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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]

The leaf area of vegetation can be expressed in terms of leaf area index (LAI). This index depends on species composition, developmental stage, prevailing site conditions, seasonality and management practices. LAI is an important ecological parameter because vegetation-atmosphere processes of the canopy are controlled by the foliage. It can be estimated with hemispherical photographs using some commercial and free softwares. The choice of the software and the settings of parameters are two fundamental aspects to obtain suitable data. The paper focuses on the comparative analysis of the LAI data obtained with three image processing softwares (Spot Light Intercept Model, Gap Light Analyzer and WinScanopy) in a case study in Italy. The data were analyzed in a qualitative and quantitative way. The Wilcoxon signed rank test showed statistically significant differences among the three softwares. WinScanopy provides lower LAI values than the other two softwares. The same non-parametric test distinguishing per forest type and forest age showed statistically significant differences among softwares in three forest types (silver fir, Norway spruce and beech forests) and in the young stands. Instead, no statistically significant differences were found in the mature stands. [ABSTRACT FROM AUTHOR]

Criteria of historical assessment are applied to the Turin Shroud to determine which hypothesis relating to the image formation process is the most likely. To implement this, a ‘Minimal Facts’ approach is followed that takes into account only physicochemical and historical data receiving the widest consensus among contemporary scientists. The result indicates that the probability of the Shroud of Turin being the real shroud of Jesus of Nazareth is very high; historians and natural theologians should therefore pay it increased attention. [ABSTRACT FROM AUTHOR]

This paper addresses change detection in multitemporal remote sensing images. After a review of the main techniques developed in remote sensing for the analysis of multitemporal data, the attention is focused on the challenging problem of change detection in very-high-resolution (VHR) multispectral images. In this context, we propose a framework that aims at defining a top–down approach to the design of the architecture of novel change-detection systems for multitemporal VHR images. The proposed framework explicitly models the presence of different radiometric changes on the basis of the properties of multitemporal images, extracts the semantic meaning of radiometric changes, identifies changes of interest with strategies designed on the basis of the specific application, and takes advantage of the intrinsic multiscale/multilevel properties of the objects and the high spatial correlation between pixels in a neighborhood. This framework defines guidelines for the development of a new generation of change-detection methods that can properly analyze multitemporal VHR images taking into account the intrinsic complexity associated with these data. In order to illustrate the use of the proposed framework, a real change-detection problem has been considered, which is described by a pair of VHR multispectral images acquired by the QuickBird satellite on the city of Trento, Italy. The proposed framework has been used for defining a system for change detection in the two images. Experimental results confirm the effectiveness of the developed system and the usefulness of the proposed framework. [ABSTRACT FROM PUBLISHER]

Abstract: “Vicus Constanciacus” is a project for the recovery, protection and evaluation of the historical and archaeological heritage of Constanciacus, an ancient small group of islands in the Northern Lagoon of Venice, of which currently just two emerged strips survive, namely: the abandoned islands of Sant’Ariano and La Cura. Exploring the area lying between the mainland and the lagoon represents an important opportunity for reconstructing the history of Venice before the well known medieval city and market arose. Through survey and excavations of the Constanciacus area, the project aims to shed new light on early patterns of occupation in the Northern Venetian Lagoon; to explore the communication network between the mainland and the sea; and to examine the evolution of settlements along the commercial routes of the Lagoon through time. The particular research environment of the Lagoon requires a close collaboration between archaeologists and scientists of differing expertise. Remote sensing (RS) in this case is critical for investigating the ancient extent and geomorphology of the ancient islands. Aerial photographs, (both vertical and oblique), and HR satellite images, are being used to identify past traces of occupation that nowadays are buried below earth surface or under the shallow waters of the Lagoon. Visual analysis of the remotely sensed data is supported by enhancement and processing techniques that emphasize the presence of anomalies of the terrain and vegetation that can be related to archaeological structures. [Copyright &y& Elsevier]

