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The agricultural sector is amidst an industrial revolution driven by the integration of sensing, communication, and artificial intelligence (AI). Within this context, the internet of things (IoT) takes center stage, particularly in facilitating remote livestock monitoring. Challenges persist, particularly in effective field communication, adequate coverage, and long-range data transmission. This study focuses on employing LoRa communication for livestock monitoring in mountainous pastures in the north-western Alps in Italy. The empirical assessment tackles the complexity of predicting LoRa path loss attributed to diverse land-cover types, highlighting the subtle difficulty of gateway deployment to ensure reliable coverage in real-world scenarios. Moreover, the high expense of densely deploying end devices makes it difficult to fully analyze LoRa link behavior, hindering a complete understanding of networking coverage in mountainous environments. This study aims to elucidate the stability of LoRa link performance in spatial dimensions and ascertain the extent of reliable communication coverage achievable by gateways in mountainous environments. Additionally, an innovative deep learning approach was proposed to accurately estimate path loss across challenging terrains. Remote sensing contributes to land-cover recognition, while Bidirectional Long Short-Term Memory (Bi-LSTM) enhances the path loss model's precision. Through rigorous implementation and comprehensive evaluation using collected experimental data, this deep learning approach significantly curtails estimation errors, outperforming established models. Our results demonstrate that our prediction model outperforms established models with a reduction in estimation error to less than 5 dB, marking a 2X improvement over state-of-the-art models. Overall, this study signifies a substantial advancement in IoT-driven livestock monitoring, presenting robust communication and precise path loss prediction in rugged landscapes. [ABSTRACT FROM AUTHOR]

This paper describes the aerosol in-situ laboratory at CIAO (CNR IMAA Atmospheric Observatory) in South Italy, outlining its configuration and detailing each instrument and sampling lines. The CIAO observatory has been collecting observations of atmospheric components since 2000. Initially the activities revolved around aerosol lidar, later radiosounding and cloud remote sensing observations were added over the years and made CIAO a leading atmospheric observatory in the Mediterranean region. In 2018, a significant upgrade started for enhancing the observational capability by adding aerosol in-situ instruments, with the objective to push new research boundaries for aerosol characterization and multi-instrumental synergistic approaches. Here, we describe each technical implementation step for building up an extensive aerosol in-situ laboratory compliant with ACTRIS (Aerosol Clouds and Trace gases Research InfraStructure) standard operating procedures. Starting from scratch, the long path initiated in 2018, with the design of the laboratory in terms of instruments, container organization, inlets and sampling lines optimizations, that required time and interactions with experts in the field. Reporting here all the details about the final solutions implemented at CIAO, this paper will be, for new aerosol in-situ laboratory, a practical guide for the implementation of the aerosol in-situ observational site. [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]

Respiratory malignancies, encompassing cancers affecting the lungs, the trachea, and the bronchi, pose a significant and dynamic public health challenge. Given that air pollution stands as a significant contributor to the onset of these ailments, discerning the most detrimental agents becomes imperative for crafting policies aimed at mitigating exposure. This study advocates for the utilization of explainable artificial intelligence (XAI) methodologies, leveraging remote sensing data, to ascertain the primary influencers on the prediction of standard mortality rates (SMRs) attributable to respiratory cancer across Italian provinces, utilizing both environmental and socioeconomic data. By scrutinizing thirteen distinct machine learning algorithms, we endeavor to pinpoint the most accurate model for categorizing Italian provinces as either above or below the national average SMR value for respiratory cancer. Furthermore, employing XAI techniques, we delineate the salient factors crucial in predicting the two classes of SMR. Through our machine learning scrutiny, we illuminate the environmental and socioeconomic factors pertinent to mortality in this disease category, thereby offering a roadmap for prioritizing interventions aimed at mitigating risk factors. [ABSTRACT FROM AUTHOR]

Understanding the response of salt marshes to flooding is crucial to foresee the fate of these fragile ecosystems, requiring an upscaling approach. In this study we related plant species and community response to multispectral indices aiming at parsing the power of remote sensing to detect the environmental stress due to flooding in lagoon salt marshes. We studied the response of Salicornia fruticosa (L.) L. and associated plant community along a flooding and soil texture gradient in nine lagoon salt marshes in northern Italy. We considered community (i.e., species richness, dry biomass, plant height, dry matter content) and individual traits (i.e., annual growth, pigments, and secondary metabolites) to analyze the effect of flooding depth and its interplay with soil properties. We also carried out a drone multispectral survey, to obtain remote sensing-derived vegetation indices for the upscaling of plant responses to flooding. Plant diversity, biomass and growth all declined as inundation depth increased. The increase of soil clay content exacerbated flooding stress shaping S. fruticosa growth and physiological responses. Multispectral indices were negatively related with flooding depth. We found key species traits rather than other community traits to better explain the variance of multispectral indices. In particular stem length and pigment content (i.e., betacyanin, carotenoids) were more effective than other community traits to predict the spectral indices in an upscaling perspective of salt marsh response to flooding. We proved multispectral indices to potentially capture plant growth and plant eco-physiological responses to flooding at the large scale. These results represent a first fundamental step to establish long term spatial monitoring of marsh acclimation to sea level rise with remote sensing. We further stressed the importance to focus on key species traits as mediators of the entire ecosystem changes, in an ecological upscaling perspective. [ABSTRACT FROM AUTHOR]

The main purpose of this paper was to study the effect of spatial variability of soil hydraulic properties (HP) and vegetation parameters (VP) (e.g., leaf-area index, LAI, and crop coefficient, Kc) on modelling agro-hydrological processes and optimising irrigation volumes at large scale. Based on this analysis, the effect of partly overlooking the spatial variability of soil HP and/or VP inputs was verified on a 140 ha irrigation sector in "Sinistra Ofanto" irrigation system in Apulia Region, Southern Italy. Five soil profiles were excavated and the HP were measured in all the soil horizons. Additionally, measurements of soil HP were taken in the surface soil layer in ninety sites distributed over the whole irrigation sector. All the HP measurements were carried out using tension infiltrometer. Remote sensing applications were used to obtain LAI and Kc using European Space Agency's (ESA) Sentinel-2 images with 10 m resolutions. First, distributed (on ninety polygons with an average area of about 1.5 ha) optimal irrigation volumes and related deep percolation volumes at a depth of 80 cm, were computed using an agro-hydrological model and accounting for the actual observed variability of soil HP and VP inputs. The sector scale irrigation and deep percolation volumes were obtained by aggregating the distributed irrigation volumes. This was considered as the reference scenario (hereafter DVS—Detailed Variability Scenario). Then, reduced variability scenarios (hereafter RVS—Reduced Variability Scenario) were considered, where the information on the actual spatial variability of the soil HP and VP was gradually overlooked to find the minimum data set needed to still have sector scale irrigation volumes and related deep percolation volumes comparable to those obtained under the DVS. Results showed that overlooking VP (RVS-VP) variability did not significantly change the optimal irrigation volumes and the deep percolation fluxes. By contrast, neglecting the HP variability (RVS-HP) showed significant effects on both the irrigation and percolation volumes compared to the DVS. The main practical finding was that, at least for the area investigated in this study, hydraulic characterization of one soil profile in an area of approximately 30 ha provides sector scale irrigation volumes and percolation fluxes comparable to those obtained under the DVS, thus by accounting for all the observed local variability. [ABSTRACT FROM AUTHOR]

• A multimodal computational approach was used for the 3D survey of a panel painting. • Data from close-range photogrammetry and laser microprofilometry were fused. • The warping arrow before and after restoration was quantified on the 3D point cloud. • Multi-temporal monitoring of the painting is computed via Cloud-to-Cloud distance. • Micrometric roughness of paint layers is measured by laser microprofilometry. This paper reports the results obtained from the workflow designed for the 3D survey of a Renaissance panel painting using close-range photogrammetry and conoscopic laser microprofilometry, before and after its restoration. The artwork under examination is the central panel of a Renaissance polyptych realized by a late Gothic painter active in central Italy in the late 15th century. This painting has had a rather troubled conservation history following numerous adverse events that have severely damaged it structurally. In preparation for restoration work, the fusion of point cloud data derived from active and passive sensors served to create a single multi-resolution model on which to conduct advanced metrological analyses for multi-temporal monitoring of the changes undergone by the panel painting during and after structural restoration. [ABSTRACT FROM AUTHOR]

