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The increasing availability of SAR images and processing results over wide areas determines the need for systematic procedures to extract the information from this dataset and exploit the enhanced quality of the displacement time series. The aim of the study is to propose a new pre-operational workflow of an A-DInSAR-based land subsidence monitoring and interpretation service. The workflow is tested in Turano Lodigiano (Lombardy region, Italy) using COSMO-SkyMed data, processed using the SqueeSAR™ algorithm, and covering the time span from 2016 to 2019. The test site is a representative peri-urban area of the Po plain susceptible to land subsidence. The results give insight about new value-added products and enable non-expert users to exploit the potential of the interferometric results. [ABSTRACT FROM AUTHOR]

The frequency and the accumulation of medium–small flood events can cause severe impacts. In a climate change context, real-time monitoring and a fast risk assessment are needed to support the post-disaster phases. The present work presents a novel methodology that leverages the potential of earth observation data to produce a proof-of-concept for flood vulnerability assessment, serving as the basis for a Map Operational Service for the Lombardy region. The proof-of-concept is related to both flood hazard estimation and vulnerability assessment, considering the evaluation of the potentialities of the synthetic aperture radar data when used to feed a downstream service. Using the city of Pavia (Italy), which was affected by a flood event in November 2019, as a case study, we present an integrated flood impact approach that includes a combination of social and physical parameters. The results contribute to a processing chain designed as a pre-operational service where each data analytic retrieves thematic products to support the exposure and damage estimates based on earth observation-derived hazard products for emergency and recovery responses. Three different satellites covered more than 40 h of the flood's evolution, supporting the great potential of the multi-sensor approach. Therefore, different sensor configurations in terms of spectral bands (X and C bands) and resolutions (from 10 to 1 m) provide a near real-time view of the event. Comparing the results obtained through the three hazard scenarios, a final social and physical Integrated Impact Index is obtained. The added value information leads to the determination of hotspots with which to prioritize effective interventions during emergency and recovery phases, crucial for capturing inherent conditions that allow communities to absorb impacts and cope with a damaging flood event. [ABSTRACT FROM AUTHOR]

Understanding crop types and their annual cycles is key to managing natural resources, especially when the pressures on these resources are attributable to climate change and social, environmental, and economic policies. In recent years, the space sector's development, with programs such as Copernicus, has enabled a greater availability of satellite data. This study uses a multi-sensor approach to retrieve crop information by developing a Proof of Concept for the integration of high-resolution SAR imagery and optical data. The main goal is to develop a pre-automatized processing chain that explores the temporal dimension of different crop. Results are related to the advantage of using a multi-sensor approach to retrieve vegetation biomass and vertical structure for the identification of phenological stages and different crops. The novelty consists of investigating the multi-temporal pattern of radiometric indices and radar backscatter to detect the different phenological stages of each crop, identifying the Day of the Year (DoY) in which the classes showed greater separability. The current study could be considered a benchmark for the exploitation of future multi-sensor missions in downstream services for the agricultural sector, strengthening the evolution of Copernicus services. [ABSTRACT FROM AUTHOR]

Imaging spectroscopy is a powerful tool used to support diverse Earth science and applications objectives, ranging from understanding and mitigating widespread impacts of climate change to management of water at farm‐scale. Community studies, such as those deployed by NASA's Surface Biology and Geology and ESA's Copernicus Hyperspectral Imaging Mission for the Environment, have offered new and tangible insights into user needs that are then incorporated into overall mission planning and design. These technologies and tools will be key to develop and consolidate downstream services for users and resource management, given the current pressures on the environment posed by climate change and population growth. This process has highlighted the degree to which planned mission capabilities are responsive to community needs. In this study, we analyze user requirements belonging to the Italian Copernicus User Forum and to the user pool of NASA's Surface Biology and Geology community for the synergic use of hyperspectral imaging technology, providing a reference for the development of earth observation services and the consolidation of existing ones. In addition, potential cross‐mission coordination is analyzed to highlight key benefits—(a) addressing shared community needs around products requiring more frequent temporal revisit and (b) shared resources and community expertise around algorithm development. This paper discusses the critical role of early engagement with users to establish a community of practice ready to work with high spatial resolution imaging spectroscopy data sets. The main outcome is a guide for the synergetic use of hyperspectral mission and data together with the identification of the main gaps between user needs and satellite capabilities influencing the development of key national and trans‐national downstream services. Plain Language Summary: In this research we have analyzed needs of users to understand how to develop and improve earth observation downstream services. Hyperspectral imaging provides information across the electromagnetic spectrum in more detail than multispectral imaging, allowing more specific analysis and accurate identification of materials and substances. The users in this study belong to the Italian Copernicus User Forum and to the NASA Surface Biology and Geology community which provided requirements respectively for ESA's Copernicus Hyperspectral Imaging Mission for the Environment and NASA's Surface Biology and Geology. This work identifies common users and examines the advantages of potential synergy between these hyperspectral satellites for responding to their needs. This analysis shows that the synergy would bring significant advantages in terms of improved revisit time and represents an opportunity to address shared community needs around product and algorithm development. Key Points: Current users' needs for hyperspectral earth observation data suggest future investment in improving spatial and temporal resolutionCoordination between missions can fill this gap especially improving temporal revisitShared community and resources can facilitate the improvement of products from these missions and their use across different sectors [ABSTRACT FROM AUTHOR]

