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Abstract Spaceborne-based monitoring for environmental purposes has become a well-established practice. The recent progress of synthetic aperture radar (SAR) sensors, including through the European Space Agency’s (ESA) Sentinel-1 constellation, has enabled the scientific community to identify and monitor several geohazards, including subsidence ground deformations. A case study in the Tuscany Region, Italy, highlights the effectiveness of interferometric synthetic aperture radar (InSAR) in detecting abrupt increases in ground deformation rates in an industrial area of Montemurlo municipality. In this case, InSAR data enabled prompt identification of the phenomenon, supporting the authorities in charge of environmental management to thoroughly investigate the situation. First, an on-site validation was performed via field surveys confirming the presence of cracks and fissures on some edifices. Further analysis, including water pumping rates, settlement gauge and topographic levelling, corroborated the InSAR data's findings regarding vertical deformation. Integration of collected data allowed for spatial identification and assessment of the subsidence bowl and its source depth recognized by the remote sensing data. The Montemurlo case offers a procedural guideline for managing abrupt accelerations, identified by InSAR data in subsidence-prone areas due to fluid overexploitation. In fact, these data proved useful in helping local authorities responsible for hydrogeomorphological risk management. With the exacerbation of deformation issues in subsidence-prone regions due to climate change, early detection and monitoring of such phenomena are increasingly crucial, with InSAR data playing a central role in achieving this goal.

Abstract Background Mass media are a new and important source of information for any natural disaster, mass emergency, pandemic, economic or political event, or extreme weather event affecting one or more communities in a country. Several techniques have been developed for data mining in social media for many natural events, but few of them have been applied to the automatic extraction of landslide events. In this study, Twitter has been investigated to detect data about landslide events in Italian-language. The main aim is to obtain an automatic text classification on the basis of information about natural hazards. The text classification for landslide events in Italian-language has still not been applied to detect this type of natural hazard. Results Over 13,000 data were extracted within Twitter considering five keywords referring to landslide events. The dataset was classified manually, providing a solid base for applying deep learning. The combination of BERT + CNN has been chosen for text classification and two different pre-processing approaches and bert-model have been applied. BERT-multicase + CNN without preprocessing archived the highest values of accuracy, equal to 96% and AUC of 0.96. Conclusions Two advantages resulted from this studio: the Italian-language classified dataset for landslide events fills that present gap of analysing natural events using Twitter. BERT + CNN was trained to detect this information and proved to be an excellent classifier for the Italian language for landslide events.

ABSTRACTWe present the geomorphological map of the upper sector of the Roncovetro active landslide (Enza Valley, Emilia-Romagna, Italy). The 1:1500 scale map provides an accurate picture of the landslide in October 2014. The map is mainly based on the data collected during an airborne LiDAR survey. The capability of LiDAR to ‘penetrate’ the vegetation cover makes these data the most complete and accurate topographic dataset of this landslide. The map shows that the upper sector of the Roncovetro landslide consists of gravity- and water runoff-related forms. Gravitational features are linked to sliding and flowing movements that characterize the short- and long-term behaviour of the landslide. By comparing the 2014 LiDAR-Digital Elevation Model (DEM) with the 1973 DEM provided by the Emilia-Romagna Region, we calculated that 6.2 ± 0.8 × 105 m3 of material has moved from the top of the Roncovetro landslide in about 40 years.

Abstract The use of light detection and ranging technologies, i.e. terrestrial laser scanner (TLS), airborne laser scanner (ALS) and mobile laser scanner (MLS), together with the unmanned aerial vehicles digital photogrammetry (UAV-DP) and satellite data are proving to be fundamental tools to carry out reliable muographic measurement campaigns. The main purpose of this paper is to propose a workflow to correctly plan and exploit these types of data for muon radiography aims. To this end, a real case study is presented: searching for hidden tombs in the Etruscan necropolis of Palazzone (Umbria, Italy). A high-resolution digital elevation model (DEM) and three-dimensional models of the ground surface/sub-surface of the study area were created by merging data obtained using different survey methods to achieve the most accurate three-dimensional environment. Indeed, the simulated muon flux transmission used to infer relative transmission values, and the estimated density distribution, depends on the reliability of the three-dimensional reconstructed ground surface model. The aim of this study is to provide knowledge on the use of TLS and UAV-DP data and GPS-acquired points within the transmission-based muography process and how these data could improve or worsen the muon imaging results. Moreover, this study confirmed that muography applications require a multidisciplinary approach.