Mapping soil organic carbon (SOC) stock can serve as a resilience indicator for climate change. As part of the carbon dioxide (CO2) sink, soil has recently become an integral part of the global carbon agenda to mitigate climate change. We used hyperspectral remote sensing to model the SOC stock in the Sele River plain located in the Campania region in southern Italy. To this end, a soil spectral library (SSL) for the Campania region was combined with an aerial hyperspectral image acquired with the AVIRIS–NG sensor mounted on a Twin Otter aircraft at an altitude of 1433 m. The products of this study were four raster layers with a high spatial resolution (1 m), representing the SOC stocks and three other related soil attributes: SOC content, clay content, and bulk density (BD). We found that the clay minerals' spectral absorption at 2200 nm has a significant impact on predicting the examined soil attributes. The predictions were performed by using AVIRIS–NG sensor data over a selected plot and generating a quantitative map which was validated with in situ observations showing high accuracies in the ground-truth stage (OC stocks [RPIQ = 2.19, R2 = 0.72, RMSE = 0.07]; OC content [RPIQ = 2.27, R2 = 0.80, RMSE = 1.78]; clay content [RPIQ = 1.6 R2 = 0.89, RMSE = 25.42]; bulk density [RPIQ = 1.97, R2 = 0.84, RMSE = 0.08]). The results demonstrated the potential of combining SSLs with remote sensing data of high spectral/spatial resolution to estimate soil attributes, including SOC stocks. [ABSTRACT FROM AUTHOR]

Monitoring soil organic carbon (SOC) typically assumes conducting a labor-intensive soil sampling campaign, followed by laboratory testing, which is both expensive and impractical for generating useful, spatially continuous data products. The present study leverages the power of machine learning (ML) and, in particular, deep neural networks (DNNs) for segmentation, as well as satellite imagery, to estimate the SOC remotely. We propose a new two-stage pipeline for remote SOC estimation, which relies on using a DNN trained to classify land cover to perform feature extraction, while the SOC estimation is performed by a different ML model. The first stage is an image segmentation DNN with the U-Net architecture, which is trained to estimate the land cover for an observed geographical region, based on multi-spectral images taken by the Sentinel-2 satellite constellation. This estimator is subsequently used to extract the latent feature vector for each of the output pixels, by rolling back from the output (dense) layer of the U-Net and accessing the last available convolutional layer of the same dimension as our desired output. The second stage is trained on a set of feature vectors extracted at the coordinates for which manual SOC measurements exist. We tested a variety of ML models and report on their performance. Using the best extremely randomized trees model, we generated a spatially continuous map of SOC estimations for the region of Tuscany, in Italy, with a resolution of 10 m, to share with the researchers as a means of validating the results and to demonstrate the efficiency of the proposed approach, which can can easily be scaled to create a global continuous SOC map. [ABSTRACT FROM AUTHOR]

In the summer of 2017, huge wildfires in the British Columbia region (Canada) led to the injection of a remarkably high concentration of biomass burning aerosol in the atmosphere. These aerosol masses reached the city of Naples, Italy, at the end of August 2017, where they were characterized by means of a multiwavelength lidar and a sun–sky–lunar photometer. Here we report on the optical and microphysical properties of this aerosol in an intriguing condition, occurring on 4 September 2017, which is characterized by an interesting multi-layered vertical distribution of the aerosol. The Lidar profiles highlighted the presence of four aerosol layers, with two located in the lower troposphere and the other two at stratospheric altitudes. A rather thorough characterization of the biomass burning aerosol was carried out. The aerosol depolarization ratio showed an increasing dependence on the altitude with averaged values of 2–4% for the tropospheric layers, which are indicative of almost spherical smoke particles, and larger values in the stratospheric layers, suggestive of aspheric particles. Lidar-derived size distributions were retrieved for the first three aerosol layers, highlighting a higher particle concentration in the fine-mode fraction for the layers observed at higher altitudes. A dominance of fine particles in the atmosphere (fine-mode fraction > 0.8) with low absorption properties (absorption AOD < 0.0025 and SSA > 0.97) was also observed over the whole atmospheric column by sun photometer data. The space-resolved results provided by the lidar data are consistent with the columnar features retrieved by the AERONET sun photometer, thus evidencing the reliability and capability of lidar characterization of atmospheric aerosol in a very interesting condition of multiple aerosol layers originating from Canadian fires overpassing the observation station. [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]

The study focuses on the integrated use of multiscale and multisensor remote sensing techniques and big data analysis for the identification of buried archaeological remains or areas of potential archaeological interest. Satellite multispectral data (at very high and high resolution), drone based visible, multispectral, and thermal imagery, and geophysical prospecting (gradiometer) were used. The ancient city of Metaponto was chosen as case study, as it was a very important city in the formative panorama of Italian Magna Graecia and it also is one of the most important and best preserved archaeological sites in southern Italy. The analysis of remote sensing data from different sensors, with different resolutions, and referable to different physical parameters, allowed to deepen archaeological knowledge on a landscape scale, as well as on a site scale, going from the analysis of traces of the ancient landscape (e.g. palaeo-channels, canalisation system, main roads), to the discovery of small features (e.g. secondary roads, houses, facilities). [ABSTRACT FROM AUTHOR]

Lake Avernus is a volcanic lake located in southern Italy. Since ancient times, it has inspired numerous myths and legends due to the occurrence of singular phenomena, such as coloring events. Only recently has an explanation been found for them, i.e., the recurring color change over time is due to the alternation of cyanobacterial blooms that are a consequence of natural nutrient inputs as well as pollution resulting from human activities. This current report specifically describes the red coloring event that occurred on Lake Avernus in March 2022, the springtime season in this region of Italy. Our innovative multidisciplinary approach, the 'Fast Detection Strategy' (FDS), was devised to monitor cyanobacterial blooms and their toxins. It integrates remote sensing data from satellites and drones, on-site sampling, and analytical/bioinformatics analyses into a cohesive information flow. Thanks to FDS, we determined that the red color was attributable to a bloom of Planktothrix rubescens, a toxin-producing cyanobacterium. Here, we report the detection and identification of 14 anabenopeptins from this P. rubescens strain, seven of which are known and seven are newly reported herein. Moreover, we explored the mechanisms and causes behind this cyclic phenomenon, confirming cyanobacteria's role as reliable indicators of environmental changes. This investigation further validates FDS's effectiveness in detecting and characterizing cyanobacterial blooms and their associated toxins, expanding its potential applications. [ABSTRACT FROM AUTHOR]

Deep-seated gravitational slope deformations (DsGSDs) are slope-scale phenomena which are widespread in mountainous regions. Despite interacting with human infrastructures and settlements, only a few cases are monitored with ground-based systems. Remote sensing technologies have recently become a consolidated instrument for monitoring and studying such widespread and slow processes. This paper proposes a three-step novel methodology to analyze the morpho-structural domain of DsGSDs by exploiting the advanced Differential Synthetic Aperture Radar Interferometry (A-DInSAR) technique through (i) the analysis of A-DInSAR measurement point density and distribution defining a coverage threshold; (ii) the assessment of the actual ground deformation with respect to the orientation of phenomena based on slope, aspect, and C-index; and (iii) ground deformation mapping with previously ranked velocity interpolation. The methodology was tested on two differently oriented phenomena: the mainly north–south-oriented Croix de Fana and the mainly east–west-oriented Valtournenche DsGSD, located in the Aosta Valley Region, northern Italy. The results show a variation in the kinematic behavior between the morpho-structural domains, while also considering any other superimposed surficial deformations. This work provides the lines for the implementation of a rapid and low-cost tool based on the use of A-DInSAR measurements which are suitable for assessing the impact of any type of DsGSD on the anthropic facilities and infrastructures in mountainous areas. [ABSTRACT FROM AUTHOR]

