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Remote images are useful tools for detecting and monitoring landslides, including shallow landslides in agricultural environments. However, the use of non-commercial satellite images to detect the latter is limited because their spatial resolution is often comparable to or greater than landslide sizes, and the spectral characteristics of the pixels within the landslide body (LPs) are often comparable to those of the surrounding pixels (SPs). The buried archaeological remains are also often characterized by sizes that are comparable to image spatial resolutions and the spectral characteristics of the pixels overlying them (OBARPs) are often comparable to those of the pixels surrounding them (SBARPs). Despite these limitations, satellite images have been used successfully to detect many buried archaeological remains since the late 19th century. In this research context, some methodologies, which examined the values of OBARPs and SBARPs, were developed to rank images according to their capability to detect them. Based on these previous works, this paper presents an updated methodology to detect shallow landslides in agricultural environments. Sentinel-2 and Google Earth (GE) images were utilized to test and validate the methodology. The landslides were mapped using GE images acquired simultaneously or nearly simultaneously with the Sentinel-2 data. A total of 52 reference data were identified by monitoring 14 landslides over time. Since remote sensing indices are widely used to detect landslides, 20 indices were retrieved from Sentinel-2 images to evaluate their capability to detect shallow landslides. The frequency distributions of LPs and SPs were examined, and their differences were evaluated. The results demonstrated that each index could detect shallow landslides with sizes comparable to or smaller than the spatial resolution of Sentinel-2 data. However, the overall accuracy values of the indices varied from 1 to 0.56 and two indices (SAVI and RDVI) achieved overall accuracy values equal to 1. Therefore, to effectively distinguish areas where shallow landslides are present from those where they are absent, it is recommended to apply the methodology to many image processing products. In conclusion, given the significant impact of these landslides on agricultural activity and surrounding infrastructures, this methodology provides a valuable tool for detecting and monitoring landslide presence in such environments. [ABSTRACT FROM AUTHOR]

In this study, we defined screening maps for Italy that classify sites based on their potential for triggering landslides. To this end, we analyzed seismic hazard maps and hazard disaggregation results on a national scale considering four spectral periods (0.01 s, 0.2 s, 0.5 s, and 1.0 s) and three return periods (475, 975, and 2475 years). First, joint distributions of magnitude ( M ) and distance ( R ) from hazard disaggregation were analyzed by means of an innovative approach based on image processing techniques to find all modal scenarios contributing to the hazard. In order to obtain the M - R scenarios controlling the triggering of earthquake-induced landslides at any computation node, mean and modal M - R pairs were compared to empirical curves defining the M - R bounds associated with landslide triggering. Three types of landslides were considered (i.e., disrupted slides and falls, coherent slides, and lateral spreads and flows). As a result, screening maps for all of Italy showing the potential for triggering landslides based on the level of seismic hazard were obtained. The maps and the related data are freely accessible. [ABSTRACT FROM AUTHOR]

One of the most frequently detected neoplasms in women in Italy is breast cancer, for which high-sensitivity diagnostic techniques are essential for early diagnosis in order to minimize mortality rates. As addressed in Part I of this work, we have seen how conditions such as high glandular density or limitations related to mammographic sensitivity have driven the optimization of technology and the use of increasingly advanced and specific diagnostic methodologies. While the first part focused on analyzing the use of a mammography machine from a physical and dosimetric perspective, in this paper, we will examine other techniques commonly used in breast imaging: contrast-enhanced mammography, digital breast tomosynthesis, radio imaging, and include some notes on image processing. We will also explore the differences between these various techniques to provide a comprehensive overview of breast lesion detection techniques. We will examine the strengths and weaknesses of different diagnostic modalities and observe how, with the implementation of improvements over time, increasingly effective diagnoses can be achieved. [ABSTRACT FROM AUTHOR]

