Plantations of fast-growing forest species such as black locust (Robinia Pseudoacacia) can contribute to energy transformation, mitigate industrial pollution, and restore degraded, marginal land. In this study, the synergistic use of Sentinel-2 and Sentinel-1 time series data is explored for modeling aboveground biomass (AGB) in black locust short-rotation plantations in northeastern Greece. Optimal modeling dates and EO sensor data are also identified through the analysis. Random forest (RF) models were originally developed using monthly Sentinel-2 spectral indices, while, progressively, monthly Sentinel-1 bands were incorporated in the statistical analysis. The highest accuracy was observed for the models generated using Sentinel-2 August composites (R2 = 0.52). The inclusion of Sentinel-1 bands in the spectral indices’ models had a negligible effect on modeling accuracy during the leaf-on period. The correlation and comparative performance of the spectral indices in terms of pairwise correlation with AGB varied among the phenophases of the forest plantations. Overall, the field-measured AGB in the forest plantations plots presented a higher correlation with the optical Sentinel-2 images. The synergy of Sentinel-1 and Sentinel-2 data proved to be a non-efficient approach for improving forest biomass RF models throughout the year within the geographical and environmental context of our study.

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Erythropotamos is a tributary of river Evros and during the last decade its drainage basin flooded many times, causing extensive damage on properties. In order to assess flood susceptibility in the aforementioned study area, the inundated areas of floods that occurred in 2010, 2017 and 2018 were initially delineated with the aid of SAR (Synthetic Aperture Radar) imagery by applying an established flood delineation methodology. Subsequently, flood susceptibility mapping was conducted for the study area by applying the Analytical Hierarchy Process (AHP). Topographical, hydrological and meteorological factors were used and each one of them was classified into three (3) flood susceptibility categories (low, medium and high). The determination of the importance for each factor over the others, which is the main objective of this research, was decided according to the proportion of the 2010 inundated area, captured by ENVISAT/ASAR imagery, which intersected with each factor's high susceptibility class. Finally, the resulting flood susceptibility map was validated according with the inundated areas of the 2017 and 2018 flood events, captured by SENTINEL -1 A/B imagery, indicating that approximately 60% of both of these areas intersected with the map's high susceptibility zone.

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The aim of this study is to assess the relationship between geological background and habitats of mushrooms. The study area is Grevena, a Prefecture of Greece well known for the great variety of the fungal flora and its distinctive geology. Thematic maps of the study area were produced with the use of GIS, taking under consideration geological formations, elevation, ecosystems and land use. Findings provide evidence that certain mushrooms are more likely to be found in specific ecosystems. The connection between forest ecosystems and the geology of the study area is more apparent, as certain forest types are related with specific geological formations; due to the insignificant presence of grasslands and riverine settings in the study area, it is not possible to assess the role of the geological formation for these mushroom habitats.