The occurrence of extreme warm events in the Arctic has been increasing in recent years in terms of their frequency and intensity. The assessment of the impact of these episodes on the snow season requires further observation capabilities, where spatial and temporal resolutions are key constraints. This study targeted the snow season of 2022 when a winter rain-on-snow event occurred at Ny-Ålesund in mid-March. The selected methodology was based on a multi-scale and multi-platform approach, combining ground-based observations with satellite remote sensing. The ground-based observation portfolio included meteorological measurements, nivological information, and the optical description of the surface in terms of spectral reflectance and snow-cover extent. The satellite data were obtained by the Sentinel-2 platforms, which provided ten multi-spectral acquisitions from March to July. The proposed strategy supported the impact assessment of heat waves in a periglacial environment, describing the relation and the timing between rain-on-snow events and the surface water drainage system. The integration between a wide range of spectral, time, and spatial resolutions enhanced the capacity to monitor the evolution of the surface water drainage system, detecting two water discharge pulsations, different in terms of duration and effects. This preliminary study aims to improve the description of the snow dynamics during those extreme events and to assess the impact of the produced break during the snow accumulation period. [ABSTRACT FROM AUTHOR]

This study is part of the requirements consolidation study for the European Copernicus Hyperspectral Imaging Mission for Environment (CHIME). It explores the value added by existing hyperspectral data of similar characteristics to CHIME, namely AVIRIS‐NG and PRecursore IperSpettrale della Missione Applicativa (PRISMA), for detecting topsoil texture properties. The spatial variability is retrieved using the linear spectral mixture analysis, an image‐based algorithm that breaks down the hyperspectral data set into fractional abundance of spectral classes within each pixel. The fractional abundance of image‐based endmembers is broken into categories to find intervals having a spatial relation with texture components in terms of fine (clay and silt) or coarse (sand) abundance. The fraction maps obtained show similar spatial patterns to the USDA soil texture classification, obtained with a geostatistical approach. Specifically, AVIRIS CHIME‐like FAM1 > 0.45 presented an agreement of 86% with clay and/or silt higher than 45% which, according to the United Stated Agriculture Department (USDA) intervals, correspond to loam‐clay loam classes. Similar results are obtained with PRISMA with FAM2 0.20–0.35, overlapping 63% of the kriging‐based USDA clay‐loam class. The fractional abundance categories showing the highest overlap percentages are correlated with the short‐wave infrared spectral range, showing average coefficients of 0.7 where wavelengths are over 1,500 nm. From 1700 nm, CHIME‐like shows values of 0.8. In conclusion, this exploratory research and results leverage the opportunity of extending the processing chain to a larger number of case studies to better understand the physical relation between the spectral reflectance captured by new spaceborne hyperspectral sensors and the spatial patterns of soil texture classes. Plain Language Summary: In the current context led by the constant availability, almost in near‐real time, of remote sensing data with high spatial, spectral and temporal resolutions, the monitoring of soil properties should no longer be solely limited to field data observations. There is a growing need of knowledge for preserving and exploiting soils and the upcoming generation of hyperspectral satellite remote sensing has popped up new opportunities for developing and adapting algorithms that break up the image pixels into several spectral behaviors that correspond to different soil properties. This paper contributes to the application of these image analysis techniques to soil texture classes retrieval considering the United Stated Agriculture Department model. The paper uses the principles of the spectral mixture concept to explore the opportunity of detecting soil textures in two farmland units and provides some highlights on the observational requirements needed to integrate the future satellite missions' architecture within the agriculture and food security application domain. Key Points: Hyperspectral imaging is used to retrieve United Stated Agriculture Department soil texture classification in two farmland units applying linear spectral mixture analysisCopernicus Hyperspectral Imaging Mission for Environment‐like and PRecursore IperSpettrale della Missione Applicativa‐derived fractional abundance intervals are found to spatially overlap the clay‐loam class obtained with field dataFractional abundances categories having the highest overlap percentage are strongly correlated with the Short‐Wave InfraRed > 1,500 nm [ABSTRACT FROM AUTHOR]

