In 2023, Greece faced its worst wildfire season, with nine major fires causing unprecedented environmental damage of 1470.31 km². This article uses Copernicus Land Monitoring Service and Sentinel-2 data, employing advanced remote sensing and GIS techniques to analyze spatial dynamics, map burn severity, assess fire extent, and highlight pre-fire tree density and land cover. The study focuses on the catastrophic fire in the Evros region and the damage to the National Forest Park of Dadia–Lefkimmi–Soufli. It also analyzes significant fires in Rhodes, Attica, Thessaly, Evia, Corfu, and Magnesia, emphasizing the compounded challenges posed by terrain, climate, and human factors in those areas. Additionally, the climate data for each affected area were compared with the weather conditions prevailing at the time of the fires. Copernicus Land Cover and Tree Density data are integrated to aid future management, assessment, and restoration. The analysis of maps and fire statistics underscores a notable pattern: areas with higher pre-fire tree density experienced correspondingly higher burn severity. This research underscores the crucial role of such data in assessing wildfire impact. In addition, compared with Copernicus Emergency Management Service, the burned area maps validate the accuracy and reliability of the utilized satellite data. The total burned area was assessed with a high accuracy rate of 96.28%.

The Urban Heat Island (UHI) phenomenon, combined with reduced vegetation and heat generated by human activities, presents a major environmental challenge for many European urban areas. The UHI effect is especially concerning in hot and temperate climates, like the Mediterranean region, during the summer months as it intensifies the discomfort and raises the risk of heat-related health issues. As a result, assessing urban heat dynamics and steering sustainable land management practices is becoming increasingly crucial. Analyzing the relationship between land cover and Land Surface Temperature (LST) can significantly contribute to achieving this objective. This study evaluates the spatial correlations between various land cover types and LST trends in Thessaloniki, Greece, using data from the Coordination of Information on the Environment (CORINE) program and advanced vegetation index techniques within Google Earth Engine (GEE). Our analysis revealed that there has been a gradual increase in average surface temperature over the past five years, with a more pronounced increase observed in the last two years (2022 and 2023) with mean annual LST values reaching 26.07 °C and 27.09 °C, respectively. By employing indices such as the Normalized Difference Vegetation Index (NDVI) and performing correlation analysis, we further analyzed the influence of diverse urban landscapes on LST distribution across different land use categories over the study area, contributing to a deeper understanding of UHI effects.

High-resolution remotely sensed data, which are characterised by their advanced spectral and spatial capabilities, provide unprecedented opportunities to monitor and analyse the dynamic structures of urban environments. Platforms like Google Earth Engine (GEE) enhance these capabilities, as they provide access to vast datasets and tools for analysing key urban parameters, including land use, vegetation cover, and surface roughness–all critical components in urban sustainability studies. This study presents a knowledge-based framework for processing high-resolution satellite imagery tailored to address the demands of sustainable urban planning in the Municipality of Kalamaria in Thessaloniki, Greece. The framework emphasises the extraction of essential urban parameters, such as the spatial distribution of built-up and green spaces, alongside the analysis of surface roughness attributes, including displacement height and roughness length. Unlike conventional methods, our framework enables a detailed intra-urban analysis as these surface roughness attributes are calculated within 200 m × 200 m sub-units. Surface roughness indicators offer essential insights into aerodynamic drag and turbulent air mixing, both of which are directly influenced by the structural characteristics of the urban landscape. Using this approach, ‘wake interference flow’ type was identified as the dominant airflow pattern in the study area. This type was observed in 105 out of 150 sub-units, suggesting that these areas likely suffer from poor air circulation and are prone to higher concentrations of air pollutants. The integration of Google Earth Engine offered a scalable and replicable solution for large-scale urban analysis making it easily adaptable to other urban areas, especially where detailed morphological datasets are unavailable. By providing a robust, scalable, and data-driven tool for assessing urban form and airflow characteristics, our study offers a significant advancement in sustainable urban planning and climate resilience strategies, with clear potential for adaptation in other cities facing similar data limitations.

