The Sorrentina Peninsula is a densely populated area with high touristic impact. It is located in a morphologically complex zone of Southern Italy frequently affected by dangerous and calamitous landslides. This work contributes to the prevention of such natural disasters by applying a GIS-based interdisciplinary approach aimed to map the areas more potentially prone to trigger slope instability phenomena. We have developed the Landslide Susceptibility Index (LSI) combining five weighted and ranked susceptibility parameters on a GIS platform. These parameters are recognized in the literature as the main predisposing factors for triggering landslides. This work combines analyses conducted on Remote Sensing, Geo-Lithology and Morphometry data and it is organized in the following logical steps: i) Multi-temporal InSAR technique was applied to Envisat-ASAR (2003–2010) and COSMO-SkyMed (2013–2015) datasets to obtain the ground displacement time series and the relative mean ground velocity maps. InSAR allowed the detection of the areas that are subjected to ground deformation and the main affected municipalities; ii) Such deformation areas were investigated through airborne photo interpretation to identify the presence of geomorphological peculiarities connected to potential slope instability. Subsequently, some of these peculiarities were checked on the field; iii) In these deformation areas the susceptibility parameters were mapped in the entire territory of Amalfi and Conca dei Marini and then investigated with a multivariate analysis to derive the classes and the respective weights used in the LSI calculation. The resulting LSI map classifies the two municipalities with high spatial resolution (2m) according to five classes of instability. The map highlights that the high/very high susceptibility zones cover 6% of the investigated territory and correspond to potential landslide source areas characterized by 25°-70° slope angles. A spatial analysis between the map of the historical landslides and the areas classified according to susceptibility allowed testing of the reliability of the LSI Index, resulting in 85% prediction accuracy.

The aim of this work is to develop and test a simple methodology for CO₂ emission retrieval applied to hyperspectral PRISMA data. Model simulations are used to infer the best SWIR channels for CO₂ retrieval purposes, the weight coefficients for a Continuum Interpolated Band Ratio (CIBR) index calculation, and the factor for converting the CIBR values to XCO₂ (ppm) estimations above the background. This method has been applied to two test cases relating to the LUSI volcanic area (Indonesia) and the Solfatara area in the caldera of Campi Flegrei (Italy). The results show the capability of the method to detect and estimate CO₂ emissions at a local spatial scale and the potential of PRISMA acquisitions for gas retrieval. The limits of the method are also evaluated and discussed, indicating a satisfactory application for medium/strong emissions and over soils with a reflectance greater than 0.1.

Surface temperatures derived by 208 ASTER and L8 satellite imagery were analysed to test multiscale and multitemporal capability through available sets of thermal data to support the volcanic monitoring of Vulcano Island in Italy. The analysis of thermal historical series derived by ASTER and L8 shows that two are the main thermally active areas: La Fossa crater and the mud pool of Fangaia. In this work we aimed to assess the correlation between the satellite-retrieved temperatures with those measured during the daytime ground field campaign conducted within the same time period and, in particular cases, simultaneously. Moreover, nighttime data acquired by an airborne and field campaign were processed with the same methodology applied to satellite data for a multiscale approach verification. Historical meteorological data acquired from a weather station were also considered. Statistically significant correlations were observed between nighttime acquisitions and meteorological data. Correlations were also significant for temperature measured during the airborne campaign, while differences up to 50% with daytime acquisition during the ground field campaigns were observed. The analysis of the results suggests that within nighttime data acquisition, differences between satellite-derived temperatures and ground temperature measurements are considerably reduced; therefore nighttime data acquisition is recommended to detect thermal anomalies.

We investigated the structure and evolution of the 2010 Eyjafjallajökull volcanic cloud and its dispersal over Iceland and Europe integrating satellite multispectral images and numerical simulations. Data acquired by Medium Resolution Imaging Spectrometer (MERIS) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) have been analyzed to quantify the cloud extent and composition. The VOL‐CALPUFF dispersal code was applied to reconstruct the transient and 3‐D evolution of the cloud. Source parameters estimated on the base of available a posteriori volcanological data sets have been used. Quantitative comparisons between satellite retrievals and modeling results were performed for two selected instants of time during the first and third eruptive phases on a regional scale. Sensitivity of the model to initial volcanological conditions has been analyzed at continental scale. Several complex non intuitive features of cloud dynamics have been highlighted and strengths and limitations of the adopted methods identified. The main findings are: the level of quantitative agreement between satellite observations and numerical results depends on ash cloud composition (particle sizes and concentration) with better agreement for smaller particles and higher concentrations; the agreement between observations and modeling outcomes also depends on the temporal stability of volcanological conditions and the complexity of the meteorological wind field; the irregular dispersion of ash, as reconstructed from satellite data and numerical modeling, can be well explained by the different response of particle sizes to strong vertical wind‐shear, and by resuspension processes acting at ground level; eruptive source conditions are the main source of uncertainty in modeling, especially during an ongoing crisis and at long‐range scales.

The areas characterized by dynamic and rapid morphological changes need accurate topography information with frequent updates, especially if these are populated and involve infrastructures. This is particularly true in active volcanic areas such as Mount (Mt.) Etna, located in the northeastern portion of Sicily, Italy. The Mt. Etna volcano is periodically characterized by explosive and effusive eruptions and represents a potential hazard for several thousands of local people and hundreds of tourists present on the volcano itself. In this work, a high-resolution, high vertical accuracy digital surface model (DSM) of Mt. Etna was derived from Pleiades satellite data using the National Aeronautics and Space Administration (NASA) Ames Stereo Pipeline (ASP) tool set. We believe that this is the first time that the ASP using Pleiades imagery has been applied to Mt. Etna with sub-meter vertical root mean square error (RMSE) results. The model covers an area of about 400 km² with a spatial resolution of 2 m and centers on the summit portion of the volcano. The model was validated by using a set of reference ground control points (GCP) obtaining a vertical RMSE of 0.78 m. The described procedure provides an avenue to obtain DSMs at high spatial resolution and elevation accuracy in a relatively short amount of processing time, making the procedure itself suitable to reproduce topographies often indispensable during the emergency management case of volcanic eruptions.

Volcanic islands are often affected by ground displacement such as slope instability, due to their peculiar morphology. This is the case of Ischia Island (Naples, Italy) dominated by the Mt. Epomeo (787 m a.s.l.), a volcano-tectonic horst located in the central portion of the island. This study aims to follow a long temporal evolution of ground deformations on the island through the interferometric analysis of satellite SAR data. Different datasets, acquired during Envisat, COSMO-SkyMed and Sentinel-1 satellite missions, are for the first time processed in order to obtain the island ground deformations during a time interval spanning 17 years, from November 2002 to December 2019. In detail, the multitemporal differential interferometry technique, named small baseline subset, is applied to produce the ground displacement maps and the associated displacement time series. The results, validated through the analysis and the comparison with a set of GPS measurements, show that the northwestern side of Mt. Epomeo is the sector of the island characterized by the highest subsidence movements (maximum vertical displacement of 218 mm) with velocities ranging from 10 to 20 mm/yr. Finally, the displacement time series allow us to correlate the measured ground deformations with the seismic swarm started with the Mw 3.9 earthquake that occurred on 21 August 2017. Such correlations highlight an acceleration of the ground, following the mainshock, characterized by a subsidence displacement rate of 0.12 mm/day that returned to pre-earthquake levels (0.03 mm/day) after 6 months from the event.