Abstract Lava overflows are highly hazardous phenomena that can occur at Stromboli. They can destabilize the crater area and the “Sciara del Fuoco” unstable slope, formed by several sector collapses, which can generate potentially tsunamigenic landslides. In this study, we have identified precursors of the October-November 2022 effusive crisis through seismic and thermal camera measurements. We analyzed the lava overflow on October 9, which was preceded by a crater-rim collapse, and the overflow on November 16. In both cases, seismic precursors anticipating the overflow onset have been observed. The analysis of the seismic and thermal data led to the conclusion that the seismic precursors were caused by an escalating degassing process from the eruptive vent, which climaxed with the overflows. Volcano deformation derived from ground-based InSAR and strainmeter data showed that inflation of the crater area accompanied the escalating degassing process up to the beginning of the lava overflows. The inflation of the crater area was especially evident in the October 9 episode, which also showed a longer seismic precursor compared to the November 16 event (58 and 40 min respectively). These results are important for understanding Stromboli’s eruptive mechanisms and open a perspective for early warning of potentially dangerous phenomena.

Wide-area ground motion monitoring is nowadays achievable via advanced Differential Interferometry SAR (A-DInSAR) techniques which benefit from the availability of large sets of Copernicus Sentinel-1 images. However, it is of primary importance to implement automated solutions aimed at performing integrated analysis of large amounts of interferometric data. To effectively detect high-displacement areas and classify ground motion sources, here we explore the feasibility of a machine learning-based approach. This is achieved by applying the random forest (RF) technique to large-scale deformation maps spanning 2015–2018. Focusing on the northern part of Italy, we train the model to identify landslide, subsidence, and mining-related ground motion with which to construct a balanced training dataset. The presence of noisy signals and other sources of deformation is also tackled within the model construction. The proposed approach relies on the use of explanatory variables extracted from the A-DInSAR datasets and from freely accessible informative layers such as Digital Elevation Model (DEM), land cover maps, and geohazard inventories. In general, the model performance is very promising as we achieved an overall accuracy of 0.97, a true positive rate of 0.94 and an F1-Score of 0.93. The obtained outcomes demonstrate that such transferable and automated approach may constitute an asset for stakeholders in the framework of geohazards risk management.

Abstract Muon radiography (muography) is an imaging technique based on atmospheric muon absorption in matter that allows to obtain two and three-dimensional images of internal details of hidden objects or structures. The technique relies on atmospheric muon flux measurements performed around and underneath the object under examination. It is a non-invasive and passive technique and thus can be thought of as a valid alternative to common prospecting techniques used in archaeological, geological and civil security fields. This paper describes muon radiography measurements, in the context of archaeological and geological studies carried out at the Temperino mine (LI, Tuscany, Italy), for the search and three-dimensional visualisation of cavities. This mine has been exploited since Etruscan times until recently (1973), and is now an active tourist attraction with public access to the tunnels. Apart from the archaeological interest, the importance of mapping the cavities within this mine lies in identifying the areas where the extraction ores were found and also in the safety issues arising from the tourist presence inside the mine. The three-dimensional imaging is achieved with two different algorithms: one involving a triangulation of two or more measurements at different locations; the other, an innovative technique used here for the first time, is based on the back-projections of reconstructed muon tracks. The latter requires only a single muographic data tacking and is to be preferred in applications where more than one site location can be difficult to access. Finally the quality of the three-dimensional muographic imaging was evaluated by comparing the results with the laser scan profiles obtained for some known cavities within the Temperino mine.

Abstract Landslides are the most frequent and diffuse natural hazards in Italy causing the greatest number of fatalities and damage to urban areas. The integration of natural hazard information and social media data could improve warning systems to enhance the awareness of disaster managers and citizens about emergency events. The news about landslide events in newspapers or crowdsourcing platforms allows fast observation, surveying and classification. Currently, few studies have been produced on the combination of social media data and traditional sensors. This gap indicates that it is unclear how their integration can effectively provide emergency managers with appropriate knowledge. In this work, rainfall, human lives, and earmarked fund data sources were correlated to “landslide news”. Analysis was applied to obtain information about temporal (2010–2019) and spatial (regional and warning hydrological zone scale) distribution. The temporal distribution of the data shows a continuous increase from 2015 until 2019 for both landslide and rainfall events. The number of people involved and the amount of earmarked funds do not exhibit any clear trend. The spatial distribution displays good correlation between “landslide news”, traditional sensors (e.g., pluviometers) and possible effects in term of fatalities. In addition, the cost of soil protection, in monetary terms, indicates the effects of events.

