Search Results (17)

Flooding triggered by intense precipitation is a significant natural hazard affecting Mediterranean regions, where complex terrain, rapid hydrological response and increasing urbanization can amplify flood impacts. This study assesses flood susceptibility in two representative Mediterranean River catchments: the Koiliaris in Crete, Greece, and the Pediaios in Cyprus. A compact Flood Hazard Index (FHI) was developed by integrating the Topographic Wetness Index (TWI), Curve Number (CN), and R20 heavy rain frequency index, representing the principal geomorphological, hydrological and climatological controls of flood generation. Spatial datasets including EU-DEM elevation data, CORINE land cover, European soil databases, and Copernicus CERRA precipitation reanalysis were combined within a GIS-based multi-criteria framework using Analytic Hierarchy Process weighting. The resulting FHI maps identify high flood susceptibility along river corridors, low-lying accumulation zones, and urbanized areas. In the Koiliaris basin, 34% of the area fell within the high and very high susceptibility classes, mainly in downstream alluvial zones, whereas in the Pediaios basin, 29% of the area fell within the high and very high susceptibility classes, concentrated around the urbanized Nicosia corridor. The analysis of historical flood events provided a qualitative consistency assessment of the FHI patterns, acknowledging that the absence of spatially explicit flood-inundation footprints limits quantitative validation. Full article

Maritime transport accounts for around 3% of global anthropogenic greenhouse gas (GHG) emissions, a share expected to grow without effective technological and regulatory intervention. Recent policy developments, including the IMO Revised GHG Strategy (2023), the extension of the EU Emissions Trading System to maritime transport, and the FuelEU Maritime Regulation, require ports and shipping stakeholders to evaluate multiple decarbonization technologies under complex and often conflicting constraints. These decisions involve trade-offs across economic, technical, environmental, social, and cyber–physical security dimensions, which are not adequately addressed by conventional decision-support tools. This paper introduces XAI–MCDA-HoDEM, an explainable multi-criteria decision framework integrating Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and SHAP-based explainability. The framework explicitly incorporates cyber–physical security as a core evaluation criterion and provides transparent, criterion-level explanations of decision outcomes. Using real-world data, the methodology is demonstrated through an illustrative case study and empirically validated at the Port of Rotterdam. Results show stable and robust rankings, alignment with observed port decarbonization strategies, and improved interpretability of decision drivers. The proposed framework supports transparent, policy-relevant decision-making for the maritime energy transition. Full article

Pinoresinol diglucoside (PDG), one of the major lignans isolated from E. ulmoides Oliver bark, has various pharmacological functions, including antihypertension and prevention of osteoporosis. However, the glycosyltransferase-encoding gene (GT) involved in regulating the glycosylation of pinoresinol to form PDG has not been reported in E. ulmoides. In this study, we screened and cloned the EuGT8 gene from E. ulmoides based on our transcriptome data. The expression pattern of the EuGT8 gene exhibited a strong positive correlation with dynamic changes in the PDG contents in three different organs of E. ulmoides. The expression level of the EuGT8 gene and PDG content were significantly decreased in asODN-EuGT8-treated shoot tips in comparison with the control group. Prokaryotic expression of the EuGT8 gene revealed that the purified EuGT8 protein could catalyze the conversion of pinoresinol into PDG. In addition, we performed transcriptional and metabolomic analyses to compare the differences between transgenic Arabidopsis and WT plants. A total of 1799 DEGs and 294 DEMs were identified in transgenic and WT plants. KEGG enrichment analysis showed that the DEGs were mainly enriched in phenylpropanoid biosynthesis, secondary metabolite biosynthesis, and starch/sucrose metabolism pathways. The DEMs were mainly enriched in ABC transporters, aminoacyl-tRNA biosynthesis, biosynthesis of amino acids, phenylpropanoid biosynthesis, and flavone and flavonol biosynthesis pathways. Correlation analysis between DEGs and DEMs identified a total of 231 DEGs associated with 38 DEMs, which were mainly distributed in multiple metabolic pathways. This finding provides both theoretical insights and genetic resources for breeding high-PDG E. ulmoides varieties, facilitating marker-assisted selection (MAS) and promoting sustainable E. ulmoides production in Guizhou. Full article

