Search

The interpretation of high-resolution remote-sensed data (i.e., LiDAR-derived DTMs, aerial photos and satellite images), compared with ground-penetrating radar surveys, historical cartography, geomorphological surveys and stratigraphic data, allowed us to map a large system of dunes near the Grado-Marano Lagoon (NE Italy) and reconstruct its evolution. Remote sensing investigations allowed us to recognize, map and interpret the sandy reliefs as a field of continental aeolian landforms extending for over 15 km2 and consisting of parabolic dunes elongated in the WSW direction. Radar soundings, together with the description of stratigraphic sections and cores, documented the internal clinostratification of the dunes, supporting their aeolian origin. Radiocarbon dating documents that the dunes formed 22 ka ago, at the end of the Last Glacial Maximum, and probably evolved until the first part of the Late Glacial, when vegetation was scarce. The landforms were fed by the sands blown from a paleochannel of Isonzo River flowing eastward of the dune's field and blown by Bora. This is a very strong katabatic wind, still characterizing the area, but that was likely much stronger during last glaciation, when it was probably sustained by a stronger wind pattern in Central Europe. [ABSTRACT FROM AUTHOR]

This paper provides an assessment of a 24 GHz multiple-input multiple-output radar as a remote sensing tool to retrieve bathymetric maps in coastal areas. The reconstruction procedure considered here exploits the dispersion relation and has been previously employed to elaborate the data acquired via X-band marine radar. The estimation capabilities of the sensor are investigated firstly on synthetic radar data. With this aim, case studies referring to sea waves interacting with a constant and a spatially varying bathymetry are both considered. Finally, the reconstruction procedure is tested by processing real data recorded at Bagnoli Bay, Naples, South Italy. The preliminary results shown here confirm the potential of the radar sensor as a tool for sea wave monitoring. [ABSTRACT FROM AUTHOR]

The Villa of Caddeddi, in the territory of Noto (Siracusa) is located on the south bank of the Tellaro river, about 3 km from its mouth. The site, interpreted as a rural luxury residence dated to the fourth–fifth century CE, was first discovered in 1972 and intermittently investigated in the subsequent decades and mostly studied from the perspectives of the splendid mosaic floors there uncovered. The excavated structure accounts for just a portion of the complex, which is partially covered by an eighteenth–nineteenth century farmhouse. After a long period of neglect, the villa, mainly known in literature for its exquisite mosaic floors, has been recently restored and opened to the public and become subject of new studies by the University of South Florida's Institute for Digital Exploration (IDEx) and the Institute of Heritage Science – CNR which between 2019 and 2022 conducted a remote sensing, using ground penetrating radar method, campaign in partnership with the Parco Archeologico e Paesaggistico di Siracusa, Eloro, Villa del Tellaro e Akrai. The results related to the digital exploration and ground penetrating radar allow new knowledge relating to the Roman villa and allows a scientific reconstruction of the villa. [ABSTRACT FROM AUTHOR]

In this paper, a method is presented to classify volcano activity into three classes, namely quiet, strombolian, and paroxysm. The method is based on training a six-layered deep neural network (DNN) model using these signals as inputs (features): time series of the number of distances of infrasonic events, radar backscatter power, RMS of tremor in five stations close to craters of the volcano, tilt derivative, and seismic tremor source depth. The method was tested on the data related to a period of five years, and the results were concluded using indexes of precision, recall, F1 score, and Cohen's Kappa coefficient were calculated to evaluate the qualification of the classification. Also, the results were compared to Bayesian network (BN), K-nearest neighbors (KNN), and decision tree (DT) methods. Decision learning trees and KNN are popular machine learning algorithms belonging to the class of supervised learning algorithms. They mimic the human level thinking and, differing from neural networks, are not black box models. The comparisons reveal the proposed method, especially in classifying both strombolian and paroxysm classes. This advantage makes the presented method a more reliable tool for practical use in the volcano monitoring control rooms. [ABSTRACT FROM AUTHOR]

