Search

The Massarosa wildfire, which occurred in July 2022 in Northwestern Tuscany (Italy), burned over 800 hectares, leading to significant environmental and geomorphological issues, including an increase in soil erosion rates. This study applied the Revised Universal Soil Loss Equation (RUSLE) model to estimate soil erosion rates with a multi-temporal approach, investigating three main scenarios: before, immediately after, and one-year post-fire. All the analyses were carried out using the Google Earth Engine (GEE) platform with free-access geospatial data and satellite images in order to exploit the cloud computing potentialities. The results indicate a differentiated impact of the fire across the study area, whereby the central parts suffered the highest damages, both in terms of fire-related RUSLE factors and soil loss rates. A sharp increase in erosion rates immediately after the fire was detected, with an increase in maximum soil loss rate from 0.11 ton × ha−1 × yr−1 to 1.29 ton × ha−1 × yr−1, exceeding the precautionary threshold for sustainable soil erosion. In contrast, in the mid-term analysis, the maximum soil loss rate decreased to 0.74 ton × ha−1 × yr−1, although the behavior of the fire-related factors caused an increase in soil erosion variability. The results suggest the need to plan mitigation strategies towards reducing soil erodibility, directly and indirectly, with a continuous monitoring of erosion rates and the application of machine learning algorithms to thoroughly understand the relationships between variables.

Deep-Seated Gravitational Slope Deformations (DSGSDs) are common in many geological environments, and due to their common limited displacement rate, they can remain unrecognized for a long time. Among the most significant events in Sicily is the Mt. San Calogero DSGSD. To contribute to a better understanding of its characteristics, including the geologic setting promoting its development, ongoing kinematics, and mechanism, a specific analysis was completed. In this paper, the results of this analysis, based on a three-folded strategy, are provided and interpreted in the context of DSGSD predisposing conditions and controlling factors. Especially, field observations associated to visual interpretation of aerial imagery were used for the identification and mapping of main geological features and landforms, high-resolution X-Band DInSAR data enabled researchers to fully characterize the deformational behavior of the slope, while a reduced complexity slope stability analysis allowed them to reconstruct the deep geometry of the DSGSD. Results from the analysis indicate that the DSGSD of Mt. San Calogero is composed of three blocks corresponding to fault-bounded tectonic elements and characterized by a specific kinematics and sensitivity to external forcing (i.e., rainfall), multiple landslides are associated to the DSGSD in the area and the deep geometry of the DSGSD is concave upward and resemble the characteristics of a rotational slide. The interpretation of the results suggests that the formation and the deformation of the Mt. San Calogero DSGSD are linked with the local and regional fault systems related to the Sicilian orogen, while shallow landslides are triggered, in clayey terrains, mostly by rainfalls. In addition, the integrated approach reveals that active tectonics and rainfalls in the San Calogero massive relief are the main driving forces of its different deformation behavior.

The presence of the olive tree in Tuscany, Italy, in its forms that have survived to the present day as an essential component of the landscape dates back many centuries. Global change is now threatening it. Therefore, it is important to find markers to enhance the olive tree environment in terms of its resilience. The aim of the research was to investigate the composition of soil bacteriomes in contrasting geochemical environments using a geochemistry approach based on the behavior of the REEs, inherited from parent rock material. Bacteriome assemblages and REE content were analyzed in 48 topsoils developed in six geochemical Tuscan environments. Combined geochemical, geoinformatic, and bioinformatic techniques highlighted the existence of four bacteriome assemblages depending on Light-REEs. Further results showed that the soil bioavailable fraction of REEs was related to parent rock materials, pH, and bacteriome composition. The most abundant bacteria were Microlunatus in graded fluvio-lacustrine soils, Gaiella in graded arenaceous soils, Bradyrizhobium in pyroclastic soils, and Rubrobacter in soils on gentle slopes of calcareous and carbonatic lithologies. This research represents a starting point to define new indicators able to assess the resilience of the olive trees in the Mediterranean landscape and characterize the territory of extra virgin olive oils.

