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The Italian national territory is characterised by the widespread presence of cavities dating back to different periods, especially in urban areas. The lack of knowledge of the position of the entrances, planimetric developments and state of preservation contributes to accentuating the unknowns related to sinkhole risk, which are directly related to potential cavity collapses with the opening of surface chasms. To deepen knowledge with a view to risk mitigation, a method has been developed to employ surveys obtained from Unmanned Aerial Vehicles (UAVs) to locate entrances even in hard-to-access urban areas. These surveys, properly supported with GNSS stations, were then integrated with cavity surveys obtained from low-cost lidar mounted on iPhones. Comparisons were made with traditional surveying techniques to better understand the reliability of the surveys made with low-cost lidar. The 3D models obtained, combined with geomechanical surveys of the rock masses hosting the cavities, allowed the application of simplified and empirical methods for an initial stability assessment. This method was tested on a portion of the municipality of Grotte di Castro (Province of Viterbo—Italy). [ABSTRACT FROM AUTHOR]

How accurately do Sentinel-2 (S2) images describe vine row spatial variability? Can they produce effective management zones (MZs) for precision viticulture? S2 and UAV datasets acquired over two years for different drip-irrigated vineyards in the Colli Morenici region (northern Italy) were used to assess the actual need to use UAV-NDVI maps instead of S2 images to obtain effective MZ maps. First, the correlation between S2 and UAV-NDVI values was investigated. Secondly, contingency matrices and dichotomous tables (considering UAV-MZ maps as a reference) were developed to compare MZ maps produced using S2 and UAV imagery. Moreover, data on grape production and quality were analyzed through linear discrimination analyses (LDA) to evaluate the effectiveness of S2-MZs and UAV-MZs to explain spatial variability in yield and quality data. The outcomes highlight that S2 images can be quite good tools to manage fertilization based on the within-field vigor variability, of which they capture the main features. Nevertheless, as S2-MZs with low and high vigor were over-estimated, S2-MZ maps cannot be used for high-accuracy input management. From the LDA results, the UAV-MZs appeared slightly more performant than the S2-MZs in explaining the variability in grape quality and yield, especially in the case of low-vigor MZs. [ABSTRACT FROM AUTHOR]

Landslides are one of the most hazardous secondary effects of earthquakes due to the potential for large-scale damage and long-term alterations to landscapes. During the 2016–2017 seismic sequence in Central Italy, many earthquake-triggered landslides (EQTLs) affected the road network and mountain trails. In this study, a methodological approach for analysing EQTLs, based on data derived from Unmanned Aerial Vehicle (UAV) surveys, is shown. The approach is applied to investigate the geometric, structural, geomechanical, and kinematic features of the Foce rockslide, which is introduced in the back analysis. The investigation involved three main steps: (i) set up of UAV-based Virtual Outcrop Models (VOMs) of the slope, (ii) a geomechanical characterisation of the rock mass through the VOM interpretation and conventional field data, and (iii) 3D Limit Equilibrium (LE) slope stability analyses. This study highlights the potential of UAV surveys for providing valuable data for stability analyses, especially in emergency conditions such as in the aftermath of seismic events. [ABSTRACT FROM AUTHOR]

The occurrence of sinkhole phenomena in Italy is a prevalent and very uncertain class of geological hazards that pose a significant threat to human infrastructure and individuals. These events are characterized by their unpredictability and the challenges associated with their accurate forecasting. Both natural and anthropic factors influence the occurrence of these events; therefore, accurate identification of the above factors is critical for effective proactive and predictive efforts. The work presented in this paper refers to a collapse that occurred in a volcanic region in northern Latium (central Italy) on 31 January 2023. The area has been monitored using drones since the early stages of the sinkhole's formation and has continued to date. Then, the collapse and the neighboring area were examined via geophysical and geochemical investigations to identify potential underlying factors. Geophysical and geochemical data were combined to provide a preliminary hypothesis on the collapse's genesis. The obtained data indicate that the structural collapse can be attributable to the fluctuation in groundwater levels as well as the development of instabilities along its banks, leading to a growth in its dimensions. [ABSTRACT FROM AUTHOR]

