Satellite remote sensing is currently an established, effective, and constantly used tool and methodology for monitoring agriculture and fertilisation. At the same time, in recent years, the need for the detection of livestock manure and digestate spreading on the soil is emerging, and the development of spectral indices and classification processes based on satellite multispectral data acquisitions is growing. However, the application of such indicators is still underutilised and, given the polluting impact of livestock manure and digestate on soil, groundwater, and air, an in-depth study is needed to improve the monitoring of this practice. Additionally, this paper aims at exposing a new spectral index capable of detecting the land affected by livestock manure and digestate spreading. This indicator was created by studying the spectral response of bare soil and livestock manure and digestate, using Copernicus Sentinel-2 MSI satellite acquisitions and ancillary datasets (e.g., soil moisture, precipitation, regional thematic maps). In particular, time series of multispectral satellite acquisitions and ancillary data were analysed, covering a survey period of 13 months between February 2022 and February 2023. As no previous indications on fertilisation practices are available, the proposed approach consists of investigating a broad-spectrum area, without investigations of specific test sites. A large area of approximately 236,344 hectares covering three provinces of the Emilia-Romagna Region (Italy) was therefore examined. A series of ground truth points were also collected for assessing accuracy by filling in the confusion matrix. Based on the definition of the spectral index, a value of the latter greater than three provides the most conservative threshold for detecting livestock manure and digestate spreading with an accuracy of 62.53%. Such results are robust to variations in the spectral response of the soil. On the basis of these very encouraging results, it is considered plausible that the proposed index could improve the techniques for detecting the spreading of livestock manure and digestate on bare ground, classifying the areas themselves with a notable saving of energy compared to the current investigation methodologies directly on the ground.

David Glacier and Drygalski Ice Tongue are massive glaciers in Victoria Land, Antarctica. The ice from the East Antarctic Ice Sheet is drained through the former, and then discharged into the western Ross Sea through the latter. David Drygalski is the largest outlet glacier in Northern Victoria Land, floating kilometers out to sea. The floating and grounded part of the David Glacier are the main focus of this article. During the XXI Italian Antarctic Expedition (2005–2006), within the framework of the National Antarctic Research Programme (PNRA), two GNSS stations were installed at different points: the first close to the grounding line of David Glacier, and the second approximately 40 km downstream of the first one. Simultaneous data logging was performed by both GNSS stations for 24 days. In the latest data processing, the kinematic PPP technique was adopted to evaluate the dominant diurnal components and the very small semi-diurnal variations in ice motion induced by the ocean tide and the mean ice flow rates of both GNSS stations. Comparison of the GNSS time series with predicted ocean tide calculated from harmonic coefficients of the nearest tide gauge stations, installed at Cape Roberts and Mario Zucchelli Station, highlight different local response of the glacier to ocean tide, with a minor amplitude of vertical motion at a point partially anchored at the bedrock close to the grounding line. During low tide, the velocity of the ice flow reaches its daily maximum, in accordance with the direction of seawater outflow from the fjord into the ocean, while the greatest daily tidal excursion generates an increase in the horizontal ice flow velocity. With the aim to extend the analysis in spatial terms, five COSMO-SkyMED Stripmap scenes were processed. The comparison of the co-registered offset tracking rates, obtained from SAR images, with the GNSS estimation shows good agreement.

Livestock manure management, especially related to soil-fertilisation practice, is responsible for most of the emissions in agriculture, and in particular the ammonia emissions (NH3), which play a key role in environmental problems, affecting water, soil and air quality. Within the digital agricultural framework, EO data contribute to agricultural-practices monitoring such as manure spreading, to mitigate pollutant emissions. This study presents a GIS-based tool on an open-source platform, developed for susceptibility estimation of sewage spreading occurrence in agricultural areas of Italy. The tool is based on the analysis of multispectral and hyperspectral satellite time series in synergy with field data and ancillary information collected from regional repositories, to produce a series of classified and prioritised spatially explicit information. Spectral analysis of satellite acquisitions enabled the identification of manure spreading with the precision of about 83%. Field campaigns from October to March were carried out to validate the spreading event. The case study demonstrates the ability of the proposed GIS-based tool in supporting the monitoring of manure spreading.

