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Multi-temporal interferometric Persistent Scatterers Interferometry (PSI) techniques derive from the elaboration of satellite Synthetic Aperture Radar (SAR) images and represent a useful tool to detect ground millimetric movements over wide areas; thanks to non-invasiveness and high accuracy. However, PSI data are relative measurements estimated along the sensor Line Of Sight and referred to a chosen stable motionless reference point, so they lack absolute reference both in time and space. In this work, we propose a methodological procedure that exploits Global Navigation Satellite System (GNSS) data acquired from permanent stations to calibrate and fix relative InSAR results into conventional geodetic reference systems. Mean yearly velocities of PSI radar targets are corrected with GNSS values throughout operative procedures used in geodesy for crustal and local deformation data. The methodology is tested in Ravenna and Ferrara cities on the north-western Adriatic coast within the eastern alluvial plain of Po river (Italy), extensively affected by subsidence with strong spatial and temporal variations. The results reveal high rates of long-term subsidence of the study area and the effectiveness of the presented methodology for producing unique ground deformation maps over wide areas. .

Landslide susceptibility assessment is vital for landslide risk management and urban planning, and the scientific community is continuously proposing new approaches to map landslide susceptibility, especially by hybridizing state-of-the-art models and by proposing new ones. A common practice in landslide susceptibility studies is to compare (two or more) different models in terms of AUC (area under ROC curve) to assess which one has the best predictive performance. The objective of this paper is to show that the classical scheme of comparison between susceptibility models can be expanded and enriched with substantial geomorphological insights by focusing the comparison on the mapped susceptibility values and investigating the geomorphological reasons of the differences encountered. To this aim, we used four susceptibility maps of the Wanzhou County (China) obtained with four different classification methods (namely, random forest, index of entropy, frequency ratio, and certainty factor). A quantitative comparison of the susceptibility values was carried out on a pixel-by-pixel basis, to reveal systematic spatial patterns in the differences among susceptibility maps; then, those patterns were put in relation with all the explanatory variables used in the susceptibility assessments. The lithological and morphological features of the study area that are typically associated to underestimations and overestimations of susceptibility were identified. The results shed a new light on the susceptibility models, identifying systematic errors that could be probably associated either to shortcomings of the models or to distinctive morphological features of the test site, such as nearly flat low altitude areas near the main rivers, and some lithological units.

Volcano slope instability manifests in many forms, ranging from steady state to punctuated movement, or shallow erosion to deep-seated spreading. The interplay of gravity, magmatic or hydrothermal fluids, and often active tectonics on a volcanic edifice results in complex spatial and temporal variations in deformation kinematics. While this makes the recognition, assessment, and monitoring of volcano slope instability challenging, advancements in Synthetic Aperture Radar (SAR) technology have significantly accelerated our knowledge of instability phenomena and our ability to assess their hazards. This review discusses the applications and challenges of SAR imagery to various slope instabilities at volcanoes around the world. SAR amplitude images are powerful tools for mapping areas of geomorphological changes. These snapshots can be combined using Interferometric SAR (InSAR) to create multi-temporal deformation maps that provide unique information on the evolution of slope failures. Space-borne InSAR has become an economic way to detect changes at volcanoes at very high resolution. Ground-Based InSAR (GBInSAR) can produce frequent SAR images (on the order of seconds to minutes), propelling InSAR from monitoring to surveillance and early-warning applications. However, interpreting InSAR-derived ground deformation signals related to volcano slope instability is still challenging. Deformation from magma rise or variations in hydrothermal systems may be inseparable from persistent, deep-seated flank motion. Similarly, shallow or localized ground motion may occur in overlap with thermal contraction or subsidence of newly emplaced lava or tephra deposits. If triggered to failure, landslides can vary over several magnitudes, from small-volume rock falls that pose only a localized hazard, to large-volume debris avalanches capable of travelling tens of kilometers away from the source. These collapses can have cascading hazards if accompanied by directed blasts or tsunamis, emphasizing the need for continued advancement of our understanding of these events and their consequences. To highlight the utility of SAR for measuring and monitoring mass movement, two case studies of Stromboli (Italy) and Pacaya (Guatemala) volcanoes are discussed in detail, where recent instability events, persistent volcanic activity, and ground truth constraints have resulted in excellent case-histories in applying SAR imagery to understand these potentially hazardous slope instabilities.

We attempt a characterization of the geotechnical and hydrological properties of hillslope deposits, with the final aim of providing reliable data to distributed catchment-scale numerical models for shallow landslide initiation. The analysis is based on a dataset built up by means of both field tests and laboratory experiments over 100 sites across Tuscany (Italy). The first specific goal is to determine the ranges of variation of the geotechnical and hydrological parameters that control shallow landslide-triggering mechanisms for the main soil classes. The parameters determined in the deposits are: grain size distribution, Atterberg limits, porosity, unit weight, in situ saturated hydraulic conductivity and shear strength parameters. In addition, mineral phases recognition via X-ray powder diffraction has been performed on the different soil types. The deposits mainly consist of well-sorted silty sands with low plastic behavior and extremely variable gravel and clay contents. Statistical analyses carried on these geotechnical and hydrological parameters highlighted that it is not possible to define a typical range of values only with relation to the main mapped lithologies, because soil characteristics are not simply dependent on the bedrock type from which the deposits originated. A second goal is to explore the relationship between soil type (in terms of grain size distribution) and selected morphometric parameters (slope angle, profile curvature, planar curvature and peak distance). The results show that the highest correlation between soil grain size classes and morphometric attributes is with slope curvature, both profile and planar.

The historical centre of Antananarivo represents one of the most important cultural heritage sites in Madagascar. During 2015, the whole city area was severely affected by geo-hydrological hazards due to cyclonic rain, resulting in severe flooding, and in widespread shallow landslides along the hillslopes. This event proved the vulnerability to geo-hydrological risk of the Upper town’s historical buildings. In October 2017, a geo-hydrological hazard mapping was performed in the Upper Town by combining field surveys, remote sensing and geomatic data analysis in a GIS environment. This provided detailed products such as a geological-geomorphological map, a map of the hydrographic network and of the creek basins, and a geodatabase to be used for detecting areas prone to erosion and geo-hydrological hazards. The final aim was to understand the geological, geomorphological and hydrographic features of the Upper Town, in order locate the more critical areas and to recommend priority works to be carried out, as a first step toward a risk management strategy and a conservation/valorization plan for building the resilience of the site. The results highlighted that a Cultural Heritage site protection strategy can be planned also in case of limited data, which is a frequent condition in developing countries.

Rock-carved cultural heritage sites are often developed in slopes formed by weak rocks, which due to their peculiar lithological, geotechnical, and morpho-structural features are characterized by excellent carvability, which at the same time makes them prone to weathering, deterioration, and slope instability issues. In this context the use of advanced close-range nondestructive techniques, such as Infrared Thermography (IRT) and Unmanned Aerial vehicle-based Digital Photogrammetry (UAV-DP) can be profitably used for the rapid detection of conservation issues (e.g., open fractures, unstable ledges-niches, water seepage and moisture) that can lead to slope instability phenomena. These techniques, when combined with traditional methods (e.g., field surveys, laboratory analysis), can provide fundamental data (such as 3D maps of the kinematic mechanisms) to implement a site-specific and interdisciplinary approach for the sustainable protection and conservation of such fragile cultural heritage sites. In this paper some examples of conservation problems in several rupestrian sites characterized by different geological contexts, from the mountainous regions of Georgia to the ancient city of Petra in Jordan, are presented, with the aim of evaluating the potential of the proposed integrated approach. The final aim is to provide conservators, practitioners, and local authorities with a useful, versatile, and low-cost methodology, to be profitably used in the protection and conservation strategies of rock-carved sites.