Italy is famous for its one-of-a-kind landscapes and the many cultural heritage sites characterizing the story of its regions. In central Italy, during the medieval age, some of them were built on the top of high and steep cliffs, often on the top of ancient ruins, to protect urban agglomerations, goods and people. The geographical locations of these centers allowed them to maintain their original conformation over time, but, at the same time, exposed them to a high risk of landslides. In this context, this research aimed to present an integrated and low-cost approach to study the potential landslide phenomena affecting two medieval towns. Field surveys and mapping were carried out through the use of innovative digital mapping tools to create a digital database directly on the field. Data gathered during field surveys were integrated with GIS analyses for an improved interpretation of the geological and geomorphological features. Due to the inaccessibility of the cliffs surrounding the two villages, a more detailed analysis of these areas was performed through the use of unmanned aerial vehicle-based photogrammetry, while advanced differential synthetic aperture radar interferometry (A-DInSAR) interpretation was undertaken to verify the stability of the buildings in proximity to the cliffs and other potential active failures. The results of the study highlighted the similar geometry and structural settings of the two areas. Kinematically, the intersection of three main joint sets tends to detach blocks (sometimes in high volumes) from the cliffs. The A-DInSAR analysis demonstrated the presence of a landslide failure along the northwest side of the Monte San Martino town. The buildings in proximity to the cliffs did not show evidence of movements. More generally, this research gives insights into the pro and cons of different survey and analysis approaches and into the benefits of their procedural integration in space and in time. Overall, the procedure developed here may be applied in similar contexts in order to understand the structural features driving slopes' instabilities and create digital databases of geological/monitoring data. [ABSTRACT FROM AUTHOR]

This work describes a procedure for building a high-quality 3D model of a rocky pinnacle in the Dolomites, Italy, using Structure from Motion (SfM) techniques. The pinnacle, known as "Campanile di Val Montanaia", is challenging to survey due to its high elevation and sub-vertical cliffs. The construction of the 3D model is the first step in a multi-disciplinary approach to characterize the rock mass and understand its behavior and evolution. This paper discusses the surveying operations, which involved climbing the pinnacle to collect Ground Control Points (GCPs) and using a UAV to capture aerial imagery. The photographs were processed using SfM software to generate point clouds, mesh, and texture, which were then used for rock mass discontinuity mapping. The study compares models of different qualities and point densities to determine the optimal trade-off between processing time and accuracy in terms of discontinuity mapping. The results show that higher quality models allow for more detailed mapping of discontinuities, with some drawbacks due to noise in the case of the densest solution (e.g., increase in frequency of outliers across the point cloud). These pros and cons are also discussed in relation to the computational cost necessary to build the models. The study also examines the limitations and challenges of performing discontinuity mapping in the different models, including subjectivity in interpretation. A further element of interest is the publication of a high-quality 3D georeferenced model of the "Campanile di Val Montanaia" to be used for several potential further applications, such as stability analyses and numerical modeling. [ABSTRACT FROM AUTHOR]

The coastal domain of central western Sicily is characterized by the presence of rocky coasts, which mainly consist of pocket beaches situated between bedrock headlands that constitute ecological niches of great touristic and economic value. In this peculiar morphodynamic system, the sedimentary contributions are mainly derived from the rockfall that affects the back of nearby cliffs or the sediment supply of small streams that flow into it. In this study, we investigated the geomorphological processes and related landforms that contribute to the evolution of a pocket beach area located in a coastal sector of NW Sicily Island. The cliffs in this are affected by several rockfalls, and deposits from these rockfalls also add to the rate of sedimentary contribution. The analysis was conducted through the application of traditional approaches and contemporary methods that have previously been used to forecast the collection of input data in the field, often under difficult conditions due to the accessibility of the sites, and which have been supported by UAV surveys. Through the analysis of the digital models of terrain and orthophotos, geometrical and multitemporal analyses of landforms were carried out. A dedicated software was utilized for the detection of rockfall runout zones and block trajectories and for defining the automatic extraction of rock mass discontinuities. The data were compared with those derived from traditional geomechanical surveys. The availability of the existing and acquired remote sensing data proved essential for this study for both defining the reference geological model and for performing the site-specific analysis of rockfall. [ABSTRACT FROM AUTHOR]

