Proposing a Governance model for environmental crises

During August 2021, a wildfire outbreak in Evia, Greece's second largest island, resulted in a major environmental and economic crisis. Apart from biodiversity and habitat loss, the disaster triggered a financial crisis because it wiped out wood-productive forests and outdoor areas that attract visitors. This crisis highlighted the need for a new governance model in order to respond to environmental crises more effectively. The aim of this study was to investigate the acceptance and attitudes of relevant stakeholders towards establishing a Hub a proposed governance model responsible for monitoring and restoring the natural capital and biodiversity after environmental crises. Results based on quantitative data collected via questionnaires showed that most respondents were positive to the Hub and perceived that its main functions should be to recommend measures after environmental crises and to facilitate cooperation among involved stakeholders. Moreover, results pointed to preferred funding sources, stakeholder groups that should participate in the Hub and key performance indicators (KPIs) for monitoring Hub's performance. The applied methodology could guide the establishment of governance models both in the study area and other countries facing environmental crises

Retrieval of Leaf Area Index Using Sentinel-2 Imagery in a Mixed Mediterranean Forest Area

Leaf area index (LAI) is a crucial biophysical indicator for assessing and monitoring the structure and functions of forest ecosystems. Improvements in remote sensing instrumental characteristics and the availability of more efficient statistical algorithms, elevate the potential for more accurate models of vegetation biophysical properties including LAI. The aim of this study was to assess the spectral information of Sentinel-2 MSI satellite imagery for the retrieval of LAI over a mixed forest ecosystem located in northwest Greece. Forty-eight field plots were visited for the collection of ground LAI measurements using an ACCUPAR LP-80: PAR & LAI Ceptometer. Spectral bands and spectral indices were used for LAI model development using the Gaussian processes regression (GPR) algorithm. A variable selection procedure was applied to improve the model’s prediction accuracy, and variable importance was investigated for identifying the most informative variables. The model resulting from spectral indices’ variables selection produced the most precise predictions of LAI with a coefficient of determination of 0.854. Shortwave infrared bands and the normalized canopy index (NCI) were identified as the most important features for LAI prediction

Predicting Tree Species Diversity Using Geodiversity and Sentinel-2 Multi-Seasonal Spectral Information

Measuring and monitoring tree diversity is a prerequisite for altering biodiversity loss and the sustainable management of forest ecosystems. High temporal satellite remote sensing, recording difference in species phenology, can facilitate the extraction of timely, standardized and reliable information on tree diversity, complementing or replacing traditional field measurements. In this study, we used multispectral and multi-seasonal remotely sensed data from the Sentinel-2 satellite sensor along with geodiversity data for estimating local tree diversity in a Mediterranean forest area. One hundred plots were selected for in situ inventory of tree species and measurement of tree diversity using the Simpson’s (D1) and Shannon (H′) diversity indices. Four Sentinel-2 scenes and geodiversity variables, including elevation, aspect, moisture, and basement rock type, were exploited through a random forest regression algorithm for predicting the two diversity indices. The multi-seasonal models presented the highest accuracy for both indices with an R2 up to 0.37. In regard to the single season, spectral-only models, mid-summer and mid-autumn model also demonstrated satisfactory accuracy (max R2 = 0.28). On the other hand, the accuracy of the spectral-only early-spring and early-autumn models was significant lower (max R2 = 0.16), although it was improved with the use of geodiversity information (max R2 = 0.25)

Vegetation Fuel Mapping at Regional Scale Using Sentinel-1, Sentinel-2, and DEM Derivatives—The Case of the Region of East Macedonia and Thrace, Greece

The sustainability of Mediterranean ecosystems, even if previously shaped by fire, is threatened by the diverse changes observed in the wildfire regime, in addition to the threat to human security and infrastructure losses. During the two previous years, destructive, extreme wildfire events have taken place in southern Europe, raising once again the demand for effective fire management based on updated and reliable information. Fuel-type mapping is a critical input needed for fire behavior modeling and fire management. This work aims to employ and evaluate multi-source earth observation data for accurate fuel type mapping in a regional context in north-eastern Greece. Three random forest classification models were developed based on Sentinel-2 spectral indices, topographic variables, and Sentinel-1 backscattering information. The explicit contribution of each dataset for fuel type mapping was explored using variable importance measures. The synergistic use of passive and active Sentinel data, along with topographic variables, slightly increased the fuel type classification accuracy (OA = 92.76%) compared to the Sentinel-2 spectral (OA = 81.39%) and spectral-topographic (OA = 91.92%) models. The proposed data fusion approach is, therefore, an alternative that should be considered for fuel type classification in a regional context, especially over diverse and heterogeneous landscapes

National Set of MAES Indicators in Greece: Ecosystem Services and Management Implications

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