Coastal managers, policymakers, and scientists use shoreline accretion/erosion trends to determine the coastline's historical evolution and generate models capable of predicting future changes. Different solutions have been developed to obtain shoreline positions from Earth observation data in recent years, the so-called Satellite-Derived Shorelines (SDS). Most of the methodologies available in the literature use multispectral optical satellite imagery. This paper proposes two new methods for shoreline mapping at the subpixel level based on PRISMA hyperspectral imagery. The first one analyses the spectral signatures along defined beach profiles. The second method uses techniques more commonly applied to multispectral image analysis, such as Spectral Unmixing algorithms and Spatial Attraction Models. The results obtained with both methodologies are validated on three Mediterranean microtidal beaches located in two different countries, Italy and Greece, using image-based ground truth shorelines manually photointerpreted and digitised. The obtained errors are around 6 and 7 m for the first and second methods, respectively. These results are comparable to the errors obtained from multispectral data. The paper also discusses the capability of the two methods to identify two different shoreline proxies. [ABSTRACT FROM AUTHOR]

The vulnerability of flood-prone areas is determined by the susceptibility of the exposed assets to the hazard. It is a crucial component in risk assessment studies, both for climate change adaptation and disaster risk reduction. In this study, we analyse patterns of vulnerability for the residential sector in a frequently hit urban area of Milan, Italy. The conceptual foundation for a quantitative assessment of the structural dimensions of vulnerability is based on the modified source–pathway–receptor–consequence model. This conceptual model is used to improve the parameterization of the flood risk analysis, describing (i) hazard scenario definitions performed by hydraulic modelling based on past event data (source estimation) and morphological features and land-use evaluation (pathway estimation) and (ii) the exposure and vulnerability assessment which consists of recognizing elements potentially at risk (receptor estimation) and event losses (consequence estimation). We characterized flood hazard intensity on the basis of variability in water depth during a recent event and spatial exposure also as a function of a building's surroundings and buildings' intrinsic characteristics as a determinant vulnerability indicator of the elements at risk. In this sense the use of a geographic scale sufficient to depict spatial differences in vulnerability allowed us to identify structural vulnerability patterns to inform depth–damage curves and calculate potential losses from mesoscale (land-use level) to microscale (building level). Results produces accurate estimates of the flood characteristics, with mean error in flood depth estimation in the range 0.2–0.3 m and provide a basis to obtain site-specific damage curves and damage mapping. Findings show that the nature of flood pathways varies spatially, is influenced by landscape characteristics and alters vulnerability spatial distribution and hazard propagation. At the mesoscale, the "continuous urban fabric" Urban Atlas 2018 land-use class with the occurrence of at least 80 % of soil sealing shows higher absolute damage values. At microscale, evidence demonstrated that even events with moderate magnitude in terms of flood depth in a complex urbanized area may cause more damage than one would expect. [ABSTRACT FROM AUTHOR]

The vulnerability of flood-prone areas is determined by the susceptibility of the exposed assets to the hazard. It is a crucial component in risk assessment studies, both for climate change adaptation and disaster risk reduction. In this study, we analyse patterns of vulnerability for the residential sector in a frequently hit urban area of Milano, Italy. The conceptual foundation for a quantitative assessment of the structural dimensions of vulnerability is based on the modified Source-Pathway-Receptor-Consequence model. This conceptual model is used to improve the parameterization of the flood risk analysis describing: (i) hazard scenarios definition performed by hydraulic modelling based on past event data (Source estimation) and morphological features and land use evaluation (Pathway estimation); (ii) the exposure and vulnerability assessment which consists of recognizing elements potentially at risk (Receptor estimation) and event losses (Consequence estimation). The structural dimension of vulnerability is mapped at building level and used in loss estimation for the residential sector at meso and micro-scale. Results produces accurate estimates of the flood characteristics, with mean error in flood depths estimation in the range 0.2-0.3 m and provide a basis to obtain site-specific damage curves and damage mapping. Findings show that the nature of flood pathways varies spatially and is influenced by landscape characteristics and alters vulnerability spatial distribution and hazard propagation. At the mesoscale, the 'Continuous urban fabric' Urban Atlas 2018 land-use class with the occurrence of at least 80 % of soil sealing shows higher absolute damage values. At microscale, evidence demonstrated that even events with moderate magnitude in terms of flood depth in a complex urbanized area may cause more damage than it would expect. [ABSTRACT FROM AUTHOR]