Landform diversity influences and interacts with both biodiversity and geodiversity and thus, they are key factors in the assessment of landscape resilience. However, research on the spatial relationships between landscape geodiversity and biodiversity is challenging because we are still lacking methods to link abiotic with biotic factors. The goal of this study is to explore and quantitatively assess the spatial relationship between geomorphometric factors and the relative distribution of rare earth elements (REEs) in soils and organism. Therefore, we selected a representative Mediterranean landscape characterized by ancient olive grove cultivations. The results show for different landforms and lithotypes a positive linear correlation in the lanthanum/samarium vs. lanthanum/ytterbium (La/Sm vs. La/Yb) signature between the bioavailable fraction of topsoil and olive drupe. Results of La/Yb vs. La/Sm reported as power function for olive drupes and topsoil follow comparable scaling ranges showing a power law of 0.83 and 0.71 respectively with an R2 0.96 vs. 0.71. A different scaling range behavior from topsoil to the related olive drupe was found for each parent rock material. Results of the Machine Learning (ML) modelling framework showed that the LaN/SmN in topsoil, were substantiality correlated to channel network base level, topographic wetness index, NDWI and valley depth. Under the physiographic environmental variables of the study area, the spatial distribution of LaN/YbN was mainly related to the lithological characteristics. Furthermore, NDVI was the most important variable to predict the fractionation ratio of LaN/YbN in olive drupe and the topographic channel network distance for LaN/SmN in olive drupe. Our findings provide new insights in the spatial distribution of REEs allowing an assessment of bio- and geodiversity of olive groves taking into account biophysical factors. Our research represents a starting point for future applications and modelling techniques to analyze at the catchment-scale the REEs biophysical fluxes and food traceability.

In the last decades, ground deformations were investigated, analysed and monitored using several methods. As a consequence of a spreading urbanization, several phenomena, e.g. landslide and subsidence, were emphasized or triggered causing not only socio-economic damages, but, in some cases, also casualties. The investigation and mapping of these phenomena are important for both local authorities and civil protection in order to promote a higher conscientious urban planning and to highlight the more hazardous areas. Furthermore, the information are a key point for social development connected to the awareness of the environment and the related risk. The Achaia prefecture, in the north-eastern Peloponnese (Greece), close to the Gulf of Patras, is an area strongly affected by subsidence and landslides. Furthermore, this is an earthquake-prone area, a factor that can trigger some mass movements. For this region, a landslide inventory was realized with the help of the interpretation of Persistent Scatterers data, for the period 1992–2008, and high-resolution optical satellite images, available until 2016, in addition to the investigation of the landslide State of Activity. Moreover, for the coastal area, a section was investigated to evidence subsidence.

The detection of active movements that could threat the infrastructures and the population is one of the main priorities of the risk management chain. Interferometric Synthetic Aperture Radar (InSAR) techniques represent one of the most useful answers to this task; however, it is difficult to manage the huge amount of information derived from the interferometric analysis. In this work, we present a procedure for deriving impact assessment maps, over a regional test site, using as starting point Sentinel-1 SAR (Synthetic Aperture Radar) images and a catalogue of elements at risk that acts as a second input of the methodology. We applied the proposed approach, named as Vulnerable Elements Activity Maps (VEAM), to the islands of Gran Canaria, La Gomera and Tenerife (Spain), where we analysed SAR images covering the time interval November 2014–September 2016. The methodology, meant to be a powerful tool for reducing the time needed for a complete analysis of a full stack of InSAR data, is ideally suited for Civil Protection Authorities. The application of the methodology allowed to detect 108 areas affected by active deformation that are threatening one or more elements at risk in 25 municipalities of the three islands.

Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progressively more relevant. Initially carried out via field sketches, the advent of satellite remote sensing opened new possibilities, reducing the cost uncertainty and safety of the previous techniques. In the present study an experimental methodology was adopted to test the potential of advanced remote sensing techniques such as multispectral Sentinel-2, PRISMA hyperspectral satellite, and UAV (unmanned aerial vehicle) remotely-sensed data for the multitemporal mapping of burned areas by soil–vegetation recovery analysis in two test sites in Portugal and Italy. In case study one, innovative multiplatform data classification was performed with the correlation between Sentinel-2 RBR (relativized burn ratio) fire severity classes and the scene hyperspectral signature, performed with a pixel-by-pixel comparison leading to a converging classification. In the adopted methodology, RBR burned area analysis and vegetation recovery was tested for accordance with biophysical vegetation parameters (LAI, fCover, and fAPAR). In case study two, a UAV-sensed NDVI index was adopted for high-resolution mapping data collection. At a large scale, the Sentinel-2 RBR index proved to be efficient for burned area analysis, from both fire severity and vegetation recovery phenomena perspectives. Despite the elapsed time between the event and the acquisition, PRISMA hyperspectral converging classification based on Sentinel-2 was able to detect and discriminate different spectral signatures corresponding to different fire severity classes. At a slope scale, the UAV platform proved to be an effective tool for mapping and characterizing the burned area, giving clear advantage with respect to filed GPS mapping. Results highlighted that UAV platforms, if equipped with a hyperspectral sensor and used in a synergistic approach with PRISMA, would create a useful tool for satellite acquired data scene classification, allowing for the acquisition of a ground truth.

