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Frequent rockfall events pose a major threat to the safe operation of the Taihang Grand Canyon Scenic Area (GCSA) in China. The traditional techniques for identifying potential rockfall sources and hazard assessment methods are often challenged in the alpine canyon landform. This study aims to establish an early identification framework for regional potential rockfall sources applicable to the canyon region and to assess rockfall hazards in potentially hazardous areas using unmanned aerial vehicle (UAV) photogrammetry. Specifically, by incorporating high-precision topographic information and geotechnical properties, the slope angle distribution method was used for static identification of potential rockfall sources. Moreover, SBAS-InSAR technology was used to describe the activity of potential rockfall sources. Finally, taking the key potentially hazardous area of the Sky City scenic spot as an example, the Rockfall Analyst tool was used to analyze the rockfall frequency, bounce height and energy characteristics based on the high-precision UAV 3D real scene model, and the analytic hierarchy process was introduced to achieve quantitative rockfall hazard assessment. The results show that the potential rockfall source areas in the Taihang GCSA is 33.47 km2 (21.47%), mainly distributed in strips on the cliffs on both sides of the canyon, of which the active rockfall source area is 2.96 km2 (8.84%). Taking the scenic spot of Sky City as example, the proposed UAV-based real scene modeling technology was proven to be able to quickly and accurately construct a 3D high-precision model of the canyon area. Moreover, the 3D rockfall simulation showed that the high-energy rockfall area was mainly distributed at the foot of the steep cliff, which mainly threatens the tourist distribution center below. The early identification and quantitative evaluation scheme of rockfall events proposed in this study can provide technical reference for the prevention and control of rockfall hazards in similar alpine valley areas.

The impact-induced rock mass fragmentation in a rockfall is analyzed by comparing the in situ block size distribution (IBSD) of the rock mass detached from the cliff face and the resultant rockfall block size distribution (RBSD) of the rockfall fragments on the slope. The analysis of several inventoried rockfall events suggests that the volumes of the rockfall fragments can be characterized by a power law distribution. We propose the application of a three-parameter rockfall fractal fragmentation model (RFFM) for the transformation of the IBSD into the RBSD. A discrete fracture network model is used to simulate the discontinuity pattern of the detached rock mass and to generate the IBSD. Each block of the IBSD of the detached rock mass is an initiator. A survival rate is included to express the proportion of the unbroken blocks after the impact on the ground surface. The model was calibrated using the volume distribution of a rockfall event in Vilanova de Banat in the Cadí Sierra, Eastern Pyrenees, Spain. The RBSD was obtained directly in the field, by measuring the rock block fragments deposited on the slope. The IBSD and the RBSD were fitted by exponential and power law functions, respectively. The results show that the proposed fractal model can successfully generate the RBSD from the IBSD and indicate the model parameter values for the case study.

The presented article attempted to analysis rockfalls susceptibility mapping which is considered as one the most important type of the land-slides with high frequent occurrence. The machine learning based multiple-layer perceptron (MLP) model was used to provide the pridicive model and hazard risk maps for studied area. This artificial neural network (ANN) was received significant success in susceptibility assessment for landslides and rockfall. The predictive model was conducted on a dataset containing 15 main triggering factors which concluded 10 regular (e.g., precipitation, slope aspect, slope angle, weathering, lithology, distance to river, main faults’ unsafe radius, landslide-prone areas, distance to roads, and distance to the cities), 5 specific (e.g., RQD, Qslope, GSI, SMR, and RHRS), and 57 historical landslides which represent different aspects of the rockfall failures at Alborz province in Iran. To susceptibility assessment, the dataset that used in ANN-based analysis was randomly divided into training (70%) and testing (30%) sets and utilized to predict the risk-able area.. The predictive model was comparatively justified by using benchmark learning classifiers concluded support-vector machine (SVM), decision tree (DT), and random forest(RF) algorithms. The confusion matrix and receiver operating characteristic curves (ROC) were used for performance analysis and obtaining models’ accuracy. According to the susceptibility results, the north part of the studied region has high risks for rockfall failures. As machine learning-based performance analysis, the MLP-based model by 0.82 accuracy/0.85 precision reached the highest rank more than other classifiers such asSVM (accuracy = 0.78/precision = 0.78), DT (accuracy = 0.70/precision = 0.73), and RF (accuracy = 0.68/precision = 0.70). Also, overall accuracy obtained from ROC, the main model MLP (AUC = 0.811) is higher than SVM (AUC = 0.780), DT (AUC = 0.740), and RF (AUC = 0.500). MLP model provide more conservative results than other classifier and RF is estimated the risk rate less than others.