Intensity–duration rainfall thresholds are commonly used in regional-scale landslide warning systems. In this manuscript, 3D thresholds are defined also considering the mean rainfall amount fallen in each alert zone (MeAR, mean areal rainfall) in Emilia Romagna region (Northern Italy). In the proposed 3D approach, thresholds are represented by a plane instead of a line, and the third dimension allows to indirectly account for the influence of complex rainfall patterns. MeAR values are calculated according to different time periods ranging from 7 to 30 days, and all threshold parameters are calibrated independently for the 8 alert zones in which the region is divided. The approach was validated and compared with classical intensity–duration thresholds, finding that the 3D threshold may be used to get better performances, especially in terms of a consistent reduction of false alarms:− 20 to − 86%, depending on the alert zone and the selected MeAR duration. These results open new encouraging perspectives for the development of the regional warning system that is operated in the study area.

SUMMARYDenoising and onset time picking of signals are essential before extracting source information from collected seismic/microseismic data. We proposed an advanced deep dual-tasking network (DDTN) that integrates these two procedures sequentially to achieve the optimal performance. Two homo-structured encoder–decoder networks with specially designed structures and parameters are connected in series for handling the denoising and detection of microseismic signals. Based on the similarity of data types, the output of the denoising network will be imported into the detection network to obtain labels for the signal duration. The procedures of denoising and duration detection can be completed in an integrated way, where the denoised signals can improve the accuracy of onset time picking. Results show that the method has a good performance for the denoising of microseismic signals that contain various types and intensities of noise. Compared with existing methods, DDTN removes the noise with a minor waveform distortion. It is ideal for recovering the microseismic signal while maintaining a good capacity for onset time picking when the signal-to-noise ratio is low. Based on that, the second network can detect a more accurate duration of microseismic signals and thus obtain more accurate onset time. The method has great potential to be extended to the study of exploration seismology and earthquakes.

The Global Navigation Satellite System (GNSS) and Synthetic Aperture Radar Interferometry (InSAR) can be combined to achieve different goals, owing to their main principles. Both enable the collection of information about ground deformation due to the differences of two consequent acquisitions. Their variable applications, even if strictly related to ground deformation and water vapor determination, have encouraged the scientific community to combine GNSS and InSAR data and their derivable products. In this work, more than 190 scientific contributions were collected spanning the whole European continent. The spatial and temporal distribution of such studies, as well as the distinction in different fields of application, were analyzed. Research in Italy, as the most represented nation, with 47 scientific contributions, has been dedicated to the spatial and temporal distribution of its studied phenomena. The state-of-the-art of the various applications of these two combined techniques can improve the knowledge of the scientific community and help in the further development of new approaches or additional applications in different fields. The demonstrated usefulness and versability of the combination of GNSS and InSAR remote sensing techniques for different purposes, as well as the availability of free data, EUREF and GMS (Ground Motion Service), and the possibility of overcoming some limitations of these techniques through their combination suggest an increasingly widespread approach.

Stromboli is an active insular volcano located in the Southern Tyrrhenian Sea and its recent volcanic activity is mostly confined within the Sciara del Fuoco (SdF, hereafter), a 2-km wide subaerial–submarine collapse scar, which morphologically dominates the NW flank of the edifice. In August-November 2014, an effusive eruption occurred along the steep SdF slope, with multiple lava flows reaching the sea. The integration of multisensor remote sensing data, including lidar, photogrammetric, bathymetric surveys coupled with SAR amplitude images collected before and after the 2014 eruption enabled to reconstruct the dynamics of the lava flows through the main morphological changes of the whole SdF slope. Well-defined and steep-sided ridges were created by lava flows during the early stages of the eruption, when effusion rates were high, favoring the penetration into the sea of lava flows as coherent bodies. Differently, fan-shaped features were emplaced during the declining stage of the eruption or in relation to lava overflows and associated gravel flows, suggesting the prevalence of volcaniclastic breccias with respect to coherent lava flows. The estimated volume of eruptive products emplaced on the SdF slope during the 2014 eruption, accounts for about 3.7 × 106 m3, 18% of which is in the submarine setting. This figure is different with respect to the previous 2007 eruption at Stromboli, when a large lava submarine delta formed. This discrepancy can be mainly related to the different elevation of the main vents feeding lava flows during the 2007 eruption (around 400 m) and the 2014 eruption (around 650 m). Besides slope accretion, instability processes were detected both in the subaerial and submarine SdF slope. Submarine slope failure mobilized at least 6 × 105 m3 of volcaniclastic material, representing the largest instability event detected since the 2007 lava delta emplacement.

Between 28 March and 1 April 2020, Stromboli volcano erupted, with overflows from the NE crater rim spreading along the barren Sciara del Fuoco slope and reaching the sea along the NW coast of the island. Poor weather conditions did not allow a detailed observation of the crater zone through the cameras monitoring network, but a clear view of the lower slope and the flows expanding in the area allowed us to characterize the flow features. This evidence was integrated with satellite, GBInSAR, and seismic data, thus enabling a reconstruction of the whole volcanic event, which involved several small collapses of the summit cone and the generation of pyroclastic density currents (PDCs) spreading along the slope and on the sea surface. Satellite monitoring allowed for the mapping of the lava flow field and the quantification of the erupted volume, and GBInSAR continuous measurements detected the crater widening and the deflation of the summit cone caused by the last overflow. The characterization of the seismicity made it possible to identify the signals that are associated with the propagation of PDCs along the volcano flank and, for the first time, to recognize the signal that is produced by the impact of the PDCs on the coast.

Landslide susceptibility maps are widely used in landslide hazard management. Although many models have been proposed, mapping unit definition is a matter that still needs to be fully examined. In the literature, the most reported mapping units are pixels and slope units, while in this work, developed in the Rio de Janeiro region (Brazil), the use of drainage basins as a mapping unit is examined; even if their use leads to the definition of maps with a coarser spatial resolution than pixels-based maps, they convey information that can be easily and rapidly handled by civil defense organizations. However, for the morphometrical characterization of entire basins, a standardized procedure does not exist, and the susceptibility results may be sensitive to the approach used. To investigate this issue, a random forest model was used to assess landslide susceptibility, using 12 independent variables: four categorical (land use, soil type, lithology and slope orientation) and eight numerical variables (slope gradient, elevation, slope curvature, profile curvature, planar curvature, flow accumulation, topographic wetness index, stream power index). For each basin, the numerical variables were aggregated according to different approaches, which, in turn, were used to set up four different model configurations: i) maximum values, ii) mean values, iii) standard deviation values, iv) joint use of all the above. The resulting maps showed noticeable differences and a quantitative validation procedure showed that the best configurations were the ones based on mean values of independent variables, and the one based on the combination of all the values of the numerical variables. The main outcomes of this work consist of a landslide susceptibility map of the study area, to be used in operational procedures of risk management and in some insights on the best approaches to aggregate raster cell data into wider spatial units.

In archaeological applications the accurate reconstruction of buried structures is mandatory. Electrical resistivity tomography is widely used for this purpose. Nevertheless, resistivity errors could be generated by wrong placement of electrodes. Papers in the literature do not discuss the influence of errors connected with the electrode position location (GPS-error). In this paper the first results of a Monte Carlo simulation analysis of data acquired on a tumulus are presented. The main research questions were: (i) if it is correct to ignore the GPS-error collect, and (ii) if a minimum threshold, that significantly affect the inversion, exists. Results, obtained considering planimetric GPS-errors of about one third of the fixed electrode distances, show that the GPS-errors affect resistivity, but the generated errors/anomalies: (a) are lower than that obtained without considering the topography, and (b) are significant from a numerical point of view, but do not affect the interpretation, being compatible with the soil resistivity ranges.