State-of-the-art snow sensing technologies currently provide an unprecedented amount of data from both remote sensing and ground sensors, but their assimilation into dynamic models is bounded to data quality, which is often low – especially in mountain, high-elevation, and unattended regions where snow is the predominant land-cover feature. To maximize the value of snow-depth measurements, we developed a random forest classifier to automatize the quality assurance and quality control (QA/QC) procedure of near-surface snow-depth measurements collected through ultrasonic sensors, with particular reference to the differentiation of snow cover from grass or bare-ground data and to the detection of random errors (e.g., spikes). The model was trained and validated using a split-sample approach of an already manually classified dataset of 18 years of data from 43 sensors in Aosta Valley (northwestern Italian Alps) and then further validated using 3 years of data from 27 stations across the rest of Italy (with no further training or tuning). The F1 score was used as scoring metric, it being the most suited to describe the performances of a model in the case of a multiclass imbalanced classification problem. The model proved to be both robust and reliable in the classification of snow cover vs. grass/bare ground in Aosta Valley (F1 values above 90 %) yet less reliable in rare random-error detection, mostly due to the dataset imbalance (samples distribution: 46.46 % snow, 49.21 % grass/bare ground, 4.34 % error). No clear correlation with snow-season climatology was found in the training dataset, which further suggests the robustness of our approach. The application across the rest of Italy yielded F1 scores on the order of 90 % for snow and grass/bare ground, thus confirming results from the testing region and corroborating model robustness and reliability, with again a less skillful classification of random errors (values below 5 %). This machine learning algorithm of data quality assessment will provide more reliable snow data, enhancing their use in snow models. [ABSTRACT FROM AUTHOR]

This paper presents a study on applying satellite Interferometric Synthetic Aperture Radar (InSAR) technology for the remote monitoring of road bridges and interpreting the results from a structural standpoint. The motivation behind this study arises from the widespread deterioration observed in many road bridges worldwide, leading to the need for large-scale, economic, and effective structural health monitoring (SHM) techniques. While traditional contact-type sensors have cost sustainability limitations, remote sensing techniques, including satellite-based InSAR, offer interesting alternative solutions. The objective of this study is three-fold: (i) to process InSAR data specifically for road bridges in operational conditions through the Multi-Temporal InSAR technique and extract displacement time series of reflective targets on their decks; (ii) to interpret the observed millimetric bridge displacements to verify the consistency with expected response to environmental loads and the possibility to detect unexpected behaviours; and (iii) to investigate the correlation between bridge displacements and environmental loads as temperature and river water flow variations. The study focuses on the multi-span prestressed concrete A22 Po River Bridge in Italy, utilising a dataset of X-Band HIMAGE mode Stripmap images acquired over eight years by the satellite constellation COSMO-SkyMed. The study demonstrates the effectiveness of InSAR-based SHM in detecting temperature-induced displacements and identifying different bridge spans simply by studying the sign of the correlation between displacements and temperature variation. It also reveals an unexpected behaviour in a portion of the bridge retrofitted to prevent scour issues a few years before the dataset start date. Furthermore, the correlation between pier displacements and river level variations underscores the importance of considering environmental factors and the geotechnical characteristics of the foundation soils in bridge monitoring. The results obtained from this study are significant with a view to using this satellite InSAR-based monitoring for early detection of anomalous bridge behaviour on a large scale. [ABSTRACT FROM AUTHOR]

Knowledge of actual crop transpiration (T) is important for advanced crop management but challenging to obtain due to the large spatial and temporal variation of T. Remote sensing offers various possibilities to assess T dynamics, while particularly sun-induced chlorophyll fluorescence (SIF) has been demonstrated as a sensitive empirical proxy for T. Despite this success, the advancement of the mechanistic understanding of how SIF relates to T dynamics is key for the future development and implementation of robust and reliable SIF-based T products. This study aims to contribute insights by experimentally assessing the sensitivity of several SIF-based T estimation strategies for evolving soil water limitation. We investigated extensive in situ and airborne data acquired during a water limitation experiment in a maize canopy in northern Italy. We evaluated five empirical strategies to integrate SIF in a T modelling framework based on the Penman-Monteith (PM) and the Ball-Berry-Leuning (BBL) concepts. Our results indicate that replacing model parameters sensitive to canopy conductance with SIF results in the best agreement between modelled and measured T under evolving water limitation. Our study contributes expanding existing knowledge with empirical insights on the sensitivity of SIF based T approaches under increasing soil water limitation at short time scales. [ABSTRACT FROM AUTHOR]

Hydrothermal alteration processes can affect the physical and chemical properties of volcanic rocks and develop via complex degassing and fluid flow systems and regimes. Although alteration can have far-reaching consequences for rock stability and permeability, little is known about the detailed structures, extent, and dynamic changes that take place in hydrothermal venting systems. By combining drone-based remote sensing with mineralogical and chemical analyses of rock and gas samples, we analyzed the structure and internal anatomy of a dynamic evolving volcanic degassing and alteration system at the La Fossa cone, Vulcano Island (Italy). From drone image analysis, we revealed a ~70,000 m² sized area subject to hydrothermal activity, for which we could determine distinct alteration gradients. By mineralogical and geochemical sampling of the zones of those alteration gradients, we study the relation between surface coloration and mineralogical and chemical composition. With increasing pixel brightness towards higher alteration gradients, we find a loss of initial mineral fraction and bulk chemical composition and a simultaneous gain in sulfur content. Using this approach, we defined and spatially constrained alteration units and compared them to the present-day thermally active surface and degassing pattern. The combined results permit us to present a detailed anatomy of the La Fossa fumarole field, highlighting 7 major units of alteration and present-day diffuse activity that, next to the high-temperature fumaroles, significantly contribute to the total activity. [ABSTRACT FROM AUTHOR]

The relationships between hillslope and fluvial processes were studied in a mountainous area of the Northern Apennines (Italy) where intermittent landslide activity has interacted for a long time with river morphodynamics. The aim of the study was to analyse such relationships in two study sites of the Scoltenna catchment. The sites were analysed in detail and monitored through time. A long-term analysis was carried out based on multitemporal photointerpretation of aerial photos. Slope morphological changes and land use modifications since 1954 were detected and compared with the evolution of the channel morphology. A short-term analysis was also performed based on two monitoring campaigns accomplished in 2021 and 2022 in order to detect possible slope displacements and channel-bed-level changes. The techniques used are global navigation satellite systems and drone photogrammetry accompanied by geomorphological surveys and mapping. The multitemporal data collected allowed us to characterise slope surface deformations and quantify morphological changes. The combination of various techniques of remote and proximal sensing proved to be a useful tool for the analysis of the surface deformations and for the investigation of the interaction between slope and fluvial dynamics, showing the important role of fluvial processes in the remobilisation of the landslide toe causing the displacement of a significant volume of sediment into the stream. [ABSTRACT FROM AUTHOR]

In Italy, the potential of geophysical prospection for the characterization of archaeological landscapes, especially in previously unexplored rural or formerly urban areas, has rarely been acknowledged or seriously tested in the field, leaving the character and density of rural settlement across time virtually unknown outside areas favourable to aerial survey. It is obvious, however, that the open countryside must conceal a wide variety of archaeological information, difficult to detect and analyse within an Italian research framework largely dominated in the past by field‐walking survey and artefact collection. In response, this article presents some initial results and practical lessons from a programme of large‐scale magnetic survey aimed at the initial exploration of the lowland landscape of the Grosseto‐Roselle valley, a few kilometres inland from the Adriatic coast, in south‐western Tuscany. It is hoped that the results briefly illustrated here will encourage others to undertake similar work elsewhere in Italy and around the Mediterranean as part of a move away from an essentially 'site‐based' approach towards a truly 'landscape' perspective. [ABSTRACT FROM AUTHOR]