This paper conceptualizes institutional resilience as a core condition of democratic sustainability in post-transition Europe. Building on neo-institutionalist approaches and recent scholarship on democratic resilience, we argue that democracies endure when three capacities align: policy coherence, procedural legitimacy, and civic/monitory participation. Using a comparative, theory-guided design, we analyze Romania, Poland, Hungary, and Slovakia (2007–2025), triangulating V-Dem/Eurostat indicators with documentary evidence (EU Rule of Law reports, CEPEJ) and interpretive analysis. Romania illustrates “reactive resilience” anchored in judicial independence and civic vigilance; Slovakia shows “restorative resilience” after corruption scandals; Poland exhibits “societal compensatory resilience,” where civic mobilization offsets institutional regression; Hungary demonstrates “instrumental resilience without democracy,” combining administrative capacity with normative decay. We integrate these findings into a three-dimensional model—institutional, normative, and communicative—showing how feedback loops convert crisis into learning. The paper concludes that sustainable democracy depends less on constitutional design alone and more on the institutionalization of learning: redundant veto points, impartial procedures that generate trust, and a monitory public sphere that sustains continuous accountability. For EU policy, the shift from conditionality to capacity (e.g., RRF) can foster endogenous resilience when supranational norms are domestically internalized rather than externally imposed. Full article

Protection of coastal cultural heritage is among the most urgent global priorities, as these sites face increasing threats from climate change, sea level rise, and human activity. This study emphasises the value of innovative geospatial tools and data ecosystems for timely risk assessment. The role of land administration systems, geospatial documentation of coastal cultural heritage sites, and the adoption of innovative techniques that combine various methodologies is crucial for timely action. The coastal management infrastructure in Greece is presented, outlining the key public authorities and national legislation, as well as the land administration and geospatial ecosystems and the various available geospatial ecosystems. We profile the Hellenic Cadastre and the Hellenic Archaeological Cadastre along with open geospatial resources, and introduce TRIQUETRA Decision Support System (DSS), produced through the EU’s Horizon project, and a Digital Twin methodology for hazard identification, quantification, and mitigation. Particular emphasis is given to the role of Digital Twin technology, which acts as a continuously updated virtual replica of coastal cultural heritage sites, integrating heterogeneous geospatial datasets such as cadastral information, photogrammetric 3D models, climate projections, and hazard simulations, allowing for stakeholders to test future scenarios of sea level rise, flooding, and erosion, offering an advanced tool for resilience planning. The approach is validated at the coastal archaeological site of Aegina Kolona, where a UAV-based SfM-MVS survey produced using high-resolution photogrammetric outputs, including a dense point cloud exceeding 60 million points, a 5 cm resolution Digital Surface Model, high-resolution orthomosaics with a ground sampling distance of 1 cm and 2.5 cm, and a textured 3D model using more than 6000 nadir and oblique images. These products provided a geospatial infrastructure for flood risk assessment under extreme rainfall events, following a multi-scale hydrologic–hydraulic modelling framework. Island-scale simulations using a 5 m Digital Elevation Model (DEM) were coupled with site-scale modelling based on the high-resolution UAV-derived DEM, allowing for the nested evaluation of water flow, inundation extents, and velocity patterns. This approach revealed spatially variable flood impacts on individual structures, highlighted the sensitivity of the results to watershed delineation and model resolution, and identified critical intervention windows for temporary protection measures. We conclude that integrating land administration systems, open geospatial data, and Digital Twin technology provides a practical pathway to proactive and efficient management, increasing resilience for coastal heritage against climate change threats. Full article

The Critical Raw Materials Act (CRMA), enacted by the European Union (EU) in May 2024, represents a strategic framework that aims to address the growing demand for critical raw materials (CRMs) and reduce dependency on non-EU sources. The present study explores the potential of CRMs recovery from granite extractive waste (EW) at a granite quarry in Buddusò (Sardinia, Italy). A significant quantity of granite EW, stored in piles within designated disposal areas at the quarry under study, is estimated in terms of mass and volume using GISs and digital elevation models (DEMs). Analysis performed using a scanning electron microscope attached to an energy-dispersive spectrometer (SEM-EDS) reveals the presence of allanite, a rare-earth-bearing mineral with substantial light rare-earth elements (LREEs), which can potentially be exploited for LREEs recovery. A combined working process including gravity and magnetic separations yields CRMs-enriched fractions with concentrations of REEs, Sc, and Ga, reaching levels of potential economic interest for different industrial applications. Despite promising concentrations, limited knowledge of allanite processing represents significant challenges for CRMs recovery from this waste. Therefore, the present study was conducted to assess the efficiency of these gravity and magnetic separation methods in order to concentrate CRMs from granite EW. Economic evaluations, including potential market value estimates, suggest that CRMs recovery from granite EW can be very profitable under optimized processing conditions. Expanding studies to other quarries in the region can provide valuable insights into the feasibility of establishing a recycling hub, offering a sustainable supply chain solution for CRMs within the EU’s strategic framework. Full article