Identification and classification of landslides is a preliminary and crucial work for landslide risk assessment and hazard mitigation. The exploitation of surface deformation velocity derived from satellite synthetic aperture radar interferometry (InSAR) is a consolidated and suitable procedure for the recognition of active landslides over wide areas. However, the calculated displacement velocity from InSAR is one-dimensional motion along the satellite line of sight (LOS), representing a major hurdle for landslide type and failure mechanism classification. In this paper, different velocity datasets derived from both ascending and descending Sentinel-1 data are employed to analyze the surface ground movement of the Huangshui region (Northwestern China). With global warming, precipitation in the Huangshui region, geologically belonging to the loess basin in the eastern edge of Qing-Tibet Plateau, has been increasing, often triggering a large number of landslides, posing a potential threat to local citizens and natural and anthropic environments. After processing both SAR data geometries, the surface motion was decomposed to obtain the two-dimensional displacements (vertical and horizontal E–W). Thus, a classification criterion of the loess landslide types and failure mode is proposed, according to the analysis of deformation direction, velocities, texture, and topographic characteristics. With the support of high-resolution images acquired by remote sensing and unmanned aerial vehicle (UAV), 14 translational slides, seven rotational slides, and 10 loess flows were recognized in the study area. The derived results may provide solid support for stakeholders to comprehend the hazard of unstable slopes and to undertake specific precautions for moderate and slow slope movements.

The rock-cut city of Vardzia is an example of the extraordinary rupestrian cultural heritage of Georgia. The site, Byzantine in age, was carved in the steep tuff slopes of the Erusheti mountains, and due to its peculiar geological characteristics, it is particularly vulnerable to weathering and degradation, as well as frequent instability phenomena. These problems determine serious constraints on the future conservation of the site, as well as the safety of the visitors. This paper focuses on the implementation of a site-specific methodology, based on the integration of advanced remote sensing techniques, such as InfraRed Thermography (IRT) and Unmanned Aerial Vehicle (UAV)-based Digital Photogrammetry (DP), with traditional field surveys and laboratory analyses, with the aim of mapping the potential criticality of the rupestrian complex on a slope scale. The adopted methodology proved to be a useful tool for the detection of areas of weathering and degradation on the tuff cliffs, such as moisture and seepage sectors related to the ephemeral drainage network of the slope. These insights provided valuable support for the design and implementation of sustainable mitigation works, to be profitably used in the management plan of the site of Vardzia, and can be used for the protection and conservation of rupestrian cultural heritage sites characterized by similar geological contexts.

The geomorphological evolution of the volcanic Island of Stromboli (Italy) between July 2010 and June 2019 has been reconstructed by using multi-temporal, multi-platform remote sensing data. Digital elevation models (DEMs) from PLÉIADES-1 tri-stereo images and from Light Detection and Ranging (LiDAR) acquisitions allowed for topographic changes estimation. Data were comprised of high-spatial-resolution (QUICKBIRD) and moderate spatial resolution (SENTINEL-2) satellite images that allowed for the mapping of areas that were affected by major lithological and morphological changes. PLÉIADES tri-stereo and LiDAR DEMs have been quantitatively and qualitatively compared and, although there are artefacts in the smaller structures (e.g., ridges and valleys), there is still a clear consistency between the two DEMs for the larger structures (as the main valleys and ridges). The period between July 2010 and May 2012 showed only minor changes consisting of volcanoclastic sedimentation and some overflows outside the crater. Otherwise, between May 2012 and May 2017, large topographic changes occurred that were related to the emplacement of the 2014 lava flow in the NE part of the Sciara del Fuoco and to the accumulation of a volcaniclastic wedge in the central part of the Sciara del Fuoco. Between 2017 and 2019, minor changes were again detected due to small accumulation next to the crater terrace and the erosion in lower Sciara del Fuoco.

Landslides recurrently impact the Italian territory, producing huge economic losses and casualties. Because of this, there is a large demand for monitoring tools to support landslide management strategies. Among the variety of remote sensing techniques, Interferometric Synthetic Aperture Radar (InSAR) has become one of the most widely applied for landslide studies. This work reviews a variety of InSAR-related applications for landslide studies in Italy. More than 250 papers were analyzed in this review. The first application dates back to 1999. The average production of InSAR-related papers for landslide studies is around 12 per year, with a peak of 37 papers in 2015. Almost 70% of the papers are written by authors in academia. InSAR is used (i) for landslide back analysis (3% of the papers); (ii) for landslide characterization (40% of the papers); (iii) as input for landslide models (7% of the papers); (iv) to update landslide inventories (15% of the papers); (v) for landslide mapping (32% of the papers), and (vi) for monitoring (3% of the papers). Sixty-eight percent of the authors validated the satellite results with ground information or other remote sensing data. Although well-known limitations exist, this bibliographic overview confirms that InSAR is a consolidated tool for many landslide-related applications.

The city of Antananarivo is located in the central highlands of Madagascar, and is the largest urban center of the island. Considering the frequent heavy cyclonic rains frequently affecting the area, its geomorphological context is particularly prone to geo-hydrological hazards, such as landslides and flash floods, as recently proved during the disastrous events of the winters of 2015 and 2018. Field data and high-resolution remote sensing data interpretation (DEMs and satellite orthophotos) were combined in order to produce detailed hydrographic and geomorphological maps. The aim was to understand the processes acting in the Analamanga hill area, with special regards to the effect of human activity in modeling the natural landforms and exacerbating the geo-hydrological hazards. The obtained maps will provide management-planning tools to be used as a first step towards a risk reduction strategy in the Antananarivo historical urban center.

Denoising methods are a highly desired component of signal processing, and they can separate the signal of interest from noise to improve the subsequent signal analyses. In this paper, an advanced denoising method based on a fully convolutional encoder&ndash, decoder neural network is proposed. The method simultaneously learns the sparse features in the time&ndash, frequency domain, and the mask-related mapping function for signal separation. The results show that the proposed method has an impressive performance on denoising microseismic signals containing various types and intensities of noise. Furthermore, the method works well even when a similar frequency band is shared between the microseismic signals and the noises. The proposed method, compared to the existing methods, significantly improves the signal&ndash, noise ratio thanks to minor changes of the microseismic signal (less distortion in the waveform). Additionally, the proposed methods preserve the shape and amplitude characteristics so that it allows better recovery of the real waveform. This method is exceedingly useful for the automatic processing of the microseismic signal. Further, it has excellent potential to be extended to the study of exploration seismology and earthquakes.

Soil sealing is the destruction or covering of natural soils by totally or partially impermeable artificial material. ISPRA (Italian Institute for Environmental Protection Research) uses different remote sensing techniques to monitor this process and updates yearly a national-scale soil sealing map of Italy. In this work, for the first time, we tried to combine soil sealing indicators as additional parameters within a landslide susceptibility assessment. Four new parameters were derived from the raw soil sealing map: Soil sealing aggregation (percentage of sealed soil within each mapping unit), soil sealing (categorical variable expressing if a mapping unit is mainly natural or sealed), urbanization (categorical variable subdividing each unit into natural, semi-urbanized, or urbanized), and roads (expressing the road network disturbance). These parameters were integrated with a set of well-established explanatory variables in a random forest landslide susceptibility model and different configurations were tested: Without the proposed soil-sealing-derived variables, with all of them contemporarily, and with each of them separately. Results were compared in terms of AUC ((area under receiver operating characteristics curve, expressing the overall effectiveness of each configuration) and out-of-bag-error (estimating the relative importance of each variable). We found that the parameter “soil sealing aggregation” significantly enhanced the model performances. The results highlight the potential relevance of using soil sealing maps on landslide hazard assessment procedures.

