Rockfall Databases Research Articles

Seasonality of rockfall triggers and conditioning factors interpreted from a lidar-derived rockfall database

Relationships between rockfall and environmental factors have been the focus of considerable research over the last several decades. Many prior studies have not quantified rockfall volume due to challenges in volume estimation without the use of modern remote sensing methods, and the role of longer-term conditioning factors on rockfall activity is often not considered. As part of a broader effort to evaluate rockfall hazard for slopes along the heavily trafficked Interstate-70 corridor in Colorado, this study takes advantage of a high temporal resolution lidar-derived rockfall database consisting of over five years' worth of rockfall data at a particular cut slope to evaluate the relationship between rockfall activity and environmental factors. The combination of monitoring duration and spatiotemporal monitoring resolution used allows for evaluation of both triggering and conditioning factors. Correlations are evaluated between rockfall metrics and weather metrics at a seasonal scale as well as the scale of individual monitoring intervals. Based on the results, it is interpreted that surficial rock material is weakened each Winter by freezing processes, large amounts of rockfall occur in the Spring as a result of the slope thawing out, elevated rockfall activity continues over the Summer as surficial rocks destabilized by freezing processes in the previous Winter are fully dislodged, and rockfall activity significantly reduces in the Fall due to the number of unstable blocks available to fall being significantly reduced. From comparison with the literature, we conclude this cycle is relatively consistent with rockfall trends observed in alpine environments, while more temperate regions exhibit notably different trends in rockfall seasonality.

Highly energetic rockfalls: back analysis of the 2015 event from the Mel de la Niva, Switzerland

Process-based rockfall simulation models attempt to better emulate rockfall dynamics to different degrees. As no model is perfect, their development is often accompanied and validated by the valuable collection of rockfall databases covering a range of site geometries, rock masses, velocities, and related energies that the models are designed for. Additionally, such rockfall data can serve as a base for assessing the model’s sensitivity to different parameters, evaluating their predictability and helping calibrate the model’s parameters from back calculation and analyses. As the involved rock volumes/masses increase, the complexity of conducting field-test experiments to build up rockfall databases increases to a point where such experiments become impracticable. To the author’s knowledge, none have reconstructed rockfall data in 3D from real events involving block fragments of approximately 500 metric tons. A back analysis of the 2015 Mel de la Niva rockfall event is performed in this paper, contributing to a novel documentation in terms of kinetic energy values, bounce heights, velocities, and 3D lateral deviations of these rare events involving block fragments of approximately 200 m3. Rockfall simulations are then performed on a “per-impact” basis to illustrate how the reconstructed data from the site can be used to validate results from simulation models.

Accuracy of Rockfall Volume Reconstruction from Point Cloud Data—Evaluating the Influences of Data Quality and Filtering

Rockfall processes are now commonly studied through monitoring campaigns using repeat lidar scanning. Accordingly, several recent studies have evaluated how the temporal resolution of data collection and various data-processing decisions can influence the apparent rockfall volumes estimated using typical rockfall database creation workflows. However, there is a lack of studies that consider how data quality and associated data-processing decisions influence rockfall volume estimation. In this work, we perform a series of tests based on an existing reference rockfall database from the Front Range of Colorado, USA, to isolate the influences of data resolution (point spacing), individual point precision, and the filter threshold applied to change results, on the volume estimates obtained for rockfalls. While the effects of individual point precision were found to be limited for typical levels of gaussian noise (standard deviation per coordinate direction ≤ 0.02 m), data resolution and change filter threshold were found to have systematic impacts on volume estimates, with the volume estimates for the smallest rockfalls decreasing substantially with increases in point spacing and change filter threshold. Because these factors disproportionately impact volume estimates for smaller rockfalls, when these factors change, the slope of the apparent power law that describes the relative frequency-volume distribution of rockfalls changes. Evidence is presented that suggests that this phenomenon can explain discrepancies between power law slopes presented in the literature based on studies focused on different scales of rockfall activity. Overall, this study demonstrates the impacts of raw data attributes on rockfall volume estimation and presents an additional effect that tends to bias rockfall frequency–magnitude power law relationships towards underestimation of the relative prevalence of small rockfalls.