Unmanned aerial vehicle (UAV) photogrammetry has recently become a widespread technique to investigate and monitor the evolution of different types of natural processes. Fluvial geomorphology is one of such fields of application where UAV potentially assumes a key role, since it allows for overcoming the intrinsic limits of satellite and airborne-based optical imagery on one side, and in situ traditional investigations on the other. The main purpose of this paper was to obtain extensive products (digital terrain models (DTMs), orthophotos, and 3D models) in a short time, with low costs and at a high resolution, in order to verify the capability of this technique to analyze the active geomorphic processes on a 12 km long stretch of the French–Italian Roia River at both large and small scales. Two surveys, one year apart from each other, were carried out over the study area and a change detection analysis was performed on the basis of the comparison of the obtained DTMs to point out and characterize both the possible morphologic variations related to fluvial dynamics and modifications in vegetation coverage. The results highlight how the understanding of different fluvial processes may be improved by appropriately exploiting UAV-based products, which can thus represent a low-cost and non-invasive tool to crucially support decisionmakers involved in land management practices.

The soil surface albedo decreases with an increasing biochar application rate as a power decay function, but the net impact of biochar application on soil temperature dynamics remains to be clarified. The objective of this study was to assess the potential of infrared thermography (IRT) sensing by monitoring soil surface temperature (SST) with a high spatiotemporal and thermal resolution in a scalable agricultural application. We monitored soil surface temperature (SST) variations over a 48 h period for three treatments in a vineyard: bare soil (plot S), 100% biochar cover (plot B), and biochar-amended topsoil (plot SB). The SST of all plots was monitored at 30 min intervals with a tripod-mounted IR thermal camera. The soil temperature at 10 cm depth in the S and SB plots was monitored continuously with a 5 min resolution probe. Plot B had greater daily SST variations, reached a higher daily temperature peak relative to the other plots, and showed a faster rate of T increase during the day. However, on both days, the SST of plot B dipped below that of the control treatment (plot S) and biochar-amended soil (plot SB) from about 18:00 onward and throughout the night. The diurnal patterns/variations in the IRT-measured SSTs were closely related to those in the soil temperature at a 10 cm depth, confirming that biochar-amended soils showed lower thermal inertia than the unamended soil. The experiment provided interesting insights into SST variations at a local scale. The case study may be further developed using fully automated SST monitoring protocols at a larger scale for a range of environmental and agricultural applications.

The use of InSAR (Interferometric Synthetic Aperture Radar) products has greatly increased in the last years because of the technological advances in terms of both acquisition sensors and processing algorithms. The development of multi-interferogram techniques and the availability of free SAR analysis tools has significantly increased the number of worldwide applications of satellite measurements for mapping and monitoring geohazards. InSAR techniques excel in determining ground deformation in urban areas, where the coherence of the radar images is high, and the obtainable results are particularly reliable. Thus, measuring urban subsidence has always been one of the main targets of the InSAR analysis. In this paper, we present a brief review on the applications, in the last decades, of both single and multi-interferogram techniques to monitor ground lowering in urban areas along the Italian Peninsula. Because of its geological context, Italy is prone to slow natural subsidence phenomena sometimes aggravated and accelerated, especially along the coasts and in urbanized areas, by anthropogenic factors (i.e., groundwater overexploitation, consolidation in recent urban expansion, geothermal activities). The review will show how the interferometric data allowed the scientific community to increase the knowledge of the phenomena, map their spatial distribution, and reconstruct their temporal evolution. The final goal of the review is to demonstrate the added value of InSAR data in supporting groundwater management and urban development in Italy.