Knowledge of forest biomass is an essential parameter for managing the forest in a sustainable way, as forest biomass data availability and reliability are necessary for forestry and forest planning, but also for the carbon market as well as to support the local economy in the mountain and inner areas. However, the accurate quantification of the above-ground biomass (AGB) is still a challenge both at the local and global levels. The use of remote sensing techniques with Unmanned Aerial Vehicle (UAV) platforms can be an excellent trade-off between resolution, scale, and frequency data of AGB estimation. In this study, we evaluated the combined use of RGB images from UAV, LiDAR data and ground truth data to estimate AGB in a forested watershed in Southern Italy. A low-cost AGB estimation method was adopted using a commercial fixed-wing drone equipped with an RGB camera, combined with the canopy information derived by LiDAR and validated by field data. Two modelling methods (stepwise regression, SR and random forest, RF) were used to estimate forest AGB. The output was an accurate maps of AGB for each model. The RF model showed better accuracy than the Steplm model, and the R2 increased from 0.81 to 0.86, and the RMSE and MAE values were decreased from 45.5 to 31.7 Mg ha-1 and from 34.2 to 22.1 Mg ha-1 respectively. We demonstrated that by increasing the computing efficiency through a machine learning algorithm, readily available images can be used to obtain satisfactory results, as proven by the accuracy of the Random forest above biomass estimation model. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of river dynamics requires the grain size distribution of bed sediments and its variation across different temporal and spatial scales. Several techniques are already available for grain size assessment based on field and remotely sensed data. However, the existing methods are only applicable on small spatial scales and on short time scales. Thus, the operational measurement of grain size distribution of riverbed sediments at the catchment scale remains an open problem. A solution could be the use of satellite images as the main imaging platform. However, this would entail retrieving information at sub‐pixel scales. In this study, we propose a new approach to retrieve sub‐pixel scale grain size class information from Copernicus Sentinel‐2 imagery building upon a new image‐based grain size mapping procedure. Three Italian gravel‐bed rivers featuring different morphologies were selected for unmanned aerial vehicle (UAV) acquisitions, field surveys and laboratory analysis meant to serve as ground truth grain size data, ranging from medium sand to coarse gravel. Grain size maps on the river bars were generated in each study site by exploiting image texture measurements, upscaled and co‐registered with Sentinel‐2 data resolution. Relationships between the grain sizes measured and the reflectance values in Sentinel‐2 imagery were analysed using a machine learning framework. Results show statistically significant predictive models (MAE of ±8.34 mm and R2 = 0.92). The trained model was applied on 300 km of the Po River in Italy and allowed us to identify the gravel–sand transition occurring along this river length. Therefore, the approach presented here—based on freely available satellite data calibrated by low‐cost drone‐derived imagery—represents a promising step towards an automated surface mean grain size mapping over long river length, easily repeated through time for monitoring purposes. [ABSTRACT FROM AUTHOR]

Miscanthus holds a great potential in the frame of the bioeconomy, and yield prediction can help improve Miscanthus' logistic supply chain. Breeding programs in several countries are attempting to produce high-yielding Miscanthus hybrids better adapted to different climates and end-uses. Multispectral images acquired from unmanned aerial vehicles (UAVs) in Italy and in the UK in 2021 and 2022 were used to investigate the feasibility of high-throughput phenotyping (HTP) of novel Miscanthus hybrids for yield prediction and crop traits estimation. An intercalibration procedure was performed using simulated data from the PROSAIL model to link vegetation indices (VIs) derived from two different multispectral sensors. The random forest algorithm estimated with good accuracy yield traits (light interception, plant height, green leaf biomass, and standing biomass) using 15 VIs time series, and predicted yield using peak descriptors derived from these VIs time series with root mean square error of 2.3 Mg DM ha−1. The study demonstrates the potential of UAVs' multispectral images in HTP applications and in yield prediction, providing important information needed to increase sustainable biomass production. [ABSTRACT FROM AUTHOR]

As solar capacity installed worldwide continues to grow, there is an increasing awareness that advanced inspection systems are becoming of utmost importance to schedule smart interventions and minimize downtime likelihood. In this work we propose a novel automatic multi-stage model to detect panel defects on aerial images captured by unmanned aerial vehicle by using the YOLOv3 network and Computer Vision techniques. The model combines detections of panels and defects to refine its accuracy and exhibits an average inference time per image of 0.98 s. The main novelties are represented by its versatility to process either thermographic or visible images and detect a large variety of defects, to prescript recommended actions to O&M crew to give a more efficient data-driven maintenance strategy and its portability to both rooftop and ground-mounted PV systems and different panel types. The proposed model has been validated on two big PV plants in the south of Italy with an outstanding AP@0.5 exceeding 98% for panel detection, a remarkable AP@0.4 (AP@0.5) of roughly 88.3% (66.9%) for hotspots by means of infrared termography and a mAP@0.5 of almost 70% in the visible spectrum for detection of anomalies including panel shading induced by soiling and bird dropping, delamination, presence of puddles and raised rooftop panels. The model predicts also the severity of hotspot areas based on the estimated temperature gradients, as well as it computes the soiling coverage based on visual images. Finally an analysis of the influence of the different YOLOv3's output scales on the detection is discussed. [ABSTRACT FROM AUTHOR]

UAVs have become a useful tool for natural hazard monitoring. In volcanic areas, they allow wider observations of the eruptive behaviour, with no risk for the operator. The SfM technique enables obtaining orthoimages of lava flows and a DEM in a short time. These data are also useful to estimate lava flow volumes and the mass output rate characterizing an eruption. We present the results of ten UAV surveys made during and after the 30 May – 6 June 2019 eruption of Etna volcano, projecting the data in a time context back until 1999. Orthoimages taken on different days allowed monitoring the morpho-structural evolution of the fissures, capturing the lava flows propagation and the accumulation of pyroclastic deposits. From 1999 to 2018, there were nine flank-eruptions and dozens of summit-eruptions, which for graphic simplicity have been grouped by year in the map. The resulting map represents the most updated of the recent lava flows of Etna. [ABSTRACT FROM AUTHOR]

