Emmanouil Psomiadis

Greek olive groves are subject to multiple thermal, water, biotic, and extreme-weather pressures, yet national-scale maps of their combined historical exposure remain limited. The present study quantified climate risk for olive cultivation across Greece using twenty agroclimatic indicators grouped into four thematic categories. Using hourly ERA5-Land data (1995–2024) and quality-controlled ESWD reports (2014–2024), each indicator recorded how often predefined adverse thresholds were met at the grid-cell level during the reference period. We integrated the resulting layers using a weighted multi-criteria decision analysis, with lethal frost applied as a separate constraint, to produce a composite spatial distribution of climate risk and district-level summaries linked to CORINE Land Cover 2018, olive grove class (2.2.3). Composite risk was spatially heterogeneous: cold and frost recurrence predominated in northern and upland areas, whereas water-related indicators occurred most persistently in southern and island districts, including eastern Crete. Most of the mapped olive grove area fell into intermediate composite classes rather than at the extremes of the score range. Comparison with a recent nationwide olive suitability assessment showed agreement in major western and southern producing districts, but also contrasting patterns where high suitability coincided with elevated recurrence-based risk. The resulting products provide a national historical baseline for climate-risk recurrence in Greek olive groves, offer a spatial basis for regionally targeted adaptation planning, and demonstrate the applicability of an AI-assisted geospatial framework for reproducible national-scale climate-risk assessment of perennial crops.

Greek olive groves are subject to multiple thermal, water, biotic, and extreme-weather pressures, yet national-scale maps of their combined historical exposure remain limited. The present study quantified climate risk for olive cultivation across Greece using twenty agroclimatic indicators grouped into four thematic categories. Using hourly ERA5-Land data (1995–2024) and quality-controlled ESWD reports (2014–2024), each indicator rec-orded how often predefined adverse thresholds were met at the grid-cell level during the reference period. We integrated the resulting layers using a weighted multi-criteria deci-sion analysis, with lethal frost applied as a separate constraint, to produce a composite spatial distribution of climate risk and district-level summaries linked to CORINE Land Cover 2018, olive-grove class (2.2.3). Composite risk was spatially heterogeneous: cold and frost recurrence predominated in northern and upland areas, whereas water-related indicators occurred most persistently in southern and island districts, including eastern Crete. Most of the mapped olive-grove area fell into intermediate composite classes ra-ther than at the extremes of the score range. Comparison with a recent nationwide olive suitability assessment showed agreement in major western and southern producing dis-tricts, but also contrasting patterns where high suitability coincided with elevated recur-rence-based risk. The resulting products provide a national historical baseline for cli-mate-risk recurrence in Greek olive groves, offer a spatial basis for regionally targeted adaptation planning, and demonstrate the applicability of an AI-Assisted geospatial framework for reproducible national-scale climate-risk assessment of perennial crops.

Accurate estimation of crop yield from remote sensing remains challenging due to the crop-specific nature of yield drivers and the difficulty of interpreting spectral indicators across agronomic systems. While many studies prioritise predictive accuracy through complex models, fewer explicitly examine the stability and physiological relevance of individual spectral and phenological indicators under controlled analytical conditions. This study investigates yield–spectral relationships in wheat and cotton using a unified Sentinel-2 indicator framework applied across multiple growing seasons in a Mediterranean agricultural environment. A consistent set of spectral and thermal indicators was derived from two phenologically targeted Sentinel-2 acquisitions per season and analysed using correlation analysis, univariate regression, constrained multivariate modelling, and recurrence analysis within an identical workflow for both crops. Distinct crop-specific patterns were observed. Wheat yield was most strongly associated with water-sensitive and canopy-related indicators, with NDWI-based metrics reaching Pearson correlations up to r = 0.85 and multivariate models explaining a substantial proportion of yield variability (up to R² ≈ 0.70) under controlled analytical conditions. In contrast, cotton yield variability was dominated by thermal accumulation, with growing degree day indicators showing correlations up to |r| = 0.59 and multivariate performance reaching R² = 0.74. Recurrence analysis indicated consistent recurrence of these indicator families across analytical stages under the examined conditions. Overall, the results indicate that parsimonious, physiologically interpretable indicator combinations can account for a meaningful proportion of yield variability without reliance on highly complex or high-dimensional modelling approaches, supporting crop-aware indicator selection for precision agriculture applications.