Surface soil moisture is a key hydrologic state variable that greatly influences the global environment and human society. Its significant decrease in the Mediterranean region, registered since the 1950s, and expected to continue in the next century, threatens soil health and crops. Microwave remote sensing techniques are becoming a key tool for the implementation of climate-smart agriculture, as a means for surface soil moisture retrieval that exploits the correlation between liquid water and the dielectric properties of soil. In this study, a workflow in Google Earth Engine was developed to estimate surface soil moisture in the agricultural fields of the Marche region (Italy) through Synthetic Aperture Radar data. Firstly, agricultural areas were extracted with both Sentinel-2 optical and Sentinel-1 radar satellites, investigating the use of Dual-Polarimetric Entropy-Alpha decomposition's bands to improve the accuracy of radar data classification. The results show that Entropy and Alpha bands improve the kappa index obtained from the radar data only by 4% (K = 0.818), exceeding optical accuracy in urban and water areas. However, they still did not allow to reach the overall optical accuracy (K = 0.927). The best classification results are reached with the total dataset (K = 0.949). Subsequently, Water Cloud and Tu Wien models were implemented on the crop areas using calibration parameters derived from literature, to test if an acceptable accuracy is reached without in situ observation. While the first model’s accuracy was inadequate (RMSD = 12.3), the extraction of surface soil moisture using Tu Wien change detection method was found to have acceptable accuracy (RMSD = 9.4).

Wetlands ecosystems support a significant percentage of the world’s biodiversity and also provide a number of ecological services, such as carbon sequestration. These fragile ecosystems need to be monitored over time in order to better understand the ecological dynamics and changes within. The study areas are the marshland of the Ostiglia and Busatello marshes. Thus, geographic and climatic aspects have been investigated in order to better explain the dynamics of this ecosystem. The aims of this study were: (i) the evaluation of satellite instruments and methods for monitoring the health state of wetland ecosystems and the impacts of Climate Change; (ii) the investigation of seasonal dynamics of this wetland. The temperature, precipitation and humidity trends have been analyzed from 2016 to 2021. Time series of spectral indices (NDVI, NDMI and NMDI) and backscatter (VV and VH) have been extracted in order to analyze the trends of seasonal variation. To better understand the backscatter variation, the correlation between spectral indices and backscatter have been computed.

In the originally published chapter “SAR and Multispectral Data Contribution to the Monitoring of Wetland Ecosystems Vulnerable to Climate Change” the first-last name order was erroneously reversed for all authors. This has been corrected.

The growing need for sustainable management approaches of crops and bare soils requires measurements at a multiple scale (space and time) field system level, which have become increasingly accurate. In this context, proximal and satellite remote sensing data cooperation seems good practice for the present and future. The primary purpose of this work is the development of a sound protocol based on a statistical comparison between Copernicus Sentinel-2 MIS satellite data and a multispectral sensor mounted on an Unmanned Aerial Vehicle (UAV), featuring spectral deployment identical to Sentinel-2. The experimental dataset, based on simultaneously acquired proximal and Sentinel-2 data, concerns an agricultural field in Pisa (Tuscany), cultivated with corn. To understand how the two systems, comparable but quite different in terms of spatial resolution and atmosphere impacts, can effectively cooperate to create a value-added product, statistical tests were applied on bands and the derived Vegetation and Soil index. Overall, as expected, due to the mentioned impacts, the outcomes show a heterogeneous behavior with a difference between the coincident bands as well for the derived indices, modulated in the same manner by the phenological status (e.g., during the canopy developments) or by vegetation absence. Instead, similar behavior between two sensors occurred during the maturity phase of crop plants.