The aim of this paper is to identify a suitable methodology to realize, in an easy and quick way, 3D models of complex structures. To achieve this aim, the first step is to build the 3D model of the scene under investigation using photogrammetric modelling. This task was carried out by the use of algorithms based on Structure from Motion (SfM) - Multi View Stereo (MVS) approaches and using camera-generated images supplied in the UAS (Unmanned Aerial system). Once built the 3D point cloud of the structure under investigation, the geometry of each element was reconstructed with 3D profile reconstruction using Rhinoceros software and a few plug-ins developed in this software. Indeed, this paper shows, through a case study of a masonry bridge of special architectural and historic value built in the middle of 1800s and located in the south of Italy, the potential of the method developed in order to manage a maintenance or restoration project. [ABSTRACT FROM AUTHOR]

There exists a need to advance our understanding of debris‐covered glacier surfaces over relatively short timescales due to rapid, climatically induced areal expansion of debris cover at the global scale, and the impact debris has on mass balance. We applied unpiloted aerial vehicle structure‐from‐motion (UAV‐SfM) and digital elevation model (DEM) differencing with debris thickness and debris stability modelling to unravel the evolution of a 0.15 km2 region of the debris‐covered Miage Glacier, Italy, between June 2015 and July 2018. DEM differencing revealed widespread surface lowering (mean 4.1 ± 1.0 m a‐1; maximum 13.3 m a‐1). We combined elevation change data with local meteorological data and a sub‐debris melt model, and used these relationships to produce high resolution, spatially distributed maps of debris thickness. These maps were differenced to explore patterns and mechanisms of debris redistribution. Median debris thicknesses ranged from 0.12 to 0.17 m and were spatially variable. We observed localized debris thinning across ice cliff faces, except those which were decaying, where debris thickened. We observed pervasive debris thinning across larger, backwasting slopes, including those bordered by supraglacial streams, as well as ingestion of debris by a newly exposed englacial conduit. Debris stability mapping showed that 18.2–26.4% of the survey area was theoretically subject to debris remobilization. By linking changes in stability to changes in debris thickness, we observed that slopes that remain stable, stabilize, or remain unstable between periods almost exclusively show net debris thickening (mean 0.07 m a‐1) whilst those which become newly unstable exhibit both debris thinning and thickening. We observe a systematic downslope increase in the rate at which debris cover thickens which can be described as a function of the topographic position index and slope gradient. Our data provide quantifiable insights into mechanisms of debris remobilization on glacier surfaces over sub‐decadal timescales, and open avenues for future research to explore glacier‐scale spatiotemporal patterns of debris remobilization. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd [ABSTRACT FROM AUTHOR]

In this work, we present the results of a rockfall trajectory study performed on the south-western slope of Mt. Catiello (Sorrento Peninsula, southern Italy). Such a study develops within a multi-methodological approach which integrates different types of remote sensing data and techniques. Specifically, ground-truth data (e.g., rock mass geo-structural information, rock block inventory) were generated by geologically-supervised interpretations of high-resolution virtual outcrop models (VOMs). These data were then used for reconstructing the in-situ fractured rock mass attributes of the Mt. Catiello peak, as provided by a Discrete Fracture Network (DFN) model, and to prepare the subsequent numerical simulations of rockfall trajectories. The resulting rockfall scenarios are consistent with the ground-truth data, both in terms of size and spatial distribution. Thus, we believe that the proposed approach can be effectively applied to other areas, characterized by similar geological features but higher levels of exposure and vulnerability. • A multi-methodological approach for rockfall simulations: from slope face to valley • Integration of UAV-SfM and satellite-based data for rock mass and debris analysis • Obtaining ground-truth information from different types of virtual outcrop models [ABSTRACT FROM AUTHOR]

The Mt. Vettore area is located in the Central Apennines (Italy), a region characterized by intense seismic activity that has recorded multiple moderate-to-high magnitude seismic sequences. The seismic activity is due to the presence of normal fault systems, among which is the Mt. Vettore Fault System (VFS), which was last activated during the 2016-17 Central Italy seismic sequence.. Moreover, the region has experienced three major tectonic phases over geological history, thus it is important to unravel their contribution to the current fracture network. Based on the integration of field observation with Unmanned Aerial Vehicle - Digital Photogrammetry data, we aim to analyze the fracture network on eight different outcrops located at different structural positions with respect to VFZ. Results show that the Late Miocene−Early Pliocene compressional phase deeply affected the present-day fracture pattern, which is especially related to the evolution of the Mt. Sibillini regional thrust and its related anticline. The present-day Quaternary extensional phase, and the associated normal faults, mostly reactivate some of the pre-existing fracture sets. • Definition of the main fracture sets in the Jurassic Calcare Massiccio formation in the Mt. Vettore Area. • Definition of the fracture intensity variation in different structural position along the study area. • Definition of the cross cutting relation between the main fracture sets. [ABSTRACT FROM AUTHOR]

