ABSTRACTSAR (Synthetic Aperture Radar) satellite interferometry is a helpful remote sensing technique for large areas analyses and monitoring, especially where the study area is difficult to access ...for practical or for legal reasons. As a result, the use of these techniques has significantly increased over the past three decades. Among the available different satellite constellations displaying different spatial and temporal resolutions, COSMO-SkyMed of the Italian Space Agency (ASI) represents a cutting-edge reality. COSMO-SkyMed constellation, launched in 2007 by ASI, is a valuable Earth observation tool that provides all-weather, day-and-night imaging capabilities with high resolution and a short revisit time. In this study, we produced an atlas for the entire Italian peninsula using two parameters (R-Index and Percentage of measurability of movement), in order to evaluate the quality and a-priori applicability of satellite interferometry data collected by the COSMO-SkyMed constellation. The atlas was obtained by means of the implementation of different model builders in the GIS (Geographical Information Systems) environment, providing a semi-automatic way to generate the above-mentioned outputs. The R-Index describes the likelihood of detecting Permanent Scatterers in mountainous areas, while the Percentage of measurability of movement indicates the percentage of real motion that interferometry can detect at a certain point in the analyzed region. A high-detail Digital Terrain Model (DTM) has been used to identify the most suitable areas for satellite interferometry monitoring and studying. The results of our analysis showed that the R-Index and the Percentage of measurability of movement could be used to pre-evaluate the quality of satellite interferometry data collected by the COSMO-SkyMed constellation. This research has important implications for disaster response, environmental monitoring, and scientific research and is one of a few cases in the world in which a unified representation for an entire country is provided.
Rainfall-induced shallow landslides represent a serious threat in hilly and mountain areas around the world. The mountainous landscape of the Cinque Terre (eastern Liguria, Italy) is increasingly ...popular for both Italian and foreign tourists, most of which visit this outstanding terraced coastal landscape to enjoy a beach holiday and to practice hiking. However, this area is characterized by a high level of landslide hazard due to intense rainfalls that periodically affect its rugged and steep territory. One of the most severe events occurred on 25 October 2011, causing several fatalities and damage for millions of euros. To adequately address the issues related to shallow landslide risk, it is essential to develop landslide susceptibility models as reliable as possible. Regrettably, most of the current land-use and urban planning approaches only consider the susceptibility to landslide detachment, neglecting transit and runout processes. In this study, the adoption of a combined approach allowed to estimate shallow landslide susceptibility to both detachment and potential runout. At first, landslide triggering susceptibility was assessed using Machine Learning techniques and applying the Ensemble approach. Nine predisposing factors were chosen, while a database of about 300 rainfall-induced shallow landslides was used as input. Then, a Geographical Information System (GIS)-based procedure was applied to estimate the potential landslide runout using the “reach angle” method. Information from such analyses was combined to obtain a susceptibility map describing detachment, transit, and runout. The obtained susceptibility map will be helpful for land planning, as well as for decision makers and stakeholders, to predict areas where rainfall-induced shallow landslides are likely to occur in the future and to identify areas where hazard mitigation measures are needed.
Landslides pose significant risks to towns and villages in Southern Italy, including the San Marco dei Cavoti hamlet (Benevento, Campania), where settlements have expanded into areas threatened by ...landslides, leading to property damage, disruption to the social fabric and loss of life. This study aims to investigate the surface deformations in the area using Differential Interferometry SAR (DInSAR) analysis on COSMO-SkyMed radar imagery and to assess the potential implications for landslide activity. The DInSAR analysis methodology allowed us to obtain high-precision results presented as time series diagrams and maps of cumulative displacement for the study area. Furthermore, the displacement rates derived from the DInSAR analysis were decomposed into vertical and horizontal components to provide better insights into the slope processes and their potential impacts on the San Marco dei Cavoti hamlet. Our significant findings revealed active slope movements and the uphill enlargement of previously inventoried landslides threatening the San Marco dei Cavoti hamlet. These insights contribute to a better understanding of the landslide dynamics in the region and highlight the areas that may require further investigation or intervention measures. In conclusion, this study demonstrates the effectiveness of DInSAR analysis in providing valuable insights into landslide dynamics and informing potential mitigation measures for at-risk communities. This technique could be applied to other landslide-prone regions to support informed decision-making and enhance the safety and resilience of affected communities.
