Search Results (27)

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. Full article

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. Full article

Rockfall presents a significant risk to the safety and economy of communities and infrastructure in mountainous regions. The recently-developed Rockfall Activity Index (RAI) utilizes high-resolution terrestrial lidar-derived digital elevation models (DEMs) of rock slopes to categorize a slope face into seven distinct morphological units, or “RAI classes”. This paper focuses on a comprehensive study conducted at four sites in Alaska, USA, where a robust lidar-based five-year inventory of 4381 rockfall events was analyzed. The primary objective was to investigate variations in failure characteristics, such as cumulative magnitude–frequency distributions, non-cumulative power–law parameters, average annual failure rates, and average failure depths, among the different RAI classes. The findings demonstrate that the seven RAI classes effectively differentiate the rock slope based on unique mass-wasting characteristics. Furthermore, the research explores spatial and temporal variations in these failure characteristics. Based on the study’s outcomes, recommendations are provided for modifying the RAI parameters for each RAI class, specifically the annual failure rate and average failure depth. These modifications aim to enhance the accuracy and effectiveness of rockfall hazard assessments. Full article

Potential rockfall source areas are widely distributed in the high mountain areas of the Tibetan Plateau, posing significant hazards to human lives, infrastructures, and lifeline facilities. In a combination of field investigation, high-precision aerial photogrammetry, and numerical simulation, we took the Maoyaba basin as an example to explore a rapid identification method for high-altitude rockfall sources. An automatic potential rockfall source identification (PRSI) procedure was introduced to simplify the process of rockfall source identification. The study revealed that rockfall sources are concentrated in areas with intense frost weathering. Our identification results were validated using rockfall inventory data detection from remote sensing images and field investigation. Of the rockfall source areas identified by the PRSI procedure, 80.85% overlapped with the remote sensing images result. The accuracy assessment using precision, recall, and F1 score was 0.91, 0.81, and 0.85, respectively, which validates the reliability and effectiveness of the PRSI procedure. Meanwhile, we compared the rockfall source distribution of five DEMs with different resolutions and four neighborhood areas. We discovered that, in addition to high-resolution DEMs (i.e., 1 m and 2 m), medium-resolution DEMs (i.e., 5 m, 12.5 m) also perform well in identifying rockfall sources. Finally, we conducted a hazard assessment based on Culmann’s two-dimensional slope stability model and rockfall hazard vector method. Appropriate protective measures should be taken at high-hazard sections to safeguard pedestrians, vehicles, and related infrastructure from rockfalls. Full article

The aim of this study was to delimit potential rockfall propagation zones based on simulated 2 m resolution rockfall trajectories using Rockyfor3D for block volume scenarios ranging from 0.05–30 m³, with explicit inclusion of the barrier effect of standing trees, for an area of approx. 7200 km² in Switzerland and Liechtenstein. For the determination of the start cells, as well as the slope surface characteristics, we used the terrain morphology derived from a 1 m resolution digital terrain model, as well as the topographic landscape model geodataset of swisstopo and information from geological maps. The forest structure was defined by individual trees with their coordinates, diameters, and tree type (coniferous or broadleaved). These were generated from detected individual trees combined with generated trees on the basis of statistical relationships between the detected trees, remote sensing-based forest structure type definitions, and stem numbers from field inventory data. From the simulated rockfall propagation zones we delimited rockfall hazard indication zones (HIZ), as called by the practitioners (because they serve as a basis for the Swiss hazard index map), on the basis of the simulated reach probability rasters. As validation, 1554 mapped past rockfall events were used. The results of the more than 89 billion simulated trajectories showed that 94% of the mapped silent witnesses could be reproduced by the simulations and at least 82% are included in the delimited HIZ. Full article

Rockfall constitutes a major threat to the safety and sustainability of transport corridors bordered by rocky cliffs. This research introduces a new approach to rockfall susceptibility modeling for the identification of potential rockfall source zones. This is achieved by developing a data-driven model to assess the local slope morphological attributes with respect to the rock slope evolution processes. The ability to address “where” a rockfall is more likely to occur via the analysis of historical event inventories with respect to terrain attributes and to define the probability of a given area producing a rockfall is a critical advance toward effective transport corridor management. The availability of high-quality digital volumetric change detection products permits new developments in rockfall assessment and prediction. We explore the potential of simulating the conceptualization of slope-scale rockfall susceptibility modeling using computer power and artificial intelligence (AI). We employ advanced 3D computer vision algorithms for analyzing point clouds to interpret high-resolution digital observations capturing the rock slope evolution via long-term, LiDAR-based 3D differencing. The approach has been developed and tested on data from three rock slopes: two in Canada and one in the UK. The results indicate clear potential for AI advances to develop local susceptibility indicators from local geometry and learning from recent rockfall activity. The resultant models produce slope-wide rockfall susceptibility maps in high resolution, producing up to 75% agreement with validated occurrences. Full article