During the 2012-2013 excavations at the Sanctuary of Punta Stilo at Kaulonia, carried out by the University of Pisa and the Scuola Normale Superiore of Pisa, close range aerial and terrestrial photogrammetric surveys were tested for the first time. The aim of the test was to verify the accuracy of the site planimetry currently used, dating back also to a century ago. The 3D data obtained have allowed new data to be acquired for correcting and updating the mapping of the site. [ABSTRACT FROM AUTHOR]

The availability of high-resolution Digital Surface Models of coastal environments is of increasing interest for scientists involved in the study of the coastal system processes. Among the range of terrestrial and aerial methods available to produce such a dataset, this study tests the utility of the Structure from Motion (SfM) approach to low-altitude aerial imageries collected by Unmanned Aerial Vehicle (UAV). The SfM image-based approach was selected whilst searching for a rapid, inexpensive, and highly automated method, able to produce 3D information from unstructured aerial images. In particular, it was used to generate a dense point cloud and successively a high-resolution Digital Surface Models (DSM) of a beach dune system in Marina di Ravenna (Italy). The quality of the elevation dataset produced by the UAV-SfM was initially evaluated by comparison with point cloud generated by a Terrestrial Laser Scanning (TLS) surveys. Such a comparison served to highlight an average difference in the vertical values of 0.05 m (RMS = 0.19 m). However, although the points cloud comparison is the best approach to investigate the absolute or relative correspondence between UAV and TLS methods, the assessment of geomorphic features is usually based on multi-temporal surfaces analysis, where an interpolation process is required. DSMs were therefore generated from UAV and TLS points clouds and vertical absolute accuracies assessed by comparison with a Global Navigation Satellite System (GNSS) survey. The vertical comparison of UAV and TLS DSMs with respect to GNSS measurements pointed out an average distance at cm-level (RMS = 0.011 m). The successive point by point direct comparison between UAV and TLS elevations show a very small average distance, 0.015 m, with RMS = 0.220 m. Larger values are encountered in areas where sudden changes in topography are present. The UAV-based approach was demonstrated to be a straightforward one and accuracy of the vertical dataset was comparable with results obtained by TLS technology. [ABSTRACT FROM AUTHOR]

Transportation networks are severely affected by natural hazards, including landslides. The prioritization of maintenance works is required to preserve the efficiency and functionality of road infrastructure. To overcome the subjectivity of traditional visual inspections for road pavement condition assessment, advanced (semi-)automatic approaches have been emerging. Still, the quantitative and objective description of damage typology and extent, and its severity classification remain the major issues for the assessment of landslide impacts on transportation routes. The objective of this work is to provide a ready-to-use tool for semi-automatic damage assessment of asphalt-paved roads in landslide-affected areas to support risk analysis and planning of mitigation measures. The use of 3D models and 2D images as reconstructed from UAV-based photogrammetry is investigated to detect longitudinal and transverse cracks on the road pavement and assess their severity in landslide areas, as a rapid, systematic, objective and less laborious alternative to traditional field surveys. A semi-automatic procedure is proposed to i) select asphalt-paved roads exposed to landslides, ii) rapidly map distresses on selected road sections, iii) quantitatively detect and describe longitudinal and transverse cracks, and iv) classify their severity according to the International Roughness Index (IRI). The procedure is applied to the Province of Como (northern Italy), where three test sites are selected for detailed analyses. The results indicate that the proposed procedure is useful for road management purposes at different levels of details by providing four outputs: i) a road damage hotspot map to detect deformations, ii) a multi-criteria binary classifier to detect pavement damage, iii) an IRI-based criterion to rate the pavement quality, and iv) a road damage severity map. • The combination of 3D models and 2D images improves road damage characterization. • Semi-automatic methods detect heterogeneous deformations on asphalt road pavements. • The International Roughness Index is used to classify the road damage severity. • Drones flying at 10 m height detect fractures wider than 1 cm on road pavements. • Landslides can cause longitudinal and transverse cracks on asphalt-paved roads. [ABSTRACT FROM AUTHOR]