The scientific evaluation of land potential for different uses is essential for sustainable land development. Our study attempts to quantify this potential for agricultural purposes at a national scale, using GIS techniques, high-resolution spatial data, and recent climate data. The land evaluation methodology we applied in our study was developed in the 1980s by soil scientists from the National Institute of Research and Development for Pedology, Agrochemistry and Environmental Protection (ICPA) and it is still the official approach for the assessment of land suitability for crops in Romania. In our study, the application of the methodology is based on high-resolution spatial data including the 25 × 25 m resolution EU-DEM, the CHESLA climate database from which mean annual temperatures and precipitations were extracted for the 1990–2019 period, the digital soil map of Romania, the European LUCAS soil database. Firstly, we compared the evolution of mean annual temperatures and precipitations for 1961–1990 and 1990–2019 periods and found that there is a significant warming trend (an overall increase of 1.27 °C for the entire country, ranging from 0.9 °C to 1.6 °C) among the major landform units and a slight precipitation increase throughout the country (68.8 mm yr⁻¹ for the whole country, ranging from 9.3 to 118.8 mm yr⁻¹). Then, we applied the land evaluation methodology for the recent period (1990–2019), starting with the digital mapping of 15 land suitability factors, which were further aggregated to achieve the land suitability index and classes for the main agricultural crops of Romania (winter wheat, maize, sunflower, potato, and vine for wine). The results show that the most suitable landform units for wheat, maize, and sunflower are the plain areas (Romanian Plain, Western Plain) with LSI average values over 60. For potato, the suitable areas (LSI over 50–60) are less extended, being found especially in the intra-mountainous depressions and cooler plateau areas, while vines find very suitable conditions (LSI over 70) at the contact of the Romanian Plain and the Subcarpathians. To assess the model performance, we determined the shares of land suitability classes within the areas occupied by the specific crops. A second validation was carried out by correlating the total crop production at the county level with the cumulated LSI values. We found that, apart from potatoes, the model performs well for the analyzed crops. However, a methodological revision is necessary to accommodate temperature and precipitation values, which did not manifest in the reference climate period (1961–1990), but which are now part of the current climate of Romania. Full article

Airborne geophysical data (AGD) have great potential to represent soil-forming factors. Because of that, the objective of this study was to evaluate the importance of AGD in predicting soil attributes such as aluminum saturation (ASat), base saturation (BS), cation exchange capacity (CEC), clay, and organic carbon (OC). The AGD predictor variables include total count (μR/h), K (potassium), eU (uranium equivalent), and eTh (thorium equivalent), ratios between these elements (eTh/K, eU/K, and eU/eTh), factor F or F-parameter, anomalous potassium (Kd), anomalous uranium (Ud), anomalous magnetic field (AMF), vertical derivative (GZ), horizontal derivatives (GX and GY), and mafic index (MI). The approach was based on applying predictive modeling techniques using (1) digital elevation model (DEM) covariates and Sentinel-2 images with AGD; and (2) DEM covariates and Sentinel-2 images without the AGD. The study was conducted in Bom Jardim, a county in Rio de Janeiro-Brazil with an area of 382,430 km², with a database of 208 soil samples to a predefined depth (0–30 cm). Non-explanatory covariates for the selected soil attributes were excluded. Through the selected covariables, the random forest (RF) and support vector machine (SVM) models were applied with separate samples for training (75%) and validation (25%). The model’s performance was evaluated through the R-squared (R²), root mean square error (RMSE), and mean absolute error (MAE), as well as null model values and coefficient of variation (CV%). The RF algorithm showed better performance with AGD (R² values ranging from 0.15 to 0.23), as well as the SVM model (R² values ranging from 0.08 to 0.23) when compared to RF (R² values ranging from 0.10 to 0.20) and SVM (R² values ranging from 0.04 to 0.10) models without AGD. Overall, the results suggest that AGD can be helpful for soil mapping. Nevertheless, it is crucial to acknowledge that the accuracy of AGD in predicting soil properties could vary depending on various common factors in DSM, such as the quality and resolution of the covariates and available soil data. Further research is needed to determine the optimal approach for using AGD in soil mapping. Full article