Urban landslides have brought challenges to developing countries undergoing urbanization. Rapid approaches to assess ground deformation are required when facing the challenge of insufficient geological survey methods. Additionally, it is indeed a challenge to map landslide-affected areas, especially precipitation-induced landslides, through optical remote sensing methods. This study applied SAR change detection methods to map the slope failure event of the San Jorge Kantutani landfill site in La Paz, Bolivia, which occurred in April 2019, and Multi-Temporal Synthetic Aperture Radar Interferometry (MTInSAR) methods to assess pre- and post-failure ground stability related to this event. We found that the amplitude information of high-resolution COSMO-SkyMed SAR imagery and its texture information can be very useful in landslide mapping, especially in situations in which optical images are not available because of complex meteorological conditions and the similar spectral characteristics between the original land cover and landslide deposits. The MTInSAR analyses found that there was already significant deformation of more than 50 mm/year along the slope direction over this site before the landslide, and such deformation could be clearly discriminated from the surrounding environment. After the landslide event and the remobilization of the landslide deposit, the slope still shows a deformation velocity of more than 30 mm/year. The SAR amplitude change detection and MTInSAR fully exploited the SAR data in landslide studies and were useful in back analyzing the occurred landslides; this could be a good method for monitoring the ground stability of La Paz or even on a national scale over the long term for reducing the catastrophic effects of geological hazards in this landslide-prone city.

The need for implementing efficient value-adding tools able to optimise Earth Observation data usage, compels the scientific community to find innovative solutions for the downstream of Earth Observation information. In this paper we present an unsupervised and automated approach based on Principal Component Analysis (PCA) and K-means clustering to detect patterns of natural or anthropogenic ground deformation from Interferometric Synthetic Aperture Radar (InSAR) Time Series. For our proof-of-concept, we focus on the Valle d’Aosta region (Northwest Italy) where mass wasting processes frequently occurs, interacting with human activities and infrastructures. The large volumes of Sentinel-1 data produced allows for retrieving horizontal and vertical Time Series from multi-geometry data fusion of Line-of-Sight (LOS) InSAR measurements. The added benefit of combining ascending/descending InSAR data and interpolating displacements in time at different time steps is here explored prior to data dimensionality reduction and feature extraction through PCA. The retrieved principal components serve as a continuous solution for cluster membership indicators in the K-means clustering method, allowing to define spatially and temporally coherent displacement phenomena. The signal of the ground deformation clusters is then deconstructed into the underlying trend and seasonality components to enhance the interpretability of the classified satellite InSAR features. Using InSAR Time series data spanning 2014–2020, the proposed approach detects several slope movements and anthropogenic deformations with both linear and seasonal displacement behaviours. The results demonstrate the potential applicability of our transferable approach to the development of automated ground motion analysis systems.

Soil thickness, intended as depth to bedrock, is a key input parameter for many environmental models. Nevertheless, it is often difficult to obtain a reliable spatially exhaustive soil thickness map in wide-area applications, and existing prediction models have been extensively applied only to test sites with shallow soil depths. This study addresses this limitation by showing the results of an application to a section of Wanzhou County (Three Gorges Reservoir Area, China), where soil thickness varies from 0 to ∼40 m. Two different approaches were used to derive soil thickness maps: a modified version of the geomorphologically indexed soil thickness (GIST) model, purposely customized to better account for the peculiar setting of the test site, and a regression performed with a machine learning algorithm, i.e., the random forest, combined with the geomorphological parameters of GIST (GIST-RF). Additionally, the errors of the two models were quantified, and validation with geophysical data was carried out. The results showed that the GIST model could not fully contend with the high spatial variability of soil thickness in the study area: the mean absolute error was 10.68 m with the root-mean-square error (RMSE) of 12.61 m, and the frequency distribution residuals showed a tendency toward underestimation. In contrast, GIST-RF returned a better performance with the mean absolute error of 3.52 m and RMSE of 4.56 m. The derived soil thickness map could be considered a critical fundamental input parameter for further analyses.

Abstract The concept of climate change has grown in recent decades, influencing the scientific community to conduct research on meteorological parameters and their variabilities. Research on global warming, as well as on its possible economic and environmental consequences, has spread over the last 20 years. Diffused changes in trends have been stated by several authors throughout the world, with different developments observed depending on the continent. Following a period of approximately 40 days of almost continuous rain that occurred from October to November 2019 across the Italian territory and caused several hazards (e.g., floods and landslides), a relevant question for decision-makers and civil protection actors emerged regarding the relative frequencies of given rainfall events in the Warning Hazard Zones (WHZs) of Italy. The derived products of this work could answer this question for both weather and hydrogeological operators thanks to the frequency and spatio-temporal distribution analyses conducted on 10-year daily rainfall data over the entire Italian territory. This work aspires to be an additional tool used to analyse events that have occurred, providing further information for a better understanding of the probability of occurrence and distribution of future events.

