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.

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.

Rockfall detection is a crucial procedure in the field of geology, which helps to reduce the associated risks. Currently, geologists identify rockfall events almost manually utilizing point cloud and imagery data obtained from different caption devices such as Terrestrial Laser Scanner (TLS) or digital cameras. Multitemporal comparison of the point clouds obtained with these techniques requires a tedious visual inspection to identify rockfall events which implies inaccuracies that depend on several factors such as human expertize and the sensibility of the sensors. This paper addresses this issue and provides an intelligent framework for rockfall event detection for any individual working in the intersection of the geology domain and decision support systems. The development of such an analysis framework presents major research challenges and justifies exhaustive experimental analysis. In particular, we propose an intelligent system that utilizes multiple machine learning algorithms to detect rockfall clusters of point cloud data. Due to the extremely imbalanced nature of the problem, a plethora of state-of-the-art resampling techniques accompanied by multiple models and feature selection procedures are being investigated. Various machine learning pipeline combinations have been examined and benchmarked applying well-known metrics to be incorporated into our system. Specifically, we developed machine learning techniques and applied them to analyze point cloud data extracted from TLS in two distinct case studies, involving different geological contexts: the basaltic cliff of Castellfollit de la Roca and the conglomerate Montserrat Massif, both located in Spain. Our experimental results indicate that some of the above-mentioned machine learning pipelines can be utilized to detect rockfall incidents on mountain walls, with experimentally validated accuracy.

El 25 de agosto de 1804 un fuerte terremoto sacudió la región del Campo de Dalías (Almería) llegando a sentirse con una intensidad EMS de IX (Mw ~6,4). En este estudio se muestran evidencias que sugieren una relación entre este episodio sísmico y la falla de Llano del Águila. Se trata de una falla normal sub vertical de dirección NW-SE y paralela a la falla de Loma del Viento, situada a unos 3 km al sur. Para la caracterización de la falla de Llano del Águila se ha llevado a cabo una nueva interpretación geomorfológica de los depósitos aluviales cuaternarios que se ven atravesados por la traza de la falla a lo largo del área de estudio. Se han identificado cuatro generaciones de abanicos aluviales provenientes de la Sierra de Gádor, y dos secciones de falla a escala cartográfica (Cantera Este y Rambla de la Maleza). La interpretación geomorfológica se basa en el análisis de fotografías aéreas históricas. Debido a la intensa antropización de la zona, los modelos digitales del terreno actuales no son útiles. Para solventar esta limitación se procesó un modelo digital de elevaciones mediante fotogrametría usando las fotos aéreas del vuelo interministerial (1977). El análisis de escarpes de falla mediante perfiles topográficos medidos en el nuevo modelo de elevaciones proporciona un salto vertical de 6,3 ± 1,9 m para la sección de la Cantera Este y de 12,1 ± 1,9 m para la sección de la Rambla de la Maleza. Estas interpretaciones han sido verificadas en el campo donde además se adquirieron nuevos datos sobre la cinemática de la falla. Todo ello ha permitido estimar la tasa de deslizamiento neta de cada sección: 0,016 ± 0,002 y 0,10 ± 0,02 mm/año para la sección de la Cantera Este y 0,031 ± 0,002 – 0,19 ± 0,.02 mm/año para la Rambla de la Maleza, respectivamente para los últimos 126 y 781 ka (Pleistoceno Medio). A partir de la longitud total de la traza de la falla se puede estimar mediante relaciones empíricas una magnitud máxima potencial de 6,59 ± 0,19.