Italian historic gardens represent a relevant asset for the tangible and intangible landscape. Along with architectural and artistic heritage they also have financial relevance, promoting several types of tourism and providing ecosystem services. The huge amount of resources needed to maintain them caused some to be partly or fully untended for many years. In order to preserve the identity of historic gardens, restoration plans must therefore consider anthropic and natural processes influencing the evolution of the different components. Through the integration of census, visual assessment and traditional topographic survey of the trees, supported by GNSS (global navigation satellite system) techniques, a procedure to collect and analyse data are under development. The work aims to optimize environmental and financial sustainability of the future restoration process. This paper presents the preliminary outcomes from the first trial occurred into the historic garden of Villa Sorra in Castelfranco Emilia (MO, Italy). The digital mapping of the trees was key to determine direct spatial relationships by using GIS ( geographical information system) tools. Preliminary data processing mainly allowed the visualization and quantification of the urgencies related to the management of the trees suffering from serious health conditions. Overall, the interdisciplinary approach proved to be fundamental for multilevel planning and improved the efficiency if compared to the traditional assessment methodology.

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

A considerable amount of the plastics produced around the world is now dispersed throughout the environment, and in particular in aquatic ecosystems. This can have damaging consequences for plants, animals and human beings. This study investigates some approaches for detection and monitoring of plastics waste in river habitats through multispectral image classification. The data are acquired using a proximity sensor in the electromagnetic spectrum range that includes the ultraviolet, visible and near infrared bands, as for the WorldView‐2 satellite. The in‐depth analysis of the spectral signatures obtained shows typical plastics trends and reflectance values in the near infrared bands. Different classification methods were compared to test their effectiveness for the isolation of plastics samples dispersed in a river habitat. This project represents the first step within a wider research programme, with the aim to define a new approach for future river pollution monitoring.

Il dataset contiene il modello digitale del terreno (DTM) della città antica di Kainua-Marzabotto. Il modello, di cui si forniscono sia il risultato finale che gli step intermedi, costituisce il punto di riferimento iniziale per il processo di ricostruzione della città etrusca di Marzabotto.

In Antarctica, the severe climatic conditions and the thick ice sheet that covers the largest and most internal part of the continent make it particularly difficult to systematically carry out geophysical and geodetic observations on a continental scale. It prevents the comprehensive understanding of both the onshore and offshore geology as well as the relationship between the inner part of East Antarctica (EA) and the coastal sector of Victoria Land (VL). With the aim to reduce this gap, in this paper multiple geophysical dataset collected since the 1980s in Antarctica by Programma Nazionale di Ricerche in Antartide (PNRA) were integrated with geodetic observations. In particular, the analyzed data includes: (i) Geodetic time series from Trans Antarctic Mountains DEFormation (TAMDEF), and Victoria Land Network for DEFormation control (VLNDEF) GNSS stations installed in Victoria Land; (ii) the integration of on-shore (ground points data and airborne) gravity measurements in Victoria Land and marine gravity surveys performed in the Ross Sea and the narrow strip of Southern Ocean facing the coasts of northern Victoria Land. Gravity data modelling has improved the knowledge of the Moho depth of VL and surrounding the offshore areas. By the integration of geodetic and gravitational (or gravity) potential results it was possible to better constrain/identify four geodynamic blocks characterized by homogeneous geophysical signature: the Southern Ocean to the N, the Ross Sea to the E, the Wilkes Basin to the W, and VL in between. The last block is characterized by a small but significant clockwise rotation relative to East Antarctica. The presence of a N-S to NNW-SSE 1-km step in the Moho in correspondence of the Rennick Geodynamic Belt confirms the existence of this crustal scale discontinuity, possibly representing the tectonic boundary between East Antarctica and the northern part of VL block, as previously proposed by some geological studies.

Marine animal forests are benthic communities dominated by sessile suspension feeders (such as sponges, corals, and bivalves) able to generate three-dimensional (3D) frameworks with high structural complexity. The biodiversity and functioning of marine animal forests are strictly related to their 3D complexity. The present paper aims at providing new perspectives in underwater optical surveys. Starting from the current gaps in data collection and analysis that critically limit the study and conservation of marine animal forests, we discuss the main technological and methodological needs for the investigation of their 3D structural complexity at different spatial and temporal scales. Despite recent technological advances, it seems that several issues in data acquisition and processing need to be solved, to properly map the different benthic habitats in which marine animal forests are present, their health status and to measure structural complexity. Proper precision and accuracy should be chosen and assured in relation to the biological and ecological processes investigated. Besides, standardized methods and protocols are strictly necessary to meet the FAIR (findability, accessibility, interoperability, and reusability) data principles for the stewardship of habitat mapping and biodiversity, biomass, and growth data.

