Published September 1, 2022 | Version v1
Presentation Open

A Mediterranean forest types' map – based on dominant species

  • 1. European Topic Centre on Data Integration and Digitalization (ETC-UMA) University of Malaga - Arquitecto Francisco Peñalosa, 18, 29010 Málaga, Spain
  • 2. Khaos Research, ITIS Software, Departamento de Lenguajes y Ciencias de la Computación, University of Málaga, 29071 Málaga, Spain
  • 3. European Environment Agency, Copenhagen, Denmark
  • 1. Department of Botany and Zoology, Faculty of Science, Masaryk University, Brno, Czech Republic
  • 2. Climpact Data Science, Nova Sophia-Regus Nova, Sophia Antipolis, France
  • 3. Department of Environmental Biology, Sapienza–University of Rome, Piazzale Aldo Moro 5, 00185, Rome, Italy
  • 4. Italian National Institute for Environmental Protection and Research (ISPRA), Via Vitaliano Brancati 48, 00144 Roma, Italy
  • 5. Department of Forest Genetics, Dendrology and Botany, Faculty of Forestry and Wood Technology, University of Zagreb, Svetošimunska 25, 10000 Zagreb, Croatia
  • 6. Department of Botany, School of Biology, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
  • 7. School for Viticulture and Enology, University of Nova Gorica, Vipavska 13, 5000 Nova Gorica, Slovenia
  • 8. University of Banja Luka, Faculty of ForestryBanja Luka, Bosnia and Herzegovina
  • 9. Research Centre of the Slovenian Academy of Sciences and Arts, Jovan Hadži Institute of Biology, Ljubljana, Slovenia
  • 10. Department of Plant and Fungal Diversity and Resources, Bulgarian Academy of Sciences, Sofia, Bulgaria
  • 11. Faculty of Science and Technology, Free University of Bozen-Bolzano, Piazza Università 5, 39100, Bolzano, Italy
  • 12. Department of Plant Biology and Ecology, University of the Basque Country UPV/EHU, Bilbao, Spain
  • 13. Faculty of Forestry, Karabuk University, Karabuk, Turkey
  • 14. Department of Biological, Geological and Environmental Sciences, University of Bologna, Via Irnerio 42, 40126, Bologna, Italy
  • 15. Dipartimento di Biologia, Università Degli Studi di Roma Tor Vergata, via della Ricerca Scientifica, 00133, Rome, Italy
  • 16. Department of Environmental Biology, Sapienza University of Rome, Italy
  • 17. University of Bologna, Bologna, Italy
  • 18. Plant Ecology and Physiology, Radboud University, Nijmegen, The Netherlands
  • 19. Institute of Biology, Faculty of Natural Sciences and Mathematics, University of Ss. Cyril and Methodius, Skopje, Republic of North Macedonia
  • 20. Departamento de Sistemas y Recursos Naturales, Universidad Politécnica de Madrid, Ciudad Universitaria s/n, 28040 Madrid, Spain
  • 21. University of Carthage, The National Research Institute of Rural Engineering, Water, and Forestry, INRGREF, Laboratory of Management and Valorization of Forest Resources, BP 10, Ariana, 2080, Tunisia
  • 22. Shouf Biosphere Reserve (SBR), Park house, El Shouf, Maasser El Shouf, Lebanon
  • 23. L2GE, Life and Earth Sciences, Lebanese University, Fanar, Lebanon
  • 24. Université Ibn Tofail Faculté des sciences humaines et sociales, Département de géographie Laboratoire « Environnement, Sociétés, Territoires » BP 242, Kénitra Maroc
  • 25. Haut Commissariat aux Eaux et Forêts et à la Lutte Contre la Désertification (HCEFLCD), B.P : 605 Rabat-Chellah
  • 26. Ifrane National Park, Azrou, Morocco
  • 27. Department of Theoretical and Applied Sciences, University of Insubria, via Dunant 3, 21100, Varese, Italy
  • 28. School of Rural and Surveying Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
  • 29. Forest Research Centre (INIA, CSIC), Ctra. La Coruña, km. 7.5, 28040 Madrid, Spain
  • 30. Área de Inventario y Estadísticas Forestales, Ministerio de Agricultura, Alimentación y Medio Ambiente, España


Forest maps are an essential tool for forest management. They help in understanding the distribution, expansion and health of forests and they give spatial and temporal context to the drivers of forest degradation and potential nature-based solutions for restoration. However, at the global and regional levels, the existing sources of forest cartography present several limitations. The definition of forests is generally too generic (broad classes of forest cover, or distinction only on coniferous, deciduous and mixed forests) and the scale is too broad. In addition, the map accuracy might me insufficient depending on the methodology used and the availability of field truthing data.

Mediterranean forests are very diverse in terms of tree species, forest types and tree density. They are generally composed of more broadleaf trees and mixed stands, often with a lower tree density than in other temperate and boreal forests. Moreover, the Mediterranean region is highly affected by human impacts and climate change. In this context, a map of Mediterranean forest types, based on dominant species, with a high temporal and spatial resolution and high map accuracy is needed to support forest management at the local scale. At a regional scale, this map supports conservation and restoration policies, such as the Convention on Biological Diversity, the UN Framework Convention on Climate Change, the European Green Deal, the Sustainable Development Goals, and the EU Forest and Biodiversity Strategies for 2030.

In the current frame of open-source Earth Observation satellite Big Data and the development of Artificial Intelligence (AI) for massive data storage and analysis, it is feasible to generate forest maps for the entire Mediterranean region on a yearly basis and at a resolution of few meters. To achieve a good map accuracy today, the bottleneck is the forest data to feed the models. There is a need of harmonizing forest databases to achieve maps at the global and regional levels with a homogeneous accuracy along with the mapped territory. In addition, the data must reach remote sensing requirements.

The map is based on Sentinel-2 multispectral imagery, NASA/JAXA ASTER Digital Elevation Model and derived thematic layers. More than 80.000 forest samples were gathered and curated for feeding the models, including approximately 100 tree species into 30 forest classes. Spectral separability analysis was used to confirm the suitability of the ecological description of forest types into remote sensing classification. This data comes from forest databases of several sources, which needed an extensive work on harmonization, as they come in different formats, collect different variables and handle different forest type’s definitions. Several National Forest Inventories (Spain, Tunisia and Lebanon) were used, together with some databases from the European Vegetation Archive (EVA). Therefore, we acknowledge the potential benefit in creating standards at European and regional levels for National Forest Inventories, for their use in remote sensing applications.


This work was developed in the framework of the EnBIC2Lab project, funded by the EU LifeWatch ERIC program, in collaboration with FAO Silva Mediterranea, Medforval network, the European Environmental Agency (EEA) and the EEA-Eionet.