On 13 January 2012, the Italian vessel, Costa Concordia, wrecked offshore Giglio Island, along the coast of Tuscany (Italy). The ship partially sunk, lying on the starboard side on a 22° steep rocky seabed, making the stability conditions of the ship critically in danger of sliding, shifting and settling. The tilted position of the ship created also pernicious conditions for the divers involved in the search and rescue operations. It became immediately clear that a continuous monitoring of the position and movements of the ship was of paramount importance to guarantee the security of the people working around and within the wreck. Starting from January 19, the Italian constellation of synthetic aperture radar (SAR) satellites, COSMO-SkyMed (CSK), was tasked to acquire high resolution images of the wreck. Thanks to CSK’s short response and revisiting time and its capability to acquire high resolution images in Spotlight mode, satellite data were integrated within the real time, ground-based monitoring system implemented to provide the civil protection authorities with a regular update on the ship stability. Exploitation of both the phase (satellite radar interferometry, InSAR) and amplitude (speckle tracking) information from CSK images, taken along the acquisition orbit, Enhanced Spotlight (ES)-29, revealed a general movement of the translation of the vessel, consistent with sliding toward the east of the hull on the seabed. A total displacement, with respect to the coastline, of 1666 mm and 345 mm of the bow and stern, respectively, was recorded, over the time period of 19 January–23 March 2012.

This paper describes the application of remote sensing techniques, based on SAR interferometry for the intensity zonation of the landslide affecting the Castagnola village (Northern Apennines of Liguria region, Italy). The study of the instability conditions of the landslide started in 2001 with the installation of conventional monitoring systems, such as inclinometers and crackmeters, ranging in time from April 2001 to April 2002, which allowed to define the deformation rates of the landslide and to locate the actual landslide sliding surface, as well as to record the intensity of the damages and cracks affecting the buildings located within the landslide perimeter. In order to investigate the past long-term evolution of the ground movements a PSI (Persistent Scatterers Interferometry) analysis has been performed making use of a set of ERS1/ERS2 images acquired in 1992–2001 period. The outcome of the PSI analysis has allowed to confirm the landslide extension as mapped within the official landslide inventory map as well as to reconstruct the past line-of-sight average velocities of the landslide and the time-series deformations. Following the high velocities detected by the PSI, and the extensive damages surveyed in the buildings of the village, the Ground-Based Interferometric Synthetic Aperture Radar (GBInSAR) system has been installed. The GBInSAR monitoring system has been equipped during October 2008 and three distinct campaigns have been carried out from October 2008 until March 2009. The interpretation of the data has allowed deriving a multi-temporal deformation map of the landslide, showing the up-to-date displacement field and the average landslide velocity. A new landslide boundary has been defined and two landslide sectors characterized by different displacement rates have been identified.

In this paper a methodology is proposed to elaborate landslide activity maps through the use of PS (Persistent Scatterer) data. This is illustrated through the case study of Tramuntana Range in the island of Majorca (Spain), where ALOS (Advanced Land Observing Satellite) images have been processed through a Persistent Scatterer Interferometry (PSI) technique during the period of 2007–2010. The landslide activity map provides, for every monitored landslide, an assessment of the PS visibility according to the relief, land use, and satellite acquisition parameters. Landslide displacement measurements are projected along the steepest slope, in order to compare landslide velocities with different slope orientations. Additionally, a ground motion activity map is also generated, based on active PS clusters not included within any known landslide phenomenon, but even moving, potentially referred to unmapped landslides or triggered by other kinds of geomorphological processes. In the Tramuntana range, 42 landslides were identified as active, four as being potential to produce moderate damage, intersecting the road Ma-10, which represents the most important road of the island and, thus, the main element at risk. In order to attest the reliability of measured displacements to represent landslide dynamics, a confidence degree evaluation is proposed. In this test site, seven landslides exhibit a high confidence degree, medium for 93 of them, and low for 51. A low confidence degree was also attributed to 615 detected active clusters with a potential to cause moderate damage, as their mechanism of the triggering cause is unknown. From this total amount, 18 of them intersect the Ma-10, representing further potentially hazardous areas. The outcomes of this work reveal the usefulness of landslide activity maps for environmental planning activities, being exportable to other radar data and different geomorphological settings.