On 20 May 2012, an Mw 5.8 earthquake, followed by an Mw 5.6 event nine days later, struck the Emilia-Romagna region in northern Italy, causing substantial damage and loss of life. Post-mainshock, several water-related phenomena were observed, such as changes in the groundwater levels in wells, the expulsion of sand–water mixtures, and widespread liquefaction evidence such as sand boils and water leaks from cracks. We analyzed the Earth's surface displacement during and after the Emilia 2012 seismic sequence using synthetic aperture radar images from the COSMO-SkyMed satellite constellation. This analysis revealed post-seismic ground subsidence between the Sant'Agostino and Mirabello villages. Specifically, the displacement time series showed a slight initial uplift followed by rapid subsidence over approximately four to five months. This widespread ground displacement pattern likely stemmed from the extensive liquefaction of saturated sandy layers at depth. This phenomenon typically induces immediate post-seismic subsidence. However, the observed asymptotic subsidence, reaching about 2.1 cm, suggested a time-dependent process related to post-liquefaction consolidation. To test this hypothesis, we analytically estimated the consolidation subsidence resulting from earthquake-induced excess pore pressure dissipation in the layered soil deposits. The simulated subsidence matched the observed data, further validating the significant role of excess pore pressure dissipation induced by earthquake loading in post-seismic ground subsidence. [ABSTRACT FROM AUTHOR]

Landslides play a significant role in the morpho-evolutional processes of slopes, affecting them globally under various geological conditions. Often unnoticed due to low velocities, they cause diffuse damage and loss of economic resources to the infrastructure or villages built on them. Recognizing and mapping mass movements is crucial for mitigating economic and social impacts. Conventional monitoring techniques prove challenging for large areas, necessitating resource-intensive ground-based networks. Leveraging abundant synthetic aperture radar (SAR) sensors, satellite techniques offer cost-effective solutions. Among the various methods based on SAR products for detecting landslides, multi-temporal differential interferometry SAR techniques (MTInSAR) stand out for their precise measurement capabilities and spatiotemporal evolution analysis. They have been widely used in several works in the last decades. Using information from the official Italian landslide database (IFFI), this study employs Sentinel-1 imagery and two new processing chains, E-PS and E-SBAS algorithms, to detect deformation areas on the slopes of Calitri, a small town in Southern Italy; these algorithms assess the cumulated displacements and their state of activity. Taking into account the non-linear trends of the scatterers, these innovative algorithms have helped to identify a dozen clusters of points that correspond well with IFFI polygons. [ABSTRACT FROM AUTHOR]

Over the past two decades, the airborne Light Detection and Ranging (LiDAR) system has become a useful tool for acquiring high-resolution topographic data, especially in active tectonics studies. Analyzing Digital Terrain Models (DTMs) from LiDAR exposes morpho-structural elements, aiding in the understanding of fault zones, among other applications. Despite its effectiveness, challenges persist in regions with rapid deformation, dense vegetation, and human impact. We propose an adapted workflow transitioning from the conventional airborne LiDAR system to the usage of drone-based LiDAR technology for higher-resolution data acquisition. Additionally, drones offer a more cost-effective solution, both in an initial investment and ongoing operational expenses. Our goal is to demonstrate how drone-based LiDAR enhances the identification of active deformation features, particularly for earthquake-induced surface faulting. To evaluate the potential of our technique, we conducted a drone-based LiDAR survey in the Casamicciola Terme area, north of Ischia Island, Italy, known for the occurrence of destructive shallow earthquakes, including the 2017 Md = 4 event. We assessed the quality of our acquired DTM by comparing it with existing elevation datasets for the same area. We discuss the advantages and limitations of each DTM product in relation to our results, particularly when applied to fault mapping. By analyzing derivative DTM products, we identified the fault scarps within the Casamicciola Holocene Graben (CHG) and mapped its structural geometry in detail. The analysis of both linear and areal geomorphic features allowed us to identify the primary factors influencing the current morphological arrangement of the CHG area. Our detailed map depicts a nested graben formed by two main structures (the Maio and Sentinella faults) and minor internal faults (the Purgatorio and Nizzola faults). High-resolution DEMs acquired by drone-based LiDAR facilitated detailed studies of the geomorphology and fault activity. A similar approach can be applied in regions where the evidence of high slip-rate faults is difficult to identify due to vegetation cover and inaccessibility. [ABSTRACT FROM AUTHOR]