Rockfall detection using LiDAR and deep learning

Rockfall risk management has traditionally been built upon scenarios based on statistical analysis of historic frequency-magnitude rockfall patterns which allows for an estimation of the return period of rockfalls within given ranges of magnitude. Τhe availability of rockfall databases is thereby of crucial importance to quantify rockfall activity. In recent years, engineering geologists have been able to leverage light detection and ranging (LiDAR) technology with automated change detection and clustering workflows, for spatially accurate rockfall monitoring and inventorization. This wealth of digital data generates new research prospects for the development of an artificial intelligence (AI) system able to analyze change detection sequences and map spatio-temporal rockfall patterns by precisely capturing the rock slope evolution in near-real-time. However, prerequisite to such an advancement is the need to efficiently automate the process of classifying surface changes corresponding to rockfall. This task typically requires intensive human effort and is prone to multiple sources of human error like subjectivity, expertise, and experience that may differ from person to person involved in a particular project. This paper investigates the potential of integrating sophisticated deep learning architectures into dynamic rockfall database population processes, with the goal of relieving experts from the daunting task of manually classifying rockfall events within large loads of change detection data. Deep neural networks based on the pioneering PointNet and PointNet++ architectures for 3D point cloud learning are developed for this purpose based on a 5-year change detection database consisting of >8000 rock slope clusters of identified change for training, with scanning intervals ranging from 5 days to 6 months. The models are tested on the 536 clusters from the two last data acquisitions to simulate the real monitoring situation and subsequently on the most frequent of the campaigns to increase the probability of working with single-event clusters. The best-performing model achieves an accuracy of about 89% and 84% on the last and shortest campaign, respectively. The optimized deep learning models are further evaluated on a geologically different rockfall database achieving almost 93% accuracy in a location where discrete geomorphologic features such as steep rock outcrops and erosion channels are present. The study shows that although it is challenging to achieve generalization in rockfall detection, site-specific training of the proposed deep learning architecture can lead to high-level performance and support further advancements in rockfall risk management.

Debris-Flow Channel Headwater Dynamics: Examining Channel Recharge Cycles With Terrestrial Laser Scanning

Debris-flows present a natural hazard to the safe operation of linear infrastructure in mountainous environments. The most significant contributor to debris-flow occurrence is a supply of readily erodible material, often created by rockfalls and other shallow landslides. The spatial distribution and total volume of storage are also critical factors, controlling the initiation location, predominant flow type, and termination location of debris-flow surges. Therefore, there is a need to be able to systematically incorporate debris recharge processes and timeframes into the monitoring and characterization of debris-flow hazards. In this work, the authors present the results of 7 years of terrestrial laser scanning (TLS) captured at the White Canyon. The White Canyon represents an analog to large scale, steep catchments to investigate the role of sediment supply on debris-flow processes. The TLS dataset was collected at monthly to quarterly intervals, providing a basis for analysis of debris transfer processes occurring on the study slope. A rockfall database of over 72,000 events was generated from 52 change detection analyses and is linked to catchment recharge and transfer processes. The results indicate that the 17 channels analyzed in the White Canyon do not directly match the conceptual models proposed from the supply theory. The channels display a variety of behaviors when exposed to the same climate signature. The temporal data acquisition rate was found to have a significant influence on the dynamics of movement that can be interpreted from TLS change detection analysis. The work highlights the need for higher frequency monitoring and the integration of climate data into the analysis, in order to better understand these dynamic processes.

Spatial data infrastructure (SDI) for inventory rockfalls with fragmentation information

The fragmentation phenomenon has a significant effect on rockfall risk assessment. This information is difficult to obtain, but it is key to improving rockfall modelling. For this reason, the RockModels team has gathered data on the fragmentation of several natural events since 2014 that nowadays wants to share them with professionals, academics and stakeholders. The best way for the dissemination of this information is the use of standard or data specifications in order to be interoperable. A fragmentation rockfall database has been created using all the gathered information, according to the INSPIRE Natural Hazard Area Data Specification currently in force. However, new tables have had to be added, since this specification does not consider fragmentation data. There are currently 6000 records of geometries of source areas, envelopes, deposits and mostly individual blocks. A web mapping application, with an automatic function for coordinate reference system transformation, has been created to facilitate access to the spatial database information. All that was developed on open-source software such as OpenLayers JavaScript library, database (PostGre-PostGIS) and the map generating Web Map Service (GeoServer). As more data are collected, the database can be easily updated and the new information will be published. Moreover, to improve data interpretation, a future task is to incorporate 3D models on the web application. The existence of this public database will facilitate research and advance in knowledge of this kind of natural hazards.Graphical abstractRockfall volume distribution inventory data.Georeferenced rockfall database including fragmentation data according to Technical Data Specifications of INSPIRE for natural hazards zone.Database mapping of rockfall inventory data in a WMS open access.Geometric modelling and geovisualization to fragmentation process in rockfall investigations.