Terraced landscapes represent one of the most widespread human-induced/man-made transformations of hilly-mountainous environments. Slope terracing produces peculiar morphologies along with unusual ...soil textures and stratigraphic features, which in turn strongly influence slope hydrology. The investigation of the hydrological features of terraced soils is of fundamental importance for understanding the hydrological dynamics occurring in these anthropogenic landscapes, especially during rainfall events. To this purpose, the availability of extensive field monitoring data series and of information on subsoil properties and structure is essential. In this study, multi-sensor hydrological data were acquired over a period longer than 2 years in the experimental site of Monterosso al Mare, in the Cinque Terre National Park (Liguria region, Italy), one of the most famous examples of terraced landscape worldwide. Monitoring data were coupled with accurate engineering-geological investigations to achieve the hydro-mechanical characterization of backfill soils and to investigate their hydrological response at both the seasonal and the single rainstorm scale. The results indicated that the coarse-grained, and anthropically remolded texture of the soils favors the rapid infiltration of rainwater, producing sharp changes in both soil volumetric water content and pore water pressure. Furthermore, the pattern of hydrological parameters showed seasonal trends outlined by alternating phases of slow drying and fast wetting. The study outcomes provide useful insights on the short and long-term evolution of hydrological factors operating in agricultural terraces. These findings represent a useful basis for a better understanding of the time-dependent processes that guide water circulation in terraced systems, which have a key role in controlling the occurrence of erosion and landslide processes.
In recent years, the study of anthropogenic sinkholes in densely urbanized areas has attracted the attention of both researchers and land management entities. The city of Naples (Italy) has been ...frequently affected by processes generating such landforms in the last decades: for this reason, an update of the sinkhole inventory and a preliminary susceptibility estimation are proposed in this work. Starting from previous data, not modified since 2010, a total of 270 new events occurred in the period February 2010–June 2021 were collected through the examination of online newspapers, local daily reports, council chronicle news and field surveys. The final consistence of the updated inventory is of 458 events occurred between 1880 and 2021, distributed through time with an increasing trend in frequency. Spatial analysis of sinkholes indicates a concentration in the central sector of the city, corresponding to its ancient and historic centre, crossed by a dense network of underground tunnels and cavities. Cavity-roof collapse is confirmed as one of the potential genetic types, along with processes related to rainfall events and service lines damage. A clear correlation between monthly rainfall and the number of triggered sinkholes was identified. Finally, a preliminary sinkhole susceptibility assessment, carried out by Frequency Ratio method, confirms the central sector of city as that most susceptible to sinkholes and emphasizes the predisposing role of service lines, mostly in the outermost areas of the city.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Landslides, a significant natural hazard driven predominantly by rainfall infiltration, pose a continuous threat to the terrain, buildings, and ecosystem of Caiazzo, located in southern Italy ...(Caserta). This research undertakes a novel approach to predict and understand the complex patterns of landslide deformations in this region. Our study leverages the advanced capabilities of COSMO-SkyMed satellite imagery, integrating a comprehensive dataset that includes factors such as elevation, slope, Topographic Wetness Index, Stream Power Index, geology, flow direction, curvature, Normalized Difference Vegetation Index, and several other relevant indices. We employ Transformer-based models, renowned for their effectiveness in capturing long-range dependencies within sequential data. The Transformer models in our study are adept at analyzing the temporal sequences of environmental factors and their intricate interactions, thereby offering a more nuanced understanding of the temporal patterns leading to landslides. These Transformer models are designed to process the entire sequence of satellite data obtained by the Coherent Pixels - Temporal Phase Coherence within the SUBSOFT package to ensure data integrity and precision, encompassing 132 images in ascending geometry and 143 in descending geometry, spanning from 2013 to 2021. By doing so, they efficiently identify critical patterns and dependencies over time, such as the interplay between rainfall events and subsequent soil and vegetation changes. The models are fine-tuned to our specific dataset, ensuring high precision and accuracy in landslide deformation prediction. Our evaluation metrics, including Mean Absolute Error, Root Mean Square Error, and R^2 score, demonstrate the superior performance of the Transformer-based approach, with significant improvements over the conventional Deep Learning model. The visual correlation of our predictions with actual landslide occurrences further corroborates the effectiveness of this method. This transformative approach not only enhances our understanding and predictive capability for landslides in Caiazzo but also sets a benchmark for landslide prediction in geologically vulnerable regions worldwide.
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