The prediction and prevention of landslide hazard is a challenging topic involving the assessment and quantitative evaluation of several elements: geological and geomorphological setting, rainfalls, and ground motion. This paper presents the multi-approach investigation of the Nevissano landslide (Asti Province, Piedmont, NW Italy). It shows a continuous and slow movement, alongside few paroxysmal events, the last recorded in 2016. The geological and geomorphological models were defined through a field survey. An inventory of the landslide's movements and rainfall records in the period 2000–2016 was performed, respectively, through archive investigations and the application of "Moving Sum of Daily Rainfall" method, allowing for the definition of rain thresholds for the landslide activation (105 mm and 193 mm, respectively, in 3 and 30 days prior to the event). The displacements over the last 8 years (2016–2023) were monitored through an innovative in-continuum monitoring inclinometric system and Earth Observation (EO) data (i.e., relying on Interferometric Synthetic Aperture Radar, or InSAR data): it gave the opportunity to validate the rainfall thresholds previously defined. This study aims to provide information to public authorities for the appropriate management of the site. Moreover, the proposed workflow could be adopted as a guideline for investigating similar situations. [ABSTRACT FROM AUTHOR]

Monitoring slow-moving landslides is a crucial task for socioeconomic risk prevention and/or mitigation. Persistent scatterer interferometric synthetic aperture radar (PS-InSAR) is an advanced remote sensing method for monitoring ground deformation. In this research, PS-InSAR time series derived from COSMO-SkyMed (descending orbit) and Sentinel-1 (ascending orbit) are analyzed for a region in Central Apennines in Italy. The sequential turning point detection method (STPD) is implemented to detect the trend turning dates and their directions in the PS-InSAR time series within areas of interest susceptible to landslides. The monthly maps of significant turning points and their directions for years 2018, 2019, 2020, and 2021 are produced and classified for four Italian administrative regions, namely, Marche, Umbria, Abruzzo, and Lazio. Monthly global precipitation measurement (GPM) images at 0. 1 ∘ × 0. 1 ∘ spatial resolution and four local precipitation time series are also analyzed by STPD to investigate when the precipitation rate has changed and how they might have reactivated slow-moving landslides. Generally, a strong correlation ( r ≥ 0.7 ) is observed between GPM (satellite-based) and local precipitation (station-based) with similar STPD results. Marche and Abruzzo (the coastal regions) have an insignificant precipitation rate while Umbria and Lazio have a significant increase in precipitation from 2017 to 2023. The coastal regions also exhibit relatively lower precipitation amounts. The results indicate a strong correlation between the trend turning dates of the accumulated precipitation and displacement time series, especially for Lazio during summer and fall 2020, where relatively more significant precipitation rate of change is observed. The findings of this study may guide stakeholders and responsible authorities for risk management and mitigating damage to infrastructures. [ABSTRACT FROM AUTHOR]

The joint exploitation of complementary information from independent satellite and ground-based SAR observations can allow reconstructing the three-dimensional (up-down, east–west, north–south) ground displacement profile. Some attempts have recently been made to complement satellite and ground-based SAR (GB-SAR) data. However, a method for generating the 3-D ground displacement time-series and evaluating the quality of these estimates is still lacking. Our research aims to develop a statistical framework that is beneficial to evaluate the quality of the reconstructed 3-D ground deformation measurements achieved by jointly using space-borne and GB-SAR systems. To this purpose, the quality of multi-platform Line-of-Sight (LOS)-projected ground displacement time series is first assessed by deriving the variance–covariance matrices of noise sources (i.e., time-inconsistent phase unwrapping mistakes, decorrelation effects, and atmospheric phase screen). Subsequently, the precision of the retrieved 3-D ground displacement products is derived. Some experiments have been performed considering the zone of Gorgoglione in southern Italy, for which three sets of SAR images are available. The first set is composed of radar images collected through a GB-SAR measurement campaign performed with the IBIS-L instrument from September 2016 to July 2017. In contrast, the spaceborne sets consist of two groups of images gathered during the same observation period by the European Copernicus Sentinel-1A/B sensors over complementary ascending (Path 146) and descending orbits (Path 124). The experimental results for the selected case-study area showed that the error bounds for the 3-D ground deformation time-series are about 3.6 mm, 3.7 mm, and 0.6 mm for the up-down, east–west, and north–south profiles, respectively. [ABSTRACT FROM AUTHOR]

One of the most promising applications of satellite data is providing users in charge of land and emergency management with information and data to support decision making for geohazard mapping, monitoring and early warning. In this work, we consider ground displacement data obtained via interferometric processing of satellite radar imagery, and we provide a novel post-processing approach based on a Functional Data Analysis paradigm capable of detecting precursors in displacement time series. The proposed approach appropriately accounts for the spatial and temporal dependencies of the data and does not require prior assumptions on the deformation trend. As an illustrative case, we apply the developed method to the identification of precursors to a mud volcano eruption in the Santa Barbara village in Sicily, southern Italy, showing the advantages of using a Functional Data Analysis framework for anticipating the warning signal. Indeed, the proposed approach is able to detect precursors of the paroxysmal event in the time series of the locations close to the eruption vent and provides a warning signal months before a scalar approach would. The method presented can potentially be applied to a wide range of geological events, thus representing a valuable and far-reaching monitoring tool. [ABSTRACT FROM AUTHOR]

