A multidisciplinary critical review of ecosystem services studies in Greece: approaches, shortcomings and the pathway to implementation

During the last two decades, ecosystem services (ES) research is used to inform the various steps of decision- and policy- making process, regarding environmental management, spatial planning and natural capital accounting. In the EU, this vast and rapid publication boom was triggered by the enactment of Action 5 of the EU Biodiversity Strategy to 2020, urging Member States to implement Mapping and Assessment of Ecosystem and their Services (MAES); few countries pioneered, while others are still lagging behind. In Greece, the implementation of MAES started in 2014 and since then an impressive progress has been made, with Greece now being among the countries with the most rapid progress. However, there are still major knowledge and data gaps on ecosystem services in Greece; know-how on specific methods, tools and practices is still to be developed. This poses obstacles in integrative efforts to identify and/or interpret the various co-variates affecting ecosystems and their services in space and time and hinders the incorporation of the ES generated information into the decision-making process. Making the first steps towards overcoming these hurdles, the present study aims to (i) synthesize the ecosystem services literature relevant to the ES implementation in Greece, (ii) validate and classify each literature source to the relevant ecosystem services categories, (iii) identify shortcomings in terms of ES assessed and data available, and (iv) critically review the variety of approaches to ES assessments that are followed. The outcomes of this study will facilitate the efficient implementation of ecosystem services assessments in Greece

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

Spectral and Spatial-Based Classification for Broad-Scale Land Cover Mapping Based on Logistic Regression

Improvement of satellite sensor characteristics motivates the development of new techniques for satellite image classification. Spatial information seems to be critical in classification processes, especially for heterogeneous and complex landscapes such as those observed in the Mediterranean basin. In our study, a spectral classification method of a LANDSAT-5 TM imagery that uses several binomial logistic regression models was developed, evaluated and compared to the familiar parametric maximum likelihood algorithm. The classification approach based on logistic regression modelling was extended to a contextual one by using autocovariates to consider spatial dependencies of every pixel with its neighbours. Finally, the maximum likelihood algorithm was upgraded to contextual by considering typicality, a measure which indicates the strength of class membership. The use of logistic regression for broad-scale land cover classification presented higher overall accuracy (75.61%), although not statistically significant, than the maximum likelihood algorithm (64.23%), even when the latter was refined following a spatial approach based on Mahalanobis distance (66.67%). However, the consideration of the spatial autocovariate in the logistic models significantly improved the fit of the models and increased the overall accuracy from 75.61% to 80.49%

Spectral and Spatial-Based Classification for Broad-Scale Land Cover Mapping Based on Logistic Regression

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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)

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

Assessing the impact of different landscape features on post-fire forest recovery with multitemporal remote sensing data: the case of Mount Taygetos (southern Greece)

Fires affecting large areas usually create a mosaic of recovering plant communities reflecting their pre-fire composition and local conditions of burning. However, post-fire recovery patterns may also reveal the effects of landscape heterogeneity on the natural regeneration process of plant communities. This study combines field data and remote sensing image interpretation techniques to assess the role of various landscape characteristics in the post-fire recovery process in a mountainous region of Greece burned by a severe wildfire. Remote sensing techniques were used to accurately map secluded, large burned areas. By introducing a temporal component, we explored the correlation between post-fire regeneration and underlying topography, soils and basement rock. Pre-fire forest cover was reduced by more than half 8 years after fire. Regarding the dominant pre-fire forest trees, Abies cephalonica did not regenerate well after fire and most pre-fire stands were converted to grasslands and shrublands. In contrast, Pinus nigra regenerated sufficiently to return to its pre-fire cover, especially in areas underlain by softer basement rock. The use of different time series of high-resolution images improved the quality of the results obtained, justifying their use despite their high cost

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