Fragmentation is a common feature of rockfall that exerts a strong control on the trajectories of the generated blocks, the impact energies, and the runout. In this paper, we present a set of four real-scale rockfall tests aimed at studying the fragmentation of the rocky blocks, from the global design of the field procedure to the data analysis and the main results. A total of 124 limestone, dacite, or granite blocks ranging between 0.2 and 5 m3 were dropped from different heights (8.5 to 23.6 m) onto four slopes with different shapes (single or double bench) and slope angles (42º to 71º). The characteristics of the blocks, in particular the size, surface texture and joint condition, were measured before the drops. The trajectories of the blocks and both the initial and the impact velocities were tracked and recorded by means of three high-speed video cameras. A total of 200 block-to-ground impacts have been studied. On average, 40% of the blocks broke upon impact on the slope or on the ground, making it necessary to measure the fragments. The initial and final sizes of the blocks/fragments were measured by hand with tape, though photogrammetric techniques (UAV and terrestrial) were also used for comparison purposes. The information gathered during the field tests provides a deep insight into the fragmentation processes. On the one hand, the high-resolution slow-motion videos help to describe when and how the block breakage takes place and the spatial distribution of the pieces. On the other hand, it is possible to compute the block trajectories, the velocities, and the energy losses using videogrammetry. The results include, for instance, a block average fragmentation of 54% and 14% for the limestone and granitoids, respectively; the systematic inventory of the size fragments, which may be used for fitting the power law distributions; and after each breakage, the total angle of aperture occupied by the fragments has been measured, with values in the range 25º–145º. To figure out the different behavior of the blocks in terms of breakage/no breakage, each block-to-ground impact has been characterized with a set of parameters describing the energy level, the robustness of the substrate, and the configuration of the block contact at the impact point, among others. All these terms are combined in a function F, which is used to adjust the field data. The adjustment has been carried out, first, for the whole 200 events and later for a subset of them. The procedure and the results are described in the paper. Although the discrimination capability of F is moderately satisfactory, it is very sensitive to the test site and setup. It must be highlighted that these field tests are a unique source of data to adjust the parameters of the numerical simulation models in use for rockfall studies and risk mitigation, especially when fragmentation during the propagation is considered.

Among the types of landslides that occur in Alpine areas, rockfalls are one of the most frequent and dangerous. Rockfalls affect every type of rock mass with greater or lesser incidence based on the intrinsic characteristics of rocks (structural setting, lithotype, geotechnical quality, weathering conditions, etc.) and climate features of geographic areas considered. In order to assess the hazard and subsequently the risk associated with these phenomena, it is first necessary to identify areas most likely to be affected. This paper presents a methodology applicable on a regional scale to identify the most susceptible Alpine areas to rockfalls. The methodology is based on statistical cross-analyses of environmental factors to identify the Alpine areas most susceptible to rockfalls. The predisposing factors considered to generate a rockfall susceptibility map are both qualitative (geological variables) and quantitative (morphometric, altimetric and climatic variables) and have been compared with a selection of historical data on rockfalls occurrence and distribution (consisting in 2800 events). As result, a classification of the Piemonte’s Alpine territory identifying 5 classes characterized by a greater or lesser propensity to generate rockfalls has been obtained. The regional susceptibility map has been subsequently used as basis to identify road networks more exposed to rockfall hazard to obtain a preliminary risk analysis at regional scale.

ContextSince the nineteenth century, rural areas have experienced progressive abandonment mostly due to socioeconomic changes, with direct and indirect effects on forest disturbance regimes occurring in these human-dominated landscapes. The role of land abandonment in modifying disturbance regimes has been highlighted for some types of disturbances, albeit being still somewhat overlooked compared to climate change.ObjectivesThis literature review is aimed at highlighting the most relevant effects of land abandonment and land-use legacy on the regime of different types of forest disturbances, providing insight into land-use change/disturbances interactions.MethodsWe searched in the Scopus and Web of Science databases for relevant studies at the global scale dealing with eight major natural disturbances: avalanche, flooding, herbivory, insect outbreak, landslide, rockfall, wildfire and windthrow. We classified papers into five relevance classes, with the highest score (4) assigned to studies quantitatively measuring the interactions between abandonment dynamics and disturbance regimes.ResultsMost papers focused on wildfires in Mediterranean Europe in the twentieth century, where landscape homogenisation and fuel build-up contributed to worsening their frequency, size and severity. Dense forests developed following land abandonment instead exert inhibiting effects toward mass movements such as avalanches, rockfalls and landslides. Regarding the other investigated disturbances, we found only a few studies presenting site-specific and partly contrasting effects.ConclusionsLand abandonment triggers ecological processes at the landscape scale, altering land cover patterns and vegetation communities, which in turn affect disturbance regimes. Implications for land and resource management mostly depend on the stage at which post-abandonment secondary succession has developed.