Clouds cover substantial parts of the Earth's surface and they are one of the most essential components of the global climate system impacting the Earth's radiation balance as well as the water cycle redistributing water around the globe as precipitation. Therefore, continuous observation of clouds is of primary interest in climate and hydrological studies. This work documents the first efforts in Italy in remote sensing clouds and precipitation using a combination of K- and W-band (24 and 94 GHz, respectively) radar profilers. Such a dual-frequency radar configuration has not been widely used yet, but it could catch on in the near future given its lower initial cost and ease of deployment for commercially available systems at 24 GHz, with respect to more established configurations. A field campaign running at the Casale Calore observatory at the University of L'Aquila, Italy, nestled in the Apennine mountain range is described. The campaign features are preceded by a review of the literature and the underpinning theoretical background that might help newcomers, especially in the Italian community, to approach cloud and precipitation remote sensing. This activity takes place in interesting time for radar sensing clouds and precipitation, stimulated both by the launch of the ESA/JAXA EarthCARE satellite missions scheduled in 2024, which will have on-board, among other instruments, a W-band Doppler cloud radar and the proposal of new missions using cloud radars currently undergoing their feasibility studies (e.g., WIVERN and AOS in Europe and Canada, and U.S., respectively). [ABSTRACT FROM AUTHOR]

Large-scale slow-moving deep-seated landslides are complex and potentially highly damaging phenomena. The detection of their dynamics in terms of displacement rate distribution is therefore a key point to achieve a better understanding of their behavior and support risk management. Due to their large dimensions, ranging from 1.5 to almost 4 km2, in situ monitoring is generally integrated using satellite and airborne remote sensing techniques. In the framework of the EFRE-FESR SoLoMon project, three test-sites located in the Autonomous Province of Bolzano (Italy) were selected for testing the possibility of retrieving significant slope displacement data from the analysis of multi-temporal airborne optic and light detection and ranging (LiDAR) surveys with digital image correlation (DIC) algorithms such as normalized cross-correlation (NCC) and phase correlation (PC). The test-sites were selected for a number of reasons: they are relevant in terms of hazard and risk; they are representative of different type of slope movements (earth-slides, deep seated gravitational slope Deformation and rockslides), and different rates of displacement (from few cm/years to some m/years); and they have been mapped and monitored with ground-based systems for many years (DIC results can be validated both qualitatively and quantitatively). Specifically, NCC and PC algorithms were applied to high-resolution (5 to 25 cm/px) airborne optic and LiDAR-derived datasets (such as hillshade and slope maps computed from digital terrain models) acquired during the 2019–2021 period. Qualitative and quantitative validation was performed based on periodic GNSS surveys as well as on manual homologous point tracking. The displacement maps highlight that both DIC algorithms succeed in identifying and quantifying slope movements of multi-pixel magnitude in non-densely vegetated areas, while they struggle to quantify displacement patterns in areas characterized by movements of sub-pixel magnitude, especially if densely vegetated. Nonetheless, in all three landslides, they proved to be able to differentiate stable and active parts at the slope scale, thus representing a useful integration of punctual ground-based monitoring systems. [ABSTRACT FROM AUTHOR]

Volcanic plume height is one the most important features of explosive activity; thus, it is a parameter of interest for volcanic monitoring that can be retrieved using different remote sensing techniques. Among them, calibrated visible cameras have demonstrated to be a promising alternative during daylight hours, mainly due to their low cost and low uncertainty in the results. However, currently these measurements are generally not fully automatic. In this paper, we present a new, interactive, open-source MATLAB tool, named 'Plume Height Analyzer' (PHA), which is able to analyze images and videos of explosive eruptions derived from visible cameras, with the objective of automatically identifying the temporal evolution of eruption columns. PHA is a self-customizing tool, i.e., before operational use, the user must perform an iterative calibration procedure based on the analysis of images of previous eruptions of the volcanic system of interest, under different eruptive, atmospheric and illumination conditions. The images used for the calibration step allow the computation of ad hoc expressions to set the model parameters used to recognize the volcanic plume in new images, which are controlled by their individual characteristics. Thereby, the number of frames used in the calibration procedure will control the goodness of the model to analyze new videos/images and the range of eruption, atmospheric, and illumination conditions for which the program will return reliable results. This also allows improvement of the performance of the program as new data become available for the calibration, for which PHA includes ad hoc routines. PHA has been tested on a wide set of videos from recent explosive activity at Mt. Etna, in Italy, and may represent a first approximation toward a real-time analysis of column height using visible cameras on erupting volcanoes. [ABSTRACT FROM AUTHOR]

Wedge-top basins represent useful tectonic elements for the characterisation of the evolution of their underlying accretionary wedge in space and time, as their final state of deformation sums up the bulk shortening and structural instability conditions of the wedge. Here, we present the geometric and kinematic patterns of deformation structures deforming the wedge-top Epiligurian basins of the Northern Apennines (Italy). Our main goals are to generate an evolutionary model to account for the syn- to post-orogenic evolution of the Epiligurian basins and to infer the building style of the Northern Apennines wedge during continental collision. Mesoscale structural analysis shows that common and widely distributed thrust and normal fault arrays deform the entire Epiligurian stratigraphic succession infilling the broadly E-vergent wedge-top basins. Thrusts are invariably cut by later NW–SE and NE-SW-striking normal and oblique fault systems characterised by fault planes that mutually intersect at all scales to form polygonal patterns. Remote sensing analysis of the tectonic structures affecting the Epiligurian formations confirms the variable orientation of both thrusts and normal faults within the different studied stratigraphic successions. As a whole, results suggest a polyphase tectonic evolution of the Epiligurian wedge-top basins during the widening of the Northern Apennines accretionary wedge towards the foreland by frontal accretion. The recognised main phases are: (i) syn-orogenic compression accommodating overall tectonic transport towards the eastern quadrants; (ii) post-orogenic extension genetically related to the extension of the inner zone of the Northern Apennines; (iii) more recent extension forming collapse-induced normal faults spatially arranged in polygonal patterns. [ABSTRACT FROM AUTHOR]

Forest fires burn various natural ecosystems worldwide and can harm the environment and human life. Accordingly, real-time monitoring of this phenomenon and early decision-making warning is vital. Active remote sensing systems, such as Synthetic Aperture Radar (SAR) sensors, provide an excellent opportunity for burn mapping because they can penetrate through clouds and smoke day and night. In this study, the potential of Sentinel-1 SAR data was investigated by deploying a deep convolutional neural network (CNN) based framework to map burn progression dynamically, in both supervised and transfer manners. Accordingly, an optimized deep architecture was designated to use SAR data features with high sensitivity to map the burned areas. The proposed method includes three main steps: 1) extraction of SAR indices, 2) training deep CNN model with a limited number of scenes and 3) assessing the transferability of the CNN for estimating burn progression for any unseen scene. Sentinel-1 SAR indices of log-ratio, radar burn difference (RBD), and difference of dual-polarization SAR vegetation index (ΔDPSVI) were obtained to be fed to the CNN. To validate the efficiency of the proposed approach, two fire events, i.e. the Derazno fire in Iran (2021) and the Rossomanno-Grottascura-Bellia fire in Italy (2017), were considered. For the scenes including training samples, the proposed method improved the overall accuracies (OAs) of classical machine learning techniques (i.e. SVM and RF) significantly (more than 4%). However, the improvement was minor when compared to a CNN using only log-ratio as the input channel (log-ratio CNN). For the scenes without training samples (unseen dates), the investigated transferred model performed substantially better (3% higher OA) compared to the other machine learning methods and the log-ratio CNN. This finding approves that the obtained SAR indices boost the transferability of the CNN model for burn progression mapping at unseen scenes. [ABSTRACT FROM AUTHOR]

Punta Licosa promontory is located in the northern part of the Cilento coast, in the southern Tyrrhenian basin. This promontory is bordered by sea cliffs connected to a wide shore platform sloping slightly towards the sea. This area has been considered stable at least since Late Pleistocene, as testified by a series of evidence well known in the literature. The aim of this research is to reconstruct the main coastal changes that have occurred in this area since the middle Holocene by means of the literature data, aerial photo interpretation, satellite images, GPS measurements, direct underwater surveys, GIS elaborations of high-resolution DTMs, bathymetric data and high-resolution orthophotos taken by UAV. Particular attention was paid to the wide platform positioned between −7.2 ± 1.2 m MSL and the present MSL, this being the coastal landform interpreted as the main consequence of sea cliff retreat. The elevation of this landform was compared with the GIA models calculated for the southern Tyrrhenian area, allowing establishing that it was shaped during the last 7.6 ± 1.1 ky BP. Moreover, the interpretation of archaeological and geomorphological markers led to the reconstruction of the shoreline evolution of this coastal sector since 7.6 ky BP. This research evaluates the cliff retreat under the effect of Holocene RSL variation on Cilento promontories, located in the western Mediterranean and characterised by the presence of monophasic platforms, and the applied method can be considered more effective and less complex and expensive if compared to other effective approaches such as those based on the usage of cosmogenic nuclides. [ABSTRACT FROM AUTHOR]