In the last few years, several advances have been made in the use of radar images to detect, map and monitor ground deformations. DInSAR (Differential Synthetic Aperture Radar Interferometry) and A-DInSAR/PSI (Advanced DInSAR/Persistent Scatterers Interferometry) technologies have been successfully applied in the study of deformation phenomena induced by, for example, active tectonics, volcanic activity, ground water exploitation, mining, and landslides, both at local and regional scales. In this paper, the existing European Space Agency (ESA) archives (acquired as part of the FP7-DORIS project), which were collected by the ERS-1/2 and ENVISAT satellites operating in the microwave C-band, were analyzed and exploited to understand the dynamics of landslide and subsidence phenomena. In particular, this paper presents the results obtained as part of the FP7-DORIS project to demonstrate that the full exploitation of very long deformation time series (more than 15 years) can play a key role in understanding the dynamics of natural and human-induced hazards.

In this paper, the authors describe the architecture of a multidisciplinary data acquisition and visualization platform devoted to the management of coastal environments. The platform integrates heterogeneous data acquisition sub-systems that can be roughly divided into two main categories: remote sensing systems and in situ sensing systems. Remote sensing solutions that are going to be implemented include aerial and underwater data acquisition while in situ sensing solutions include the use of Radio Frequency IDentification (RFID) tracers, Wireless Sensor Networks and imaging techniques. All the data collected by these subsystems are stored, integrated and fused on a single platform that is also in charge of data visualization and analysis. This last task is carried out according to the paradigm of Augmented Virtuality that foresees the augmentation of a virtually reconstructed environment with data collected in the real world. The described solution proposes a novel holistic approach where different disciplines concur, with different data acquisition techniques, to a large scale definition of coastal dynamics, in order to better describe and face the coastal erosion phenomenon. The overall framework has been conceived by the so-called Team COSTE, a joint research team between the Universities of Pisa, Siena and Florence.

This work is focused on deformation activity mapping and monitoring using Sentinel-1 (S-1) data and the DInSAR (Differential Interferometric Synthetic Aperture Radar) technique. The main goal is to present a procedure to periodically update and assess the geohazard activity (volcanic activity, landslides and ground-subsidence) of a given area by exploiting the wide area coverage and the high coherence and temporal sampling (revisit time up to six days) provided by the S-1 satellites. The main products of the procedure are two updatable maps: the deformation activity map and the active deformation areas map. These maps present two different levels of information aimed at different levels of geohazard risk management, from a very simplified level of information to the classical deformation map based on SAR interferometry. The methodology has been successfully applied to La Gomera, Tenerife and Gran Canaria Islands (Canary Island archipelago). The main obtained results are discussed.

In this study, we present the detection and characterization of ground displacements in the urban area of Pisa (Central Italy) using Interferometric Synthetic Aperture Radar (InSAR) products. Thirty RADARSAT-2 and twenty-nine COSMO-SkyMed images have been analyzed with the Small BAseline Subset (SBAS) algorithm, in order to quantify the ground subsidence and its temporal evolution in the three-year time interval from 2011 to 2014. A borehole database was reclassified in stratigraphical and geotechnical homogeneous units, providing the geological background needed for the local scale analysis of the recorded displacements. Moreover, the interferometric outputs were compared with the last 30 years’ urban evolution of selected parts of the city. Two deformation patterns were recorded by the InSAR data: very slow vertical movements within the defined stability threshold (±2.5 mm/yr) and areas with subsidence rates down to −5 to −7 mm/yr, associated with high peak velocities (−15 to −20 mm/yr) registered by single buildings or small groups of buildings. Some of these structures are used to demonstrate that the high subsidence rates are related to the recent urbanization, which is the trigger for the accelerated consolidation process of highly compressible layers. Finally, this urban area was a valuable test site for demonstrating the different results of the C- and X-band data processing, in terms of the density of points and the quality of the time series of deformation.

Permanent Scatterer Interferometry (PSI) has been used to detect and characterize the subsidence of the Pisa urban area, which extends for 33 km2 within the Arno coastal plain (Tuscany, Italy). Two SAR (Synthetic Aperture Radar) datasets, covering the time period from 1992 to 2010, were used to quantify the ground subsidence and its temporal evolution. A geotechnical borehole database was also used to make a correspondence with the detected displacements. Finally, the results of the SAR data analysis were contrasted with the urban development of the eastern part of the city in the time period from 1978 to 2013. ERS 1/2 (European Remote-Sensing Satellite) and Envisat SAR data, processed with the PSInSAR (Permanent Scatterer InSAR) algorithm, show that the investigated area is divided in two main sectors: the southwestern part, with null or very small subsidence rates (