European Forest Types: toward an automated classification

  • Francesca Giannetti
  • Anna Barbati
  • Leone Davide Mancini
  • Davide Travaglini
  • Annemarie Bastrup-Birk
  • Roberto Canullo
  • Susanna Nocentini
  • Gherardo Chirici
Original Paper
Part of the following topical collections:
  1. ICP Forests


Key message

The outcome of the present study leads to the application of a spatially explicit rule-based expert system (RBES) algorithm aimed at automatically classifying forest areas according to the European Forest Types (EFT) system of nomenclature at pan-European scale level. With the RBES, the EFT system of nomenclature can be now easily implemented for objective, replicable, and automatic classification of field plots for forest inventories or spatial units (pixels or polygons) for thematic mapping.


Forest Types classification systems are aimed at stratifying forest habitats. Since 2006, a common scheme for classifying European forests into 14 categories and 78 types (European Forest Types, EFT) exists.


This work presents an innovative method and automated classification system that, in an objective and replicable way, can accurately classify a given forest habitat according to the EFT system of nomenclature.


A rule-based expert system (RBES) was adopted as a transparent approach after comparison with the well-known Random Forest (RF) classification system. The experiment was carried out based on the information acquired in the field in 2010 ICP level I plots in 17 European countries. The accuracy of the automated classification is evaluated by comparison with an independent classification of the ICP plots into EFT carried out during the BioSoil project field survey. Finally, the RBES automated classifier was tested also for a pixel-based classification of a pan-European distribution map of beech-dominated forests.


The RBES successfully classified 94% of the plots, against a 92% obtained with RF. When applied to the mapped domain, the accuracy obtained with the RBES for the beech forest map classification was equal to 95%.


The RBES algorithm successfully automatically classified field plots and map pixels on the basis of the EFT system of nomenclature. The EFT system of nomenclature can be now easily and objectively implemented in operative transnational European forest monitoring programs.


European Forest Type Expert system Classification GIS Vegetation Algorithm ICP forests 



This research was carried out in the context of ICP Forest group “Upscaling & Spatially explicit estimation of biophysical variables with remote sensing”, coordinated by Prof. Gherardo Chirici. The authors wish to thank the guest editor Walter Seidling and the two anonymous reviewers for their positive contribution to improving the manuscript.

This research was cofunded by the Accademia Italiana di Scienze Forestali and by geoLAB—Laboratory of Forest Geomatics at the Department of Agriculture, Alimentary and Forest Systems of the Università degli Studi di Firenze in the framework of a PhD Student Scholarship to Francesca Giannetti.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material