The widespread diffusion of the wild boar on the Italian territory and its consistent use for hunting have created the possibility to conduct multiple studies on the pathologies afflicting this ungulate. Nevertheless, in the last two decades, only some pathologies such as classical and African Swine Fever, Tuberculosis, Brucellosis from Brucella suis have benefited from substantial public funding and the consequent great interest from the scientific world, while less attention was addressed to parasitic diseases including sarcoptic mange. Therefore, to fill this gap, the purpose of this study was to contribute to the knowledge of sarcoptic mange in the wild boar population in Aosta Valley in the Northwest of Italy, including sympatric species as foxes. Due to past field surveys, it has been possible to find a possible role of snow metrics in the spread of this pathogen. Even if there are only empirical evidence and the mechanism remain unknown remote sensing analysis considering snow metrics were performed to provide to veterinarians, foresters, biologists, and ecologists new tools to better understand wield board dynamics and join to ordinary tool an instrument to enhance management and planning strategies. The snow metrics (SM) were derived from USGS NASA Landsat 8 L2A retrieved from Theia CNES platform and processed in Orfeo Toolbox LIS extension package. The relationship between SM and the disease spread was tested per each Aosta Valley municipality obtaining LISA maps for each hunting season. The results have showed that this parasite is present in an endemic form even if with rather low prevalence values, equal to 1.2% in the season hunting season 2013/2014, and equal to 7.5% in the hunting season 2014/2015. Moreover, within simultaneous given values of SM, sarcoptic mange seem to find good conditions for spreading. [ABSTRACT FROM AUTHOR]

Afforestation is one of the most effective processes for removing carbon dioxide from the atmosphere and combating global warming. Landsat data and machine learning approaches can be used to map afforestation (i) indirectly, by constructing two maps of the same area over different periods and then predicting changes, or (ii) directly, by constructing a single map and analyzing observations of change in both the response and remotely sensed variables. Of crucial importance, no comprehensive comparisons of direct and indirect approaches for afforestation monitoring are known to have been conducted to date. Afforestation maps estimated through the analysis of remotely sensed data may serve as intermediate products for guiding the selection of samples and the production of statistics. In this and similar studies, a huge effort is dedicated to collecting validation data. In turn, those validation datasets have varying sampling intensities in different areas, which complicates their use for assessing the accuracies of new maps. As a result, the work done to collect data is often not sufficiently exploited, with some validation datasets being used just once. In this study, we addressed two main aims. First, we implemented a methodology to reuse validation data acquired via stratified sampling with strata constructed from remote sensing maps. Second, we used this method for acquiring data for comparing map accuracy estimates and the precision of estimates for direct and indirect approaches for country-wide mapping of afforestation that occurred in Italy between 1985 and 2019. To facilitate these comparisons, we used Landsat imagery, random forest classification, and Google Earth Engine. The herein-presented method produced different accuracy estimates with 95% confidence interval and for different map classes. Afforestation accuracies ranged between 53 ± 5.9% for the indirect map class inside the buffer—defined as a stratum within 120 m of the forest/non-forest mask boundaries—and 26 ± 3.4% for the direct map outside the buffer. The accuracy in non-afforestation map classes was much greater, ranging from 87 ± 1.9% for the indirect map inside the buffer to 99 ± 1.3% for the direct map outside the buffer. Additionally, overall accuracies (with 95% CI) were estimated with large precision for both direct and indirect maps (87 ± 1.3% and 89 ± 1.6%, respectively), confirming (i) the effectiveness of the method we introduced for reusing samples and (ii) the relevance of remotely sensed data and machine learning for monitoring afforestation. [ABSTRACT FROM AUTHOR]

Structural Health Monitoring (SHM) represents a very powerful tool to assess the health condition of buildings. In recent years, the growing availability of high-resolution SAR satellite images has made possible the application of multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques for structural monitoring purposes, with high precision, low costs, timesaving, and the possibility to investigate wide areas. However, a comprehensive validation of the effectiveness of MT-InSAR in this application field has not been achieved yet. For this reason, in this paper a comparison between interferometric data and on-site measurement of displacements is proposed. The application case study is the Civic Tower of the city of L'Aquila (Abruzzo Region, Italy). After the seismic events that affected the area in 2009, an on-site monitoring system was installed on the tower to detect any changes in the damage pattern in the period 2010–2013. Furthermore, images acquired by COSMO-SkyMed constellation in Stripmap mode (~3 m resolution) during the same period were processed by the Permanent Scatterer-InSAR (PSI) technique to estimate the deformation of the structure and the surrounding area. The obtained results indicate that both methods are consistent in the measurement of displacement trends of the building and a slight rotation/displacement of the tower was detected. Such evidence highlights both the huge potential and the limitations of using InSAR techniques for SHM. [ABSTRACT FROM AUTHOR]

Forest ecosystems' structure and biomass monitoring are crucial for understanding the contribution of forests to the global greenhouse gas balance. NASA's Global Ecosystem Dynamics Investigation (GEDI) mission collects waveform lidar data to estimate Above Ground Biomass Density (AGBD). While of great interest, GEDI data are challenging to download and pre-process and require coding expertise, limiting their usage. In this paper, we introduce GEDI4R, an open-source R package providing efficient methods for downloading, reading, clipping, visualizing, and exporting GEDI data. GEDI4R was tested over the whole of Italy, and more than 11 million GEDI pulses were downloaded in less than 10 hours. The GEDI pulse density in forests ranged between 132 per km2 (in the Friuli Venezia Giulia Italian administrative region) and 61 pulses per km2 (in Trentino Alto-Adige). A regional-level comparison between the official growing stock volume estimates reported in the last Italian forest inventory and the AGBD extracted from the GEDI data acquired over the forest revealed large correlations (r2 = 0.77). Our package facilitates the usage of GEDI AGBD data, which provides innovative information to monitor carbon cycle dynamics at the global scale. [ABSTRACT FROM AUTHOR]

Detection of change through remote sensing (RS) is widely used in Earth observation and environment surveys, whereas the introduction of bibliometric methods to the development, application, and identification of trends in RS change detection (RSCD) remain limited. Based on the 5,012 published academic studies in the Web of Science Core Collection (WOSCC) database between 2000 and 2022, and CiteSpace software, the publications built the RSCD knowledge mapping about cooperation network, literature co-citation, keyword co-occurrence, and burst detection analyses. The result shown that: (1) There has been a significant increasing trend in RSCD-related literature over the last two decades. Among these, Remote Sensing and IEEE journals had the highest number of publications. (2) China, the United States, and Italy, are the top three in the number of publications, mainly by various universities and research institutes. Bruzzone Lorenzo, Gong Maoguo, Bovolo Francesca, and other authors have made important contributions. (3) Among highly cited literature, the "changed object" was the first focus of CD research, and 17 research clusters were identified, including semantic, terrain correction, land cover, synthetic aperture radar, and the unsupervised. (4) Main research topics included CD models, unsupervised CD algorithms, and land cover classification. Research hots included deep learning, misregistration, image segmentation, and Google Earth Engine. This study provided the multidimensional references for researchers, practitioners, and institutions in the current trends, topics, and hots of RSCD. [ABSTRACT FROM AUTHOR]