Los deslizamientos y desprendimientos son un peligro común en áreas montañosas como los Pirineos. Aun así, en ocasiones han sido poco estudiados por la dificultad de acceso y, por tanto, la dificultad en la adquisición de datos, y la baja densidad de población en estas zonas. La Tartera de Cambrils, en la comarca del Solsonès, Cataluña, es un canchal producto de antiguos deslizamientos y desprendimientos de roca que ponen en peligro infraestructuras del pueblo de Cambrils, con una concurrencia creciente debido al incremento de actividades turísticas en la zona. Este estudio quiere determinar los procesos que causaron los deslizamientos iniciales, los procesos que ocurren en la ladera hoy en día y los factores que los causan. Para cumplir este objetivo, se ha consultado información bibliográfica y se ha llevado a cabo trabajo de campo para construir una cartografía geológica y geomorfológica, y se han adquirido datos LiDAR, con un Escáner Laser Terrestre, y fotografías del talud. Con los datos LiDAR y las fotografías se producen nubes de puntos tridimensionales, a partir de las que se estudian las discontinuidades y se analiza la estabilidad del talud. La continuidad, buzamiento y separación de la estratificación y la fracturación de la roca, dificultan el deslizamiento planar de bloques de roca y, por el contrario, facilitan el deslizamiento en cuña y el vuelco. El análisis de las discontinuidades permite estimar la posibilidad de futuros desprendimientos de roca sobre las distintas infraestructuras y proporciona datos fundamentales para planear futuras medidas de protección.

Intense rainfall events often trigger landslides and torrential flows, which are not only hazardous processes on their own, but can also generate cascading hazards through sudden and massive sediment delivery to river networks. Slope processes are therefore key drivers of geomorphic change in mountainous catchments, enhancing hillslope-channel connectivity and promoting rapid channel reorganisation. In light of the above, it is essential to characterise structural and functional connectivity (Heckmann et al., 2018), as well as geomorphic organisation before and after intense precipitation events, to better evaluate flood hazards and associated risks. Against this background, the January 2020 Gloria storm affected the Tordera River basin (Catalonia, NE Spain), where more than 480 mm of rainfall was recorded over 96 hours, with 24-hour accumulation over 200 mm, causing widespread sediment mobilisation and channel changes, as well as significant damage due to flooding and landslides.In this study, we aim to evaluate: 1) how pre-event hillslope-channel connectivity influences the geomorphic response to extreme floods and the post-event geomorphic changes through an integrated analysis of the index of connectivity (IC); 2) the spatial distribution patterns of erosion and sedimentation through the geomorphic mapping of the active riverbed and sediment bars (both active and stable), before and after the Gloria storm, and the existing inventory of landslides caused by the event. High-resolution DTMs (Digital Terrain Models) were generated from airborne LiDAR surveys conducted in 2011, 2016, and 2023. The IC was derived from a pre-event DTM to characterise structural sediment connectivity following Cavalli et al. (2013), while erosion and sedimentation processes were quantified using the difference between DTMs (DTMs of Difference, DoDs) for pre-event (2016-2011) and post-event (2023-2016) periods. GIS-based geomorphic mapping of active channels and sediment bars before and after Gloria was used to assess event-scale channel reorganisation.Preliminary results indicate a clear spatial correspondence between pre-event connectivity patterns and the magnitude of the geomorphic change observed during that extreme flood. Areas characterised by high pre-event erosion rates, identified from 2016-2011 DoDs, largely coincide with sectors where numerous landslides were triggered during the Gloria storm. High connectivity values also correspond to areas dominated by erosion and deposition in the 2016-2011 DoDs, highlighting the role of pre-event structural connectivity in conditioning sediment transfer pathways. Furthermore, active bars mapped after the event predominantly overlap with areas affected by pre-event erosion, whereas bars that remained stable during the storm are mainly associated with zones characterised by pre-event deposition. The active channel also experienced noticeable widening during the event, while the majority of vegetated bars that were stable before Gloria remained stable throughout the storm, reinforcing the link between pre-event geomorphic organisation and flood response. These findings highlight the importance of pre-event structural connectivity in controlling geomorphic response during extreme rainfall events, providing valuable insight for hazard assessment and river management. Cavalli, M. et al. (2013). Geomorphometric assessment of spatial sediment connectivity in small Alpine catchments. Geomorphology, 188, 31-41.Heckmann, T. et al. (2018). Indices of sediment connectivity: opportunities, challenges and limitations. Earth-Science Reviews, 187, 77-108.