A complete landslide dam hazard management incorporates two assessment phases: the damming probability and the breach hazard. A prompt evaluation of the dam stability is crucial during the emergency to mitigate its consequences, but a reliable risk assessment can be realized only after the event has occurred, when the available time is very short. Therefore, it is necessary to develop tools able to help in mapping the spatial probability of damming over large areas for land-use planning, in order to better constrain consequence analysis and risk scenarios for setting up mitigation measures. In this work, a semi-automated GIS-based mapping methodology, based on a statistical correlation of morphometric parameters described by a morphological index, is proposed to spatially assess the likelihood of a river obstruction by landslide damming through two main mechanisms: the reactivation of existing landslides and the formation of new landslides. The two mapping methods (damming predisposition and damming probability) were used on a test area, the Arno River basin in Italy. The Eastern part of the basin resulted as the most susceptible to damming events in the whole basin. These are the highest mountain ridges in the basin (about 1600 m a.s.l.), characterized by calcareous, arenaceous, and marl lithology. The results are confirmed by the high concentration of the known historical landslide dams in the area according to existing inventories.

Reliable automatic microseismic waveform detection with high efficiency, precision, and adaptability is the basis of stability analysis of the surrounding rock mass. In this paper, a convolutional neural network (CNN)-based microseismic detection network (CNN-MDN) model was established and well trained to a high degree of accuracy using a dataset with 16,000 preprocessed waveforms. By comparison with other methods, 4000 waveforms were tested to evaluate the precision, recall, and F1-score. The results revealed that the CNN-MDN demonstrated the highest performance in microseismic detection. Moreover, the low sensitivity of the CNN-MDN to noise of different intensities was proved by testing on semi-synthetic data. The model also possesses good generalization ability and superior performance capability for microseismic detection under different geological structure backgrounds, and it can correctly detect the microseismic events with Mw ≥ 0.5. These preliminary results show that the CNN-MDN can be directly applied to unprocessed microseismic data and has great potential in real-time microseismic monitoring applications.

The mission geosynchronous – continental land atmosphere sensing system (G-CLASS) is designed to study thediurnal water cycle, using geosynchronous radar. Although the water cycle is vital to human society, processes on timescalesless than a day are very poorly observed from space. G-CLASS, using C-band geosynchronous radar, could transform this. Itsscience objectives address intense storms and high resolution weather prediction, and significant diurnal processes such assnow melt and soil moisture change, with societal impacts including agriculture, water resource management, flooding, andlandslides. Secondary objectives relate to ground motion observations for earthquake, volcano, and subsidence monitoring. Theorbit chosen for G-CLASS is designed to avoid the geosynchronous protected region and enables integration times of minutesto an hour to achieve resolutions down to ~20 m. Geosynchronous orbit (GEO) enables high temporal resolution imaging (up toseveral images per hour), rapid response, and very flexible imaging modes which can provide much improved coverage at lowlatitudes. The G-CLASS system design is based on a standard small geosynchronous satellite and meets the requirements ofESA's Earth Explorer 10 call. Peer Reviewed Objectius de Desenvolupament Sostenible::15 - Vida d'Ecosistemes Terrestres Objectius de Desenvolupament Sostenible::13 - Acció per al Clima

This paper presents the preliminary results of the IPL project 196 “Development and applications of a multi-sensor drone for geohazards monitoring and mapping.” The objective of the project is to test the applicability of a multi-sensor drone for the mapping and monitoring of different types of geohazards. The Department of Earth Sciences of the University of Florence has developed a new type of drone airframe. Several survey campaigns were performed in the village of Ricasoli, in the Upper Arno river Valley (Tuscany, Italy) with the drone equipped with an optical camera to understand the possibility of this rising technology to map and characterize landslides. The aerial images were combined and analyzed using Structure-from-Motion (SfM) software. The collected data allowed an accurate reconstruction and mapping of the detected landslides. Comparative analysis of the obtained DTMs also permitted the detection of some slope portions being prone to failure and to evaluate the area and volume of the involved mass.

We communicate the results of a preliminary investigation aimed at improving a state-of-the-art RSLEWS (regional-scale landslide early warning system) based on rainfall thresholds by integrating mean soil moisture values averaged over the territorial units of the system. We tested two approaches. The simplest can be easily applied to improve other RSLEWS: it is based on a soil moisture threshold value under which rainfall thresholds are not used because landslides are not expected to occur. Another approach deeply modifies the original RSLEWS: thresholds based on antecedent rainfall accumulated over long periods are substituted with soil moisture thresholds. A back analysis demonstrated that both approaches consistently reduced false alarms, while the second approach reduced missed alarms as well.

Stromboli (Aeolian Archipelago, Italy) is an active volcano that is frequently affected by moderate to large mass wasting, which has occasionally triggered tsunamis. With the aim of understanding the relationship between the geomorphologic evolution and slope instability of Stromboli, remote sensing information from space-born Synthetic Aperture Radar (SAR) change detection and interferometry (InSAR) () and Ground Based InSAR (GBInSAR) was compared with field observations and morphological analyses. Ground reflectivity and SqueeSAR™ (an InSAR algorithm for surface deformation monitoring) displacement measurements from X-band COSMO-SkyMed satellites (CSK) were analysed together with displacement measurements from a permanent-sited, Ku-band GBInSAR system. Remote sensing results were compared with a preliminary morphological analysis of the Sciara del Fuoco (SdF) steep volcanic flank, which was carried out using a high-resolution Digital Elevation Model (DEM). Finally, field observations, supported by infrared thermographic surveys (IRT), allowed the interpretation and validation of remote sensing data. The analysis of the entire dataset (collected between January 2010 and December 2014) covers a period characterized by a low intensity of Strombolian activity. This period was punctuated by the occurrence of lava overflows, occurring from the crater terrace evolving downslope toward SdF, and flank eruptions, such as the 2014 event. The amplitude of the CSK images collected between February 22nd, 2010, and December 18th, 2014, highlights that during periods characterized by low-intensity Strombolian activity, the production of materials ejected from the crater terrace towards the SdF is generally low, and erosion is the prevailing process mainly affecting the central sector of the SdF. CSK-SqueeSAR™ and GBInSAR data allowed the identification of low displacements in the SdF, except for high displacement rates (up to 1.5 mm/h) that were measured following both lava delta formation after the 2007 eruption and the lava overflows of 2010 and 2011. After the emplacement of the 2014 lava field, high displacements in the central and northern portions of the SdF were recorded by the GBInSAR device, whereas the spaceborne data were unable to detect these rapid movements. A comparison between IRT images and GBInSAR-derived displacement maps acquired during the same time interval revealed that the observed displacements along the SdF were related to the crumbling of newly emplaced 2014 lava and of its external breccia. Detected slope instability after the 2014 flank eruption was related to lava accumulation on the SdF and to the difference in the material underlying the 2014 lava flow: i) lava flows and breccia layers related to the 2002–03 and 2007 lava flow fields in the northern SdF sector and ii) loose volcaniclastic deposits in the central part of the SdF. This work emphasizes the importance of smart integration of spaceborne, SAR-derived hazard information with permanent-sited, operational monitoring by GBInSAR devices to detect areas impacted by mass wasting and volcanic activity.