Plastic pollution has become one of the main global environmental emergencies. A considerable part of used plastics materials is dispersed or accumulated in the environment with a significant damaging impact on many terrestrial and aquatic ecosystems.Artificial Intelligence has proven a fundamental approach in last years for the detection of plastics waste in the aquatic habitats: several groups have recently tried to tackle such problem by developing some machine learning-based methods and multispectral or RGB imagery. This study compares the results obtained by two machine learning classifiers, namely Random Forests and Support Vector Machine, to detect macroplastic in the fluvial habitat through multispectral imagery. The acquisition of images has been made with a hand-held multispectral camera called MAIA-WV2. Despite the obtained results are quite good in terms of accuracy in a random validation dataset, some issues, mostly related to the presence of white rocks and glares on water have still to be properly solved.

The recent availability of unmanned aerial systems (UASs) and the joint use of methodologies provided by geomatics engineering, photogrammetry, and computer vision technologies have established new opportunities for environmental monitoring purposes. This is especially the case for traditional survey applications where labour-intensive investigations are required, such as for coastal environments, where logistic difficulties can compromise the quality of the overall product and make the work of the surveyors difficult. The high-resolution and flexible overflights offered by unmanned aerial vehicle (UAV) sensors make these instruments more suitable for a wide range of coastal applications. In particular, all applications that require investigations at desired periods and revisit times of the coastal settings can benefit from the new paradigm offered by UAV surveys. The objective of this chapter is to acquaint the reader with the potentiality of UAS-based surveys on coastal environments through a discussion of examples available in the very recent literature and through the skills achieved by the authors in this field. After a short introduction on UAV models that can be used to perform aerial surveys and a short description on the available methodologies to process the data acquired by visible and multispectral sensors, this chapter will focus on several UAS applications referred to coastal environments.

One of the most important ice-stream of the Victoria Land (VL) is the David Glacier, which produces 100 km long floating sea-ward ice tongues in the Ross Sea, the Drygalski Ice Tongue (DIT). The ice-tongue slides down into the sea increasing its velocity rates and together with ice-stream movement sometime produce characteristic ice-quakes. This paper shows the effects of the sea tidal variation on both horizontal and vertical components of movement at a portion of DIT. Ocean tide is usually modelled by a series of harmonic coefficients (amplitude and phase), which are estimated through several systems of measurement. For the study area, these data are made available by the Antarctic Tide Gauge (ATG) database. Moreover, tidal data recorded by a multiparameter underwater tide gauge, which was installed at Mario Zucchelli Station (MZS), the Italian Antarctic Base, in February 2006, are being processed again. The kinematic Precise Point Positioning (PPP) processing was adopted for the analysis of 24 days of acquisition performed with a GPS receiver located in the initial part of DIT, about 46 km seaward from the Ice Fault David Cauldron. The analysis of harmonic tidal components has shown that PPP solutions show correct values of diurnal and semi-diurnal tidal components and therefore can provide valuable information in the coastal area covered by ice tongues.

