The main purpose of this study is to explore the relationship between three field-based fire severity indices (Composite Burn Index-CBI, Geometrically structure CBI, weighted CBI) and spectral indices derived from Sentinel 2A and Landsat-8 OLI imagery on a recent large fire in Thasos, Greece. We employed remotely sensed indices previously used from the remote sensing fire community (Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), differenced NDVI, differenced NBR, relative differenced NBR, Relativized Burn Ratio) and seven Sentinel 2A-specific indices considering the availability of spectral information recorded in the red-edge spectral region. The statistical correlation indicated a slightly stronger relationship between the differenced NBR and the GeoCBI for both Sentinel 2A (r = 0.872) and Landsat-8 OLI (r = 0.845) imagery. Predictive local thresholds of dNBR values showed slightly higher classification accuracy for Sentinel 2A (73.33%) than Landsat-8 OLI (71.11%), suggesting the adequacy of Sentinel 2A for forest fire severity assessment and mapping in Mediterranean pine ecosystems. The evaluation of the classification thresholds calculated in this study over other fires with similar pre-fire conditions could contribute in the operational mapping and reconstruction of the historical patterns of fire severity over the Eastern Mediterranean region.

Fire management implications and the design of conservation strategies on fire prone landscapes within the UNESCO World Heritage Properties require the application of wildfire risk assessment at landscape level. The objective of this study was to analyze the spatial variation of wildfire risk on Holy Mount Athos in Greece. Mt. Athos includes 20 monasteries and other structures that are threatened by increasing frequency of wildfires. Site-specific fuel models were created by measuring in the field several fuel parameters in representative natural fuel complexes, while the spatial extent of the fuel types was determined using a synergy of high-resolution imagery and high temporal information from medium spatial resolution imagery classified through object-based analysis and a machine learning classifier. The Minimum Travel Time (MTT) algorithm, as it is embedded in FlamMap software, was applied in order to evaluate Burn Probability (BP), Conditional Flame Length (CFL), Fire Size (FS), and Source-Sink Ratio (SSR). The results revealed low burn probabilities for the monasteries; however, nine out of the 20 monasteries have high fire potential in terms of fire intensity, which means that if an ignition occurs, an intense fire is expected. The outputs of this study may be used for decision-making for short-term predictions of wildfire risk at an operational level, contributing to fire suppression and management of UNESCO World Heritage Properties.

Land-Use/Land-Cover (LULC) products are a common source of information and a key input for spatially explicit models of ecosystem service (ES) supply and demand. Global, continental, and regional, readily available, and free land-cover products generated through Earth Observation (EO) data, can be potentially used as relevant to ES mapping and assessment processes from regional to national scales. However, several limitations exist in these products, highlighting the need for timely land-cover extraction on demand, that could replace or complement existing products. This study focuses on the development of a classification workflow for fine-scale, object-based land cover mapping, employed on terrestrial ES mapping, within the Greek terrestrial territory. The processing was implemented in the Google Earth Engine cloud computing environment using 10 m spatial resolution Sentinel-1 and Sentinel-2 data. Furthermore, the relevance of different training data extraction strategies and temporal EO information for increasing the classification accuracy was also evaluated. The different classification schemes demonstrated differences in overall accuracy ranging from 0.88% to 4.94% with the most accurate classification scheme being the manual sampling/monthly feature classification achieving a 79.55% overall accuracy. The classification results suggest that existing LULC data must be cautiously considered for automated extraction of training samples, in the case of new supervised land cover classifications aiming also to discern complex vegetation classes. The code used in this study is available on GitHub and runs on the Google Earth Engine web platform.

Research Highlights: The developed National Set of Indicators for the Mapping and Assessment of Ecosystems and their Services (MAES) implementation in Greece at the national level sets the official, national basis on which future studies will be conducted for MAES reporting for the achievement of targets within the National and the European Union (EU) biodiversity Strategy. Background and Objectives: Greece is currently developing and implementing a MAES nation-wide program based on the region’s unique characteristics following the proposed methodologies by the European Commission, in the frame of the LIFE-IP 4 NATURA project (Integrated actions for the conservation and management of Natura 2000 sites, species, habitats and ecosystems in Greece). In this paper, we present the steps followed to compile standardized MAES indicators for Greece that include: (a) collection and review of the available MAES-related datasets, (b) shortcomings and limitations encountered and overcome, (c) identification of data gaps and (d) assumptions and framework setting. Correspondence to EU and National Strategies and Policies are also examined to provide an initial guidance for detailed thematic studies. Materials and Methods: We followed the requirements of the EU MAES framework for ecosystem services and ecosystem condition indicator selection. Ecosystem services reported under the selected indicators were assigned following the Common International Classification of Ecosystem Services. Spatial analysis techniques were applied to create relevant thematic maps. Results: A set of 40 MAES indicators was drafted, distributed in six general indicator groups, i.e., Biodiversity, Environmental quality, Food, material and energy, Forestry, Recreation and Water resources. The protocols for the development and implementation of an indicator were also drafted and adopted for future MAES studies in Greece, providing guidance for adaptive development and adding extra indicators when and where needed. Thematic maps representing ecosystem services (ES) bundles and ES hotspots were also created to identify areas of ES importance and simultaneously communicate the results at the national and regional levels.