A big challenge in terms or landslide risk mitigation is represented by increasing the resiliency of society exposed to the risk. Among the possible strategies with which to reach this goal, there is the implementation of early warning systems. This paper describes a procedure to improve early warning activities in areas affected by high landslide risk, such as those classified as critical infrastructures for their central role in society. This research is part of the project LEWIS (Landslides Early Warning Integrated System): An Integrated System for Landslide Monitoring, Early Warning and Risk Mitigation along Lifelines. LEWIS is composed of a susceptibility assessment methodology providing information for single points and areal monitoring systems, a data transmission network and a data collecting and processing center (DCPC), where readings from all monitoring systems and mathematical models converge and which sets the basis for warning and intervention activities. The aim of this paper is to show how logistic issues linked to advanced monitoring techniques, such as big data transfer and storing, can be dealt with compatibly with an early warning system. Therefore, we focus on the interaction between an areal monitoring tool (a ground-based interferometric radar) and the DCPC. By converting complex data into ASCII strings and through appropriate data cropping and average, and by implementing an algorithm for line-of-sight correction, we managed to reduce the data daily output without compromising the capability for performing.

On February 14, 2010, a large landslide affected the urban centre of San Fratello town (Sicily Island, Southern Italy), causing severe damage to buildings, roadways, and infrastructure, as well as about 2000 evacuees. This large complex landslide, covering more than 1 km2 in extension, represents one of the major phenomena that ever occurred in Sicily. In order to manage the landslide risk, the civil protection system was activated as part of the initial response to the emergency (the “emergency phase”). This involved the Civil Protection Departments both at national (DPC) and regional (DRPC) levels, as well as scientific institutions (namely “Competence centres”, CdCs), local administration personnel, and technicians. On March 8, 2010, during the post-event recovery phase, a ground-based synthetic aperture radar (GB-InSAR) system was installed in order to monitor the ground surface deformation, assess the landslide residual risk, and analyse its displacement trend. Accurate field surveys and building inspections were also performed for a validation of the GB-InSAR data, in order to map the ground deformation, plan building evacuation-demolishment, as well as check the efficiency of the landslide mitigation works. This paper describes the outcomes of the 57 month monitoring campaign (March 2010–December 2014), reporting the use of GB-InSAR data for near real-time monitoring, mapping, and post-emergency/recovery management activities. The final aim was to provide the civil protection personnel with a reliable, rapid, and easy communication system of the monitoring results, designed to an enhance understanding of the landslide phenomena, and to support the decision-making process.

The objective of this work is twofold: (i) automatically setting up a landslide inventory using a state-of-the art semantic engine based on data mining on online news and (ii) evaluating if the automatically generated inventory can be used to validate a regional scale landslide warning system based on rainfall-thresholds. The semantic engine scanned internet news in real time in a 50 months test period. At the end of the process, an inventory of approximately 900 landslides was automatically set up for the Tuscany region (23,000 km 2 , Italy). Using a completely automated procedure, the inventory was compared with the outputs of the regional landslide early warning system and a good correspondence was found, e.g. 84% of the events reported in the news is correctly identified by the warning system. On the basis of the obtained results, we conclude that automatic validation of landslide models using geolocalized landslide events feedback is possible. The source of data for validation can be obtained directly from the Internet channel using an appropriate semantic engine dedicated to perform a monitoring of the Google News aggregator. Moreover, validation statistics can be used to evaluate the effectiveness of the predictive model and, if deemed necessary, an update of the rainfall thresholds could be performed to obtain an improvement of the forecasting effectiveness of the warning system. In the near future, the proposed procedure could operate in continuous time and could allow for a periodic update of landslide hazard models and landslide early warning systems with minimum or none human intervention.

Classification and regression problems are a central issue in geosciences. In this paper, we present Classification and Regression Treebagger (ClaReT), a tool for classification and regression based on the random forest (RF) technique. ClaReT is developed in Matlab and has a simple graphic user interface (GUI) that simplifies the model implementation process, allows the standardization of the method, and makes the classification and regression process reproducible. This tool performs automatically the feature selection based on a quantitative criterion and allows testing a large number of explanatory variables. First, it ranks and displays the parameter importance; then, it selects the optimal configuration of explanatory variables; finally, it performs the classification or regression for an entire dataset. It can also provide an evaluation of the results in terms of misclassification error or root mean squared error. We tested the applicability of ClaReT in two case studies. In the first one, we used ClaReT in classification mode to identify the better subset of landslide conditioning variables (LCVs) and to obtain a landslide susceptibility map (LSM) of the Arno river basin (Italy). In the second case study, we used ClaReT in regression mode to produce a soil thickness map of the Terzona catchment, a small sub-basin of the Arno river basin. In both cases, we performed a validation of the results and a comparison with other state-of-the-art techniques. We found that ClaReT produced better results, with a more straightforward and easy application and could be used as a valuable tool to assess the importance of the variables involved in the modeling.

This study aimed at evaluating and mapping Ground Subsidence Susceptibility (GSS) in the Grosseto plain (Tuscany Region, Italy) by exploiting multi-temporal satellite InSAR data and by applying two parallel approaches; a bivariate statistical analysis (Frequency Ratio) and a mathematical probabilistic model (Fuzzy Logic operator). The Grosseto plain experienced subsidence and sinkholes due to natural causes in the past and it is still suffering slow-moving ground lowering. Five conditioning subsidence-related factors were selected and managed in a GIS environment through an overlay pixel-by-pixel analysis. Firstly, multi-temporal ground subsidence inventory maps were prepared in the study area by starting from two inventories referred to distinct temporal intervals (2003–2009 and 2014–2019) derived from Persistent Scatterers Interferometry (PSI) data of ENVISAT and SENTINEL-1 satellites. Then, the susceptibility modelling was performed through the Frequency Ratio (FR) and Fuzzy Logic (FL) approaches. These analyses led to slightly different scenarios which were compared and discussed. Results show that flat areas on alluvial and colluvial deposits with thick sedimentary cover (higher than 20 m) on the bedrock in the central and eastern sectors of the plain are the most susceptible to land subsidence. The obtained FR- and FL-based GSS maps were finally validated with a ROC (Receiver Operating Characteristic) analysis, in order to estimate the overall performance of the models. The AUC (Area Under Curve) values of ROC analysis of the FR model were higher than the ones of FL model, suggesting that the former is a better and more appropriate predictor for subsidence susceptibility analysis in the study area. In conclusion, GSS maps provided a qualitative overview of the subsidence scenarios and may be helpful to predict and preliminarily identify high-risk areas for environmental local authorities and decision makers in charge of land use planning in the study area. Finally, the presented methodologies to derive GSS maps are easily reproducible and could also be applied and tested in other test sites worldwide, in order to check the modeling performance in different environmental settings.

Satellite interferometric data are widely exploited for ground motion monitoring thanks to their wide area coverage, cost efficiency and non-invasiveness. The launch of the Sentinel-1 constellation opened new horizons for interferometric applications, allowing the scientists to rethink the way in which these data are delivered, passing from a static view of the territory to a continuous streaming of ground motion measurements from space. Tuscany Region is the first worldwide example of a regional scale monitoring system based on satellite interferometric data. The processing chain here exploited combines a multi-interferometric approach with a time-series data mining algorithm aimed at recognizing benchmarks with significant trend variations. The system is capable of detecting the temporal changes of a wide variety of phenomena such as slow-moving landslides and subsidence, producing a high amount of data to be interpreted in a short time. Bulletins and reports are derived to the hydrogeological risk management actors at regional scale. The final output of the project is a list of potentially hazardous and accelerating phenomena that are verified on site by field campaign by completing a sheet survey in order to qualitatively estimate the risk and to suggest short-term actions to be taken by local entities. Two case studies, one related to landslides and one to subsidence, are proposed to highlight the potential of the monitoring system to early detect anomalous ground changes. Both examples represent a successful implementation of satellite interferometric data as monitoring and risk management tools, raising the awareness of local and regional authorities to geohazards.