Purpose: The fear of COVID-19 infection may discourage patients from going to the hospital even in case of sudden onset of disabling symptoms. There is growing evidence of the reduction of stroke admissions and higher prevalence of severe clinical presentation. Yet, no studies have investigated the perfusion pattern of acute strokes admitted during the lockdown. We aimed to evaluate the effects of the COVID-19 pandemic on hyper-acute stroke CT perfusion (CTP) pattern during the first months of the pandemic in Italy. Methods: In this retrospective observational study, we analyzed CTP images and clinical data of ischemic stroke patients admitted between 9 March and 2 June 2020 that underwent CTP (n = 30), to compare ischemic volumes and clinical features with stroke patients admitted during the same period in 2019 (n = 51). In particular, CTP images were processed to calculate total hypoperfused volumes, core volumes, and mismatch. The final infarct volumes were calculated on follow-up CT. Results: Significantly higher total CTP hypoperfused volume (83.3 vs 18.5 ml, p = 0.003), core volume (27.8 vs 1.0 ml, p < 0.001), and unfavorable mismatch (0.51 vs 0.91, p < 0.001) were found during the COVID-19 period compared to no-COVID-19 one. The more unfavorable perfusion pattern at admission resulted in higher infarct volume on follow-up CT during COVID-19 (35.5 vs 3.0 ml, p < 0.001). During lockdown, a reduction of stroke admissions (− 37%) and a higher prevalence of severe clinical presentation (NIHSS ≥ 10; 53% vs 36%, p = 0.029) were observed. Conclusion: The results of CTP analysis provided a better insight in the higher prevalence of major severity stroke patients during the COVID-19 period. [ABSTRACT FROM AUTHOR]

With the aim of unveiling evidence of Late Quaternary faulting, a series of Ground Penetrating Radar (GPR) profiles were acquired across the Campotenese continental basin (Mt. Pollino region) in the southern Apennines active extensional belt (Italy). A set of forty-nine 300 MHz and 500 MHz GPR profiles, traced nearly perpendicular to a buried normal fault, were acquired and carefully processed through a customized workflow. The data interpretation allowed us to reconstruct a pseudo-3D model depicting the boundary between the Mesozoic bedrock and the sedimentary fill of the basin, which were in close proximity to the fault. Once reviewing and defining the GPR signature of faulting, we highlight in our data how near surface alluvial and colluvial sediments appear to be dislocated by a set of conjugate (west and east-dipping) discontinuities that penetrate inside the underlying Triassic dolostones. Close to the contact between the continental deposits and the bedrock, some buried scarps which offset wedge-shaped deposits are interpreted as coseismic ruptures, subsequently sealed by later deposits. Although the use of pseudo-3D GPR data implies more complexity linking the geophysical features among the radar images, we have reconstructed a reliable subsurface fault pattern, discriminating master faults and a series of secondary splays. We believe our contribution provides an improvement in the characterization of active faults in the study area which falls within the Pollino seismic gap and is considered potentially prone to severe surface faulting. Our aim is for our approach and workflow to be of inspiration for further studies in the region as well as for similar high seismic hazard areas characterized by scarcity of near-surface data. [ABSTRACT FROM AUTHOR]

Leaf spot disease is one of the most common fungal diseases that cause immense and sometimes irreparable damage to poplar trees. In this regard, conventional methods for detection of fungal contamination are time-consuming, costly and difficult. Therefore, in this study, in order to distinguish healthy leaves from infected ones, a modified image processing algorithm by using the Laplacian threshold was used. In this study, the illumination wavelengths have been selected using a UV-Visible Spectrophotometer device (PG Instruments, Model T92+ Double® Beam, England). The images were recorded using a 1260 lux light intensity and a digital camera mounted on the stereomicroscope (SN 287050, Italy) with a resolution of 5 megapixels. The experimental data were analyzed based on a completely randomized design with three replications. Analysis of variance (ANOVA) test was used to determine the significance of treatments performance. According to the results, the red spectrum with 680nm wavelength showed the highest contaminated surface on the leaf (190500 μm²). In contrast, the yellow spectrum with a wavelength of 585 nm determined the lowest amount of contaminated surface (65781 μm²). The blue and green spectra showed roughly the same performance in early detection of fungal contamination. Overall obtained results showed that the red spectrum with wavelength of 680 nm is more reliable for early poplar leaves’ surface contamination detection in compare to blue (470 nm), green (550 nm) and yellow (585 nm) wavelengths with 13%, 14% and 55.8% improvements, respectively. The method presented in this study can be used to identify the quality and the health of biological products and disease progression, significantly easier and faster in compare to the conventional methods. [ABSTRACT FROM AUTHOR]

