An example of the combined use of UAV photogrammetry and rockfall numerical simulation is described. A case of fragmental rockfall occurred on 17 November 2018 in Cárcavos, a site located in the Spanish municipality of Ayna (Albacete). The event caused a great social alarm as some infrastructure was affected. By using Unmanned Aerial Vehicle (UAV) photogrammetry, a high-resolution 3D model has been generated from point cloud data, and distribution and size of the fragmented rocks (more than 600 boulders) determined. The analysis has been performed through numerical simulations to: (1) reproduce the paths followed by the real blocks; and (2) estimate the speed and energy of the blocks, together with their heights, impacts and stopping points. Accordingly, source areas have been identified, including the potential source areas and unstable blocks on the slope. In addition, the exposed elements at risk (buildings, facilities, infrastructures, etc.) have been identified, and the effectiveness of mitigation measures against future events evaluated.

Coastal regions in Southwest Europe have experienced major interventions and transformations of the territory with unprecedented urban development, primarily related to growing tourism activity. The coast is the place where marine and terrestrial processes converge, making it highly vulnerable to the effects of climate change. However, the lack of information on the frequency of these extreme weather events and their impacts on the coast hampers an accurate analysis of the consequences of global change. This paper provides a detailed analysis of the extreme weather events (EWE) that have affected the Atlantic and Mediterranean coasts of Southwest Europe during the period from 1 January 2009 to 28 February 2020, as well as a quantification of their impacts: fatalities, injuries and economic damage. Official sources from France, Portugal and Spain were consulted, along with technical reports, scientific articles, etc., to generate a unified database. A total of 95 significant extreme events have caused 168 fatalities, 137 injuries and almost €4000 M in direct economic losses. Cyclone Xynthia (February 2010) on the French Atlantic coast stands out, having caused 47 fatalities, 79 injuries and substantial economic losses valued at €3000 M. The study shows a slight upward trend in the number of events recorded, especially during the last three years of the analysis, as well as in human losses and damages. The results reveal a higher exposure of the Mediterranean coast of Southwest Europe when compared to the Atlantic, especially the Spanish Mediterranean coast, with 61% of the fatalities recorded there during the study period. This is primarily due to a model of exponential tourism growth on the Mediterranean coast, with an enormous urban and infrastructure development during the last decades. Traditionally, the Mediterranean coast is less prepared to reduce the effects of marine storms, extreme events that are becoming more frequent and virulent in the context of climate and global change. This work highlights the need to create a continuous monitoring system–at the European level–of the impacts of extreme weather events on the coast, where 40% of the European population is concentrated. This observatory should serve as a source of information for risk mitigation policies (predictive, preventive and corrective), as well as for emergency management during disasters.

Deformation processes, both natural (e.g., subsidence, landslides, active tectonics) and induced (e.g., associated with mining, construction. groundwater exploitation), result in significant socioeconomic losses worldwide. Accurate detection and classification of these processes are crucial for effective risk management. In this study, we present a novel approach for the automatic classification of deformation processes using Interferometric Synthetic Aperture Radar (InSAR) data and machine learning techniques. Specifically, we use a decision tree-based classification algorithm to train a model capable of recognizing and distinguishing different types of deformation processes using time series of displacements, grouped into Active Deformation Areas (ADAs). We test this methodology in a large area in SE Spain. Our results demonstrate promising performance, with an Area Under the Curve (AUC) > 0.95, identifying several covariates of morphometric, geological, hydrogeological, and geotechnical nature as key factors. This automatic classification of InSAR data holds significant implications for risk management associated with ground deformation, providing a potentially valuable tool for decision makers in urban planning and land management officials.

Detecting and monitoring slope movements in mining areas is essential to better understand their causes and mitigate their adverse consequences. Satellite radar interferometry (InSAR) techniques allow to generate deformation maps at high resolution (both spatial and temporal), especially since 2014, when the European Space Agency's Sentinel-1 mission (6-day revisit frequency) became operational. The application of InSAR is, however, constrained by a number of limitations. One of the most important of them relates to its ability to measure only one component (or, at best, two components, provided that ascending and descending data are available) of the surface displacement (i.e., the line-of-sight component). In addition to this, InSAR offers a very low sensitivity in the north-south (NS) direction, which makes it difficult to study, solely on the basis of InSAR data, phenomena characterized by a strong NS component. In this context, this work discusses the potential role of UAV-based SfM image correlation as a possible data source to resolve the NS component of the motion, which in turn allows resolving, in the strict sense of the term, the three components of the motion from (at least) one ascending and one descending InSAR dataset.In this work we present the results of a local-scale study carried out in El Feixolín (León), a former open-pit and underground mining area affected by a rapid (1.67 m/year according to in situ measurements), large slope movement. Results include ground displacement velocity data obtained using (i) FASTVEL (and Sentinel-1 ascending and descending imagery), an on-demand, unsupervised InSAR processing service available on the Geohazards Exploitation Platform (GEP) (https://geohazards-tep.eu/), (ii) image correlation techniques (applied on UAV-based SfM orthoimagery) and (iii) DGNSS techniques. Further, this study provides as final result a dataset of 3D displacement velocity values (InSAR 3D dataset) derived by integrating the InSAR data obtained in ascending (InSAR ASC dataset) and descending (InSAR DES dataset) geometry, with the data obtained in NS direction through image correlation (SfM NS dataset). Comparison of the results with the data acquired in situ through DGNSS surveying revealed Root Mean Square Error (RMSE) values of 0.05, 0.23, 0.16 and 0.03 m/year (and relative RMSE values of 34, 67, 13 and 19%), respectively for the InSAR ASC, InSAR DES, SfM NS and InSAR 3D datasets, highlighting the effectiveness of UAV-based SfM image correlation for deriving NS ground deformation data to support InSAR processing and obtain 3D ground deformation vectors.