In recent years digital sensors have been successfully integrated on board Unmanned Aerial Vehicles (UAV) to assess crop vigour, vegetation coverage, and to quantify the 'greenness' of foliage as indirect measurements of crop nitrogen status. The classical approach of precision agriculture has involved the use of multispectral sensors onboard UAV and the development of numerous vegetation indices associated with vegetation parameters, such as the mostly used Normalized Difference Vegetation Index (NDVI). However, the main negative issue when dealing with multi and hyper-spectral reflectance measuring tools is their high cost and complexity from the operational point of view. As a low-cost alternative, vegetation indices derived from Red Green Blue (RGB) cameras have been employed for remote-sensing assessment, providing data on different stress conditions and species. Digital images record information as amounts of RGB light emitted for each pixel of the image; however, the intensity of red and blue will often alter how green an image appears. To simplify the interpretation of digital colour data, recent studies have suggested converting RGB values to the more intuitive Hue, Saturation, and Brightness (HSB) colour spectrum, and then into a single measure of dark green colour, the Dark Green Color Index (DGCI). In this study, NDVI acquired by a ground-based handheld crop sensor and by a multispectral camera mounted on board a UAV has been compared with DGCI calculated from images taken with a commercial digital camera on board a UAV, trying to quantify the colour of turfgrass that had received different nitrogen (N) rates. The objectives of the trial were to study an affordable easy-to-use tool evaluating the relationship among NDVI, DGCI and leaf nitrogen content on turfgrass.

Coastal dunes provide the hinterland with natural protection from marine dynamics. The specialized plant species that constitute dune vegetation communities are descriptive of the dune evolution status, which in turn reveals the ongoing coastal dynamics. The aims of this paper were to demonstrate the applicability of a low-cost unmanned aerial system for the classification of dune vegetation, in order to determine the level of detail achievable for the identification of vegetation communities and define the best-performing classification method for the dune environment according to pixel-based and object-based approaches. These goals were pursued by studying the north-Adriatic coastal dunes of Casal Borsetti (Ravenna, Italy). Four classification algorithms were applied to three-band orthoimages (red, green, and near-infrared). All classification maps were validated through ground truthing, and comparisons were performed for the three statistical methods, based on the k coefficient and on correctly and incorrectly classified pixel proportions of two maps. All classifications recognized the five vegetation classes considered, and high spatial resolution maps were produced (0.15 m). For both pixel-based and object-based methods, the support vector machine algorithm demonstrated a better accuracy for class recognition. The comparison revealed that an object approach is the better technique, although the required level of detail determines the final decision.

In order to make inferences on the geodynamics of Antarctica, geodetic and gravimetric maps derived from past and new observations can be used. This paper provides new insights into the geodynamics of Antarctica by integrating data at regional and continental scales. In particular, signatures of geodynamic activity at a regional extent have been investigated in Victoria Land (VL, Antarctica) by means of Global Navigation Satellite System (GNSS) permanent station observations, data from the VLNDEF (Victoria Land Network for Deformation control) discontinuous network, and gravity station measurements. At the continental scale, episodic GNSS observations on VLNDEF sites collected for 20 years, together with continuous data from the International GNSS Service (IGS) and Polar Earth Observing Network (POLENET) sites, were processed, and the Euler pole position assessed with the angular velocity of the Antarctic plate. Both the Bouguer and the free-air gravity anomaly maps were obtained by integrating the available open-access geophysics dataset, and a compilation of 180 gravity measurements collected in the VL within the Italian National Program for Antarctic Research (PNRA) activities. As a result, new evidence has been detected at regional and continental scale. The main absolute motion of VL is towards SE (Ve 9.9 ± 0.26 mm/yr, Vn −11.9 ± 0.27 mm/yr) with a pattern similar to the transforms of the Tasman and Balleny fracture zones produced as consequence of Southern Ocean spreading. Residual velocities of the GNSS stations located in VL confirm the active role of the two main tectonic lineaments of the region, the Rennick–Aviator and the Lillie–Tucker faults with right-lateral sense of shear. The resulting VL gravity anomalies show a NW region characterized by small sized Bouguer anomaly with high uplift rates associated and a SE region with low values of Bouguer anomaly and general subsidence phenomena. The East and West Antarctica are characterized by a different thickness of the Earth’s crust, and the relative velocities obtained by the observed GNSS data confirm that movements between the two regions are negligible. In East Antarctica, the roots of the main subglacial highlands, Gamburtsev Mts and Dronning Maud Land, are present. The Northern Victoria Land (NVL) is characterized by more scattered anomalies. These confirm the differences between the Glacial Isostatic Adjustment (GIA) modeled and observed uplift rates that could be related to deep-seated, regional scale structures.