Salt marshes are important coastal areas that consist of a vegetated intertidal marsh platform and a drainage network of tidal channels. How salt marshes and their drainage networks develop is not fully understood, but it has been shown that the biogeomorphic interactions and feedbacks between vegetation development and channel formation play an important role. We examined the relationships among tidal channel sinuosity, marsh roughness, vegetation type (pioneer, Elymus athericus or Phragmites australis), and patch size at different spatial scales using a high‐resolution vegetation map (derived from aerial photography) and lower‐resolution satellite imagery processed with linear spectral mixture analysis. The patch‐size distribution in all vegetation types corresponded to a power law, suggesting the presence of self‐organizational processes. While small vegetation patches are more dominant in pioneer vegetation, they were present in all vegetation types. The largest patch size is restricted to E. athericus. We observed an inverse logarithmic relationship between channel sinuosity and vegetation patch size in all vegetation types. The fact that this relationship is observed in both pioneer and later successional stages suggests that after the establishment of a drainage network in the dynamic pioneer stages of salt marsh development, the later stages of salt marsh succession largely inherit the meandering pattern of the early successional stages. Our study confirms recent evidence that no significant changes in the specific features of tidal channel networks (e.g., channel width, drainage density, and efficiency) take place during the later stages of salt marsh development. Key Points: The imprint of biogeomorphic processes on salt marsh drainage network sinuosity during the pioneer stages is inherited by later successional vegetation typesVegetation patch‐size distributions in salt marsh vegetations from the pioneer to late successional stages correspond to power laws, suggesting the presence of self‐organizational processesVegetation patch size in salt marshes shows an inverse logarithmic relationship with tidal channel sinuosity, irrespective of vegetation type and elevation [ABSTRACT FROM AUTHOR]

Physical, chemical and biological characteristics of seawaters are primary descriptors for understanding environmental patterns and improving maritime spatial planning for potential aquaculture uses. By analyzing these descriptors in spatial and temporal dimensions, it is possible to characterize the potential productivity performances of different locations for specific aquaculture species. We developed a toolbox that, starting from the actual competing uses of the maritime space, aims at: (a) identifying sites with conditions feasible for aquaculture fish growth (feasibility scenario); and (b) assessing their different productivity performances in terms of potential fish harvest (suitability scenario). The toolbox is being designed in the Mediterranean, northern Adriatic Sea, but because of its modularity/multi-stage process, it can be easily adapted to other areas, or scaled to larger areas. The toolbox, representing a pre-operational Copernicus downstreaming service that integrates data and products from different sources (in situ, Earth Observation and modeling), is innovative because it is based more on parameters relevant for fish vitality than on those oriented to farm functioning. Stakeholders and farmers involved in the maritime spatial planning can use resulting scenarios for decision-making and market-trading processes. [ABSTRACT FROM AUTHOR]

In the near future, the oceans will be subjected to a massive development of marine infrastructures, including offshore wind, tidal and wave energy farms and constructions for marine aquaculture. The development of these facilities will unavoidably exert environmental pressures on marine ecosystems. It is therefore crucial that the economic costs, the use of marine space and the environmental impacts of these activities remain within acceptable limits. Moreover, the installation of arrays of wave energy devices is still far from being economically feasible due to many combined aspects, such as immature technologies for energy conversion, local energy storage and moorings. Therefore, multi-purpose solutions combining renewable energy from the sea (wind, wave, tide), aquaculture and transportation facilities can be considered as a challenging, yet advantageous, way to boost blue growth. This would be due to the sharing of the costs of installation and using the produced energy locally to feed the different functionalities and optimizing marine spatial planning. This paper focuses on the synergies that may be produced by a multi-purpose offshore installation in a relatively calm sea, i.e., the Northern Adriatic Sea, Italy, and specifically offshore Venice. It analyzes the combination of aquaculture, energy production from wind and waves, and energy storage or transfer. Alternative solutions are evaluated based on specific criteria, including the maturity of the technology, the environmental impact, the induced risks and the costs. Based on expert judgment, the alternatives are ranked and a preliminary layout of the selected multi-purpose installation for the case study is proposed, to further allow the exploitation of the synergies among different functionalities. [ABSTRACT FROM AUTHOR]