The 2016 Italian seismic sequence was characterized by three main shocks that occurred on August 24th, on October 26th and 30th. The latter, a Mw 6.5 event, is the strongest seismic event recorded in Italy since the 1980, Mw 6.9 Irpinia Earthquake. In this time span between the first and the third shock of the 2016 sequence, more than 1000 seismic events of Mw greater than 3.0 and shallow hypocentral depth between 7 and 11 km were recorded, triggering more than of 1300 observed landslides. In this paper, the identification of the triggering models for four selected rock slides was performed together with the mechanical characterization of the affected rock masses both in situ and in laboratory. Large scale morphostructural setting was analysed to verify the role of regional tectonics in the failures. Discontinuity sets were identified at detail-scale using three-dimensional (3D) point clouds developed from structure from motion (SfM) reconstruction using unmanned aerial vehicles (UAVs). In situ and laboratory tests coupled to data from UAV 3D models were used for geomechanical characterization. Data were finally adopted as input of limit equilibrium static analyses, which allowed a better comprehension of the stability conditions prior to seismic events and clarify some aspects of the mobilized strength. • 3D Rockslides models were obtained from UAV surveys. • Structure from Motion (SfM) permitted to characterize the discontinuty sets. • The tectonic pattern of the area is controlled by NW-SE Quaternary faults. • Rockslides are locally controlled by more ancient discontinuity systems. • Failures of rock bridges were involved in the sliding. [ABSTRACT FROM AUTHOR]

The utility of new imaging technologies to better understand hazardous geological environments cannot be overstated. The combined use of unmanned aerial vehicles (UAV) and digital photogrammetry (DP) represents a rapidly evolving technique that permits geoscientists to obtain detailed spatial data. This can aid in rapid mapping and analyses of dynamic processes that are modifying contemporary landscapes, particularly through the creation of a time series of digital data to help monitor the geomorphological evolution of volcanic structures. Our study comprises a short-term (in geological terms) monitoring program of the dynamic and diffuse Pisciarelli degassing structure caused by the interplay between intensive rainfall and hydrothermal activity. This area, an unstable fumarole field located several hundred meters east of the Solfatara Crater of the Campi Flegrei caldera (southern Italy), is characterized by consistent soil degassing, fluid emission from ephemeral vents, and hot mud pools. This degassing activity is episodically accompanied by seismic swarms and macroscopic morphology changes such as the appearance of vigorously degassing vents, collapsing landslides, and bubbling mud. In late-2019 and 2020, we performed repeated photogrammetric UAV surveys using the Structure from Motion (SfM) technique. This approach allowed us to create dense 3D point clouds and digital orthophotos spanning one year of surveys. The results highlight the benefits of photogrammetry data using UAV for the accurate remote monitoring and mapping of active volcanoes and craters in harsh and dangerous environments. [ABSTRACT FROM AUTHOR]

Xylella fastidiosa (Xf) is a well-known bacterial plant pathogen mainly transmitted by vector insects and is associated with serious diseases affecting a wide variety of plants, both wild and cultivated; it is known that over 350 plant species are prone to Xf attack. In olive trees, it causes olive quick decline syndrome (OQDS), which is currently a serious threat to the survival of hundreds of thousands of olive trees in the south of Italy and in other countries in the European Union. Controls and countermeasures are in place to limit the further spreading of the bacterium, but it is a tough war to fight mainly due to the invasiveness of the actions that can be taken against it. The most effective weapons against the spread of Xf infection in olive trees are the detection of its presence as early as possible and attacks to the development of its vector insects. In this paper, image processing of high-resolution visible and multispectral images acquired by a purposely equipped multirotor unmanned aerial vehicle (UAV) is proposed for fast detection of Xf symptoms in olive trees. Acquired images were processed using a new segmentation algorithm to recognize trees which were subsequently classified using linear discriminant analysis. Preliminary experimental results obtained by flying over olive groves in selected sites in the south of Italy are presented, demonstrating a mean Sørensen–Dice similarity coefficient of about 70% for segmentation, and 98% sensitivity and 93% precision for the classification of affected trees. The high similarity coefficient indicated that the segmentation algorithm was successful at isolating the regions of interest containing trees, while the high sensitivity and precision showed that OQDS can be detected with a low relative number of both false positives and false negatives. [ABSTRACT FROM AUTHOR]

The adoption of precision agriculture has the potential to increase the environmental sustainability of cropping systems as well as farmers' income. Farmers in transition to precision agriculture need low-input and effective protocols to delineate homogenous management zones to optimize their actions without past knowledge e.g., yield maps. Different approaches have been developed so far, based on the analysis of the within-field variability in crop and soil properties, but procedures were rarely suited for operational conditions. We identified here a low-inputs protocol to map management zones from soil electrical conductivity and/or crop vegetation indices, using a winter wheat field in northern Italy as a pilot case. The reliability of the alternative data sources was evaluated at three crop development stages using a yield map as reference. Red-edge and NIR (NDRE) bands were the most reliable data sources for management zones identification, with 62%, 68%, and 74% of correct classifications at early tillering, stem elongation, and late booting, respectively. Our work identifies a minimum dataset for accurate management zones' definition and highlights that in-season monitoring based on the red-edge band was able to reliably identify management zones already at early tillering, despite minor differences in crop growth. [ABSTRACT FROM AUTHOR]