Abstract: Subduction zones are the place in the world where fluids are transported from the foredeep to the mantle and back-to-the-surface in the back-arc. The subduction of an oceanic plate implies the transportation of the oceanic crust to depth and its methamorphization. Oceanic sediments release water in the (relatively) shallower part of the subduction zone, while dehydration of the subducted basaltic crust allows fluid circulation at larger depths. While the water budget in oceanic subduction has been deeply investigated, less attention has been given to the fluids implied in the subduction of a continental margin (i.e. in continental subduction). In this study, we use teleseismic receiver function (RF) analysis to image the process of water migration at depth, from the subducting plate to the mantle wedge, under the Northern Apennines (NAP, Italy). Harmonic decomposition of the RF data-set is used to constrain both isotropic and anisotropic structures. Isotropic structures highlight the subduction of the Adriatic lower crust under the NAP orogens, from 35–40km to 65km depth, as a dipping low S-velocity layer. Anisotropic structures indicate the presence of a broad anisotropic zone (anisotropy as high as 7%). This zone develops in the subducted Adriatic lower crust and mantle wedge, between 45 and 65km depth, directly beneath the orogens and the more recent back-arc extensional basin. The anisotropy is related to the metamorphism of the Adriatic lower crust (gabbro to blueschists) and its consequent eclogitization (blueschists to eclogite). The second metamorphic phase releases water directly in the mantle wedge, hydrating the back-arc upper mantle. The fluid migration process imaged in this study below the northern Apennines could be a proxy for understanding other regions of ongoing continental subduction. [Copyright &y& Elsevier]

Abstract: In recent years great interest has been posed in imaging techniques like X-ray computed microtomography which in a nondestructive way produce three-dimensional (3D) images of the internal structure of, e.g. porous media. A major challenge lies in the quantitative analysis of the resulting images that allows a more comprehensive and objective characterization of the sample under investigation. A software able to handle and process large 3D image datasets with common hardware is therefore necessary in order to extract morphological and textural information directly from the images. In the present paper the Pore3D software library developed by the SYRMEP research group of the Elettra Synchrotron Light Laboratory in Trieste (Italy) is presented. The library consists of several state-of-the-art functions and procedures for performing filtering, segmentation and quantitative analysis of 3D images. The current status of the project and some applications are here reported. [Copyright &y& Elsevier]

Temporary arrays installed in urban areas for investigating the upper-most geological structure typically comprised of a limited number of stations and are arranged in geometries constrained by environmental boundaries. Therefore, it is expected that the frequency–wavenumber images are significantly blurred by the array transfer function and are corrupted by noise. In this paper, the effect of the Richardson–Lucy regularization method applied to the problem of deblurring frequency–wavenumber images is investigated. The images are computed by analysing data from two small-aperture 2-D arrays, installed with different configurations in a test-site within the town of Potenza (Southern Italy) for near-surface investigations. We show that removing the effects of the array response from the frequency–wavenumber images improve the phase-velocity estimation, reducing the relevant level of uncertainty. Furthermore, the Richardson–Lucy regularization method is effective in reducing the level of noise related to spatial aliasing by eliminating spurious peaks, allowing the maxima related to different seismic sources to be better discriminated. [ABSTRACT FROM AUTHOR]

Abstract: Pixel detector R&D for HEP and imaging applications require an easily configurable and highly versatile DAQ system able to drive and read out many different chip designs in a transparent way, with different control logics and/or clock signals. An integrated, real-time data collection and analysis environment is essential to achieve fast and reliable detector characterization. We present a DAQ system developed to fulfill these specific needs, able to handle multiple devices at the same time while providing a convenient, ROOT based data display and online analysis environment. [Copyright &y& Elsevier]

In areas with complex three-dimensional features, slope and aspect interact with light conditions and significantly affect the spatial structure of images acquired by remote sensing instruments (for example, by changing the distribution of shadows and affecting the texture of high resolution imagery). In this scenario, this paper analyses the potential and the effectiveness of an automatic classification system to identify three fundamental vegetation classes (forest, grassland and crops) in the complex topography of the Italian Alps (Autonomous Province of Trento, Italy). This classification system is based on the fusion of spectral information provided by the SPOT-5 multi-spectral channels (Ground Instantaneous Field of View, GIFOV, equal to 10 m) and textural information extracted from airborne digital orthophotos (GIFOV equal to 1 m) and is designed to be user-friendly. The texture of the digital orthophotos was modelled using defined bidirectional variograms, thereby extracting additional information unavailable in first-order texture analyses. Using SPOT-5 multi-spectral information alone, the classification accuracy in the investigated alpine area was equal to 87.5%, but increased to 92.1% when texture information was included. In particular, the texture information significantly increased the classification accuracy for crops (from 68.9% to 87.9%), especially orchards that tend to be classified as lowland deciduous forests, and herbaceous crops (such as maize) that are often misclassified as grasslands. A further simple majority analysis increased the ability of detecting grassland, crops and urban zones. The combination of the majority analysis and the proposed automatic classification system seems an effective approach to classifying vegetation types in highly fragmented and complex Alpine landscapes on a regional scale. [ABSTRACT FROM AUTHOR]