Accurate estimation of crop yield from remote sensing remains challenging due to the crop-specific nature of yield drivers and the difficulty of interpreting spectral indicators across agronomic systems. While many studies prioritise predictive accuracy through complex models, fewer explicitly examine the stability and physiological relevance of in-dividual spectral and phenological indicators under controlled analytical conditions. This study investigates yield–spectral relationships in wheat and cotton using a harmonised Sentinel-2 indicator framework applied across multiple growing seasons in a Mediterra-nean agricultural environment. A consistent set of spectral and thermal indicators was derived from two phenologically targeted Sentinel-2 acquisitions per season and analysed using correlation analysis, univariate regression, constrained multivariate modelling, and recurrence analysis within an identical workflow for both crops. Distinct crop-specific patterns were observed. Wheat yield was most strongly associated with water-sensitive and canopy-related indicators, with NDWI-based metrics reaching Pearson correlations up to r = 0.85 and multivariate models explaining a substantial proportion of yield varia-bility (up to R² ≈ 0.82) under controlled analytical conditions. In contrast, cotton yield var-iability was dominated by thermal accumulation, with growing degree day indicators showing correlations up to |r| = 0.59 and multivariate performance reaching R² = 0.76. Recurrence analysis confirmed the stability of these indicator families across analytical stages. Overall, the results indicate that parsimonious, physiologically interpretable indi-cator combinations can account for a substantial proportion of yield variability without reliance on black-box modelling, supporting crop-aware indicator selection for precision agriculture applications.

Wildfires increasingly threaten Mediterranean landscapes, particularly in regions like Attica, Greece, where urban sprawl, agricultural abandonment, and climatic conditions heighten the risk at the Wildland–Urban Interface (WUI). The Mediterranean basin, recognized as one of the global wildfire “hotspots”, has witnessed a steady increase in both fire severity, frequency, and burned area during the last four decades, a trend amplified by urban sprawl and agricultural land abandonment. This study represents the first integrated, region-wide mapping of the WUI and associated wildfire risk in Attica, the most densely urbanized area in Greece and one of the most fire-exposed metropolitan regions in Southern Europe, utilizing advanced techniques such as Earth Observation and GIS analysis. For this purpose, various geospatial datasets were coupled, including Copernicus High Resolution Layers, multi-decadal Landsat fire history archive, UCR-STAR building footprints, and CORINE Land Cover, among others. The research delineated WUI zones into 40 interface and intermix categories, revealing that WUI encompasses 26.29% of Attica, predominantly in shrub-dominated areas. An analysis of fire frequency history from 1983 to 2023 indicated that approximately 102,366 hectares have been affected by wildfires. Risk assessments indicate that moderate hazard zones are most prevalent, covering 36.85% of the region, while approximately 25% of Attica is classified as moderate, high, or very high susceptibility zones. The integrated risk map indicates that 37.74% of Attica is situated in high- and very high-risk zones, principally concentrated in peri-urban areas. These findings underscore Attica’s designation as one of the most fire-prone metropolitan regions in Southern Europe and offer a viable methodology for enhancing land-use planning, fuel management, and civil protection efforts.

Wildfires increasingly threaten Mediterranean landscapes, particularly in regions like Attica, Greece, where urban sprawl, agricultural abandonment, and climatic conditions enhance the risk associated with the Wildland-Urban Interface (WUI). The Mediterranean basin, recognized as one of the global wildfire “hotspots”, has witnessed a steady increase in both fire severity, frequency, and burned area during the last four decades, a trend amplified by urban sprawl and agricultural land abandonment. This study represents the first integrated, region-wide mapping of the WUI and associated wildfire risk in Attica, the most densely urbanized area in Greece and one of the most fire-exposed metropolitan regions in southern Europe, utilizing advanced techniques such as Earth Observation and GIS analysis. For this purpose, various geospatial datasets were combined, including Copernicus High Resolution Layers, a multi-decadal Landsat fire history archive, UCR-STAR building footprints, and CORINE Land Cover, among others. The research delineated WUI zones classified into 40 categories of interface and intermix, revealing that WUI encompasses 26.29% of Attica, predominantly within shrub-dominated areas. An analysis of fire frequency history from 1983 to 2023 indicated that approximately 102,366 hectares have been affected by wildfires. Risk assessments highlighted that moderate hazard zones are most prevalent, covering 36.85% of the region, while approximately 25% of Attica is classified within moderate, high, and very high susceptibility zones. The integrated risk map indicates that 37.74% of Attica is situated in high- and very high-risk zones, principally concentrated in peri-urban areas. These findings underscore Attica’s designation as one of the most fire-prone metropolitan regions in southern Europe, presenting a viable methodology for enhancing land-use planning, fuel management, and civil protection efforts.