As reflectance measured via remote sensing is connected to plant light use and morpho-structural features, it can be used to derive spectral proxies of functional traits, or spectro-functional traits. Focusing on disentangling intraspecific trait variability in nature, we evaluated the links between haplotype and spectro-functional traits in Phragmites australis populations. Haplotypes sequencing and multi-seasonal satellite data were used to evaluate the temporal dynamics of spectro-functional traits for reed stands sampled from seven wetlands in Central Italy, investigating meteo-climatic drivers, the differences across ecological statuses, sites, and haplotypes, and quantifying intraspecific variability due to haplotype or phenotypic plasticity. Five haplotypes were identified, including an unedited one, which explained a substantial portion of intraspecific variability in canopy traits, differing for aquatic and terrestrial stands. We found that meteo-climatic factors impact on aquatic reeds traits (not over terrestrial ones) and a dualism between most and less common haplotypes, pointing to different evolutionary strategies. Dynamics in reed canopy traits were linked to ecological status, site and haplotype, with signs of haplotype-variable effects of dieback on aquatic stands. Evaluating the spectro-functional variability over reed haplotypes may provide a straightforward approach for monitoring the genotype–phenotype relations across scales and assessing their ecological drivers. [ABSTRACT FROM AUTHOR]

Earth flows are complex gravitational events characterised by a heterogeneous displacement pattern in terms of scale, style, and orientation. As a result, their monitoring, for both knowledge and emergency purposes, represents a relevant challenge in the field of engineering geology. This paper aims to assess the capabilities, peculiarities, and limitations of different remote sensing monitoring techniques through their application to the Pietrafitta earth flow (Southern Italy). The research compared and combined data collected during the main landslide reactivations by different ground-based remote sensors such as Robotic Total Station (R-TS), Terrestrial Synthetic Aperture Radar Interferometry (T-InSAR), and Terrestrial Laser Scanner (TLS), with data being derived by satellite-based Digital Image Correlation (DIC) analysis. The comparison between R-TS and T-InSAR measurements showed that, despite their different spatial and temporal resolutions, the observed deformation trends remain approximately coherent. On the other hand, DIC analysis was able to detect a kinematic process, such as the expansion of the landslide channel, which was not detected by the other techniques used. The results suggest that, when faced with complex events, the use of a single monitoring technique may not be enough to fully observe and understand the processes taking place. Therefore, the limitations of each different technique alone can be solved by a multi-sensor monitoring approach. [ABSTRACT FROM AUTHOR]

Afforestation processes, natural and anthropogenic, involve the conversion of other land uses to forest, and they represent one of the most important land use transformations, influencing numerous ecosystem services. Although remotely sensed data are commonly used to monitor forest disturbance, only a few reported studies have used these data to monitor afforestation. The objectives of this study were two fold: (1) to develop and illustrate a method that exploits the 1985–2019 Landsat time series for predicting afforestation areas at 30 m resolution at the national scale, and (2) to estimate afforestation areas statistically rigorously within Italian administrative regions and land elevation classes. We used a Landsat best-available-pixel time series (1985–2019) to calculate a set of temporal predictors that, together with the random forests prediction technique, facilitated construction of a map of afforested areas in Italy. Then, the map was used to guide selection of an estimation sample dataset which, after a complex photointerpretation phase, was used to estimate afforestation areas and associated confidence intervals. The classification approach achieved an accuracy of 87%. At the national level, the afforestation area between 1985 and 2019 covered 2.8 ± 0.2 million ha, corresponding to a potential C-sequestration of 200 million t. The administrative region with the largest afforested area was Sardinia, with 260,670 ± 58,522 ha, while the smallest area of 28,644 ± 12,114 ha was in Valle d'Aosta. Considering elevation classes of 200 m, the greatest afforestation area was between 400 and 600 m above sea level, where it was 549,497 ± 84,979 ha. Our results help to understand the afforestation process in Italy between 1985 and 2019 in relation to geographical location and altitude, and they could be the basis of further studies on the species composition of afforestation areas and land management conditions. [ABSTRACT FROM AUTHOR]

During forensic archaeological research conducted in 2021 in the Comacchio Lagoon (FE), we detected new data concerning the ancient local hydrography to the west of Argine Agosta, an ancient bank, in Valle Fossa di Porto. A systematic campaign of geophysical surveys for heritage and archaeology was conducted with a hydrographic drone equipped with a single-beam bathymetric sensor echo sounder, sub-bottom profiler, and side-scan sonar. In combination with the indirect non-invasive investigations, systematic surveys of the walkable areas were also completed. These remote-sensing studies have relocated the River Vatrenus palaeo-watercourse, while visual census have brought to light a system of wooden palisades with floors made of wood associated with some scattered finds belonging to the material culture dating back to the Mediaeval and Renaissance periods. A preview of the results of this case study is presented here, contributing to the reconstruction of the ancient landscape and waterscape area of the Valle Fossa di Porto. [ABSTRACT FROM AUTHOR]

Earth observation data are useful to analyze the impact of climate-related variables on geomorphological processes. This work aims at evaluating the impact of rainfall on slow-moving landslides, by means of a quantitative procedure for identifying satellite-based displacement clusters, comparing them with rainfall series, and applying statistical tests to evaluate their relationships at the regional scale. The chosen study area is the Basento catchment in the Basilicata region (southern Italy). Rainfall series are gathered from rain gauges and are analyzed to evaluate the presence of temporal trends. Ground displacements are obtained by applying the P-SBAS (Parallel Small BAseline Subset) to three datasets of Sentinel-1 images: T146 ascending orbit, and T51 and T124 descending orbits, for the period 2015–2020. The displacement series of the pixels located in areas mapped as landslides by the Italian Landslide Inventory and sited within rain gauge influence regions (defined as 10 km circular buffers) are studied. Those displacement series are analyzed and compared to the rainfall series to search for correlations, by employing statistical and non-parametric tests. In particular, two landslides are selected and investigated in detail. Significant results were obtained for the T124 descending orbit for both landslides, for a 3-day cumulative rainfall and a 7-day delay of the slope response. Challenges in the whole procedure are highlighted and possible solutions to overcome the raised problems are proposed. Given the replicability of the proposed quantitative procedure it might be applied to any study area. [ABSTRACT FROM AUTHOR]

The agricultural sector, the largest and least efficient water user, is facing important challenges in sustaining food production and careful water use. The objective of this study is to improve farm and irrigation district water use efficiency by developing an operational procedure for smart irrigation and optimizing the exact water use and relative water productivity. The SIM (smart irrigation monitoring and forecasting) optimization irrigation strategy, based on soil moisture (SM) and crop stress thresholds, was implemented in the Chiese (North Italy) and Capitanata (South Italy) Irrigation Consortia. The system is based on the energy–water balance model FEST-EWB (Flashflood Event-based Spatially distributed rainfall runoff Transformation Energy–Water Balance model), which was pixelwise calibrated with remotely sensed land surface temperature (LST), with mean areal absolute errors of approximately 3 °C, and then validated against local measured SM and latent heat flux (LE) with RMSE values of approximately 0.07 and 40 Wm−2, respectively. The effect of the optimization strategy was evaluated on the reductions in irrigation volume and on the different timing, from approximately 500 mm over the crop season in the Capitanata area to approximately 1000 mm in the Chiese district, as well as on cumulated drainage and ET fluxes. The irrigation water use efficiency (IWUE) indicator appears to be higher when applying the SIM strategy than when applying the traditional irrigation strategy: greater than 35% for the tomato fields in southern Italy and 80% for maize fields in northern Italy. [ABSTRACT FROM AUTHOR]

Background. Volunteers' participation in scientific research has increased in recent decades. Citizen science (CS) data have been used in quantitative ecology to analyse species ranges by means of species distribution models. We investigated the Italian distribution of five large saproxylic beetles (big five), to describe their niche space, paramount areas for their conservation, and conservation gaps. Methods. CS data from two projects, climate and environmental variables were used to produce Habitat suitability (HS) maps for each species and averaged HS maps. The big five's conservation status was assessed interpolating HS maps with the distribution of protected areas, concomitantly identifying conservation gaps. Results. The pre-alpine and Apennines arcs, north-eastern Sicily and eastern Sardinia, were identified as conservation's hotspots. Ranking HS levels from minimum to optimal, the extent of conservation gaps decreases as environmental suitability for the big five increases. Conclusions. For the first time in Italy, CS data have been used to investigate niche space of the largest protected saproxylic beetles and analyse the distribution of their suitable habitat. The resulting HS raster maps and vector layers, reporting HS value in all Italian protected areas (n° 3771), were provided and discussed, reporting an application example for conservation purposes. [ABSTRACT FROM AUTHOR]