Landslides represent a major geomorphological feature influencing the evolution of the southern Apennine slopes with earth flows being one of the most representative types of landslide. The development of earth flows in the southern Apennines is facilitated by the widespread occurrence of clayey lithologies. Earth flows can either produce marked scars along the slopes, when their activity is high, or give rise to bumpy areas that are frequently covered by vegetation, when their activity is low. In this latter case, although the risk is significantly reduced, the earth flow can still represent a problem for buildings and infrastructure. The Picerno earth flow in the Basilicata region is an example of a low-activity earth flow. This NW-SE–oriented landslide extends for a length of ~ 5.5 km, has an average width of ~ 680 m, and affects the village of Picerno. Our investigations mainly focused on the terminal portion of the landslide and revealed that some significant linear infrastructures (e.g., the Potenza–Naples railway) and important provincial and municipal roads are affected by the earth flow. In order to better define the amount of displacement characterizing the Picerno earth flow and gain insights into the lateral extension and the depth of the main detachment surface, we undertook a detailed geological and geomorphological survey. Interferometry synthetic aperture radar data provided by the processing of SAR images, with the aim of highlighting the regions of the landslide that are currently active, together with electrical resistivity tomography data which have been utilized to define the geometry of the landslide body. Our results provide useful suggestions for planning appropriate actions aimed at stabilizing the landslide body. [ABSTRACT FROM AUTHOR]

A case history is presented describing the revision of the 3D seismic volume for an onshore hydrocarbon field in Italy. One of the key elements for the success of the project was the contribution of the geological information to seismic data processing and imaging, which was deemed since the beginning as an indispensable guide to drive and constraint the seismic reprocessing. In particular, two critical steps in which the integration between geology and geophysics played a relevant role are discussed here: the estimation of static corrections for data preconditioning before imaging, and the velocity model building for depth imaging. The paper includes also a proposal for a stratigraphic revision of the allochthonous sequence of the Val d’Agri area. The outcomes of the project comprised the release of a reprocessed seismic volume in time domain, the creation of the first Pre-Stack Depth Migrated volume of the field, together with the revision of some stratigraphic boundaries, and created the conditions for a revision of the geological model of the field. The project lasted approximately two years and was executed by a multidisciplinary group comprising geophysicists, geologists, sedimentologists, and structuralists. The collaboration with Shell, joint venture partner, was also an important factor for the success of the project. [ABSTRACT FROM AUTHOR]

This paper describes the results of a field investigation with the objective of evaluating the possibility to produce drone-derived 3D digital point clouds sufficiently dense and accurate to determine discontinuity surface roughness characteristics. A discontinuous rock mass in Italy was chosen as the investigation site and Structure from Motion and Multi-View Stereo techniques adopted for producing three-dimensional point clouds from the two-dimensional image sequences. Since the roughness of discontinuities depends on direction, scale and resolution of the sampling, data were always collected along the maximum slope gradient. The scale effect was evaluated by analysing discontinuity profiles of different lengths (10 cm, 30 cm, 60 cm and 100 cm), with measurements taken from drone flights flown at different distances from the rocky slopes (10 m, 20 m and 30 m). The accuracy of the derived joint roughness coefficients was evaluated by direct comparison with discontinuity profiles measured during fieldwork using conventional techniques and from contemporaneous terrestrial laser scanning. Results from this research show that 3D digital point clouds, derived from the processing of drone-flight images, were successfully used for reliable representation of discontinuity roughness for profiles longer than 60 cm, whereas less reliable results were achieved for shorter profile lengths. This, even if strictly related to this case study since several factors can affect the minimum profile length, represents a significant contribution to improve the knowledge on the use of remotely captured data for characterising the discontinuities in natural or man-made rock outcrops, particularly where access difficulties do not allow conventional engineering-geological surveys to be undertaken. [ABSTRACT FROM AUTHOR]