The Mediterranean region, identified by scientists as a ‘climate hot spot’, is experiencing warmer and drier conditions, along with an increase in the intensity and frequency of extreme weather events. One such extreme phenomena is droughts. The recent wildfires in this region are a concerning consequence of this phenomenon, causing severe environmental damage and transforming natural landscapes. However, droughts involve a two-way interaction: On the one hand, climate change and various human activities, such as urbanization and deforestation, influence the development and severity of droughts. On the other hand, droughts have a significant impact on various sectors, including ecology, agriculture, and the local economy. This study investigates drought dynamics in four Mediterranean countries, Greece, France, Italy, and Spain, each of which has experienced severe wildfire events in recent years. Using satellite-based Earth observation data, we monitored drought conditions across these regions over a five-year period that includes the dates of major wildfires. To support this analysis, we derived and assessed key indices: the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI). High-resolution satellite imagery processed within the Google Earth Engine (GEE) platform enabled the spatial and temporal analysis of these indicators. Our findings reveal that, in all four study areas, peak drought conditions, as reflected in elevated NDDI values, were observed in the months leading up to wildfire outbreaks. This pattern underscores the potential of satellite-derived indices for identifying regional drought patterns and providing early signals of heightened fire risk. The application of GEE offered significant advantages, as it allows efficient handling of long-term and large-scale datasets and facilitates comprehensive spatial analysis. Our methodological framework contributes to a deeper understanding of regional drought variability and its links to extreme events; thus, it could be a valuable tool for supporting the development of adaptive management strategies. Ultimately, such approaches are vital for enhancing resilience, guiding water resource planning, and implementing early warning systems in fire-prone Mediterranean landscapes.

Floods caused by extreme weather events critically impact human and natural systems. Remote sensing can be a very useful tool in mapping these impacts. However, processing and analyzing satellite imagery covering extensive periods is computationally intensive and time-consuming, especially when data from different sensors need to be integrated, hampering its operational use. To address this issue, the present study focuses on mapping flooded areas and analyzing the impacts of the 2023 Storm Daniel flood in the Thessaly region (Greece), utilizing Earth Observation and GIS methods. The study uses multiple Sentinel-1, Sentinel-2, and Landsat 8/9 satellite images based on backscatter histogram statistics thresholding for SAR and Modified Normalized Difference Water Index (MNDWI) for multispectral images to delineate the extent of flooded areas triggered by the 2023 Storm Daniel in Thessaly region (Greece). Cloud computing on the Google Earth Engine (GEE) platform is utilized to process satellite image acquisitions and track floodwater evolution dynamics until the complete drainage of the area, making the process significantly faster. The study examines the usability and transferability of the approach to evaluate flood impact through land cover, linear infrastructure, buildings, and population-related geospatial datasets. The results highlight the vital role of the proposed approach of integrating remote sensing and geospatial analysis for effective emergency response, disaster management, and recovery planning.

Accurate land cover mapping is a fundamental tool for environmental management and ecosystem monitoring. This study presents a comparative evaluation of four deep learning architectures, namely a Convolutional Neural Network (CNN), UNet++, ConvNeXt and Vision Transformer (ViT), for land cover classification into five primary classes: water, cropland, forest, low/natural vegetation and built-up. The broader Lake Kerkini basin was selected as the primary training and evaluation area. The multispectral input data were generated through Google Earth Engine and consisted of multi-seasonal Sentinel-2 composite mosaics for the 2021 mapping year, covering winter, spring, summer and autumn. To obtain a more reliable performance estimate and mitigate the effects of spatial autocorrelation, a four-fold spatial cross-validation approach was implemented. Under this spatial validation framework, the convolution-based architectures achieved the strongest performance. CNN obtained the highest numerical fold-mean performance, with an overall accuracy of 81.53% and a macro-averaged F1 score (Macro-F1) of 80.09%, followed closely by UNet++ and ConvNeXt. Non-parametric repeated-measures statistical testing indicated a significant overall architecture effect, with CNN, UNet++ and ConvNeXt showing broadly comparable fold-level Macro-F1 performance, while the tested ViT configuration trained from scratch ranked last across all spatial folds. Regional transferability was further evaluated in the nearby independent Lake Doirani region, where the convolutional architectures, particularly CNN and UNet++, showed strong agreement with the WorldCover-derived reference labels without fine-tuning. Finally, feature-importance analysis indicated that specific spectral-seasonal channels, especially the Blue band (B2) in winter and the Short-Wave Infrared band (B12) in summer, were consistently influential in the models’ predictions. Overall, under the tested 2021 Mediterranean case-study conditions, the results highlight the importance of spatially rigorous validation and show that the evaluated convolution-based configurations achieved stronger performance than the tested ViT configuration trained from scratch.