Eight years after the first record in Italy, Kiwifruit Decline (KD), a destructive disease causing root rot, has already affected more than 25% of the area under kiwifruit cultivation in Italy. Diseased plants are characterised by severe decay of the fine roots and sudden wilting of the canopy, which is only visible after the season's first period of heat (July–August). The swiftness of symptom appearance prevents correct timing and positioning for sampling of the disease, and is therefore a barrier to aetiological studies. The aim of this study is to test the feasibility of thermal and multispectral imaging for the detection of KD using an unsupervised classifier. Thus, RGB, multispectral and thermal data from a kiwifruit orchard, with healthy and diseased plants, were acquired simultaneously during two consecutive growing seasons (2017–2018) using an Unmanned Aerial Vehicle (UAV) platform. Data reduction was applied to the clipped areas of the multispectral and thermal data from the 2017 survey. Reduced data were then classified with two unsupervised algorithms, a K-means and a hierarchical method. The plant vigour (canopy size and presence/absence of wilted leaves) and the health shifts exhibited by asymptomatic plants between 2017 and 2018 were evaluated from RGB data via expert assessment and used as the ground truth for cluster interpretation. Multispectral data showed a high correlation with plant vigour, while temperature data demonstrated a good potential use in predicting health shifts, especially in highly vigorous plants that were asymptomatic in 2017 and became symptomatic in 2018. The accuracy of plant vigour assessment was above 73% when using multispectral data, while clustering of the temperature data allowed the prediction of disease outbreak one year in advance, with an accuracy of 71%. Based on our results, the unsupervised clustering of remote sensing data could be a reliable tool for the identification of sampling areas, and can greatly improve aetiological studies of this new disease in kiwifruit. [ABSTRACT FROM AUTHOR]

In this century, one of the main objectives of agriculture is sustainability addressed to achieve food security, based on the improvement of use efficiency of farm resources, the increasing of crop yield and quality, under climate change conditions. The optimization of farm resources, as well as the control of soil degradation processes (e.g., soil erosion), can be realized through crop monitoring in the field, aiming to manage the local spatial variability (time and space) with a high resolution. In the case of high profitability crops, as the case of vineyards for high-quality wines, the capability to manage and follow spatial behavior of plants during the season represents an opportunity to improve farmer incomes and preserve the environmental health. However, any field monitoring represents an additional cost for the farmer, which slows down the objective of a diffuse sustainable agriculture. Satellite multispectral images have been widely used for production management in large areas. However, their observation is limited by the pre-defined and fixed scale with relatively coarse spatial resolution, resulting in limitations in their application. In this paper, encouraged by recent achievements in convolutional neural network (CNN), a multiscale full-connected CNN is constructed for the pan-sharpening of Sentinel-2A images by UAV images. The reconstructed data are validated by independent multispectral UAV images and in-situ spectral measurements. The reconstructed Sentinel-2A images provide a temporal evaluation of plant responses using selected vegetation indices. The proposed methodology has been tested on plant measurements taken either in-vivo and through the retrospective reconstruction of the eco-physiological vine behavior, by the evaluation of water conductivity and water use efficiency indexes from anatomical and isotopic traits recorded in vine trunk wood. In this study, the use of such a methodology able to combine the pro and cons of space-borne and UAVs data to evaluate plant responses, with high spatial and temporal resolution, has been applied in a vineyard of southern Italy by analyzing the period from 2015 to 2018. The obtained results have shown a good correspondence between the vegetation indexes obtained from reconstructed Sentinel-2A data and plant hydraulic traits obtained from tree-ring based retrospective reconstruction of vine eco-physiological behavior. • CNN is constructed for pan-sharpening of Sentinel-2A images by UAV images. • Reconstructed data are validated on MS UAV images and in-situ spectral measures. • Reconstructed VIs from Sentinel-2A data agreed with dendroecological plant traits. [ABSTRACT FROM AUTHOR]