  1. Ackers SH, Davis RJ, Olsen KA, Dugger KM (2015) The evolution of mapping habitat for northern spotted owls (Strix occidentalis caurina): a comparison of photo-interpreted, Landsat-based, and lidar-based habitat maps. Remote Sens Environ 156:361–373. CrossRefGoogle Scholar
  2. Adamo M, Tarantino C, Lucas RM,Tomaselli V, Sigismondi A,Mairota P, Blonda P (2015) Combined Use of Expert Knowledge and Earth Observation Data for the Land Cover Mapping of an Italian Grassland Area: An EODHaM System Application. 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). N.p., 3065–3068. doi: 10.1109/IGARSS.2015.7326463
  3. Andrew M (1996) Information Systems project redefinition in New Zealand : will we ever learn?.Aus. Comput J 28:27–40Google Scholar
  4. Barbati A, Corona P, Marchetti M (2006) European forest types. Categories and types for sustainable forest management and reporting. European Environment Agency, EEA Technical report No. 9/2006, ISSN 1725-2237. Available at: Accessed 20 November 2017. Accessed 20 Nov 2017
  5. Barbati A, Corona P, Marchetti M (2007) A Forest typology for monitoring sustainable forest management: the case of European Forest Types. Plant Biosyst. 141(1):93–103. CrossRefGoogle Scholar
  6. Barbati A, Arianoutsou M, Corona P, de las Heras J, Fernandes P, Moreira F, Papageorgiou K, Vallejo R, Xanthopoulos G (2010) Post-fire forest management in southern Europe: a COST action for gathering and disseminating scientific knowledge. IForest 3(1):5–7. CrossRefGoogle Scholar
  7. Barbati A, Corona P, Marchetti M (2011) Annex 1: Pilot Application of the European Forest Types. In Michalak R. (eds.), FOREST EUROPE, UNECE and FAO 2011. State of Europe’s Forests 2011. Status and trends in Sustainable Forest Management in Europe. Oslo: 259–273Google Scholar
  8. Barbati A, Marchetti M, Chirici G, Corona P (2014) European Forest Type and Forest Europe SFM indicators: tools for monitoring progress on forest biodiversity conservation. Forest Ecol Manag 321:145–151. CrossRefGoogle Scholar
  9. Bastrup-Birk A, Neville P, Chirici G, Houston T (2007) The BioSoil Forest biodiversity field manual. ICP Forests, HamburgGoogle Scholar
  10. Beard JS, Beeston GR, Harvey JM, Hopkins AJM, Shepherd DP (2013) The vegetation of Western Australia at the 1:3,000,000 scale. Explanatory memoir. Second edition. Conserv Sci Western Aust 9:1–152Google Scholar
  11. Bingyuan C, Ma S, Cao H (2014) Ecosystem assessment and fuzzy systems management. Springer (ISBN 978-3-319-03448-5), New York/London, p 529Google Scholar
  12. Bohn U, Neuhäusl R, unter Mitarbeit von / with contributions by Gollub G, Hettwer C, Neuhäuslová Z, Raus, Th, Schlüter H, Weber H (2000/2003): Karte der natürlichen Vegetation Europas / Map of the Natural Vegetation of Europe. Maßstab / Scale 1 : 2 500 000. Münster (Landwirtschaftsverlag), Bundesamt für Naturschutz (BfN)/Federal Agency for Nature Conservation Konstantinstr. 110, 53179 Bonn, Germany. Avilable on line Accessed 20 November 2011
  13. Bravo-Oviedo A, Pretzsch H, Ammer C, Andenmatten E, Barbati A, Barreiro S, Brang P, Bravo F, Coll L, Corona P, Den Ouden J, Ducey MJ, Forrester DI, Giergiczny M, Jacobsen JB, Lesinski J, Löf M, Mason B, Matovic B, Metslaid M, Morneau F, Motiejunaite J, O’Reilly C, Pach M, Ponette Q, Del Rio M, Short I, Skovsgaard JP, Soliño M, Spathelf P, Sterba H, Stojanovic D, Strelcova K, Svoboda M, Verheyen K, Von Lüpke N, Zlatanov T (2014) European mixed forests: definition and research perspectives. Forest Systems 23(3):518–533. CrossRefGoogle Scholar
  14. Breiman L (2001) Random forests. Mach Learn 45(1):5–32. CrossRefGoogle Scholar
  15. Bruelheide H (2000) A new measure of fidelity and its application to defining species groups. J Veg Sci 11(2):167–178. CrossRefGoogle Scholar
  16. Brus DJ, Hengeveld GM, Walvoort DJJ, Goedhart PW, Heidema AH, Nabuurs GJ, Gunia K (2011) Statistical mapping of tree species over Europe. Eur J For Res 131(1):145–157CrossRefGoogle Scholar
  17. Buffa G, Villani M (2012) Are the ancient forests of the Eastern Po Plain large enough for a long term conservation of herbaceous nemoral species? Plant Biosyst 146(4):970–984. CrossRefGoogle Scholar
  18. Capelo J, Masquita S, Costa JC, Ribeiro S, Arsénio P, Neto C, Monteiro-Henriques T, Aguiar C, Honrado J, Espírito-Santo D, Lousã M (2007) A methodological approach to potential vegetation modeling using GIS techniques and phytosociological expert-knowledge: application to mainland Portugal. Phytocoenologia 37(3–4):399–415. CrossRefGoogle Scholar