In 2011 Brazil experienced the worst disaster in the country&rsquo, s history. There were 918 deaths and thousands made homeless in the mountainous region of Rio de Janeiro State due to several landslides triggered by heavy rainfalls. This area constantly suffers high volumes of rain and episodes of landslides. Due to these experiences, we used the MaCumBa (Massive CUMulative Brisk Analyser) software to identify rainfall intensity&ndash, duration thresholds capable of triggering landslides in the most affected municipalities of this region. More than 3000 landslides and rain data from a 10-year long dataset were used to define the thresholds and one year was used to validate the results. In this work, a set of three thresholds capable of defining increasing alert levels (moderate, high and very high) has been defined for each municipality. Results show that such thresholds may be used for early alerts. In the future, the same methodology can be replicated to other Brazilian municipalities with different datasets, leading to more accurate warning systems.

The European Space Agency satellites Sentinel-1 radar and Sentinel-2 optical data are widely used in water surface mapping and management. In this work, we exploit the potentials of both radar and optical images for satellite-based quick detection and extent mapping of inundations/water raising events over Shkod&euml, r area, which occurred in the two last years (2017&ndash, 2018). For instance, in March 2018 the Shkod&euml, r district (North Albania) was affected twice by the overflow of the Drin and Buna (Bojana) Rivers and by the Shkod&euml, r lake plain inundation. Sentinel-1 radar data allowed a rapid mapping of seasonal fluctuations and provided flood extent maps by discriminating water surfaces (permanent water and flood areas) from land/non-flood areas over all the informal zones of Shkod&euml, r city. By means of Sentinel-2 data, two color composites maps were produced and the Normalized Difference Water Index was estimated, in order to further distinguish water/moisturized soil surfaces from built-up and vegetated areas. The obtained remote sensing-based maps were combined and discussed with the urban planning framework in order to support a sustainable urban and environmental management. The provided multi-temporal analysis could be easily exploited by the local authorities for flood prevention and management purposes in the inherited territorial context. The proposed approach outputs were validated by comparing them with official Copernicus EMS (Emergency Management Service) maps available for one of the chosen events. The comparison shows good accordance results. As for a further enhancement in the future perspective, it is worth to highlight that a more accurate result could be obtained by performing a post-processing edit to further refine the flooded areas, such as water mask application and supervised classification to filter out isolated flood elements, to remove possible water-lookalikes and weed out false positives.

The 6-days repeatability of Sentinel-1 constellation allows building up an interferometric stack with unprecedented velocity. Easily updatable hot-spot analyses, frequently repeated following the update of Sentinel-1 images, represent very useful tools for MTInSAR (Multi-Temporal Interferometric Synthetic Aperture Radar) data analysis. Mountain regions are a challenging environment for interferometric analyses because of their climatic, morphological and land cover characteristics. In this context, MTInSAR data can retrieve reliable information over wide areas, with high cost-benefits ratio and where the installation of ground-based devices is not feasible. Considering the well-known limitations of interferometric techniques (such as fast motions or temporal and spatial decorrelation), hot-spot analyses are a viable solution for semi-automatic ground movements extraction over mountain regions. In this work, we present an example of a hot-spot analysis applied to a large stack of MTInSAR products generated by means of SqueeSAR technique over an alpine region (Valle d’Aosta, north-western Italy). The obtained outputs allow detecting 277 moving areas connected to landslides and mass wasting processes in general. These products are intended to be implemented in the landslide risk management chain of the region, focusing on landslide state of activity definition and landslide mapping.

The investigation of large landslides in high alpine environments is often hindered by the difficult accessibility of the mountainous terrain. Efforts are typically concentrated on the remote measurement of the surface displacements, in order to define the general slope dynamics and identify phases of increasing activity. The characterization of such phenomena is challenging, due to their complex nature as well as the limitations of monitoring techniques. Appropriately integrating monitoring data from different sources can help reduce uncertainties, yet it is seldom done. In this paper, the outcomes of GNSS, satellite InSAR, and GBInSAR campaigns performed at the Bosmatto landslide (Northwestern Alps, Italy) are presented. The joint analysis provided a comprehensive view of the deformation field of the landslide, which revealed a gradually decreasing dip angle of the calculated movement vectors from head to toe. The instability was interpreted as a 2.5 – 3.5 ∗ 106 m3 rockslide, moving at peak velocities >50 mm/y according to a broadly roto-translational mechanism. The impact of the seasonal snow cover on the reliability of the interferometric acquisitions was also evaluated. Advantages and implications offered by the combination of multiple monitoring techniques are highlighted.

In the last decade satellite remote sensing has become an effective tool for monitoring geo-hazard-induced ground motions, and has been increasingly used by the scientific community. Direct and indirect costs due to geo-hazards are currently rising, causing serious socio-economics and casualty losses. Therefore, creating a priority list turns out to be essential to highlight the most relevant ground deformations and to better focus risk management practices at regional scale. The Sentinel-1 constellation, thanks to the 6-days repeatability and the free availability of the data, allows to easily update the geo-hazard-induced ground motions, compared to other kind of satellite sensors. In this work, a hot-spot-like method is presented by filtering a large stack of Sentinel-1 images processed by means of the SqueeSAR algorithm. Three periods, with six months repetitiveness, have been analysed in order to evaluate the behaviour and evolution of deformation clusters. The target area is Tuscany Region, located in the central part of Italy and affected by a wide gamma of geohazards, ranging from landslides to large subsidence areas. The final output is a geo-database of ground motions that can be used by regional authorities to prioritize and to effectively plan local risk reduction actions. © 2019, © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

In many landslide studies, the possibility to predict future behaviour is still a major concern. To date, early-warning systems have mostly relied on the availability of detailed, high-frequency data from sensors installed in situ. Methods deducing reliable failure predictions have been largely applied at local scale, where in situ monitoring systems can be installed.The same purpose could not be chased through spaceborne monitoring applications, as these could not yield information acquired in sufficiently systematic fashion: the low data sampling frequency of most of the satellite systems hampered the possibility to retrieve the necessary details of tertiary creep characterized by accelerating deformation. So far, the lack of systematic information on ground displacement acquired at regional scale was another serious limit hampering the application of failure prediction methods at wide scale. Such limitations can be partially solved through the exploitation of new generation spaceborne platforms.The launch of Sentinel-1 mission opened a new opportunity for InSAR monitoring applications thanks to the increased acquisition frequency, the regularity of acquisitions and the policy on data access. We demonstrate the potential of satellite Interferometric Synthetic Aperture Radar (InSAR) to identify precursors to catastrophic slope failures.Here we present three sets of Sentinel-1 constellation images processed by means of multi-interferometric analysis. We detect clear trends of accelerating displacement prior to the catastrophic failure of three large slopes of very different nature: an open-pit mine slope, a natural rock slope in alpine terrain, and a tailings dam embankment. We determine that these events could have been located several days or weeks in advance. The results highlight that satellite InSAR may now be used to support decision making and enhance predictive ability for this type of hazard.