Recent Digital Surface, Elevation, and Bathymetric Models (DSM/DEM/DBM) aim to provide high-resolution and accurate height and depth information needed for a variety of surveying, geodetic, geophysical, and other applications. In this study, first we aim to validate in two test areas some of the most used models, i.e., ASTER GDEM; AW3D30 DSM; Copernicus DEM; EU-DEM; GEBCO 2020; NASADEM HGT; SRTM15+ and SRTM Global, using GNSS; spirit leveling; and gravity measurements. The validation is performed along two traverses of 14.5 and 12.0 km each in Northern and Central Greece, respectively. Since these models are based on geoid heights obtained from global geopotential models, we also investigate their influence on the validation results. Next, we carry out comparisons between GEBCO 2020, SRTM15+, and the Greek Seas DTM, with depths derived from in situ coastal measurements in six different areas in Northern Greece. From the analysis, we conclude that the heights obtained from the Copernicus DEM provide the best overall results in terms of mean value and standard deviation while also showing consistent results in the two test areas. Similarly, the Greek Seas DTM shows better consistency with the measured depths in the coastal test areas. Full article

Xanthomonas citri pv. citri (Xcc) and X. citri pv. aurantifolii (Xca), causal agents of citrus bacterial canker, are both regulated by the European Union to prevent their introduction. Xcc is responsible for severe outbreaks of citrus production worldwide, therefore, a prompt and reliable detection is advisable for the early detection of this bacterium either in symptomatic or asymptomatic plant material. The current EPPO (European and Mediterranean Plant Protection Organization) diagnostic protocol, PM 7/44(1), includes several diagnostic tests even if new assays have been developed in the latter years for which validation data are needed. Recently, a test performance study was organized within the Valitest EU Project to validate Xcc diagnostic methods and provide evidence on the most reliable assays; however, the influence of DNA extraction methods (DEM) on the reliability of the detection has never been assessed. In this study we evaluate four different DEM, by following two different approaches: (i) a comparison by real-time PCR standard curves of bacterial DNA versus bacterial DNA added to plant DNA (lemon, leaves and fruit; orange fruit); and (ii) the evaluation of performance criteria of spiked samples (plant extract added with ten-fold diluted bacterial suspensions at known concentrations). Droplet digital PCR is developed and compared with real-time PCR, as the detection method. Full article

The success rate in landslide susceptibility mapping efforts increased with the advancements in machine learning algorithms and the availability of geospatial data with high spatial and temporal resolutions. Existing data-driven susceptibility mapping models are not globally applicable due to the high variability of landslide conditioning parameters and the limitations in the availability of up-to-date and accurate data. Among numerous applications, landslide susceptibility maps are essential for site selection and health monitoring of engineering structures, such as dams, for increasing their lifetime and to prevent from disastrous events caused by the damages. In this study, landslide susceptibility mapping performance of XGBoost algorithm was evaluated in a landslide-prone area in the upper basin of Ataturk Dam, which is a prime investment located in the southeast of Turkey. The study area has a size of 2718.7 km² with an elevation difference of ca. 2000 m and contains 27 lithological units. EU-DEM v1.1 from the Copernicus Programme was used to derive the geomorphological features. High classification accuracy with area under curve value of 0.96 could be obtained from the XGBoost algorithm. According to the results, the main factors controlling the landslides in the study area are the lithology, altitude and topographic wetness index. Full article