In the last decade, modelling hurricanes in potentially affected areas using geographical information systems (GIS) and geospatial cyberinfrastracture (GCI) has become a major topic of research. Despite some basic approaches, some unsolved questions are still under discussion. The disastrous effects of hurricanes on communities are well known, however there is a need to better understand the hazard contributions of the different components related to a hurricane, such as storm surges, floods and high winds. In this paper, the selected approach is to determine an onset zoning from a set of attributes that are considered to govern the hurricane while examining the influence of each individual component that produces the final exposure. To this end, this study assesses the different components using parameters derived from topography, bathymetry and hurricane physical indexes. Key attributes are the river network, the topography, the wetness index and the offline bathymetry. Complementary data include the CMORPH rain dataset and the hurricane track together with its structure model, both based on National Oceanic and Atmospheric Administration (NOAA) datasets. Total hazard results were then overlaid with population data in the overall assessment of elements at risk. The approach, which made use of a number of available global and free datasets, was then validated on a regional basis using ground data collected by the World Food Programme (WFP) over the study area (Central America region) for a specific hurricane. [ABSTRACT FROM AUTHOR]

One characteristic of a Geographic Information System (GIS) is that it addresses the necessity to handle a large amount of data at multiple scales. Lands span over an area greater than 15 million km2 all over the globe and information types are highly variable. In addition, multi-scale analyses involve both spatial and temporal integration of datasets deriving from different sources. The currently worldwide used system of latitude and longitude coordinates could avoid limitations in data use due to biases and approximations. In this article a fast and reliable algorithm implemented in Arc Macro Language (AML) is presented to provide an automatic computation of the surface area of the cells in a regularly spaced longitude-latitude (geographic) grid at different resolutions. The approach is based on the well-known approximation of the spheroidal Earth's surface to the authalic (i.e. equal-area) sphere. After verifying the algorithm's strength by comparison with a numerical solution for the reference spheroidal model, specific case studies are introduced to evaluate the differences when switching from geographic to projected coordinate systems. This is done at different resolutions and using different formulations to calculate cell areas. Even if the percentage differences are low, they become relevant when reported in absolute terms (hectares). [ABSTRACT FROM AUTHOR]

The current Copernicus evolution aims to meet horizontal users' needs in order to widen uptake of the Copernicus monitoring products by non-traditional users. In 2019, the European Commission initiated a coordinated action to evaluate the current and potential uptakes of Copernicus products, and for the monitoring and protection of European Cultural and Natural Heritage in a future climate change scenario. An interaction matrix was developed, circulated to and fulfilled by users in order to collect their needs and identify the main gaps in terms of monitoring data and information. The results show what users require from Copernicus to face the daily challenges of preserving and protecting CH features. Moreover, the interaction with users identified a data and information access model that best maximizes uptake by the users. The present work illustrates the user requirement coordination mechanism adopted by the European Copernicus Cultural Heritage Task Force; synthesises the results achieved in terms of gap analysis; and assesses the current and potential uptake of Copernicus data, services, and products in support of the monitoring and protection of European cultural heritage. It also provides recommendation about the implementation of infrastructural solutions to improve Copernicus services data and information access by cultural heritage users. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) is a key biophysical variable for agroecosystem monitoring, as well as a relevant state variable in crop modelling. For this reason, temporal and spatial determination of LAI are required to improve the understanding of several land surface processes related to vegetation dynamics and crop growth. Despite the large number of retrieved LAI products and the efforts to develop new and updated algorithms for LAI estimation, the available products are not yet capable of capturing site-specific variability, as requested in many agricultural applications. The objective of this study was to evaluate the potential of non-parametric approaches for multi-temporal LAI retrieval by Sentinel-2 multispectral data, in comparison with a VI-based parametric approach. For this purpose, we built a large database combining a multispectral satellite data set and ground LAI measurements collected over two growing seasons (2018 and 2019), including three crops (i.e., winter wheat, maize, and alfalfa) characterized by different growing cycles and canopy structures, and considering different agronomic conditions (i.e., at three farms in three different sites). The accuracy of parametric and non-parametric methods for LAI estimation was assessed by cross-validation (CV) at both the pixel and field levels over mixed-crop (MC) and crop-specific (CS) data sets. Overall, the non-parametric approach showed a higher accuracy of prediction at pixel level than parametric methods, and it was also observed that Gaussian Process Regression (GPR) did not provide any significant difference (p-value > 0.05) between the predicted values of LAI in the MC and CS data sets, regardless of the crop. Indeed, GPR at the field level showed a cross-validated coefficient of determination (R2CV) higher than 0.80 for all three crops. [ABSTRACT FROM AUTHOR]