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.

Performing a reliable stability analysis of a landslide slope requires a good understanding of the internal geometries and an accurate characterisation of the geotechnical parameters of the identified strata. Geotechnical models are commonly based on geomorphological data combined with direct and intrusive geotechnical investigations. However, the existence of numerous empirical correlations between seismic parameters (e.g., S-wave velocity) and geotechnical parameters in the literature has made it possible to investigate areas that are difficult to reach with direct instrumentation. These correlations are often overlooked even though they enable a reduction in investigation costs and time. By means of geophysical tests, it is in fact possible to estimate the N-SPT value and derive the friction angle from results obtained from environmental seismic noise measurements. Despite the empirical character and a certain level of uncertainty derived from the estimation of geotechnical parameters, these are particularly useful in the preliminary stages of an emergency, when straight data are not available and on all those soils where other direct in situ tests are not reliable. These correlations were successfully applied to the Theilly landslide (Western Alps, Italy), where the geotechnical model was obtained by integrating the results of a multi-parameter geophysical survey (H/V seismic noise and ground-penetrating radar) with stratigraphic and geomorphological observations, digital terrain model and field survey data. The analysis of the triggering conditions of the landslide was conducted by means of hydrological–geotechnical modelling, evaluating the behaviour of the slope under different rainfall scenarios and considering (or not) the stabilisation interventions present on the slope. The results of the filtration analyses for all events showed a top-down saturation mechanism, which led to the formation of a saturated face with a maximum thickness of 5 m. Stability analyses conducted for the same events showed the development of a shallow landslide in the first few metres of saturated soil. The modelling results are compatible with the actual evolution of the phenomenon and allow us to understand the triggering mechanism, providing models to support future interventions.

On 13 January 2012, the Italian vessel Costa Concordia wrecked on the shore of Giglio Island, about 15 km off the coast of southern Tuscany (Italy), causing the loss of 32 lives. It is considered one of the worst disasters in maritime history. Salvage operations started immediately after the wreck with the coordination of the Italian National Civil Protection Department and the technological support of several Research Centers, which were activated for the management of the consequent emergency phase. A multi-parametric and multiplatform monitoring system was promptly implemented, involving several advanced remote sensing techniques, among which was Infrared Thermography (IRT). In this framework, IRT monitoring was performed during a 35-day period (25 January–1 March 2012), using a terrestrial, hand-held thermal camera. Six different thermal images were acquired daily from the island’s coastline in three different positions, both in daylight and night times. The aim was to detect thermal anomalies connected to possible deformations of the vessel and oil spills. Between 3–4 February, IRT successfully revealed on oil spill drifting from the stern of the wreck towards the island harbor. Furthermore, the wreck’s thermal dilatation was also analyzed during a 24-day close-range monitoring, providing interesting insights for the interpretation of the deformation monitoring results. This paper presents the outcomes of these innovative and experimental monitoring activities, with the aim of testing the potential of IRT as a versatile and operative tool to be used in maritime and environmental disaster response.

In Italy, three different operational continuous monitoring experiences based on the exploitation of Multi Temporal Synthetic Aperture Radar data (MTInSAR) Sentinel-1 data are here depicted, and the results obtained in one year have been analysed. Tuscany region (Central Italy) has been the first region to implement such service, followed by Valle d’Aosta and Veneto regions (North-West and North-East Italy, respectively). In detail, the services benefit from regularly updated deformation maps (every 12 days) to promptly detect anomalies of deformation, i.e., trend variations in the time series of displacement. In this work, anomalies detected between September 2019 and September 2020 are thus correlated with several types of factors, either related to the environment, intrinsic of the data or derived from ancillary data. A statistical analysis has been performed on the three regions, and are discretized into five macro-areas, namely: i) spatial and temporal statistics, related to the geographic setting and the temporal distribution of the anomalies; ii) parametric, i.e., related to the interferometric processing; iii) triggering factors; iv) environmental and geological factors; v) urban setting. The results derived from the analysis of this work show the obvious differences between the three regions, highlighting distinct distributions of the anomalies according to the different settings of each study area. Furthermore, results were analyzed, to provide a summary of the main findings obtained, giving a first evaluation of the services and hypothesizing future further improvements and applications.