Agricultural lands are the widest Human-modified ecosystems, making crop production the most extensive form of land use on Earth. However, in conventional agricultural land management, soil erosion may be boosted up to 1–2 orders of magnitude higher than the natural rates of soil production, making unproductive about the 30% of the world’s arable. Nowadays in Europe, vineyards represent the most erosion-prone agricultural lands, especially in Mediterranean countries, showing the highest erosion rates in comparison to other type of land uses. Prosecco wine is produced in NE Italy by a rate of 400 M bottles per year, with the fastest growing demand in the global market at present. A production of 90 M bottles year-1 is currently running in the historical Prosecco DOCG (215 km2), in a steep hilly landscape of Veneto Region (Conegliano-Valdobbiadene). To sustain wine production, agricultural intensification is at present increasing, by re-setting of hillslopes and land use changes towards new vineyard plantations. The aim of this study is to estimate and to map potential soil erosion rate, calculating a sort of “soil footprint” for wine production in different agricultural land-management scenarios. RUSLE model was adopted to estimate potential soil erosion in Mg ha−1 year−1, by using high resolution topographic data (LiDAR), 10 years rainfall data analysis, detailed land use and local soil characteristics. For a conventional land-management scenario the estimated that total potential soil erosion in the Prosecco DOCG area is 411,266 Mg year-1, with an erosion rate of 19.5 Mg ha year-1. Modelled soil erosion is mainly clustered on steep slopes, with rates higher than 40 Mg ha-1 year-1. In Prosecco vineyards potential soil erosion could reach 300,180 Mg year-1, by a mean rate of 43.7 Mg ha-1 year-1, which is 31 times higher than the upper limit of tolerable soil erosion threshold defined for Europe. In contrast, simulation of different nature-based scenarios (hedgerows, buffer strips, and grass cover) showed soil erosion could be effectively reduced: a 100% inter-row grass cover showed a reduction of almost 3 times in vineyards (from 43.7 to 14.6 Mg ha-1 year-1), saving about 50% of soil in the whole Prosecco DOCG. The soil footprint modelled for a conventional land-management scenario is about 3.3 kg every bottle produced; in contrast it would be reduced to 1.1 kg/bottle in the completely green land-management scenario. This study, as the first estimation of potential soil erosion at Prosecco DOCG scale, suggests that an integrated and public soil erosion monitoring system is strongly needed in viticultural area, by implementing direct/indirect field measures with spatial analyses at agricultural landscape scale. [ABSTRACT FROM AUTHOR]

The digital representation of hydraulic engineering works such as dykes or barriers is a sector that is becoming increasingly important. In general, however, the 3D relief of semi-submerged structures is still a problem. The Geomatics Laboratory in collaboration with CNR-IPCF of Messina as part of a broader project to analyze the variations of the coast lines and the seabed of the province of Reggio Calabria (southern Italy), has developed an experimental system of acquisition and processing of digital images acquired via UAV and (Remotely operated vehicle) ROV. The present note describes the design and construction characteristics of the ROV realized and used, the advantages and limitations of the use of a small and low-cost ROV, as well as the results obtained during the experimentation phase. The process of surveying of the sub-emerged structure inquired and the relative construction of a unique 3D model are also described. The two surveys were first elaborated separately and then united in a single reference system by means of the visible markers in the area straddling the sea surface. Finally, the bathymetry values obtained from the classic echo sounder system and those obtained from the model obtained were compared. [ABSTRACT FROM AUTHOR]

The paper presents an innovative approach for improving the orientation results when terrestrial and UAV images are jointly processed. With the existing approaches, the processing of images coming from different platforms and sensors leads often to noisy and inaccurate 3D reconstructions, due to the different nature and properties of the acquired images. In this work, a photogrammetric pipeline is proposed to filter and remove bad computed tie points, according to some quality feature indicators. A completely automatic procedure has been developed to filter the sparse point cloud, in order to improve the orientation results before computing the dense point cloud. We report some tests and results on a dataset of about 140 images (Modena cathedral, Italy). The effectiveness of the filtering procedure was verified using some internal quality indicators, external checks (ground truth data) and qualitative visual analyses. [ABSTRACT FROM AUTHOR]

One of the problems of wound specialists is that they may not have enough time to assess the length and width of the wound. This problem is aggravated when they have to visit the wounds virtually. This paper presents a novel AI-based method for evaluation of the wound healing process. In this algorithm, a neural network is used to detect a combination of color spectra next to each other and a neural network is used to detect the distance of the wound from the camera via hand detection. Finally, the dimensions of the wound are obtained along with the analysis of its texture. The neural networks used are a deep networks and use intensive learning algorithms with less computational complexity than deep networks, which can be implemented on mobile processors as well. Networks are modified in a way that the additional connections between different nodes are removed without any change in performance. In this way, time and computational complexity are reduced. Then through the server, images of the wound and its analysis can be sent to different users, including doctors and medical centers. [ABSTRACT FROM AUTHOR]