Quantitative hazard analysis of rockfalls is a fundamental tool for sustainable risk management, even more so in places where the preservation of natural heritage and people’s safety must find the right balance. The first step consists in determining the magnitude-frequency relationship, which corresponds to the apparently simple question: how big and how often will a rockfall be detached from anywhere in the cliff? However, there is usually only scarce data on past activity from which to derive a quantitative answer. Methods are proposed to optimize the exploitation of multi-source inventories, introducing sampling extent as a main attribute for the analysis. This work explores the maximum possible synergy between data sources as different as traditional inventories of observed events and current remote sensing techniques. Both information sources may converge, providing complementary results in the magnitude-frequency relationship, taking advantage of each strength that overcomes the correspondent weakness. Results allow characterizing rockfall detachment hazardous conditions and reveal many of the underlying conditioning factors, which are analyzed in this paper. High variability of the hazard over time and space has been found, with strong dependencies on influential external factors. Therefore, it will be necessary to give the appropriate reading to the magnitude-frequency scenarios, depending on the application of risk management tools (e.g., hazard zoning, quantitative risk analysis, or actions that bring us closer to its forecast). In this sense, some criteria and proxies for hazard assessment are proposed in the paper. Full article

We examine the coseismic influence of the 5 July 2019, M_W7.1 Ridgecrest and the 24 June 2020 M_W5.8 Owens Lake earthquakes on rockfall distributions in two undisturbed high-altitude areas of the southern Sierra Nevada Mountains, California, USA. These events occurred within the geologically recent (<2 Mya) Walker Lane/eastern California shear zone. While both study areas are characterized as plutonic, the Owens Lake event largely affected terrain that was formerly glaciated and oversteepened while the Ridgecrest event affected non-glaciated terrain. Our inventory of rockfall locations was derived from analysis of Sentinel-2 images acquired just prior to and immediately after the events. This difference mapping approach using readily-available Sentinel-2 imagery allows for rapid rockfall and landslide mapping. GIS analysis shows that even though the total area assessed for both earthquakes was similar (~1500 km²), the significantly lower magnitude Owens Lake event produced nearly twice as many (102) mappable rockslides as the significantly stronger Ridgecrest event (58), a difference likely due to slope oversteepening in the formerly glaciated area. Significant seismic amplification by topography and reactivation of preexisting failures was apparent for both areas. Inclusion of these factors may improve failure predictions and rockfall probability estimation. Full article

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. Full article

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. Full article

Generating an unbiased inventory of mass movements is challenging, particularly in a large region such as China. However, due to the enormous threat to human life and property caused by the increasing number of mass movements, it is imperative to develop a reliable nationwide mass movement susceptibility model to identify mass movement-prone regions and formulate appropriate disaster prevention strategies. In recent years, the mixed-effects models have shown their unique advantages in dealing with the biased mass movement inventory, yet there are no relevant studies to compare different mixed-effects models. This research compared three mixed-effects models to explore the most plausible and robust susceptibility mapping model, considering the inherently heterogeneously complete mass movement information. Based on a preliminary data analysis, eight critical factors influencing mass movements were selected as basis predictors: the slope, aspect, profile curvature, plan curvature, road density, river density, soil moisture, and lithology. Two additional factors, namely, the land use and geological environment division, representing the inventory bias were selected as random intercepts. Subsequently, three mixed-effects models—Statistical-based generalized linear mixed-effects model (GLMM), generalized additive mixed-effects model (GAMM), and machine learning-based tree-boosted mixed-effects model (TBMM)—were adopted. These models were used to evaluate the susceptibility of three distinct types of mass movements (i.e., 28,814 debris flows, 54,586 rockfalls and 108,432 landslides), respectively. The results were compared both from quantitative and qualitative perspectives. The results showed that TBMM performed best in all three cases with AUROCs (Area Under the Receiver Operating Characteristic curve) of cross-validation, spatial cross-validation, and predictions on simulated highly biased inventory, all exceeding 0.8. In addition, the spatial prediction patterns of TBMM were more in line with the natural geomorphological underlying process, indicating that TBMM can better reduce the impact of inventory bias than GLMM and GAMM. Finally, factor contribution analysis showed the key role of topographic factors in predicting the occurrence of mass movements, followed by road density and soil moisture. This study contributes to assessing China’s overall mass movement susceptibility situation and assisting policymakers in master planning for risk mitigation. Further, it demonstrates the tremendous potential of TBMM for mass movement susceptibility assessment, despite inherent biases in the inventory. Full article