Coastal wetlands represent particularly valuable natural resources, characterized by the interaction between their geomorphological and biological components. Their adaptation to the changing conditions depends on the rate and extent of spatial and temporal processes and their response is still not fully understood. This work aims at detecting and improving the understanding of the transition dynamics on eco-geomorphological structures in a coastal wetland ecosystem. The approach could support sustainable habitat management improving the detection and optimizing the offer of Earth Observation (EO) products for coastal system monitoring. Such course of action will strengthen evidence-based policy making, surface biophysical data sovereignty and the Space Data downstream sector through remote sensing techniques thanks to the capability of investigating larger scale and short-to-long-term dynamics. The selected case study is the Lido basin (Venice Lagoon, Italy). Our methodology offers a support in the framework of nature-based solutions, allowing the identification of ecosystem-level indicators of the surface biophysical properties influencing stability and evolution of intertidal flats on which a conceptual model is implemented. Landsat satellite imagery is used to delineate the spatial and temporal variability of the main vegetation and sediment typologies in 1990–2011. Within this period, specific anthropic activities were carried out for morphological restoration and flood protection interventions. Specifically, the lower saltmarsh shows its more fragmented part in the Baccan islet, a residual sandy spit in front of the Lido inlet. The area covered by Sarcocornia-Limonium, that triggers sediment deposition, has fluctuated yearly, from a minimum coverage of 13% to a maximum of 50%. The second decade (2001–2009) is identified as the period with major changes of halophytic and Algae-Biofilm cover typologies distribution. The power law and related thresholds, representing the patch size frequency distribution, is an indicator of the ecosystem state transition dynamics. The approach, based on multi-temporal and spatial EO analysis, is scalable elsewhere, from regional to local-to-global scale, considering the variability of climate data and anthropogenic activities. The present research also supports sustainable habitat management, improving the detection, and optimizing the offer of EO products for coastal system monitoring. [ABSTRACT FROM AUTHOR]

In the light of the "Biological Diversity" concept, habitats are cardinal pieces for biodiversity quantitative estimation at a local and global scale. In Europe EUNIS (European Nature Information System) is a system tool for habitat identification and assessment. Earth Observation (EO) data, which are acquired by satellite sensors, offer new opportunities for environmental sciences and they are revolutionizing the methodologies applied. These are providing unprecedented insights for habitat monitoring and for evaluating the Sustainable Development Goals (SDGs) indicators. This paper shows the results of a novel approach for a spatially explicit habitat mapping in Italy at a national scale, using a supervised machine learning model (SMLM), through the combination of vegetation plot database (as response variable), and both spectral and environmental predictors. The procedure integrates forest habitat data in Italy from the European Vegetation Archive (EVA), with Sentinel-2 imagery processing (vegetation indices time series, spectral indices, and single bands spectral signals) and environmental data variables (i.e., climatic and topographic), to parameterize a Random Forests (RF) classifier. The obtained results classify 24 forest habitats according to the EUNIS III level: 12 broadleaved deciduous (T1), 4 broadleaved evergreen (T2) and eight needleleaved forest habitats (T3), and achieved an overall accuracy of 87% at the EUNIS II level classes (T1, T2, T3), and an overall accuracy of 76.14% at the EUNIS III level. The highest overall accuracy value was obtained for the broadleaved evergreen forest equal to 91%, followed by 76% and 68% for needleleaved and broadleaved deciduous habitat forests, respectively. The results of the proposed methodology open the way to increase the EUNIS habitat categories to be mapped together with their geographical extent, and to test different semi-supervised machine learning algorithms and ensemble modelling methods. [ABSTRACT FROM AUTHOR]

According to the global growth of the "Blue economy", coastal zones are under pressure from both land and marine side economic activities. The fragmentation of sectorial interests and legislation along the coasts has led to the need for bridging knowledge (data/information and methods/tools) and governance (decision-makers at every level) in order to ensure sustainable economic development and social and ecosystem resilience. This poses the need for an interaction process that associates user needs to the European and national legislative framework to create a policy-oriented demand of Copernicus Earth Observation services in coastal areas. Such goals need a strong and effective system to monitor compliance and to assess the progress of the legislation. This study aims at identifying potential gaps in the current Copernicus product offer for the monitoring of the coastal sector through the elicitation of stakeholder requirements. The methodology is applied to the Italian landscape of users, but it is scalable at European level. The results provide a clear overview of the coastal user requirements, highlighting the common need of integrated information for the management, and represents the basis for defining the coastal services. [ABSTRACT FROM AUTHOR]