In situ and remote-sensing measurements have been used to characterize the run-up phase and the phenomena that occurred during the August⁻November 2014 flank eruption at Stromboli. Data comprise videos recorded by the visible and infrared camera network, ground displacement recorded by the permanent-sited Ku-band, Ground-Based Interferometric Synthetic Aperture Radar (GBInSAR) device, seismic signals (band 0.02⁻10 Hz), and high-resolution Digital Elevation Models (DEMs) reconstructed based on Light Detection and Ranging (LiDAR) data and tri-stereo PLEIADES-1 imagery. This work highlights the importance of considering data from in situ sensors and remote-sensing platforms in monitoring active volcanoes. Comparison of data from live-cams, tremor amplitude, localization of Very-Long-Period (VLP) source and amplitude of explosion quakes, and ground displacements recorded by GBInSAR in the crater terrace provide information about the eruptive activity, nowcasting the shift in eruptive style of explosive to effusive. At the same time, the landslide activity during the run-up and onset phases could be forecasted and tracked using the integration of data from the GBInSAR and the seismic landslide index. Finally, the use of airborne and space-borne DEMs permitted the detection of topographic changes induced by the eruptive activity, allowing for the estimation of a total volume of 3.07 ± 0.37 × 106 m3 of the 2014 lava flow field emplaced on the steep Sciara del Fuoco slope.

Strombolian activity varies in magnitude and intensity and may evolve into a threat for the local populations living on volcanoes with persistent or semi-persistent activity. A key example comes from the activity of Stromboli volcano (Italy). The “ordinary” Strombolian activity, consisting in intermittent ejection of bombs and lapilli around the eruptive vents, is sometimes interrupted by high-energy explosive events (locally called major or paroxysmal explosions), which can affect very large areas. Recently, the 3 July 2019 explosive paroxysm at Stromboli volcano caused serious concerns in the local population and media, having killed one tourist while hiking on the volcano. Major explosions, albeit not endangering inhabited areas, often produce a fallout of bombs and lapilli in zones frequented by tourists. Despite this, the classification of Strombolian explosions on the basis of their intensity derives from measurements that are not always replicable (i.e., field surveys). Hence the need for a fast, objective and quantitative classification of explosive activity. Here, we use images of the monitoring camera network, seismicity and ground deformation data, to characterize and distinguish paroxysms, impacting the whole island, from major explosions, that affect the summit of the volcano above 500 m elevation, and from the persistent, mild explosive activity that normally has no impact on the local population. This analysis comprises 12 explosive events occurring at Stromboli after 25 June 2019 and is updated to 6 December 2020.

Background Rockfall events are one of the most dangerous phenomena that often cause several damages both to people and facilities. During recent years, the scientific community focused the attention at evaluating the effectiveness of seismological methods in monitoring these phenomena. In this work, we present a quick and practical method to locate the rebounds of some man-induced boulders falls from a landslides crown located in the Northern Apennines (Central Italy). The reconstruction of the trajectories was obtained by means of back analysis performed through a Matlab code that takes into account both the DEM (Digital Elevation Model) of the ground, the geotechnical-geophysical characteristics of the slope and the arrival times of the seismic signals generated by the rock impacts on the ground. Results The localization results have been compared with GPS coordinates of the points and videos footage acquired during the simulations, in order to assess the reliability of the method. In most cases, the retrieved impact points match with the real trajectories, showing a high reliability. Furthermore, four different cases have been identified as a function of the geomechanical, geophysical and morphological conditions. Due to the latter ones, in some case it was necessary to assume different values for the propagation velocity of the elastic waves in the ground, here assumed to be isotropic and homogeneous. Conclusions This work aims at evaluating the effectiveness of a quick and practical method to locate rockfall events using a small-aperture seismic network. The obtained results indicate that the technique can provide quantitative information about the area most prone to impact of detached blocks. The method still presents some uncertainty, but reducing some of the approximations (e.g. by better constraining the velocity model), it could lead to prompt and more accurate results, easily applicable to hazard estimates.

In this paper the mapping of subsidences in Tuscany (Italy) is presented. To achieve our goal satellite SAR data processed with persistent scatters interferometry (PSI) technique have been used. Several subsiding areas have been identified and three of them have been analyzed in detail and subsequently compared with literature work both to validate the results and to analyze the evolution of the identified subsidences. In general, this comparison confirmed the quality of the analyses and, furthermore, lead to the identification of an important ground uplift close to Prato city, an area that was historically affected by subsidence.

Predicting the time of failure is a topic of major concern in the field of geological risk management. Several approaches, based on the analysis of displacement monitoring data, have been proposed in recent years to deal with the issue. Among these, the inverse velocity method surely demonstrated its effectiveness in anticipating the time of collapse of rock slopes displaying accelerating trends of deformation rate. However, inferring suitable linear trend lines and deducing reliable failure predictions from inverse velocity plots are processes that may be hampered by the noise present in the measurements; data smoothing is therefore a very important phase of inverse velocity analyses. In this study, different filters are tested on velocity time series from four case studies of geomechanical failure in order to improve, in retrospect, the reliability of failure predictions: Specifically, three major landslides and the collapse of an historical city wall in Italy have been examined. The effects of noise on the interpretation of inverse velocity graphs are also assessed. General guidelines to conveniently perform data smoothing, in relation to the specific characteristics of the acceleration phase, are deduced. Finally, with the aim of improving the practical use of the method and supporting the definition of emergency response plans, some standard procedures to automatically setup failure alarm levels are proposed. The thresholds which separate the alarm levels would be established without needing a long period of neither reference historical data nor calibration on past failure events.

Nowadays many developing countries need effective measures to reduce the disaster related risks. Structural interventions are the most effective to achieve these aims. Nevertheless, in the absence of adequate financial resources different low-cost strategies can be used to minimize losses. The purpose of this paper is to demonstrate that the disaster risk reduction can be gathered building a community coping capacity. In the case study, flood and seismic analyses have been carried out using relatively simple and low-cost technologies, fundamental for governments and research institutions of poorly developed countries. In fact, through the acquisition and dissemination of these basic information, a reduction of vulnerability and risk can be achieved. In detail, two methodologies for the evaluation of hydraulic and seismic vulnerability were tested in the Dajç municipality (Northern Albania), a high-seismicity region that is also severely affected by floods. Updated bathymetric, topographic and hydraulic data were processed with HEC-RAS software to identify sites potentially affected by dykes overflowing. Besides, the soil-structure interaction effects for three strategic buildings were studied using microtremors and the Horizontal to Vertical Spectral Ratio method. This flood and seismic vulnerability analysis was then evaluated in terms of costs and ease of accessibility in order to suggest the best use both of the employed devices and the obtained information for designing good civil protection plans and to inform the population about the right behaviour in case of threat.

Three-quarters of the world population suffered natural disasters at least once between 1980 and 2000. Furthermore, last years' chronicles showed us several geological events that repededly affected urban landscapes. As a consequence beside the loss of human lives and environmental degradation, lots of students were excluded from school, many of whom never to return. Natural hazards have physical, educational, economic and psychosocial impacts. Nowadays it is well ascertained that to decrease this impact by means of structural interventions requires considerable economic resources. Nevertheless, the comparision consequences of similar events in different contexts, it emerges that risk awareness of the population and education to emergency procedures have a positive impact on the occurrence of victims. It follows that the ability of school occupants exposed to hazards to resist and recover from the effects of a hazard in a timely and efficient manner plays a key role to reduce risk, moreover resulting in a cheaper and faster strategy of intervention.This paper presents a new cost-effective methodology and procedure to rapidly assess the geohazard safety classification (GSC) of schools and provides useful information to local decision makers. The GSC, based on the concepts of hazard, vulnerability and resilience, can be calculated integrating ancillary data by means of rapid and not invasive field surveys and questionnaires distributed to the schools employees. Moreover, it can be easily read and understand since it uses the same type of scale as energy efficiency, to indicate the occupants' safety in case of adverse events related to geo-hydrologic hazards.This new rapid assessment methodology have been tested on 10 schools in Tuscany (Italy) and the present paper shows how the GSC can be strongly affected by the lack of ancillary information or the incompleteness of the Risk Assessment Document as well as the school occupants poor perception of geo-hydrologic risk. However, this limitation is at the same time one of the strengths of the GSC: investing in education is a cost-effective way to increase substantially the GSC.

