, the point spacing, depending on the variation in point spacing. When the M3C2 projection diameter becomes larger than the changing area on the rock slope, the change cannot be identified and extracted. Inventory summaries and illustrations depict the influence of spatial averaging on the semi-automated rockfall extraction, and suggestions are made for selecting the optimal projection diameter. Recommendations are made to improve the methods used to semi-automatically extract rockfall from sequential point clouds.

The Importance of Monitoring Interval for Rockfall Magnitude‐Frequency Estimation

AbstractRockfalls commonly exhibit power law volume‐frequency distributions, where fewer large events are observed relative to more numerous small events. Within most inventories, the smallest rockfalls are the most difficult to detect and so may not be adequately represented. A primary challenge occurs when neighboring events within a single monitoring interval are recorded as one, producing ambiguity in event location, timing, volume, and frequency. Identifying measurement intervals that minimize these uncertainties is therefore essential. To address this, we use an hourly data set comprising 8,987 3‐D point clouds of a cliff that experiences frequent rockfalls. Multiple rockfall inventories are derived from this data set using change detections for the same 10‐month period, but over different monitoring intervals. The power law describing the probability distribution of rockfall volumes is highly sensitive to monitoring interval. The exponent,β, is stable for intervals >12 hr but increases nonlinearly over progressively short timescales. This change is manifested as an increase in observed rockfall numbers, from 1.4 × 103(30 day intervals) to 1.4 × 104(1 hr intervals), and a threefold reduction in mean rockfall volume. When the monitoring interval exceeds 4 hr, the geometry of detected rockfalls becomes increasingly similar to that of blocks defined by rock mass structure. This behavior change reveals a time‐dependent component to rockfall occurrence, where smaller rockfalls (identifiable from more frequent monitoring) are more sensitive to progressive deformation of the rock mass. Acquiring complete inventories and attributing discrete controls over rockfall occurrence may therefore only be achievable with high‐frequency monitoring, dependent upon local lithology.

GIS-Based Rockfall Susceptibility Zoning in Greece

The assessment of rockfall risks on human activities and infrastructure is of great importance. Rock falls pose a significant risk to (a) transportation infrastructure, (b) inhabited areas, and (c) Cultural Heritage sites. The paper presents a method to assess rockfall susceptibility at national scale in Greece, using a simple rating approach and Geographic Information Systems (GIS) techniques. An extensive inventory of rockfalls for the entire country was compiled for the period between 1935 and 2019. The rockfall events that were recorded are those which have mainly occurred as distinct rockfall episodes in natural slopes and have impacted human activities, such as roads, inhabited areas, and archaeological sites. Through a detailed analysis of the recorded data, it was possible to define the factors which determine the occurrence of rockfalls. Based on this analysis, the susceptibility zoning against rockfalls at the national scale was prepared, using a simple rating approach and GIS techniques. The rockfall susceptibility zoning takes into account the following parameters: (a) the slope gradient, (b) the lithology, (c) the annual rainfall intensity, (d) the earthquake intensity, and (e) the active fault presence. Emphasis was given on the study of the earthquake effect as a triggering mechanism of rockfalls. Finally, the temporal and spatial frequency of the recorded events and the impact of rockfalls on infrastructure assets and human activities in Greece were evaluated.

Detection and analysis of mass wasting events in chalk sea cliffs using UAV photogrammetry

Coastal cliff recession represents a significant risk to both people and infrastructure, it is therefore important that we are able to efficiently monitor these environments to inform future management decisions. Through the use of UAV digital photogrammetry, we obtain point clouds to develop monthly models of the sea cliffs at Telscombe, East Sussex, UK between August 2016 and July 2017. The models captured were accurate to 0.05 m and had an average point density of 351 pts./m2. Using the methodology presented we were able to automatically detect rockfalls by undertaking a 2.5D surface change detection which populated monthly inventories through volumetric estimations. A total of 10,085 failures were observed with an estimated volumetric flux of 3889.4 m3 over the 12 month period of data collection. Due to the high frequency of data capture, successive block failures in the Newhaven Chalk formation were observed. The largest failure within the 12 month period was estimated at 2546.8 m3 and followed significant toe erosion due to wave action. The steepening of the cliff face was modelled through limit equilibrium analysis to determine the reduction in factor of safety for the months preceding failure. We then present a magnitude-frequency analysis using negative power laws from the monthly rockfall inventories for the entire study area. The negative power law models produced a strong correlation across all months with r2 values ranging from 0.97 to 0.99. The normalised power law scaling parameters ranged from 1.421 to 1.955 for β and from 33.79 to 904.14 for s. The observed rollover in power laws presented in previous research matches the resolution of the data presented in this study. Our results show that this method of data capture is comparable to existing methods whilst offering significant benefits in field surveying time and cost.

Combining temporal 3-D remote sensing data with spatial rockfall simulations for improved understanding of hazardous slopes within rail corridors

Abstract. Remote sensing techniques can be used to gain a more detailed understanding of hazardous rock slopes along railway corridors that would otherwise be inaccessible. Multiple datasets can be used to identify changes over time, creating an inventory of events to produce magnitude–frequency relationships for rockfalls sourced on the slope. This study presents a method for using the remotely sensed data to develop inputs to rockfall simulations, including rockfall source locations and slope material parameters, which can be used to determine the likelihood of a rockfall impacting the railway tracks given its source zone location and volume. The results of the simulations can be related to the rockfall inventory to develop modified magnitude–frequency curves presenting a more realistic estimate of the hazard. These methods were developed using the RockyFor3D software and lidar and photogrammetry data collected over several years at White Canyon, British Columbia, Canada, where the Canadian National (CN) Rail main line runs along the base of the slope. Rockfalls sourced closer to the tracks were more likely to be deposited on the track or in the ditch, and of these, rockfalls between 0.1 and 10 m3 were the most likely to be deposited. Smaller blocks did not travel far enough to reach the bottom of the slope and larger blocks were deposited past the tracks. Applying the results of the simulations to a database of over 2000 rockfall events, a modified magnitude–frequency can be created, allowing the frequency of rockfalls deposited on the railway tracks or in ditches to be determined. Suggestions are made for future development of the methods including refinement of input parameters and extension to other modelling packages.