Landslide hazard management usually requires time-consuming campaigns of data acquisition, elaboration, and modeling. However, in the post-emergency phase management, time is a factor, and simpler but faster methods of analysis are needed to support decisions even in the short-term. This paper analyzes the Theilly landslide (Western Italian Alps), which was recently affected by a series of reactivations. While some instrumental campaigns are being carried out to support the design of protection measures, simple tools are also needed to assess the hazard of future reactivations and to evaluate the possibility of damming the torrent at the footslope. Therefore, state-of-the-art empirical methods were used and customized for the specific case study: a set of intensity–duration rainfall thresholds depicting increasing hazard levels was defined to monitor and forecast possible reactivations, while a methodology based on hydro-morphometric indices was applied to the case of study, to assess the possible evolution scenarios (landslide that does not dam the river, formation of a stable dam, formation of an unstable dam), based on the landslide volume. The proposed empirical methodologies have the advantage of requiring only ready-available input data and quick elaborations, thus allowing the rapid set up of tools that could be used for hazard management.

The Sciara del Fuoco (SdF) collapse scar at Stromboli is an active volcanic area affected by rapid morphological changes due to explosive/effusive eruptions and mass-wasting processes. The aim of this paper is to demonstrate the importance of an integrated analysis of multi-temporal remote sensing (photogrammetry, COSMO-SkyMed Synthetic Aperture Radar amplitude image) and marine geophysical data (multibeam and side scan sonar data) to characterize the main morphological, textural, and volumetric changes that occurred along the SdF slope in the 2020–2021 period. The analysis showed the marked erosive potential of the 19 May 2021 pyroclastic density current generated by a crater rim collapse, which mobilized a minimum volume of 44,000 m3 in the upper Sciara del Fuoco slope and eroded 350,000–400,000 m3 of material just considering the shallow-water setting. The analysis allowed us also to constrain the main factors controlling the emplacement of different lava flows and overflows during the monitored period. Despite the morphological continuity between the subaerial and submarine slope, textural variations in the SdF primarily depend on different processes and characteristics of the subaerial slope, the coastal area, the nearshore, and “deeper” marine areas.

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.

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.

UAV-based photogrammetry is becoming increasingly popular even in application fields that, until recently, were deemed unsuitable for this technique. Depending on the characteristics of the investigated scenario, the generation of three-dimensional (3D) topographic models may in fact be affected by significant inaccuracies unless site-specific adaptations are implemented into the data collection and processing routines. In this paper, an ad hoc procedure to exploit high-resolution aerial photogrammetry for the multitemporal analysis of the unstable Sciara del Fuoco (SdF) slope at Stromboli Island (Italy) is presented. Use of the technique is inherently problematic because of the homogeneous aspect of the gray ash slope, which prevents a straightforward identification of match points in continuous frames. Moreover, due to site accessibility restrictions enforced by local authorities after the volcanic paroxysm in July 2019, Ground Control Points (GCPs) cannot be positioned to constrain georeferencing. Therefore, all 3D point clouds were georeferenced using GCPs acquired in a 2019 (pre-paroxysm) survey, together with stable Virtual Ground Control Points (VGCPs) belonging to a LiDAR survey carried out in 2012. Alignment refinement was then performed by means of an iterative algorithm based on the closest points. The procedure succeeded in correctly georeferencing six high-resolution point clouds acquired from April 2017 to July 2021, whose time-focused analysis made it possible to track several geomorphological structures associated with the continued volcanic activity. The procedure can be further extended to smaller-scale analyses such as the estimation of locally eroded/accumulated volumes and pave the way for rapid UAV-based georeferenced surveys in emergency conditions at the SdF.

Velocity is one of the most important parameters to evaluate the damaging potential of a mass movement, but its assessment, especially for extremely rapid landslides, is a complex task. In the literature, several models to assess mass movement velocity exist, but they usually require many detailed parameters, and therefore, they are applicable only to a single slope and not usable for regional-scale analyses. This study aims to propose a simple morphometric methodology, based on the spatialisation of the Energy Line method, to determine the velocity of shallow landslides and rockfalls at a regional scale. The proposed method requires a limited amount of input data (landslide perimeters and a digital elevation model), and its application can be carried out using GIS software and a Matlab code. The test area of this work is the Valle d’Aosta Region (Northern Italy), selected due to its peculiar geological and geomorphological setting that makes this region susceptible to the occurrence of both shallow landslides and rockfalls. Since measured velocity values for rockfalls and shallow landslides were not available, the results obtained with the proposed method have been validated through the implementation of a model in the literature, namely the Gravitational Process Path (GPP) model, for some selected landslides.