Multi-method monitoring of rockfall activity along the classic route up Mont Blanc (4809 m a.s.l.) to encourage adaptation by mountaineers

Abstract. There are on average 35 fatal mountaineering accidents per summer in France. On average, since 1990, 3.7 of them have occurred every summer in the Grand Couloir du Goûter, on the classic route up Mont Blanc (4809 m a.s.l.). Rockfall is one of the main factors that explain this high accident rate and contribute to making it one of the most accident-prone areas in the Alps for mountaineers. In this particular context, the objective of this study is to document the rockfall activity and its triggering factors in the Grand Couloir du Goûter in order to disseminate the results to mountaineers and favour their adaptation to the local rockfall hazard. Using a multi-method monitoring system (five seismic sensors, an automatic digital camera, three rock subsurface temperature sensors, a traffic sensor, a high-resolution topographical survey, two weather stations and a rain gauge), we acquired a continuous database on rockfalls during a period of 68 d in 2019 and some of their potential triggering factors (precipitation, ground and air temperatures, snow cover, frequentation by climbers). At the seasonal scale, our results confirm previous studies showing that rockfalls are most frequent during the snowmelt period in permafrost-affected rockwalls. Furthermore, the unprecedented time precision and completeness of our rockfall database at high elevation thanks to seismic sensors allowed us to investigate the factors triggering rockfalls. We found a clear correlation between rockfall frequency and air temperature, with a 2 h delay between peak air temperature and peak rockfall activity. A small number of rockfalls seem to be triggered by mountaineers. Our data set shows that climbers are not aware of the variations in rockfall frequency and/or cannot/will not adapt their behaviour to this hazard. These results should help to define an adaptation strategy for climbers. Therefore, we disseminated our results within the mountaineering community thanks to the full integration of our results into the management of the route by local actors. Knowledge built during this experiment has already been used for the definition and implementation of management measures for the attendance in summer 2020.

Development of Improved Semi-Automated Processing Algorithms for the Creation of Rockfall Databases

While terrestrial laser scanning and photogrammetry provide high quality point cloud data that can be used for rock slope monitoring, their increased use has overwhelmed current data analysis methodologies. Accordingly, point cloud processing workflows have previously been developed to automate many processes, including point cloud alignment, generation of change maps and clustering. However, for more specialized rock slope analyses (e.g., generating a rockfall database), the creation of more specialized processing routines and algorithms is necessary. More specialized algorithms include the reconstruction of rockfall volumes from clusters and points and automatic classification of those volumes are both processing steps required to automate the generation of a rockfall database. We propose a workflow that can automate all steps of the point cloud processing workflow. In this study, we detail adaptions to commonly used algorithms for rockfall monitoring use cases, such as Multiscale Model to Model Cloud Comparison (M3C2). This workflow details the entire processing pipeline for rockfall database generation using terrestrial laser scanning.

Computational Geometry-Based Surface Reconstruction for Volume Estimation: A Case Study on Magnitude-Frequency Relations for a LiDAR-Derived Rockfall Inventory