Sea level rise is one of the main risk factors for the preservation of cultural heritage sites located along the coasts of the Mediterranean basin. Coastal retreat, erosion, and storm surges are posing serious threats to archaeological and historical structures built along the coastal zones of this region. In order to assess the coastal changes by the end of 2100 under the expected sea level rise of about 1 m, we need a detailed determination of the current coastline position based on high resolution Digital Surface Models (DSM). This paper focuses on the use of very high-resolution Unmanned Aerial Vehicles (UAV) imagery for the generation of ultra-high-resolution mapping of the coastal archaeological area of Pyrgi, Italy, which is located near Rome. The processing of the UAV imagery resulted in the generation of a DSM and an orthophoto with an accuracy of 1.94 cm/pixel. The integration of topographic data with two sea level rise projections in the Intergovernmental Panel on Climate Change (IPCC) AR5 2.6 and 8.5 climatic scenarios for this area of the Mediterranean are used to map sea level rise scenarios for 2050 and 2100. The effects of the Vertical Land Motion (VLM) as estimated from two nearby continuous Global Navigation Satellite System (GNSS) stations located as close as possible to the coastline are included in the analysis. Relative sea level rise projections provide values at 0.30 ± 0.15 cm by 2050 and 0.56 ± 0.22 cm by 2100 for the IPCC AR5 8.5 scenarios and at 0.13 ± 0.05 cm by 2050 and 0.17 ± 0.22 cm by 2100, for the IPCC Fifth Assessment Report (AR5) 2.6 scenario. These values of rise correspond to a potential beach loss between 12.6% and 23.5% in 2100 for Representative Concentration Pathway (RCP) 2.6 and 8.5 scenarios, respectively, while, during the highest tides, the beach will be provisionally reduced by up to 46.4%. In higher sea level positions and storm surge conditions, the expected maximum wave run up for return time of 1 and 100 years is at 3.37 m and 5.76 m, respectively, which is capable to exceed the local dune system. With these sea level rise scenarios, Pyrgi with its nearby Etruscan temples and the medieval castle of Santa Severa will be exposed to high risk of marine flooding, especially during storm surges. Our scenarios show that suitable adaptation and protection strategies are required. [ABSTRACT FROM AUTHOR]

The coastal system, with its physical chemical and biological components, is the place where the lithosphere, atmosphere and hydrosphere intersect and interact, and in which human activity has a considerable impact on the balance of the whole ecosystem. The aim of this study is to assess the health of the coastal environment in a natural protected area in Policoro (Italy). Sand samples have been collected and analysed to detect the presence of environmental contaminants and pollutants (heavy metals). A photogrammetric survey was carried out using an unmanned aerial vehicle (UAV), and an updated orthophoto of the area was obtained using the Structure from Motion (SfM) processing technique. Geostatistical techniques have been used to assess the distribution of the contaminants in the study area. [ABSTRACT FROM AUTHOR]

Precision agriculture is considered to be a fundamental approach in pursuing a low-input, high-efficiency, and sustainable kind of agriculture when performing site-specific management practices. To achieve this objective, a reliable and updated description of the local status of crops is required. Remote sensing, and in particular satellite-based imagery, proved to be a valuable tool in crop mapping, monitoring, and diseases assessment. However, freely available satellite imagery with low or moderate resolutions showed some limits in specific agricultural applications, e.g., where crops are grown by rows. Indeed, in this framework, the satellite's output could be biased by intra-row covering, giving inaccurate information about crop status. This paper presents a novel satellite imagery refinement framework, based on a deep learning technique which exploits information properly derived from high resolution images acquired by unmanned aerial vehicle (UAV) airborne multispectral sensors. To train the convolutional neural network, only a single UAV-driven dataset is required, making the proposed approach simple and cost-effective. A vineyard in Serralunga d'Alba (Northern Italy) was chosen as a case study for validation purposes. Refined satellite-driven normalized difference vegetation index (NDVI) maps, acquired in four different periods during the vine growing season, were shown to better describe crop status with respect to raw datasets by correlation analysis and ANOVA. In addition, using a K-means based classifier, 3-class vineyard vigor maps were profitably derived from the NDVI maps, which are a valuable tool for growers.

Storm surge and wave runup are key determinants of the potential for beach overwashing during storm events. However, the prediction and quantification of wave runup on embayed beaches is strongly influenced by particular characteristics (e.g., irregular morphology, low tides, absence of swell, etc.) which differ from those on open beaches, and have rarely been investigated in literature. In the present paper, a model framework aimed at predicting wave-induced runup on an embayed sandy beach is validated by means of measurements derived from a video-monitoring station, recently installed in South Italy, during two storm events in 2016. The numerical approach employs MeteOcean forecasted waves within SWAN and SWASH models (in both 2-d and 1-d mode). The combination of multibeam and d-RTK surveys with Unmanned Aerial Vehicle (UAV) imagery provides high resolution depth grid (0.015 m), particularly required in shallow waters, where wave hydrodynamics is highly influenced by the bottom. The results show and discuss the agreement between video measurements and 2-d predictions of runup. A sensitivity analysis of the Manningfls roughness factor is needed in 1-d simulations. The accuracy of the empirical formulas in predicting wave runup in an embayed beach is also investigated, showing mainly an overestimation of the observations. [ABSTRACT FROM AUTHOR]