Deltaic systems are broadly recognized as vulnerable hot spots at the interface between land and sea and are highly exposed to harmful natural and manmade threats. The vulnerability to these threats and the interactions of the biological, physical, and anthropogenic processes in low-lying coastal plains, such as river deltas, requires a better understanding in terms of vulnerable systems and to support sustainable management and spatial planning actions in the context of climate change. This study analyses the potential of Bayesian belief network (BBN) models to represent conditional dependencies in vulnerability assessment for future sea level rise (SLR) scenarios considering ecological, morphological and social factors using Earth observation (EO) time series dataset. The BBN model, applied in the Po Delta region in the northern Adriatic coast of Italy, defines relationships between twelve selected variables classified as driver factors (DF), land cover factors (LCF), and land use factors (LUF) chosen as critical for the definition of vulnerability hot spots, future coastal adaptation, and spatial planning actions to be taken. The key results identify the spatial distribution of the vulnerability along the costal delta and highlight where the probability of vulnerable areas is expected to increase in terms of SLR pressure, which occurs especially in the central and southern delta portion. [ABSTRACT FROM AUTHOR]

Identifying fire-affected areas is of key importance to support post-fire management strategies and account for the environmental impact of fires. The availability of high spatial and temporal resolution optical satellite data enables the development of procedures for detailed and prompt post-fire mapping. This study proposes a novel approach for integrating multiple spectral indices to generate more accurate burned area maps by exploiting Sentinel-2 images. This approach aims to develop a procedure to combine multiple spectral indices using an adaptive thresholding method and proposes an agreement index to map the burned areas by optimizing omission and commission errors. The approach has been tested for the burned area classification of four study areas in Italy. The proposed agreement index combines multiple spectral indices to select the actual burned pixels, to balance the omission and commission errors, and to optimize the overall accuracy. The results showed the spectral indices singularly performed differently in the four study areas and that high levels of commission errors were achieved, especially for wildfires which occurred during the fall season (up to 0.93) Furthermore, the agreement index showed a good level of accuracy (minimum 0.65, maximum 0.96) for all the study areas, improving the performance compared to assessing the indices individually. This suggests the possibility of testing the methodology on a large set of wildfire cases in different environmental conditions to support the decision-making process. Exploiting the high resolution of optical satellite data, this work contributes to improving the production of detailed burned area maps, which could be integrated into operational services based on the use of Earth Observation products for burned area mapping to support the decision-making process. [ABSTRACT FROM AUTHOR]

Evolution in the Copernicus Space Component is foreseen in the mid-2020s to meet priority user needs not addressed by the existing infrastructure, and/or to reinforce existing services. In this context, the European Commission is intending to evaluate the overall potential utility of a complementary Copernicus hyperspectral mission to be added to the Copernicus Sentinels fleet. Hyperspectral imaging is a powerful remote sensing technology that, allowing the characterization and quantification of Earth surface materials, has the potential to deliver significant enhancements in quantitative value-added products. This study aims to illustrate the interaction methodology that was set up to collect and assess user-driven requirements in different thematic areas to demonstrate the potential benefit of a future Copernicus hyperspectral mission. Therefore, an ad hoc interaction matrix was circulated among several user communities to gather preferences about hyperspectral-based products and services. The results show how the involvement of several user communities strengthens the identification of these user requirements. Moreover, the requirement evaluation is used to identify potential opportunities of hyperspectral imaging in addressing operational needs associated with policy obligations at European, national, and local levels. The frequency distribution of spectral range classes and spatial and temporal resolutions are also derived from the preference expressed by the user communities in each thematic area investigated. [ABSTRACT FROM AUTHOR]

Coastal sand dunes are highly dynamic aeolian landforms where different spatial patterns can be observed due to the complex interactions and relationships between landforms and land cover. Sediment distribution related to vegetation types is explored here on a single ridge dune system by using an airborne hyperspectral and light detection and ranging (LiDAR) remote sensing dataset. A correlation model is applied to describe the continuum of dune cover typologies, determine the class metrics from landscape ecology and the morphology parameters, and extract the relationship intensity among them. As a main result, the mixture of different vegetation types such as herbaceous, shrubs, and trees classes shows to be a key element for the sediment distribution pattern and a proxy for dune sediment retention capacity, and the anthropic fingerprints can play an even major role influencing both ecological and morphological features. The novelty of the approach is mostly based on the synergistic use of LiDAR with hyperspectral that allowed (i) the benefit from already existing processing methods to simplify the way to obtain thematic maps and coastal metrics and (ii) an improved detection of natural and anthropic landscape. [ABSTRACT FROM AUTHOR]