The continuous monitoring of displacements occurring on the Earth surface by exploiting MTInSAR (Multi Temporal Interferometry SAR) Sentinel-1 data is a solid reality, as testified by the ongoing operational ground motion service in the Tuscany region (Central Italy). In this framework, anomalies of movement, i.e., accelerations or deceleration as seen by the time series of displacement of radar targets, are identified. In this work, a Machine Learning algorithm such as the Random Forest has been used to assess the probability of occurrence of the anomalies induced by slope instability and subsidence. About 20,000 anomalies (about 7000 and 13,000 for the slope instability and the subsidence, respectively) were collected between 2018 and 2020 and were used as input, while ten different variables were selected, five related to the morphological and geological setting of the study area and five to the radar characteristics of the data. The resulting maps may provide useful indications of where a sudden change of displacement trend may occur, analyzing the contribution of each factor. The cross-validation with the anomalies collected in a following timespan (2020–2021) and with official landslide and subsidence inventories provided by the regional authority has confirmed the reliability of the final maps. The adoption of a map for assessing the probability of the occurrence of MTInSAR anomalies may serve as an enhanced geohazard prevention measurement, to be periodically updated and refined in order to have the most precise knowledge possible of the territory.

Two paroxysmal explosions occurred at Stromboli on 3 July and 28 August 2019, the first of which caused the death of a young tourist. After the first paroxysm an effusive activity began from the summit vents and affected the NW flank of the island for the entire period between the two paroxysms. We carried out an unsupervised analysis of seismic and infrasonic data of Strombolian explosions over 10 months (15 November 2018–15 September 2019) using a Self-Organizing Map (SOM) neural network to recognize changes in the eruptive patterns of Stromboli that preceded the paroxysms. We used a dataset of 14,289 events. The SOM analysis identified three main clusters that showed different occurrences with time indicating a clear change in Stromboli’s eruptive style before the paroxysm of 3 July 2019. We compared the main clusters with the recordings of the fixed monitoring cameras and with the Ground-Based Interferometric Synthetic Aperture Radar measurements, and found that the clusters are associated with different types of Strombolian explosions and different deformation patterns of the summit area. Our findings provide new insights into Strombolian eruptive mechanisms and new perspectives to improve the monitoring of Stromboli and other open conduit volcanoes.

One of the main constraints in assessing shallow landslide hazards through physically based models is the need to characterize the geotechnical parameters of the involved materials. Indeed, the quantity and quality of input data are closely related to the reliability of the results of every model used, therefore data acquisition is a critical and time-consuming step in every research activity. In this perspective, we reviewed all official certificates of tests performed through 30 years at the Geotechnics Laboratory of the Earth Science Department (University of Firenze, Firenze, Italy), compiling a dataset in which 380 points are accurately geolocated and provide information about one or more geotechnical parameters used in slope stability modeling. All tests performed in the past (in the framework of previous research programs, agreements of cooperation, or to support didactic activities) were gathered, homogenized, digitalized, and geotagged. The dataset is based on both on-site tests and laboratory tests, it accounts for 40 attributes, among which 13 are descriptive (e.g., lithology or location) and 27 may be of direct interest in slope stability modeling as input parameters. The dataset is made openly available and can be useful for scientists or practitioners committed to landslide modeling.

Abstract We present the continuous monitoring of ground deformation at regional scale using ESA (European Space Agency) Sentinel-1constellation of satellites. We discuss this operational monitoring service through the case study of the Tuscany Region (Central Italy), selected due to its peculiar geological setting prone to ground instability phenomena. We set up a systematic processing chain of Sentinel-1 acquisitions to create continuously updated ground deformation data to mark the transition from static satellite analysis, based on the analysis of archive images, to dynamic monitoring of ground displacement. Displacement time series, systematically updated with the most recent available Sentinel-1 acquisition, are analysed to identify anomalous points (i.e., points where a change in the dynamic of motion is occurring). The presence of a cluster of persistent anomalies affecting elements at risk determines a significant level of risk, with the necessity of further analysis. Here, we show that the Sentinel-1 constellation can be used for continuous and systematic tracking of ground deformation phenomena at the regional scale. Our results demonstrate how satellite data, acquired with short revisiting times and promptly processed, can contribute to the detection of changes in ground deformation patterns and can act as a key information layer for risk mitigation.

Abstract 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.