The percentage of discoveries due to traditional and non-traditional searches for homicide graves is still meager in Italy. A critical exam of the scientific approaches followed by Italian law enforcements, in recent cases of unsuccessful ground searches for homicide graves, should be seriously done for evidencing the possible errors done. In order to assist law enforcements in the ground searches, the present research reports on: i) the appearance of a clandestine grave with the related main characteristics; ii) the factors influencing the choice of the burial site; iii) the modern approaches usable during ground search activities as proposed by UK experts; iv) the main results of remote sensing investigations and the Red-Amber-Green search prioritization system carried out for the search for three different clandestine graves. [ABSTRACT FROM AUTHOR]

A hyperspectral image is classically a three-way (or tensor) block of data. In order to extract information from it, it has to be classified using image classifiers. Since classifiers are traditionally twoway classifiers, the hyperspectral data is unfolded into a two-way data. Processing the hyperspectral data as a two-way data often reduces the accuracy of the classification. This research explores the novel application of Tensor Partial Least Squares (TPLS) for hyperspectral image classification. TPLS has been proven to be more robust than the two-way PLS. Unlike the two-way classifiers, the TPLS utilises the hyperspectral data as a three-way (tensor) data. Two hyperspectral images of Indian Pines region in Northwest Indiana, USA and University of Pavia, Italy are used as test beds for the experiment. The results extracted by the model are the X loadings, Y loadings, X scores, and Y scores. The computed training mean r square values for Indian Pines and University of Pavia are 0:90616±74, and 0:91556±63 respectively. The results of the experiment show that the TPLS performed better than the unfolded PLS, but fell short of the notable traditional classifiers. [ABSTRACT FROM AUTHOR]

We report on the characterization of columnar aerosol optical and microphysical properties in the Naples Mediterranean area over a period of five years by the ground-based sun–sky–lunar photometer operating at our observational station in the frame of the AERONET network. Statistical and climatological analyses of daily mean values of aerosol optical depth at 440 nm (AOD440) and Ångström exponent at 440/870 nm (α440/870) allowed for highlighting their typical seasonal behavior. In particular, we observe higher mean values of the AOD440 during summer or spring, which are consistent with an increased frequency of both Saharan dust transport events and biomass burning episodes affecting the measurement area in these periods of the year. Conversely, α440/870 does not show any typical seasonal trend. In order to gain information on the different aerosol contributions along the atmospheric column, the frequency distributions of AOD440 and α440/870 were analyzed and fitted by a superposition of Gaussian functions. The most populated modes are centered at α440/870 = 1.26 ± 0.07 and AOD440 = 0.16 ± 0.01. These values are associated with continental polluted aerosol mixed with sea salt aerosol and correspond to the background conditions typically observed in clear atmospheric conditions. Daily size distributions averaged over each month highlight that the fine aerosol component always prevails over the coarse fraction, except for the few months in which Saharan dust events are particularly frequent. Finally, the mean value of the SSA at 440 nm resulted as 0.94 ± 0.05, while the refractive index real and imaginary part were 1.47 ± 0.07 and (6.5 ± 0.2) × 10−3, respectively. These values are in agreement with those observed in other Mediterranean sites located in Southern Italy, evidencing a rather characteristic feature of the geographical region. [ABSTRACT FROM AUTHOR]

The occurrence of extreme windstorms and increasing heat and drought events induced by climate change leads to severe damage and stress in coniferous forests, making trees more vulnerable to spruce bark beetle infestations. The combination of abiotic and biotic disturbances in forests can cause drastic environmental and economic losses. The first step to containing such damage is establishing a monitoring framework for the early detection of vulnerable plots and distinguishing the cause of forest damage at scales from the management unit to the region. To develop and evaluate the functionality of such a monitoring framework, we first selected an area of interest affected by windthrow damage and bark beetles at the border between Italy and Austria in the Friulian Dolomites, Carnic and Julian Alps and the Carinthian Gailtal. Secondly, we implemented a framework for time-series analysis with open-access Sentinel-2 data over four years (2017–2020) by quantifying single-band sensitivity to disturbances. Additionally, we enhanced the framework by deploying vegetation indices to monitor spectral changes and perform supervised image classification for change detection. A mean overall accuracy of 89% was achieved; thus, Sentinel-2 imagery proved to be suitable for distinguishing stressed stands, bark-beetle-attacked canopies and wind-felled patches. The advantages of our methodology are its large-scale applicability to monitoring forest health and forest-cover changes and its usability to support the development of forest management strategies for dealing with massive bark beetle outbreaks. [ABSTRACT FROM AUTHOR]

Calibrating land surface phenology (LSP) with tree rings is important to model spatio-temporal variations in forest productivity. We used MODIS (resolution: 250 m) NDVI, WDRVI and EVI series 2000–2014 to derive LSP metrics quantifying phenophase timing and canopy photosynthetic rates of 26 European beech forests covering a large thermal gradient (5–16 °C) in Italy. Average phenophase timing changed greatly with site temperature (e.g. growing season 70 days longer at low- than high-elevation); average VI values were affected by precipitation. An annual temperature about 12 °C (c. 1100 m asl) represented a bioclimatic threshold dividing warm from cold beech forests, distinguished by different phenology-BAI (basal area increment) relationships and LSP trends. Cold forests showed decreasing VI values (browning) and delayed phenophases and had negative BAI slopes. Warmer forests tended to increase VI (greening), and positive BAI slopes. NDVI peak, commonly used in global trend assessments, changed with elevation in agreement with changes in wood production. A cross-validation modelling approach demonstrated the ability of LSP to predict average BAI and its interannual variability. Merging sites into bioclimatic groups improved models by amplifying the signal in growth or LSP. NDVI had highest performances when informing on BAI trends; WDRVI and EVI were mostly selected for modelling mean and interannual BAI. WDRVI association with tree rings, tested in this study for the first time, showed that this VI is highly promising for studying forest dynamics. MODIS LSP can quantify forest functioning changes across landscapes and model interannual spatial variations and trends in productivity dynamics under climate change. [ABSTRACT FROM AUTHOR]

Fully-autonomous vehicles, both aerial and ground, could provide great benefits in the Agriculture 4.0 framework when operating within cooperative architectures, thanks to their ability to tackle difficult tasks, particularly within complex irregular and unstructured scenarios such as vineyards on sloped terrains. A decentralised multi-phase approach has been proposed as an alternative to more common cooperative schemes. When perennial crops are considered, it is advantageous to build a simplified geometrical (and georeferenced) crops model, which can be identified by using 3D point clouds acquired during a-priori explorative missions by unmanned aerial vehicles. This model can be used to plan the tasks to be performed within the crops by the in-field aerial and ground drones. In this companion paper, the proposed strategy is applied to a specific case study involving a vineyard on a sloped terrain, located in the Barolo region in Piedmont, Italy. Ad-hoc technologies and guidance, navigation and control algorithms were designed and implemented. The main objectives were to improve the autonomous driving capabilities of the drones involved and to automate the process of retrieving low-complexity maps from the data collected with preliminary remote sensing missions to make them available for the autonomous navigation by a quadrotor and an unmanned 4-wheel steering ground vehicle within the vine rows. Preliminary results highlight the benefits achievable by exploiting the tailored technologies selected and applied to improve each of the analysed mission phases. • Autonomous ground and aerial vehicles can lead to improve agriculture management. • A collaborative scheme based on a multi-phase approach is proposed. • A specific case study involving a vineyard located in the Barolo is presented. • Design of advanced GNC schemes are proposed for autonomous navigation of drones. [ABSTRACT FROM AUTHOR]