The Italian vessel Costa Concordia wrecked on January 13th 2012 offshore the Giglio Island (Tuscany, Italy), with the loss of 32 lives. Salvage operation of the vessel started immediately after the wreck. This operation was the largest and most expensive maritime salvage ever attempted on a wrecked ship and it ended in July 2014 when the Costa Concordia was removed from the Giglio Island, and dragged in the port of Genoa where it was dismantled. The refloating and removal phases of the Costa Concordia were monitored, in the period between 14th and 27th of July, exploiting SAR (Synthetic Aperture Radar) images acquired by the X-band COSMO-SkyMed satellite constellation in crisis mode. The main targets of the monitoring system were: (i) the detection of possible spill of pollutant material from the vessel and (ii) to exclude that oil slicks, illegally produced by other vessels, could be improperly linked to the naval convoy during its transit along the route between the Giglio Island and the port of Genoa. Results point out that the adopted monitoring system, through the use of the COSMO-SkyMed constellation, can be profitably employed to monitor emergency phases related to single ship or naval convoy over wide areas and with a suitable temporal coverage. Furthermore, the refloating and removal phases of the Costa Concordia were a success because no pollution was produced during the operations.

Several countries worldwide are funding large-scale programs to mitigate landslide risk by implementing engineering remedial works. However, the overall effectiveness of such measures is rarely monitored, and they are typically performed at the slope scale without fully exploiting the wide-area capabilities of remote sensing technologies. A multi-scale and multi-source monitoring procedure for evaluating the slope stability and the effectiveness of related remedial works was proposed in this study and applied in the middle section of the Three Gorges Reservoir Area (TGRA), China. The area is highly exposed to landslide hazards, and a massive program of engineering remedial works was recently implemented. Satellite interferometric synthetic aperture radar (InSAR)-based techniques were first exploited at the regional scale with the objective to provide a general overview of the deformative scenario and to highlight localized problems (active landslides or high deformation zones) to be further investigated, then, local-scale field investigation and multi-source ground monitoring data were employed to verify the deforming states of active landslides and to evaluate the effectiveness of the landslide engineering remedial works. The results indicated that, among the 310 mapped landslides in the study area, 52 were identified to be active and in a slow-moving state by satellite InSAR, Among the 58 controlled landslides, 9 of them were suspected to be active in a slow-moving state and require further concern. Particular attention was paid to two controlled landslides that were found in a continuously and progressively deforming state. We observed that the regional-scale program of slope stabilization was highly successful, however, the variation of the surrounding environmental setting could have led to landslide reactivation or partial invalidation of the landslide remedial works. The proposed multi-scale and multi-source monitoring framework is low-cost, easy to perform, and very straightforward to communicate to citizens and authorities. It can be easily implemented with very wide areas to assess the slope stability and to investigate the effectiveness of large-scale governmental risk mitigation programs, identifying precursor signals that could allow for intervention before reaching critical conditions.

Risk communication is a mutual process of understanding the risk among stakeholders and represents a measure to integrate lay knowledge into measures to prevent, mitigate, and deal with risk. Flood crisis communication, which takes place in the face of incoming danger, needs a practical and operative approach to cope with potentially destructive, large-scale natural disasters. Since floods are one of the most predictable georisks, communication is an efficient means to reduce risk, especially by reducing people's exposure. Many authors discussed the nature of risk communication. Regarding flood risk, most works are devoted to long-term communication plans, but more practical indications on how to communicate during - or shortly before - an (expected) emergency are lacking. Therefore, we present here a framework providing recommendations on what should be the information conveyed in a flood warning message and with what communication medium it should be issued, depending on the criticality level of the expected flood, on the resources of the institution in charge for the warning and on the specific advantages of each medium. Therefore, the framework shown in this paper provides an easy to employ handbook for civil protection operators to define the suitable content, shape and medium for warning messages toward the population. This framework addresses common issues such as the possibility of false alarms or the lack of personnel in charge of risk communication, as well as the role of social media (and their limits), that in many cases is still not well understood.

On 3 July 2019, Stromboli volcanic island experienced a paroxysmal explosion that triggered wildfires on vegetated areas in the south, southwestern, and eastern part of the island. This study analyzes the runoff and the transport of sediment originating from rainfall, to verify whether the vegetation loss due to wildfire changed the hydrogeological structure of the affected area and the flooding hazard. A preliminary hydrological study was conducted to analyze the superficial runoff due to rainfall. According to local planning, the hydrogeological study and flood risk assessment were carried out for the return periods corresponding to 50, 100, and 300 years. The flooding levels were calculated using the hydrodynamic module of the IBER software. The IBER sediment transport module was applied in a non-stationary regime for erosion and sedimentation analysis. The results showed that the fire caused an increase of the water discharge rates between 0.06 and 0.16 m2/s, for the 50 year return period, in the Ginostra inhabited area. The great differences of the flood levels between pre- and post-eruptive scenarios, for the highest return periods, were recognized. The analysis of sediment transport showed that rains could exert an erosion and re-sedimentation effect that would transport from 0.1 m to more than 1 m of re-mobilized material in the Ginostra area, which could cause inconvenience in the inhabited area of the island.

The blocking of river courses by mass movements is very common in mountainous areas with deep and narrow valleys. Landslide dams may pose serious threats to people and their livelihoods downstream in the case of abrupt dam failure. Since the publication of benchmark reviews of Costa and Schuster (1988) and Korup (2002) , there is a growing number of studies focusing on the formation, stability, and short-term impacts of landslide dams. This review combines the insights of all these studies, builds on current concepts of landslide dams, and suggests ways to unify terminologies and classifications. We furthermore present a new worldwide database compiled from literature data. It contains 410 landslide dams >1 million m3 in volume that were formed since 1900 since these have the most complete data entries. These data show that dam longevity is, among other factors, correlated with the type of landslide forming the dam. Those formed by rock/debris avalanches and rockslides have longest lifespans. However, the influence of landslide type or material on dam longevity decreases with time after dam formation. To ensure consistency in the next database generation, we suggest guidelines for data collection to provide a solid basis for evaluating dam stability and governing factors. A preliminary classification matrix for landslide dam stability that combines topographic setting and the internal structure of the dam body is another outcome of our review. Furthermore, an evaluation of the various geomorphic stability indices proposed in the literature regarding their suitability and limitations in assessing dam formation and stability shows that they predict the probability of dam formation reasonably well, but that their application to longevity estimates requires further assessment. The geomorphic impacts of landslide dams in the short-, medium- and long-term are summarized and illustrated with key examples. Finally, for a better understanding of the factors controlling dam stability, we recommend to (1) include dam composition and sedimentary structures in future case studies, (2) maintain and update the worldwide database for sound statistical analyses, (3) refine landslide dam stability indices and test them for different landslide types, and (4) study hazard cascades related to multiple dams in one watershed. For long-term landscape evolution studies, we suggest to (5) quantify terrestrial sediment flux related to landslide dams, (6) detect ancient landslide dams in river profiles, and (7) further exploit the sediment archives in former impoundment areas.