Landslide detection and mapping are essential issues for reducing impact of such natural disasters, and for improving the future built-up expansion and planning strategies, especially in developing countries where a reasonable land-use design is an important concern for sustainable growth and environmental management. Armenia is a landlocked country and its urban development is strongly tied to the improvement of infrastructures, which must takes into account the environmental setting and the slope instability of the area, in order to identify risks and possible damages to settlements and economic activities. The use of satellite-based Earth Observation data has advanced significantly in the last decade and has turned out to be very useful for measuring and monitoring slow-moving surface deformation phenomena with millimetric precision. In this framework, this study aims at providing a remote sensing-based Landslide Inventory Map (LIM) and a Landslide Susceptibility Map (LSM) over Dilijan (Armenia) area, performed within the Secondary Cities Urban Development in Armenia project. In particular, LIM and LSM in the study area were produced by using ground deformation measurements derived from satellite Synthetic Aperture Radar (SAR) data, acquired by ALOS and ENVISAT sensors from 2003 up to 2010, and integrated with photo-interpretation of recent optical images and morphological analysis of Digital Elevation Model (DEM). Given the extensive presence of vegetation in the area of interest, satellite SAR images were processed to produce both SqueeSAR™ and Temporary Coherent Scatterers data, which are PSI (Persistent Scatterer Interferometry) data conceived as evolution of PSInSAR™ approach and particularly suited for non-urban and rural areas characterized by low density of coherent terrain benchmarks over time. Landslide mapping produced through this work identifies the most hazardous landslide-affected and landslide-prone areas around Dilijan city, and can be used for further estimating environmental risks for urban infrastructure development in the area.

The accurate evaluation of landslide-induced damage is a necessity for planning of proper and effective mitigation measures. It requires the implementation of field investigations to identify structural failures to more effectively trace landslide boundaries. Many methods have been proposed to classify landslide-induced damage of buildings. The existing methods demonstrate several advantages and drawbacks depending on the parameters considered, as lack of some important features and difficulties in applicability. A new classification approach of landslide-induced damage of facilities is proposed, which specifically focuses on assessing of damage degree and its relationship to the ground motion intensity and impact severity. The new approach is designed in two steps: a chart utilized during surveys to quantify cracks on structures and ground surface; an a posteriori ranking of structures performed using a cell-grid matrix. Furthermore, a damage recording scheme useful for field surveying is proposed. This approach considers several parameters derived from different existing methodologies by smoothing out drawbacks and homogenizing the considered features. The resulting approach provides a new procedure of landslide-induced damage assessment adoptable in case of private dwellings, as it does not require internal accessibility, and it is exploitable for different landslide events and for different kinds of structures and facilities.

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.

The mission geosynchronous – continental land atmosphere sensing system (G-CLASS) is designed to study the diurnal water cycle, using geosynchronous radar. Although the water cycle is vital to human society, processes on timescales less than a day are very poorly observed from space. G-CLASS, using C-band geosynchronous radar, could transform this. Its science objectives address intense storms and high resolution weather prediction, and significant diurnal processes such as snow melt and soil moisture change, with societal impacts including agriculture, water resource management, flooding, and landslides. Secondary objectives relate to ground motion observations for earthquake, volcano, and subsidence monitoring. The orbit chosen for G-CLASS is designed to avoid the geosynchronous protected region and enables integration times of minutes to an hour to achieve resolutions down to ∼20 m. Geosynchronous orbit (GEO) enables high temporal resolution imaging (up to several images per hour), rapid response, and very flexible imaging modes which can provide much improved coverage at low latitudes. The G-CLASS system design is based on a standard small geosynchronous satellite and meets the requirements of ESA's Earth Explorer 10 call.

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.

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.

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.

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.

Pre- event and event landslide deformations have been detected and measured for the landslide that occurred on 3 December 2013 on the south-western slope of the Montescaglioso village (Basilicata Region, southern Italy). In this paper, ground displacements have been mapped through an integrated analysis based on a series of high resolution SAR (Synthetic Aperture Radar) images acquired by the Italian constellation of satellites COSMO-SkyMed. Analysis has been performed by exploiting both phase (through multi-image SAR interferometry) and amplitude information (through speckle tracking techniques) of the satellite images. SAR Interferometry, applied to images taken before the event, revealed a general pre-event movement, in the order of a few mm/yr, in the south-western slope of the Montescaglioso village. Highest pre-event velocities, ranging between 8 and 12 mm/yr, have been recorded in the sector of the slope where the first movement of the landslide took place. Speckle tracking, applied to images acquired before and after the event, allowed the retrieval of the 3D deformation field produced by the landslide. It also showed that ground displacements produced by the landslide have a dominant SSW component, with values exceeding 10 m for large sectors of the landslide area, with local peaks of 20 m in its central and deposit areas. Two minor landslides with a dominant SSE direction, which were detected in the upper parts of the slope, likely also occurred as secondary phenomena as consequence of the SSW movement of the main Montescaglioso landslide.

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.

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–decoder neural network is proposed. The method simultaneously learns the sparse features in the time–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–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.

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.