Modeling of synthetic aperture radar (SAR) imaging distortions induced by topography is addressed and a novel radiometric calibration method is proposed in this paper. An analytical formulation of the problem is primarily provided in purely geometrical terms, by adopting the theoretical notions of the differential geometry of surfaces. The novel and conceptually simple formulation relies on a cylindrical coordinate system, whose longitudinal axis corresponds to the sensor flight direction. A 3D representation of the terrain shape is then incorporated into the SAR imaging model by resorting to a suitable parametrization of the observed ground surface. Within this analytical framework, the area-stretching function quantitatively expresses in geometrical terms the inherent local radiometric distortions. This paper establishes its analytical expression in terms of the magnitude of the gradient of the look-angle function uniquely defined in the image domain, thus resulting in being mathematically concise and amenable to a straightforward implementation. The practical relevance of the formulation is also illustrated from a computational perspective, by elucidating its effective discrete implementation. In particular, an inverse cylindrical mapping approach is adopted, thus avoiding the drawback of pixel area fragmentation and integration required in forward-mapping-based approaches. The effectiveness of the proposed SAR radiometric calibration method is experimentally demonstrated by using COSMO-SkyMed SAR data acquired over a mountainous area in Italy. [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 agricultural and forestry sector is constantly evolving, also through the increased use of precision technologies including Remote Sensing (RS). Remotely biomass estimation (WaSfM) in wood production forests is already debated in the literature, but there is a lack of knowledge in quantifying pruning residues from canopy management. The aim of the present study was to verify the reliability of RS techniques for the estimation of pruning biomass through differences in the volume of canopy trees and to evaluate the performance of an unsupervised segmentation methodology as a feasible tool for the analysis of large areas. Remote sensed data were acquired on four uneven-aged and irregularly spaced chestnut orchards in Central Italy by an Unmanned Aerial Vehicle (UAV) equipped with a multispectral camera. Chestnut geometric features were extracted using both supervised and unsupervised crown segmentation and then applying a double filtering process based on Canopy Height Model (CHM) and vegetation index threshold. The results show that UAV monitoring provides good performance in detecting biomass reduction after pruning, despite some differences between the trees' geometric features. The proposed unsupervised methodology for tree detection and vegetation cover evaluation purposes showed good performance, with a low undetected tree percentage value (1.7%). Comparing crown projected volume reduction extracted by means of supervised and unsupervised approach, R2 ranged from 0.76 to 0.95 among all the sites. Finally, the validation step was assessed by evaluating correlations between measured and estimated pruning wood biomass (Wpw) for single and grouped sites (0.53 < R2 < 0.83). The method described in this work could provide effective strategic support for chestnut orchard management in line with a precision agriculture approach. In the context of the Circular Economy, a fast and cost-effective tool able to estimate the amounts of wastes available as by-products such as chestnut pruning residues can be included in an alternative and virtuous supply chain. [ABSTRACT FROM AUTHOR]

In NE Italy, fast-moving landslides represent a significant threat both to the population and to the built environment. In the eastern portion of the Italian Alps, rockfalls are common and are often responsible for casualties or severe damage to infrastructure. To assess rockfall susceptibility, field surveys are crucial to accurately recognize the source areas of falling rock phenomena, to quantify the volumes of unstable blocks and identify the possible block trajectories.This work takes in detailed geological and geomorphological mapping, through the identification of dislodged blocks, fractures and fallen blocks on a much-frequented limestone cliff in the Trieste Karst used as a climbing spot. Moreover, a popular tourist path (the Napoleonica) lies at the foot of the climbing cliffs.The first identification of the most hazardous sections of the climbing sites were performed starting from field surveys and aerial photo analysis, morphometric and geomechanical characterization of the rock masses together with mapping activities.The field data were implemented with the analysis of UAV (Unmanned Aircraft Vehicle) images and videos; this innovative technique allows to reach and study hard-to-reach sites with high resolution and precision. The main aim of the study is to perform a semiautomatic morphometric and geomechanical characterization of two of the most hazardous sections. This analysis allowed to extract orientation and spacing of discontinuities of the studied walls and to recognize and measure unstable or pre-dislodged blocks by means of 3D orthophotos obtained from the UAV image processing. The main output of this work is a GIS-based map that will be the starting point for future 2D and 3D simulations in order to provide a rockfall susceptibility evaluation of the study area.Moreover, the methodology used could serve as model for the study of the other popular climbing sites, where the geological, geomorphological and climatic conditions are similar. The latters in fact are the predisposing factors to slope failures and consequently could cause casualties during climbing activities.The final outputs could form an important basis for the growth of investment in tourism policies aimed at increasing the quality of local attractions. [ABSTRACT FROM AUTHOR]

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