Both in the literature and in practical applications, several works have dealt with rockfall analysis and the planning of mitigation measures. It is also possible to find inventories and papers that describe historical events. However, it is challenging to find methodologies or studies about inventorying rockfall mitigation or their efficiency over time. In Italy, many rockfall barriers and other mitigation solutions have been built in the last decades, and one of the most urgent problems is their correct management and maintenance. Lauria, a small town in southern Italy, can be considered an example of this common condition exacerbated by a wildfire in 2017. This work presents a methodology for assessing rockfall risk and creating a geodatabase of mitigation structures focused on small communities. We used digitalization of archival sources to reconstruct and geocode the record of mitigation works. An available database of historical landslides was used to reconstruct the most relevant rockfall events. Moreover, we coupled this with Sentinel-2 images and high-resolution orthophotos to map the wildfire area. Data obtained from the UAV-LiDAR survey were used to map the mitigation structures. The aim was to create a reliable state-of-the-art method, described in an operational monograph, to be used by experts for the design of new rockfall mitigation structures in both an affordable and efficient way. Moreover, we created a simple webGIS and a 3-D interactive view, helpful in disseminating rockfall hazards and mitigation strategies among the population at risk. Full article

An assessment of ground instabilities’ causative factors remains a topical subject. Such studies are rare, and evaluation techniques are still under development. The choice of evaluation technique should take into account the materials available and the objective sought. Statistical analysis methods are the most widely used, with multivariate analysis being the most accurate. The present work evaluates the weights of the influences of the different factors of ground instability of the coastal region between Tetouan and Jebha through multiple correspondence analysis (MCA) and principal component analysis (PCA). The application of both methods requires an accurate ground instability inventory with study sites that are well documented through modalities of causative factors and other descriptive data. The performed MCA shows that lithology has a significant influence on the type of existing instability. It also helped classify the instabilities into five distinct classes according to their modalities and specify the factors that differentiate the classes. The PCA shows that lithology is the most influential factor in landslides, contrary to rockfalls, where a variety of factors can be preponderant. Full article

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. Full article

Rainfall-induced landslides pose a significant threat to the lives and property of residents in the southeast mountainous and hilly area; hence, characterizing the distribution pattern and effective susceptibility mapping for rainfall-induced landslides are regarded as important and necessary measures to remediate the damage and loss resulting from landslides. From 10 June 2019 to 13 June 2019, continuous heavy rainfall occurred in Longchuan County, Guangdong Province; this event triggered extensive landslide disasters in the villages of Longchuan County. Based on high-resolution satellite images, a landslide inventory of the affected area was compiled, comprising a total of 667 rainfall-induced landslides over an area of 108 km². These landslides consisted of a large number of shallow landslides with a few flowslides, rockfalls, and debris flows, and the majority of them occurred in Mibei and Yanhua villages. The inventory was used to analyze the distribution pattern of the landslides and their relationship with topographical, geological, and hydrological factors. The results showed that landslide abundance was closely related to slope angle, TWI, and road density. The landslide area density (LAD) increased with the increase in the above three influencing factors and was described by an exponential or linear relationship. In addition, southeast and south aspect hillslopes were more prone to collapse than the northwest­–north aspect ones because of the influence of the summer southeast monsoon. A new open-source tool named MAT.TRIGRS(V1.0) was adopted to establish the landslide susceptibility map in landslide abundance areas and to back-analyze the response of the rainfall process to the change in landslide stability. The prediction results were roughly consistent with the actual landslide distribution, and most areas with high susceptibility were located on both sides of the river valley; that is, the areas with relatively steep slopes. The slope stability changes in different periods revealed that the onset of heavy rain on 10 June 2019 was the main triggering factor of these group‑occurring landslides, and the subsequent rainfall with low intensity had little impact on slope stability. Full article