Entering the European Union, the Republic of Croatia took over the INSPIRE directive called the National Spatial Data Infrastructure. A large amount of spatial data can be found through the National Spatial Data Infrastructure Geoportal. Data are available for viewing or downloading via different services, such as a web mapping service or web feature service. Although different spatial data are available, it is hard to access useful information through Geoportal. The aim of this paper is to prepare a spatial database which will gather different spatial data related to environmental engineering and present different queries and the visualization of the results. The main data used are related to protected areas in the Republic of Croatia, which register the environmental pollutants, air quality, exploitation and research fields of mineral resources, waste management, water management, and so forth. Alongside the national spatial data, the Copernicus Land monitoring service EU-DEM, the digital elevation model, is used. The classification of Sentinel-2 MSI data is used to provide land cover. Remotely sensed data are used in queries where aspect, slope, and land cover affect the results. Two predefined SQL queries are discussed. The first query discusses the danger of landslides, and the second query discusses threats from illegal landfills and the effect that they have on the environment. Predefined SQL queries enable users to quickly access needed data, even when the original data is updated. All data, databases, visualization, and results are presented in open access software. Full article

Recent developments in the fields of geographical object-based image analysis (GEOBIA) and ensemble learning (EL) have led the way to the development of automated processing frameworks suitable to tackle large-scale problems. Mapping riverscape units has been recognized in fluvial remote sensing as an important concern for understanding the macrodynamics of a river system and, if applied at large scales, it can be a powerful tool for monitoring purposes. In this study, the potentiality of GEOBIA and EL algorithms were tested for the mapping of key riverscape units along the main European river network. The Copernicus VHR Image Mosaic and the EU Digital Elevation Model (EU-DEM)—both made available through the Copernicus Land Monitoring Service—were integrated within a hierarchical object-based architecture. In a first step, the most well-known EL techniques (bagging, boosting and voting) were tested for the automatic classification of water, sediment bars, riparian vegetation and other floodplain units. Random forest was found to be the best-to-use classifier, and therefore was used in a second phase to classify the entire object-based river network. Finally, an independent validation was performed taking into consideration the polygon area within the accuracy assessment, hence improving the efficiency of the classification accuracy of the GEOBIA-derived map, both globally and by geographical zone. As a result, we automatically processed almost 2 million square kilometers at a spatial resolution of 2.5 meters, producing a riverscape-units map with a global overall accuracy of 0.915, and with per-class F1 accuracies in the range 0.79–0.97. The obtained results may allow for future studies aimed at quantitative, objective and continuous monitoring of river evolutions and fluvial geomorphological processes at the scale of Europe. Full article

Digital elevation models (DEMs) are a widely used form of topographic information, with some of the most popular being the Shuttle Radar Topography Mission (SRTM) DEM and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM). These two sources of topographical information are the main constituents of the European Union Digital Elevation Model (EU-DEM), which is a relatively new dataset of the EU’s Copernicus Land Monitoring Service. In this context, the purpose of this study was to validate EU-DEM for its vertical accuracy and to compare it with SRTM DEM and ASTER GDEM data. This was achieved in a Geographic Information System (GIS) environment, using extensive—in the order of tens of thousands of points—geodetic Global Navigation Satellite System (GNSS) measurements and appropriate pre-processing steps. The absolute elevation errors results had a Root Mean Square Error (RMSE) of 2.7 m at a 90% confidence level and characterize the performance of EU-DEM from local to regional scale, generally confirming that it is an enhanced source of elevation information when compared with its predecessors. Full article

This paper describes a new multi-criteria land evaluation method, based on geomorphology and land cover, for the automated detection of suitable terrain for the sport of foot orienteering (footO). Reference data, in the form of areas already mapped and used for footO, was used to define criteria for geomorphology and land cover, and represents an expert knowledge component. The motivation for this research is that orienteering maps are often drawn for unfamiliar terrain that organizers of the event or mapmakers need to determine in advance, usually from base maps or by random reconnaissance. In a presented case study of Croatia and Slovenia, the geomorphology was derived from Digital Elevation Model over Europe (EU-DEM). The slope and aspect define components of the direction of the surface, and we tested the usability of these simple terrain parameters for the task. The CORINE dataset was used for the definition of the land cover. The results of the case study give potentially suitable areas for foot orienteering in Croatia and Slovenia, and in neighboring areas. Evaluation of the results, using reference areas as the control, proved that the proposed methodology gives a reliable indication of terrain suitability for orienteering. The method is simple, straightforward, and can be performed using standard GIS with common raster algorithms. Full article