Land subsidence affects many areas of the world, posing a serious threat to human structures and infrastructures. It can be effectively monitored using ground-based and remote sensing techniques, such as the Global Navigation Satellite System (GNSS) and Interferometric Synthetic Aperture Radar (InSAR). GNSS provides high precision measurements, but in a limited number of points, and is time-consuming, while InSAR allows one to obtain a very large number of measurement points, but only in areas characterized by a high and constant reflectivity of the signal. The aim of this work is to propose an approach to combine the two techniques, overcoming the limits of each of them. The approach was applied in the Po River Delta (PRD), an area located in Northern Italy and historically affected by land subsidence. Ground-based GNSS data from three continuous stations (CGNSS) and 46 non-permanent sites (NPS) measured in 2016, 2018, and 2020, and Sentinel-1 and COSMO-SkyMed SAR data acquired from 2016 to 2020, were considered. In the first phase of the method, InSAR processing was calibrated and verified through CGNSS measurements; subsequently, the calibrated interferometric data were used to validate the GNSS measurements of the NPS. In the second phase, the datasets were integrated to provide an efficient monitoring system, extracting high-resolution deformation maps. The results showed a good agreement between the different sources of data, a high correlation between the displacement rate and the age of the emerged surfaces composed of unconsolidated fine sediments, and high land subsidence rates along the coastal area (up to 16–18 mm/year), where the most recent deposits outcrop. The proposed approach makes it possible to overcome the disadvantages of each technique by providing more complete and detailed information for a better understanding of the ongoing phenomenon. [ABSTRACT FROM AUTHOR]

Although hail is a well-known meteorological hazard, it is hard to find long records of hail observed at the ground with high spatial resolution. Most hail climatologies are based on remote sensing observations or inhomogeneous networks of human observers. In the plain of Friuli Venezia Giulia (northeast Italy), a hailpad network of 367 stations has operated since 1988. During the 1988–2016 warm seasons, 7782 hailpads were impacted by hailstones and more than one million dents were observed and automatically analyzed, even though only 63% of them were associated with valid hailstone dents. In this work, this large quantity of direct hail observations is used to build a hail climatology in terms of hailstone size, areal density, and flux of kinetic energy. From the empirical distributions of data collected, it is possible to fit statistical distributions to the different hailstone/hailpad behaviors. In particular, it is also possible to find an approximate estimation of the flux of kinetic energy based only on the largest hail diameter observed on the hailpad. Last, temporal and spatial distributions of these characteristics are investigated. Hailstones are larger along a southwestern-to-northeastern alley, which is parallel to the main pre-Alpine crest, with the very largest sizes being more frequent on the southwestern corner. The only hail climate change signal that one can infer from the analysis of these multidecadal trends is that, in more recent years, hailstorms seem to produce fewer and larger hailstones, on average. [ABSTRACT FROM AUTHOR]

As there is an urgent need to protect rapidly declining global diversity, it is important to identify methods to quickly estimate the diversity and heterogeneity of a region and effectively implement monitoring and conservation plans. The combination of remotely sensed and field-collected data, under the paradigm of the Spectral Variation Hypothesis (SVH), represents one of the most promising approaches to boost large-scale and reliable biodiversity monitoring practices. Here, the potential of SVH to capture information on plant diversity at a fine scale in an ecological network (EN) embedded in a complex landscape has been tested using two new and promising methodological approaches: the first estimates α and β spectral diversity and the latter ecosystem spectral heterogeneity expressed as Rao's Quadratic heterogeneity measure (Rao's Q). Both approaches are available thanks to two brand-new R packages: "biodivMapR" and "rasterdiv". Our aims were to investigate if spectral diversity and heterogeneity provide reliable information to assess and monitor over time floristic diversity maintained in an EN selected as an example and located in northeast Italy. We analyzed and compared spectral and taxonomic α and β diversities and spectral and landscape heterogeneity, based on field-based plant data collection and remotely sensed data from Sentinel-2A, using different statistical approaches. We observed a positive relationship between taxonomic and spectral diversity and also between spectral heterogeneity, landscape heterogeneity, and the amount of alien species in relation to the native ones, reaching a value of R2 = 0.36 and R2 = 0.43, respectively. Our results confirmed the effectiveness of estimating and mapping α and β spectral diversity and ecosystem spectral heterogeneity using remotely sensed images. Moreover, we highlighted that spectral diversity values become more effective to identify biodiversity-rich areas, representing the most important diversity hotspots to be preserved. Finally, the spectral heterogeneity index in anthropogenic landscapes could be a powerful method to identify those areas most at risk of biological invasion. [ABSTRACT FROM AUTHOR]

In the second half of the twentieth century, after the end of the Second World War, a considerable anthropogenic pressure was observed in most of the Mediterranean territories. This process has caused the expansion of existing settlements and the construction of numerous new towns, often located very close to rivers. A frequent consequence of this process is the transformation of several rivers through planform changes, narrowing, channelization and culverting to recover spaces where inhabited centers expanded, and the construction of structures interacting with rivers. This issue is very important in territories such as the Metropolitan City of Reggio Calabria, in southern Italy, which is an interesting case study due to the considerable anthropogenic pressures observed in the last 70 years. The main goal of this paper is to evaluate the effects of anthropogenic pressure in the last 70 years on some rivers of the Metropolitan City of Reggio Calabria in terms of the following issues: planform changes, channelization, culverting, and the presence of structures and infrastructures interacting with rivers. The specific goals of this paper are the quantification of the effects of anthropogenic pressure on the rivers of the study area analyzing sixteen parameters, the identification of possible conditions of hydraulic hazard through the analysis of past events, and the proposal of structural and non-structural mitigation interventions. In many rivers of the study area, the significant effects of anthropogenic pressure are visible through rivers that pass above highways, barred rivers, rivers replaced by roads and numerous crossing roads with a missing levee. [ABSTRACT FROM AUTHOR]

In the last decade, research on Corylus avellana has focused on improving field techniques and hazelnut quality; however, climatic change and sustainability goals call for new agronomic management strategies. Precision management technologies could help improve resource use efficiency and increase grower income, but research on remote sensing systems and especially on drone devices is still limited. Therefore, the hazelnut is still linked to production techniques far from the so-called Agriculture 4.0. Unmanned aerial vehicles platforms are becoming increasingly available to satisfy the demand for rapid real-time monitoring for orchard management at spatial, spectral, and temporal resolutions, addressing the analysis of geometric traits such as canopy volume and area and vegetation indices. The objective of this study is to define a rapid procedure to calculate geometric parameters of the canopy, such as canopy area and height, by methods using NDVI and CHM values derived from UAV images. This procedure was tested on the young Corylus avellana tree to manage a hazelnut orchard in the early years of cultivation. The study area is a hazelnut orchard (6.68 ha), located in Bernalda, Italy. The survey was conducted in a six-year-old irrigated hazelnut orchard of Tonda di Giffoni and Nocchione varieties using multispectral UAV. We determined the Projected Ground Area and, on the Corylus avellana canopy trough, the vigor index NDVI (Normalized Difference Vegetation Index) and the CHM (Canopy Height Model), which were used to define the canopy and to calculate the tree crown area. The projection of the canopy area to the ground measured with NDVI values > 0.30 and NDVI values > 0.35 and compared with CHM measurements showed a statistically significant linear regression, R2 = 0.69 and R2 = 0.70, respectively. The ultra-high-resolution imagery collected with the UAV system helped identify and define each tree crown individually from the background (bare soil and grass cover). Future developments are the construction of reliable relationships between the vigor index NDVI and the Leaf Area Index (LAI), as well as the evaluation of their spatial-temporal evolution. [ABSTRACT FROM AUTHOR]

Natural disasters have a significant impact on urban areas, resulting in loss of lives and urban services. Using satellite and aerial imagery, the rapid and automatic assessment of at-risk located buildings from can improve the overall disaster management system of urban areas. To do this, the definition, and the implementation of models with strong generalization, is very important. Starting from these assumptions, the authors proposed a deep learning approach based on the U-Net model to map buildings that fall into mapped landslide risk areas. The U-Net model is trained and validated using the Dubai's Satellite Imagery Dataset. The transferability of the model results are tested in three different urban areas within Calabria Region, Southern Italy, using natural color orthoimages and multi-source GIS data. The results show that the proposed methodology can detect and predict buildings that fall into landslide risk zones, with an appreciable transferability capability. During the prevention phase of emergency planning, this tool can support decision-makers and planners with the rapid identification of buildings located within risk areas, and during the post event phase, by assessing urban system conditions after a hazard occurs. [ABSTRACT FROM AUTHOR]