Accurate digital elevation models represent the basic tool for a large spectrum of applications, including geological, architectonic, archaeological, and urbanistic studies. However, aggressive urbanization may significantly alter the morphology of areas of interest. Such is the case of the active Campi Flegrei caldera in southern Italy, where all the buildings, facilities, infrastructures, quarries, and landfills altered the original volcanic morphology. To avoid these limitations, we analyzed a set of vintage aerial photographs acquired in 1943 by Italy's Military Geographic Institute (IGM). We reconstructed the study area's topography before the deep anthropic modification from the 1960s onwards by applying Structure-from-Motion photogrammetric processing. On the reconstructed orthomosaic and a historical-Digital Surface Model (hDMS), we conducted geomorphic analyses along 18 longitudinal stream profiles outside, across the border, and inside the caldera, underlining that the rectilinear sections with preferred orientations and increased incision values suggest a strong structural underpinning on valley incision and stream paths. The analysis of the spatial distribution of sinuosity, SL index, Chi (χ) value and knickpoint maps suggests the presence of tectonic lineaments that influence the stream network from the pre-caldera (>40 ka) to recent times, producing articulated caldera and craters geometry as well as affecting the localization of volcanic vents. The comparison between the 1943 hDSM and 2013 LiDAR (Light Detection and Ranging) DSM allowed us to spotlight the areas that show the most significant differences due to anthropic intervention that obliterated critical features, thus supporting our motivation to use this base. Overall, the results indicate that long-lived volcano-tectonic and tectonic structures control the orientation of drainage patterns and their re-arrangements during volcano-tectonic deformation phases. Finally, a comprehensive structural map, based on the merged 1943 hDSM and the bathymetric DEM, is presented together with a conceptual evolutionary model of the stream network across the caldera border. Under the proper acquisition conditions, vintage aerial photographs can provide a useful tool for morphological analysis and can be applied to several topics in the geosciences. • The original topography of Campi Flegrei is restored using historical aerial photographs. • Several geomorphic indices highlight the activity of volcano-tectonic structures. • Rectilinear and deeply incised valleys occur in different sectors. • NE–SW and NW–SE oriented tectonic features are overprinted by E–W oriented features. • Tectonic lineaments control the occurrence of volcanism through time. [ABSTRACT FROM AUTHOR]

Ground deformation monitoring is a crucial task in geohazard management to ensure the safety of lives and infrastructure. Persistent scatterer interferometric synthetic aperture radar (PS-InSAR) is an advanced technique for measuring small displacements on the Earth's surface. Estimated PS-InSAR time series acquired by Sentinel-1 satellites provide a great opportunity for effective monitoring of ground deformation in recent years. However, challenges arise when processing these time series due to their non-uniform sampling, noise from atmosphere and preprocessing issues including phase unwrapping and others. Therefore, estimating the location and direction of trend turning in such time series, as an indicator of ground deformation, is not an easy task. In this work, a sequential turning point detection method (STPD) is proposed and compared with other change point detection methods. Using a large set of simulated time series with various noise types, it is shown that STPD outperforms other methods in terms of overall accuracy and root mean square error for location and direction of trend turnings. As a case study, STPD is applied to detect turning points within PS-InSAR time series for the province of Frosinone in Italy and classified using topography and land cover/use. In addition, an area susceptible to landslides is selected to estimate the starting dates of potential slow-moving landslides. It is also shown that the turning points in the local precipitation time series have a high correlation with the ones in the PS-InSAR time series, indicating that precipitation is a major triggering factor of the displacements in the area. The STPD can rapidly and effectively detect locations and directions of trend turnings and is freely available online in both MATLAB and python. • A robust trend turning point detection method is proposed, namely, STPD. • A normalized index is proposed to distinguish jumps from turning points in STPD. • The STPD can process one million time series of size 100 within an hour. • The STPD was successfully applied to PS-InSAR time series for Frosinone in Italy. • Most displacement turning points were correlated with precipitation turning points. [ABSTRACT FROM AUTHOR]