The San Rossore Natural Park, located on the Tuscany (Italy) coast, has been utilized over the last 10 years for many remote sensing campaigns devoted to coastal zone monitoring. A wet area is located in the south-west part of the Natural Park and it is characterized by a system of ponds and dunes formed by sediment deposition occurring at the Arno River estuary. The considerable amount of collected data has permitted us to investigate the evolution of wetland spreading and land coverage as well as to retrieve relevant biogeochemical parameters, e.g. green biomass, from remote sensing images and products. This analysis has proved that the monitoring of coastal wetlands, characterized by shallow waters, moor and dunes, demands dedicated aerospace sensors with high spatial and spectral resolution. The outcomes of the processing of images gathered during several remote sensing campaigns by airborne and spaceborne hyperspectral sensors are presented and discussed. A particular effort has been devoted to sensor response calibration and data validation due to the complex heterogeneity of the observed natural surfaces. [Copyright &y& Elsevier]

Abstract.  High quality CCD images obtained at two different observatories in North Italy allowed the identification of four morphological structures near the nuclear region of the comet Ikeya-Zhang (I-Z): haloes, jets, shells and spirals. The interpretation of the nature of these structures has been attempted by means of a comparison of different up-to-date image processing techniques, which led to a single common estimate of the rotation period (p=1.48?0.20 days). [ABSTRACT FROM AUTHOR]

We use morphological image processing for classifying spatial patterns at the pixel level on binary land-cover maps. Land-cover pattern is classified as 'perforated,' 'edge,' 'patch,' and 'core' with higher spatial precision and thematic accuracy compared to a previous approach based on image convolution, while retaining the capability to label these features at the pixel level for any scale of observation. The implementation of morphological image processing is explained and then demonstrated, with comparisons to results from image convolution, for a forest map of the Val Grande National Park in North Italy. [ABSTRACT FROM AUTHOR]

The automatic or semi-automatic research of archaeological findings includes several methodologies and algorithms of computer vision. The reconstruction of a scene is one of the key steps to meeting that challenge. This paper addresses a methodology for the reconstruction of underwater scenes with mosaicing techniques. The reconstruction of various scenes will be a video mosaic of sea-bottom landscapes starting from single video frames. The methodology is based on the evaluation of optic flow between frames, and its motion estimation has been evaluated on features extracted from the common areas of pairs of consecutive frames. This approach takes the motion model from a geometric projection framework. The estimation of camera movement is a second key point in the mosaicing problem. The methodology used should be robust enough to produce a good performance because of the high level of noise and turbulence involved in sea-bottom video acquisition. For this purpose, geometrical transformations have been used to map each frame into a unique big common reference frame with dimensions similar to that of the union of frames. [ABSTRACT FROM AUTHOR]

Abstract: Due to the extreme fragility of paper-based artefacts, few techniques are available for scientific investigation and characterisation of ancient drawings or paper-based artefacts. Image reflectance spectroscopy represents an almost unique tool for scientific analysis on precious drawings, for which even micro-invasive techniques of analysis cannot be used. Indeed, beside the high fragility of the support, drawings and graphical works are typically characterised by a very limited number of artistic materials, which in principle could give information on the work of art. In this paper an interdisciplinary study on a set of drawings by Parmigianino (1503–1540), selected from the collection of the Uffizi Gallery in Florence, is presented. Non-invasive measurements of image spectroscopy (IS) have been employed to support the work of art-historians in the critical re-examination and interpretation of the graphical work of the artist. The multispectral analysis has been carried out over the extended spectral region (400–1700 nm), in order to provide simultaneous indications both on the pictorial materials and on possibly underlying hatches. The data processing has been performed by means of Principal Component Analysis (PCA) and the elaboration of each case has been addressed to specifically respond to questions related to the art-historical problem. [Copyright &y& Elsevier]

This paper aims to analyze specific instances which can aid in the understanding of the manner in which cinematic propaganda was used to distribute and represent images of fascist identity in Italy. The moving image and its associated discourses have been re-fragmented and subject to analysis in view of their historical, political and cultural contexts. Within studies of fascism there exists a broad field of cultural/political research on the propagandistic aspects of the fascist regime. There has been constant interest in film propaganda with a focus on Italy and on newsreels. This paper has investigated examples of cinematographic newsreel and documentary distribution. However, what lies beyond its scope is a discussion of LUCE’s stylistic, iconographic choices within its films, newsreels and documentaries, which were aimed at supporting fascism.

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]

Describes the above-named upcoming conference event. May include topics to be covered or calls for papers. [ABSTRACT FROM AUTHOR]