Key to the quantification of rockfall hazard is an understanding of its magnitude-frequency behaviour. Remote sensing has allowed for the accurate observation of rockfall activity, with methods being developed for digitally assembling the monitored occurrences into a rockfall database. A prevalent challenge is the quantification of rockfall volume, whilst fully considering the 3D information stored in each of the extracted rockfall point clouds. Surface reconstruction is utilized to construct a 3D digital surface representation, allowing for an estimation of the volume of space that a point cloud occupies. Given various point cloud imperfections, it is difficult for methods to generate digital surface representations of rockfall with detailed geometry and correct topology. In this study, we tested four different computational geometry-based surface reconstruction methods on a database comprised of 3668 rockfalls. The database was derived from a 5-year LiDAR monitoring campaign of an active rock slope in interior British Columbia, Canada. Each method resulted in a different magnitude-frequency distribution of rockfall. The implications of 3D volume estimation were demonstrated utilizing surface mesh visualization, cumulative magnitude-frequency plots, power-law fitting, and projected annual frequencies of rockfall occurrence. The 3D volume estimation methods caused a notable shift in the magnitude-frequency relations, while the power-law scaling parameters remained relatively similar. We determined that the optimal 3D volume calculation approach is a hybrid methodology comprised of the Power Crust reconstruction and the Alpha Solid reconstruction. The Alpha Solid approach is to be used on small-scale point clouds, characterized with high curvatures relative to their sampling density, which challenge the Power Crust sampling assumptions.

Rockfall susceptibility along the regional road network of Aosta Valley Region (northwestern Italy)

ABSTRACT Rockfalls are a major hazard in mountain areas. They can endanger human settlements and infrastructures, and, every year, cause multiple damage and victims. An investigation of those areas more susceptible to rockfall represents a key approach to improve the analysis and management of rockfall impact and consequences. The proposed procedure involves the study of occurred rockfall databases and thematic maps to compute a susceptibility map, by the employment of Analytical Hierarchic Process. The computation is focused on the road network of Aosta Valley Region (northwestern Italy) and its proximity. The model results highlight the importance of morphometric factors on the investigated phenomena. The outcomes of the analysis were also validated by comparing rockfall databases with the receiver operating characteristic curve, in order to confirm their reliability. The results of the procedure are a starting point for a detailed planning action in order to manage the hazard related to these phenomena.

A New Methodology for Mapping Past Rockfall Events: From Mobile Crowdsourcing to Rockfall Simulation Validation

Rockfalls are one of the most common natural hazards in mountainous areas that pose high risk to people and their activities. Rockfall risk assessment is commonly performed with the use of models that can simulate the potential rockfall source, propagation and runout areas. The quality of the models can be improved by collecting data on past rockfall events. Mobile crowdsourcing is becoming a common approach for collecting field data by using smartphones, the main advantages of which are the use of a harmonised protocol, and the possibility of creating large datasets due to the simultaneous use by multiple users. This paper presents a new methodology for collecting past rockfall events with a mobile application, where the locations and attributes of rockfall source areas and rockfall deposits are collected, and the data are stored in an online database which can be accessed via the WebGIS platform. The methodology also presents an approach for calculating an actual source location based on viewshed analysis which greatly reduces the problem of field mapping of inaccessible source areas. Additionally, we present a rockfall database in the Alpine Space that has been created by the presented methodology, and an application of collected data for the calibration and validation of two rockfall models (CONEFALL and Rockyfor3D).

Dating of rockfall damage in trees yields insights into meteorological triggers of process activity in the French Alps

Rockfall release is a rather unpredictable process. As a result, the occurrence of rockfall often threatens humans and (infra)structures. The assessment of potential drivers of rockfall activity therefore remains a major challenge, even if the relative influence of rainfall, snowmelt, or freeze–thaw cycles has long been identified in short‐term monitoring projects. In the absence of longer‐term assessments of rockfall triggers and possible changes thereof, our knowledge of rockfall dynamics remains still lacunary as a result of the persisting scarcity of exhaustive and precise rockfall databases. Over the last decades, several studies have employed growth disturbances (GDs) in tree‐ring series to reconstruct rockfall activity. Paradoxically, these series were only rarely compared to meteorological records. In this study, we capitalize on the homogeneity of a centennial‐old reforestation plot to develop two reconstructions – R1 including only growth suppressions, and R2 based on injuries – with limited biases related to decreasing sample size and changes in exposed diameters back in time. By doing so, our study also and quite clearly highlights the large potential that protection forests have in terms of yielding reliable, multidecadal rockfall reconstructions. From a methodological perspective, we find no synchronicity between R1 and R2, as well as an absence of meteorological controls on rockfall processes in R1. This observation pleads for a careful selection of GDs in future reconstructions. In terms of process dynamics, we demonstrate that summer intense rainfall events (>10 mm day−1) are the main drivers for rockfall activity at our study site. Despite the stringency of our detection procedure, correlations between rockfall activity and meteorological variables remain comparable to those reported in previous studies, as a result of the complexity and multiplicity of triggering factors. We therefore call for a more systematic coupling of tree‐ring analysis with rockfall and microclimatic monitoring in future studies. © 2020 John Wiley & Sons, Ltd.