This article presents an approach to identify Green Infrastructure (GI), its benefits and condition. This information enables environmental agencies to prioritise conservation, management and restoration strategies accordingly. The study focuses on riparian areas due to their potential to supply Ecosystem Services (ES), such as water quality, biodiversity, soil protection and flood or drought risk reduction. Natural Water Retention Measures (NWRM) related to agriculture and forestry are the type of GI considered specifically within these riparian areas. The approach is based on ES condition indicators, defined by the European Environment Agency (EEA) to support the policy targets of the 2020 Biodiversity Strategy. Indicators that can be assessed through remote sensing techniques are used, namely: capacity to provide ecosystem services, proximity to protected areas, greening response and water stress. Specifically, the approach uses and evaluates the potential of freely available products from the Copernicus Land Monitoring Service (CLMS) to monitor GI. Moreover, vegetation and water indices are calculated using data from the Sentinel-2 MSI Level-2A scenes and integrated in the analysis. The approach has been tested in the Italian Po river basin in 2018. Firstly, agriculture and forest NWRM were identified in the riparian areas of the river network. Secondly, the Riparian Zones products from the CLMS local component and the satellite-based indices were linked to the aforementioned ES condition indicators. This led to the development of a pixel-based model that evaluates the identified GI according to: (i) its disposition to provide riparian regulative ES and (ii) its condition in the analysed year. Finally, the model was used to prioritise GI for conservation or restoration initiatives, based on its potential to deliver ES and current condition. [ABSTRACT FROM AUTHOR]

Nature-based solutions are increasingly relevant tools for spatial and environmental planning, climate change adaptation (CCA), and disaster risk reduction (DRR). For this reason, a wide range of institutions, governments, and financial bodies are currently promoting the use of green infrastructure (GI) as an alternative or a complement to traditional grey infrastructure. A considerable amount of research already certifies the benefits and multi-functionality of GI: natural water retention measures (NWRMs), as GIs related specifically to the water sector are also known, are, for instance, a key instrument for the prevention and mitigation of extreme phenomena, such as floods and droughts. However, there are persisting difficulties in locating and identifying GI and one of the most promising solutions to this issue, the use of satellite-based data products, is hampered by a lack of well-grounded knowledge, experiences, and tools. To bridge this gap, we performed a review of the Copernicus Global Land Service (CGLS) products, which consist of freely-available bio-geophysical indices covering the globe at mid-to-low spatial resolutions. Specifically, we focused on vegetation and energy indices, examining previous research works that made use of them and evaluating their current quality, aiming to define their potential for studying GI and especially NWRMs related to agriculture, forest, and hydro-morphology. NWRM benefits are also considered in the analysis, namely: (i) NWRM biophysical impacts (BPs), (ii) ecosystem services delivered by NWRMs (ESs), and (iii) policy objectives (POs) expressed by European Directives that NWRMs can help to achieve. The results of this study are meant to assist GI users in employing CGLS products and ease their decision-making process. Based on previous research experiences and the quality of the currently available versions, this analysis provides useful tools to identify which indices can be used to study several types of NWRMs, assess their benefits, and prioritize the most suitable ones. [ABSTRACT FROM AUTHOR]

The presence and distribution of green vegetation cover in the biosphere are of paramount importance in investigating cause-effect phenomena at the land/atmosphere interface, estimating primary production rates as part of global carbon and water cycle assessments and evaluating soil protection and land use change over time. The fraction of green vegetation cover (FCover) as estimated from satellite observations has already been demonstrated to be an extraordinarily useful product for understanding vegetation cover changes, for supporting ecosystem service assessments over areas with variable extents and for processes spanning a variable period of time (abrupt events or long-term processes). This study describes a methodology implemented to estimate global FCover (from 2001 to 2015) by applying a linear spectral mixture analysis with global endmembers to an entire temporal series of MODIS satellite observations and gap-filling missing FCover observations in temporal series using the DINEOF algorithm. The resulting global MODV1 FCover product was validated with two global validation datasets and showed an overall good thematic absolute accuracy (RMSE = 0.146) consistent with the validation performance of other FCover global products. Basic statistics performed on the product show changes in average and trend values and allow for the quantification of gross vegetation loss and gain over different temporal scales. To demonstrate the capacity of this global product to monitor specific dynamics, a multitemporal analysis was performed on selected sites and vegetation responses (i.e., cover changes), and specific dynamics resulting from cause-effect phenomena are briefly discussed. The product is intended to be used for monitoring vegetation dynamics, but it also has the potential to be integrated in other modeling frameworks (e.g., the carbon cycle, primary production, and soil erosion) in conjunction with other spatial datasets such as those on climate and soil type. [ABSTRACT FROM AUTHOR]