This study is focused on wide-area deformation monitoring initiatives based on the differential interferometric SAR technique (DInSAR). In particular, it addresses the use of advanced DInSAR (A-DInSAR) techniques, which are based on large sets of synthetic aperture radar (SAR) and Copernicus Sentinel-1 images. Such techniques have undergone a dramatic development in the last twenty years: they are now capable to process big sets of SAR images and can be exploited to realize a wide-area A-DInSAR monitoring. The study describes several initiatives to establish wide-area ground motion services (GMS), both at county- and region-level. In the second part of the study, some of the key technical aspects related to wide-area A-DInSAR monitoring are discussed. Finally, the last part of the study is devoted to the European ground motion service (EGMS), which is part of the Copernicus land monitoring service. It represents the most important wide-area A-DInSAR deformation monitoring system ever developed. The study describes its main characteristics and its main products. The end of the production of the first EGMS baseline product is foreseen for the last quarter of 2021.

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.

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.

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.

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.

The launch of the medium resolution Synthetic Aperture Radar (SAR) Sentinel-1 constellation in 2014 has allowed public and private organizations to introduce SAR interferometry (InSAR) products as a valuable option in their monitoring systems. The massive stacks of displacement data resulting from the processing of large C-B and radar images can be used to highlight temporal and spatial deformation anomalies, and their detailed analysis and postprocessing to generate operative products for final users. In this work, the wide-area mapping capability of Sentinel-1 was used in synergy with the COSMO-SkyMed high resolution SAR data to characterize ground subsidence affecting the urban fabric of the city of Pistoia (Tuscany Region, central Italy). Line of sight velocities were decomposed on vertical and E–W components, observing slight horizontal movements towards the center of the subsidence area. Vertical displacements and damage field surveys allowed for the calculation of the probability of damage depending on the displacement velocity by means of fragility curves. Finally, these data were translated to damage probability and potential loss maps. These products are useful for urban planning and geohazard management, focusing on the identification of the most hazardous areas on which to concentrate efforts and resources.

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 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.

Synthetic Aperture Radar Interferometry (InSAR) techniques have been long exploited for detecting and mapping slow-moving ground surface displacements due to their millimeter accuracy, non-invasiveness and wide area coverage. A review on different applications of Persistent Scatterers InSAR approaches, proposed and applied over Tuscany region (Italy) across time, is here presented. The study area is characterized by both subsidence of alluvial plains and landslides on hilly and mountainous reliefs. Tuscany has a leading role in Italy in the field of interferometric applications: the first InSAR analyses, which date back to 2003, were performed at local basin scale, by exploiting various PSI-based approaches for risk mapping. The first InSAR applications at regional scale date back to 2009, relying on historical SAR archives of ERS and ENVISAT satellites for updating subsidence and landslide inventory maps at a certain temporal date. Nowadays, the availability of Sentinel-1 SAR data with a regular and systematic 6-days acquisitions plan, allows near-real time monitoring of deformative scenario at regional scale rather than solely mapping of geo-hydrological phenomena. Most recent innovative InSAR applications over Tuscany region scan the territory, exploiting the regular repeat pass of Sentinel-1, and promptly highlight the sites affected by the highest ground movements with high temporal frequency. Such approaches permit us to pass from a static ‘picture’ of regional slope instability to a weekly updated ‘movie’ with improved detail, useful for civil protection practices. These last ongoing works significantly enhance the value of multi-temporal InSAR approaches for investigating and managing geo-hazards over the Region.

We propose a methodology to couple rainfall thresholds and susceptibility maps for dynamic landslide hazard assessment at regional scale. Both inputs are combined in a purposely-built hazard matrix to get a spatially and temporally variable definition of landslide hazard: while statistical rainfall thresholds are used to accomplish a temporal forecasting with very coarse spatial resolution, landslide susceptibility maps provide static spatial information about the probability of landslide occurrence at fine spatial resolution. The test site is the Northern part of Tuscany (Italy), where a recent landslide susceptibility map and a set of recently updated rainfall thresholds are available. These products were modified and updated to meet the requirements of the proposed procedure: the susceptibility map was reclassified and the threshold set was expanded defining additional thresholds. The hazard matrix combines three susceptibility classes (S1, low susceptibility; S2 medium susceptibility; S3 high susceptibility) and three rainfall rate classes (R1, R2, R3), defining five hazard classes, from H0 (null hazard) to H4 (high hazard). A key passage of the procedure is the appropriate calibration and validation of the matrix, letting the hazard classes have a precise meaning in terms of expected consequences and hazard management. The employ of the proposed procedure in a regional warning system brings two main advantages: (i) it is possible to better hypothesize when and where landslide are expected and with which hazard degree, thus fostering a more effective hazard and risk management (e.g., setting priorities of intervention); (ii) the spatial resolution of the regional scale warning system is markedly refined because from time to time the areas where landslides are expected represent only a fraction of the alert zone.