Quantifying rock fall probabilities and their temporal distribution associated with weather seasonality

Quantifying rock fall hazards requires information about their frequency and volumes. Previous studies have focused on quantifying rock fall volume–frequency relationships or the weather conditions antecedent to rock fall occurrences, and their potential use as prediction tools. This paper is focused on quantifying rock fall occurrence probabilities and presents approaches for quantifying rock fall temporal distributions. In particular, von Mises distributions allow direct correlation between seasonal weather variations and rock fall occurrences. The approaches are illustrated using a rock fall database along a railway corridor in the Canadian Cordillera, in which rock fall occurrences were correlated to the morphology and lithology. A Binomial probability distribution applied to the annual rock fall frequency suggests an average daily rock fall probability of 1 × 10−2 across the study area. However, circular (von Mises) distributions associated with weather trends in the area, and fitted to monthly rock fall records, allow estimation of daily rock fall probabilities in different months. This approach allows a direct correlation between rock fall frequencies and seasonal variations in weather conditions. The results suggest daily rock fall probabilities between 4 × 10−3 and 8 × 10−3 for April through July and up to 2.1 × 10−2 in October. Moreover, local peaks in rock fall monthly records are quantitatively explained through the seasonality of weather conditions. Similar values are obtained when applying the Binomial distribution to monthly records. However, this last approach does not show strong distribution fits and does not allow a correlation between rock fall frequencies and seasonal weather variations.

Influence of meteorological factors on rockfall occurrence in a middle mountain limestone cliff

Abstract. The influence of meteorological conditions on rockfall occurrence has been often highlighted, but knowledge of it is still not sufficient due to the lack of exhaustive and precise rockfall databases. In this study, rockfalls have been detected in a limestone cliff by annual terrestrial laser scanning, and dated by photographic survey over a period of 2.5 years. A near-continuous survey (one photo every 10 min) with a wide-angle lens has made it possible to date 214 rockfalls larger than 0.1 m3, and a monthly survey with a telephoto lens has dated 854 rockfalls larger than 0.01 m3. Analysis of the two databases shows that the rockfall frequency can be multiplied by a factor as high as 7 during freeze–thaw episodes and 26 when the mean rainfall intensity (since the beginning of the rainfall episode) is higher than 5 mmh−1. Based on these results, a three-level scale has been proposed for predicting the temporal variations of rockfall frequency. The more precise database and freeze–thaw episode definition make it possible to distinguish different phases in freeze–thaw episodes: negative temperature cooling periods, negative temperature warming periods and thawing periods. It appears that rockfalls occur more frequently during warming and thawing periods than during cooling periods. It can be inferred that rockfalls are caused by thermal ice dilatation rather than by dilatation due to the phase transition. But they may occur only when the ice melts, because the cohesion of the ice–rock interface can be sufficient to hold the rock compartment which has been cut.

A regional rockfall database: the Eastern Alps test site

Landslide databases are essential tools for risk assessment. In the Italian portion of the South-Eastern Alps these archives are managed by the Veneto Region and by the provinces of Bolzano and Trento, as part of the Italian Inventory of Landslides. About 750 rockfall events recorded in these archives were checked, put together and completed to create a new integrated database. Information on 24 new rockfalls were added from different research sources. Descriptive statistic shows that the most affected slopes with the highest frequency of rockfall are the southern ones. The distribution of rockfalls during the year presents three peaks in March, June and November. The most commonly affected lithologies are massive dolomites, effusive rocks and mica schists. Roads proved to be the most commonly involved feature. The hypsographic curves obtained from the DEMs of the provinces of Belluno, Bolzano and Trento have shown that the different areas potentially affected by periglacial processes experience rockfalls during summer and autumn, thus representing a risk to tourists and hikers. This study is the first attempt to collate and extend the available information in the various rockfall databases in the Eastern Italian Alps, with the aim at creating uniform data collection for the whole Alpine range.