Hosting precious superb murals and painted sculptures from the fourth to the fourteenth century, the UNESCO word heritage site Mogao Grottoes are one of the most famous sites of Buddhist art. Although several reinforcement projects had been implemented, frequent rockfalls originated from the slope atop the cliff of Mogao Grottoes can be observed, representing a significant risk to both relics and tourists. Therefore, it is important to assess the rockfall hazard of the slope of Mogao Grottoes for the preventive protection as well as to inform future management decisions. In this study, rockfall hazard of the slope of Mogao Grottoes was assessed by using Analytic Hierarchy Process (AHP), Fuzzy-AHP (F-AHP) and AHP-TOPSIS methods and then the performances were evaluated by receiver operating characteristic curve (ROC) and area under curve (AUC). To execute these methods, a high-resolution digital surface model was first captured through UAV digital photogrammetry technology without contact. Belonging to four hazard levels (Very high, high, low and very low), the slope was divided into 10 units based on the slope topography. The hazard assessment result shows that 80% of the total slope units are at low rockfall hazard level and 20% slope units are at very high/high level, respectively. Moreover, loose debris and rainfall are confirmed as the most active causative factors. The result of AUC, ROC and recorded rockfall events reveal that the F-AHP is the most suitable method. Besides, this study also holds the potential applications of the F-AHP in the rockfall assessment for many other grottoes carved in conglomerate strata in Dunhuang.

Rockfall is a prevalent geological hazard threatening lives and infrastructure. Beyond static assessment, real-time dynamic monitoring is crucial to capture sudden events, ensuring timely warning and risk mitigation. Leveraging low cost, flexible deployment, and high spatiotemporal resolution, PTZ (Pan-Tilt-Zoom) cameras combined with deep learning provide a potential solution. However, the small size, background similarity, rapid and complex motion of rockfall objects present significant challenges for reliable monitoring. To tackle these issues, this paper proposes a rockfall detection and tracking framework integrating motion awareness and physical constraints. First, a lightweight temporal module is incorporated into YOLOv8 to enhance perception of small and dim moving objects. Second, a novel matching strategy based on Euclidean distance and motion direction replaces the traditional IoU in ByteTrack to improve tracking stability. Trajectory fragmentation caused by missed detections is mitigated via a frame-difference-based completion mechanism. Adaptability to unseen environments is further bolstered through a generalization training strategy. A custom dataset combining real-world surveillance and simulated rockfalls was constructed. Experiments demonstrate superior detection, robust tracking, and strong generalization under complex backgrounds and limited frame rates. Deployment on NVIDIA Orin edge platforms facilitates real-time monitoring and supports practical PTZ-based early warning for geological hazards.

Hazard and risk assessment procedures of different types of rockfall were analyzed to compare their outcomes when they are applied to the same case study. Although numerous methodologies are available in literature, rockfall hazard and risk analyses are often limited to standard estimations, affected by a margin of uncertainty, especially when relevant engineering projects are about to be realized. Based on the design purpose, different types of approaches can be chosen among the qualitative and quantitative ones available in literature, which allow different levels of analysis. One of the main criticisms related to rockfall events is the risk affecting linear structures, such as road or railways, due both to their strategic relevance for trade and communications and to the great entity of the exposed value (traffic units) traveling along them. In this perspective, a comparison between the qualitative method known as Evolving Rockfall Hazard Assessment (EHRA), the semi-quantitative modified Rockfall Hazard Rating System (RHRS) and the quantitative Rockfall Risk Management (RoMa) approach is herein commented according to a practical application to a case study. It is the case of the rockfall threat along slopes crossed by a strategic road connecting two of the most known spots of eastern Sicily (Italy), at the Taormina tourist complex. Data were retrieved from both recent literature and technical surveys on field. Achieved results highlight how the approaches are affected by a different level of detail and uncertainty, arising also by some necessary assumption that must be taken into account, especially when mitigation measures or territory planning have to be designed. Achieved results can be also taken into account for similar studies worldwide, in order to choose the most suitable procedure based on the design purpose. This is indeed crucial in the perspective of the optimization of time and economic resources in the territorial planning practice.

Proximity remote sensing techniques, both land- and drone-based, allow for a significant improvement of the quality and quantity of raw data employed in the analysis of rockfall phenomena. In particular, the large amount of data these techniques can provide allows for the use of probabilistic approaches to rock mass characterization, with particular reference to block volume and shape definition. These, in return, are key parameters required for a proper rockfall hazard assessment and the optimization of countermeasures design. This study aims at providing a sort of guide, starting from the data gathering phase to the processing, up to the implementation of the outputs in a probabilistic-based scenario, which is able to associate a probability of not being exceeded with total kinetic energy values. By doing so, we were able to introduce a new approach for the choice of design parameters and the evaluation of the effectiveness of mitigation techniques. For this purpose, a suitable case study located in Varaita Valley (Cuneo, Italy) has been selected. The area has been surveyed, and a model of the slope and a digital model of the rock faces have been defined. The results show that a 6.5 m3 block has a probability of not being exceeded of 75%; subsequent simulations show that the level of kinetic energy involved in such a rockfall is extremely high. Some mitigation techniques are discussed.