Precision irrigation plays a crucial role in managing crop production in a sustainable and environmentally friendly manner. This study builds on the results of the GreenWaterDrone project, aiming to estimate, in real time, the actual water requirements of crop fields using the crop water stress index, integrating infrared canopy temperature, air temperature, relative humidity, and thermal and near-infrared imagery. To achieve this, a state-of-the-art aerial micrometeorological station (AMMS), equipped with an infrared thermal sensor, temperature–humidity sensor, and advanced multispectral and thermal cameras is mounted on an unmanned aerial system (UAS), thus minimizing crop field intervention and permanently installed equipment maintenance. Additionally, data from satellite systems and ground micrometeorological stations (GMMS) are integrated to enhance and upscale system results from the local field to the regional level. The research was conducted over two years of pilot testing in the municipality of Trifilia (Peloponnese, Greece) on pilot potato and watermelon crops, which are primary cultivations in the region. Results revealed that empirical irrigation applied to the rhizosphere significantly exceeded crop water needs, with over-irrigation exceeding by 390% the maximum requirement in the case of potato. Furthermore, correlations between high-resolution remote and proximal sensors were strong, while associations with coarser Landsat 8 satellite data, to upscale the local pilot field experimental results, were moderate. By applying a comprehensive model for upscaling pilot field results, to the overall Trifilia region, project findings proved adequate for supporting sustainable irrigation planning through simulation scenarios. The results of this study, in the context of the overall services introduced by the project, provide valuable insights for farmers, agricultural scientists, and local/regional authorities and stakeholders, facilitating improved regional water management and sustainable agricultural policies.

Understanding a catchment’s geomorphological and erosion processes is essential for sustainable land management and soil conservation. This study investigates the Xynias drained lake catchment in Central Greece using a twofold geospatial modelling approach that combines morphometric analysis with the Revised Universal Soil Loss Equation (RUSLE) to evaluate the area’s landscape evolution, surface drainage features, and soil erosion processes. The catchment exhibits a sixth-order drainage network with a dendritic and imperfect pattern, shaped by historical lacustrine conditions and the carbonate formations. The basin has an elongated shape with steep slopes, high total relief, and a mean hypsometric integral value of 26.3%, indicating the area is at an advanced stage of geomorphic maturity. The drainage density and frequency are medium to high, reflecting the influence of the catchment’s relatively flat terrain and carbonate formations. RUSLE simulations also revealed mean annual soil loss to be 1.16 t ha⁻¹ y⁻¹ from 2002 to 2022, along with increased erosion susceptibility in hilly and mountainous areas dominated by natural vegetation. In comparison to these areas, agricultural regions displayed less erosion risk. These findings demonstrate the effectiveness of combining GIS with remote sensing for detecting erosion-prone areas, informing conservation initiatives. Along with the previously stated results, more substantial conservation efforts and active land management are required to meet the Sustainable Development Goals (SDGs) while considering the monitored land use changes and climate parameters for future catchment management.

This research examines the complex interactions between natural vegetation and urban infrastructure within the Wildland-Urban Interface (WUI), focusing on the region of Attica, Greece. Human-built environments and wild vegetation converge in areas where conditions favor the propagation of wildfires. The presence of diverse fuel sources, both natural and artificial, fosters the development of conditions conducive to the rapid spread of wildfires. Consequently, these areas are particularly vulnerable to natural hazards. The Attica region is among the most densely populated in Greece, with a population density of over 3.8 million inhabitants. The region is characterized by extensive and unregulated buildings, which renders it a suitable subject for studying WUI. The study addresses the relationship between the increasing frequency of wildfires and the impact of urban sprawl concerning future fire risk, highlighting the critical need for effective risk management strategies. To achieve its objectives, freely available advanced geospatial data from digital and satellite sources was utilised, such as data on urban structures (UCR-Star building footprints from 2014–2021) and vegetation (Corine Land Cover 2018, forest maps from 2022, and high-resolution vegetation data from the Copernicus Database) to map the WUI areas. Historical fire records (Fire frequency) were derived from Landsat satellite imagery (1983–2023), while topographic maps (1988–1994) were processed to create Digital Elevation Models (DEMs) and slope maps, and climatic data modified the Fire Weather Index (FWI) at the 90th percentile for RCP 4.5 projections. The methodology employed a three-stage process to map the WUI, integrating fuel type mapping, dwelling characterisation, and classification of WUI types. The wildfire risk was assessed through a Geographic Information System (GIS)-based model combining hazard (fire history, weather, topography, and fuel types) and susceptibility (land cover and WUI categories) to identify high-risk areas in Attica. The spatial analysis performed the spatial extent of the WUI in Attica, which was estimated to be 26% of the whole region. Furthermore, 37% of the study area was classified as high or very high risk, underscoring the region’s vulnerability. Temporal fire mapping from 1983 to 2023 provided a comprehensive understanding of fire dynamics over four decades, allowing detailed analysis of the relationship between WUI expansion and fire occurrence. Overall, more than 102,000 hectares in Attica have been affected by wildfires, covering over one-third of the region. The findings outline a strong correlation between urban development and wildfire risk, thus offering valuable insights into the factors contributing to fire vulnerability in WUI areas. These findings contribute to the scientific discourse and a solid foundation for developing evidence-based policies to improve fire prevention, response, and resilience in areas where urban and natural landscapes intersect.