Statistical correlation between meteorological and rockfall databases

Abstract. Rockfalls are a major and essentially unpredictable sources of danger, particularly along transportation routes (roads and railways). Thus, the assessment of their probability of occurrence is a major challenge for risk management. From a qualitative perspective, it is known that rockfalls occur mainly during periods of rain, snowmelt, or freeze–thaw. Nevertheless, from a quantitative perspective, these generally assumed correlations between rockfalls and their possible meteorological triggering events are often difficult to identify because (i) rockfalls are too rare for the use of classical statistical analysis techniques and (ii) not all intensities of triggering factors have the same probability. In this study, we propose a new approach for investigating the correlation of rockfalls with rain, freezing periods, and strong temperature variations. This approach is tested on three French rockfall databases, the first of which exhibits a high frequency of rockfalls (approximately 950 events over 11 years), whereas the other two databases are more typical (approximately 140 events over 11 years). These databases come from (1) national highway RN1 on Réunion, (2) a railway in Burgundy, and (3) a railway in Auvergne. Whereas a basic correlation analysis is only able to highlight an already obvious correlation in the case of the "rich" database, the newly suggested method appears to detect correlations even in the "poor" databases. Indeed, the use of this method confirms the positive correlation between rainfall and rockfalls in the Réunion database. This method highlights a correlation between cumulative rainfall and rockfalls in Burgundy, and it detects a correlation between the daily minimum temperature and rockfalls in the Auvergne database. This new approach is easy to use and also serves to determine the conditional probability of rockfall according to a given meteorological factor. The approach will help to optimize risk management in the studied areas based on their meteorological conditions.

GIS-based evaluation of rockfall risk along routes in Greece

This article presents a GIS-based system, designed to assist in the management of rockfall risk along Greek routes. The system was developed in two stages; the field data collection led to the implementation of a rockfall rating system with its fundamental parameters, while the data process concluded to a rockfall data base and a GIS-based interface. The fundamental parameters were derived from the rockfall hazard rating system developed by Pierson et al. at the Oregon State Highway Division and provide a coherent approach to decide the type and the cost of protection measures to be applied in an area affected by rockfalls and presents highest rockfall risk. The system presented in this paper as well as the original system comprises exponential scoring functions that represent the increased hazard and reflected in nine categories forming the classification. The method presented in this paper modified certain categories from the original system which were described qualitatively and may lead to quite subjective estimations. These categories are ditch effectiveness; climate and presence of water on slope as well as rockfall history. Moreover, the original category “Geologic characteristic” was changed to Structural and Discontinuities Index; an index that relates blockiness of rock mass and orientation of joints with their weathering condition and their roughness. This index follows the classification of weathering and joint’s roughness suggested by International Society of Rock Mechanics, while other modifications regarding the categories “decision sight distance” and “roadway width” were applied based on Greek standards. An application of this modified method to a 3 km road which connects Athinios port and Fira, the capital of Santorini island, Greece, a high traffic intensity road where rockfalls periodically cause traffic interruptions is presented. The method was applied in fifteen cross sections of slopes adjacent to the road and the analyses showed increased risk and the need for urgent remedial works.

New York State Department of Transportation Rock Slope Rating Procedure and Rockfall Assessment

The New York State Department of Transportation’s (DOT’s) Geotechnical Engineering Bureau has significant responsibilities in the field of rock and soil mechanics. Not the least of these is minimizing rockfalls and landslides and quickly responding when they occur. The Geotechnical Engineering Bureau has a long history of gathering and managing information about rock and soil slopes to anticipate needs and allocate resources. In 1988, the New York State DOT began to maintain rock slope, earth slope, and rockfall databases. Potential rockfall sites were identified and evaluated according to procedures presented by FHWA. In 1992, the New York State DOT devised its own rock slope rating system. To date, more than 2,800 rock slopes have been evaluated using the New York State DOT rating procedures. In addition, more than 475 rockfall incidents have been cataloged and incorporated into the New York State DOT databases. These databases have been integrated into a geographic information system environment.