This study presents a comprehensive geospatial framework for assessing coastal vulnerability and ecosystem service distribution along the Greek coastline, one of the longest and most diverse in Europe. The framework integrates two complementary components: a Coastal Erosion Vulnerability Index applied to all identified beach units, and Coastal Flood Risk Indexes focused on low-lying and urbanized coastal segments. Both indices draw on harmonized, open-access European datasets to represent environmental, geomorphological, and socio-economic dimensions of risk. The Coastal Erosion Vulnerability Index is developed through a multi-criteria approach that combines indicators of physical erodibility, such as historical shoreline retreat, projected erosion under climate change, offshore wave power, and the cover of seagrass meadows, with socio-economic exposure metrics, including land use composition, population density, and beach-based recreational values. Inclusive accessibility for wheelchair users is also integrated to highlight equity-relevant aspects of coastal services. The Coastal Flood Risk Indexes identify flood-prone areas by simulating inundation through a novel point-based, computationally efficient geospatial method, which propagates water inland from coastal entry points using Extreme Sea Level (ESL) projections for future scenarios, overcoming the limitations of static ‘bathtub’ approaches. Together, the indices offer a spatially explicit, scalable framework to inform coastal zone management, climate adaptation planning, and the prioritization of nature-based solutions. By integrating vulnerability mapping with ecosystem service valuation, the framework supports evidence-based decision-making while aligning with key European policy goals for resilience and sustainable coastal development.

This contribution presents a new approach for assessing/ranking the vulnerability of beaches to mean and extreme sea level rise at regional (island) scales. It combines socio-economic information with beach erosion projections from morphodynamic models to rank beach vulnerability in a structured, ‘holistic’ manner. It involves the collation of various beach geo-spatial environmental and socio-economic data, which are then combined with erosion projections under different climatic scenarios. A Strengths–Weaknesses–Opportunities–Threats (SWOT) framework is employed for the indicator selection, and multi-criteria methods (Analytical Hierarchy Process—AHP, Technique for Order of Preference by Similarity to Ideal Solution—TOPSIS, Preference Ranking Organization Method for Enrichment Evaluations—PROMETHEE II) are then used to optimize indicator weights and rank beach vulnerability. Framework implementation in Lesvos and Kos has shown that there will be significant effects of the mean and (particularly) of the extreme sea levels on the carrying capacity and the capability of the beaches to buffer backshore assets, in the absence of appropriate adaptation measures. As the proposed approach relies on widely available information on many of the socio-economic indicators required to assess the beach’s significance/criticality, it can provide a reproducible and transferable methodology that can be applied at different locations and spatial scales.

This study assesses the exposure of the 155 Greek seaports to marine flooding and extreme heat under climate change. Flood exposure was estimated through a threshold approach that compared projected mean and extreme sea levels to high-resolution port quay elevation data. It was found that while relatively few ports will face quay inundation, the majority will experience operational disruptions due to insufficient freeboard for berthing of commercial vessels under both the mean (80%) and extreme sea (96%) levels by 2050. For selected ports, 2-D flood modelling was undertaken that showed that the used ‘static’ flood threshold approach likely underestimates flood exposure. Future heat exposure was studied through the comparison of extreme temperature and humidity projections to operational and health/safety thresholds. Port infrastructure and personnel/users will be exposed to large material, operational and health risks, whereas energy demand will rise steeply. Deadly heat days (due to mean temperature/humidity combination) will increase, particularly at island ports: 20% of Greek ports might face more than 50 such days annually by end-century. As ports are associated with large urban clusters, these findings suggest a broader health risk. Our findings suggest an urgent climate adaptation need given the strategic socio-economic importance of ports.

Ports are vital components of global and regional supply chains, supporting trade, transport connectivity, and socio-economic development. However, their functionality is increasingly threatened by climatic hazards such as sea-level rise and heat stress, both of which are projected to intensify under future climate change. This study presents a comprehensive framework for assessing the criticality of ports within a national network, demonstrated through its application to the Greek port system, which encompasses a multitude of ports of all types from large international hubs to small island ones. The framework combines openly accessible geospatial and socio-economic data with projections of exposure to sea-level rise and extreme heat within a structured multi-criteria decision-making (MCDM) approach, enabling the identification of critical ports and the prioritization of adaptation needs. Results show that large mainland ports dominate in socio-economic importance and network centrality, while smaller island ports are vital locally due to limited redundancy and high exposure to climatic hazards. By 2100, nearly all ports are projected to experience freeboard reductions below operational thresholds and increased heat-related stress. These results highlight the need for targeted adaptation measures, including engineering interventions for mainland ports and redundancy-enhancing actions for island ports. The proposed framework provides a replicable, data-driven tool to guide evidence-based prioritization of adaptation investments and strengthen climate-resilient maritime transport and coastal management, thereby contributing to the achievement of Sustainable Development Goals (SDGs) 1.5, 9 and 13.

This study introduces a geospatial comprehensive methodological system aimed at evaluating the suitability and need for agricultural digital solutions (ADSs) across Europe. This system integrates a diverse range of factors, including geophysical characteristics, climate patterns, and socioeconomic conditions, evaluated at regional- and farm-specific levels. By leveraging open-source Earth observations and socioeconomic data, we develop multiple performance, environmental, and socioeconomic similarity indexes that compare regions based on shared characteristics, such as soil quality, climate, and socioeconomic factors. Using advanced statistical and multi-criteria analysis tools, these indexes are tailored to different stages of agricultural production, enabling region-specific assessments that identify and prioritize the needs for digital solutions across Europe. The results indicate that the developed indexes effectively categorize regions based on comparable characteristics, facilitating the targeted recommendation of ADSs. Additionally, a connectivity performance index is created to assess the local deployment model of agricultural digital solutions (cloud, edge, or mixed), ensuring that the recommendations for technological implementation are feasible and effective given the local connectivity conditions.

Retrieval of soil organic carbon (SOC) from uncrewed aerial vehicle (UAV) hyperspectral imagery depends on how laboratory measurements are linked to pixels at centimetre resolution. This study presents a quality-controlled workflow in which radiometric calibration, harmonisation against proximal spectroradiometer measurements, pixel-level screening, spectral support definition and model validation are treated as interdependent components of a single procedure. Imagery was acquired with a tunable Fabry–Pérot interferometer camera in six bands between 670 and 1100~nm over eight bare soil fields in Flanders, Belgium (85 topsoil samples) and five fields in Central Macedonia, Greece (42 samples). Single pixel spectra, neighbourhood aggregation and local pixel augmentation were compared at radii of 0.25, 0.50 and 1.00~m using Random Forest, XGBoost, a 1D convolutional neural network and TabPFN, with all pixels from a given sampling location assigned to the same cross-validation fold. In Flanders, 23 of 24 neighbourhood configurations reduced RMSE relative to single pixel extraction, the best reaching $R^2 = 0.784$ and RMSE =0.155% SOC. In Central Macedonia, adverse acquisition conditions and a narrow target range prevented a usable SOC model, although electrical conductivity, CaCO₃ and pH reached $R^2$ between 0.61 and 0.69. These results indicate that the spectral support should be defined to match the field sampling support, and that this correspondence belongs alongside radiometric calibration, pixel screening and validation in a reproducible workflow for field-scale soil mapping.