  19. Caudullo G, Pasta S, Giannetti F, Barbati A, Chirici G (2016) European forest classifications in San-Miguel. In: Ayanz J, de Rigo D, Caudullo G, Houston Durrant T, Mauri A (eds) European Atlas of Forest Tree Species. Publication Office of the European Union, Luxembourg, pp 32–33Google Scholar
  20. Chen M, Yao Z (2008) Classification techniques of neural networks using improved genetic algorithms. In: Proceedings of 2nd International Conference on Genetic and Evolutionary Computing,Washington. Article number 4637407, 115–119Google Scholar
  21. Chytrý M (2012) Vegetation of the Czech Republic: diversity, ecology, history and dynamics. Preslia 84(3):427–504Google Scholar
  22. Chytrý M, Tichý L, Holt J, Botta-Dukàt Z (2002) Determination of diagnostic species with statistical fidelity measures. J Veg Sci 13(1):79–90. CrossRefGoogle Scholar
  23. Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20:37–46CrossRefGoogle Scholar
  24. Cook SE, Corner R, Grealish GJ, Gessler PE, Chartres CJ (1996) A rule-based system to map soil properties. Soil Sci Soc Am J 60(1996):1893–1900. CrossRefGoogle Scholar
  25. Corona P (2016) Consolidating new paradigms in large-scale monitoring and assessment of forest ecosystems. Environ Res 144(Pt B):8–14. CrossRefPubMedGoogle Scholar
  26. Corona P, Ferrari B, Cartisano R, Barbati A (2014) Calibration assessment of forest flammability potential in Italy. IForest 7(5):300–305. CrossRefGoogle Scholar
  27. COUNCIL OF EUROPE (1979)- ETS 104 – Conservation of Wildlife and Natural Habitats, Bern, 19.IX.1979. available at Accessed 20 November 2017
  28. de Rigo D, Caudullo G, San-Miguel-Ayanz J (2016) European tree species distribution with Constrained Spatial Multi-Frequency Analysis. EUR - Scientific and Technical Research. Publications Office of the European Union. Available on line at
  29. Dengler J, Löbel S, Dolnik C (2009) Species constancy depends on plot size—a problem for vegetation classification and how it can be solved. J Veg Sci 20(4):754–766. CrossRefGoogle Scholar
  30. Douda J, Boublík K, Slezák M, Biurrun I, Nociar J, Havrdová A, Doudová J, Aćić S, Brisse H, Brunet J, Chytrý M, Claessens H, Csiky J, Didukh Y, Dimopoulos P, Dullinger S, FitzPatrick Ú, Guisan A, Horchler PJ, Hrivnák R, Jandt U, Kącki Z, Kevey B, Landucci F, Lecomte H, Lenoir J, Paal J, Paternoster D, Pauli H, Pielech R, Rodwell JS, Roelandt B, Svenning JC, Šibík J, Šilc U, Škvorc Ž, Tsiripidis I, Tzonev RT, Wohlgemuth T, Zimmermann NE (2016) Vegetation classification and biogeography of European floodplain forests and alder cars. Appl Veg Sci 19(1):147–163. CrossRefGoogle Scholar
  31. Duveneck MJ, Thompson JR, Wilson BT (2015) An imputed forest composition map for New England screened by species range boundaries. Forest Ecol Manag 347:107–115. CrossRefGoogle Scholar
  32. EEA (2006) European forest types. Categories and types for sustainable forest management and reporting. European Environment Agency, EEA Technical report No. 9/2006. ISSN 1725-2237 Available at: Accessed 20 November 2017
  33. EEA (2013) Digital Elevation Model over Europe (EU-DEM). European Environment Agency Available on-line at
  34. EEA (2013a) Copernicus Initial Operations 2011–2013 Land Monitoring Service pan-European Component, High Resolution Layer Permanent Water Bodies (PWB). Available at. Accessed 30 January 2016. In: European Environment Agency (2013) GIO land (GMES/Copernicus initial operations land) High Resolution Layers (HRLs) – summary of product specifications, European Environment Agency, Copenhagen K, Denmark
  35. EEA (2013b) Copernicus Initial Operations 2011–2013 Land Monitoring Service pan-European Component, High Resolution Layer Permanent Wetlands (WET) Available at Accessed 30 January 2016. In: European Environment Agency (2013). GIO land (GMES/Copernicus initial operations land) High Resolution Layers (HRLs) – summary of product specifications, European Environment Agency, Copenhagen K, Denmark
  36. EEA (2015a) Linking in situ vegetation data to the EUNIS habitat classification: results for forest habitats. European EnvironmentAgency, EEA Report No. 18/2015. ISSN 1725-2237 Available on-line < Accessed 20 November 2017>
  37. EEA (2015b) The biogeographical regions dataset. Europe. European Environment Agency Available on-line at: : Accessed 20 November 2017
  38. Ewald J (2003) A critique for phytosociology. J Veg Sci 14(2):291–296. CrossRefGoogle Scholar
  39. Faber-Langendoen D, Keeler-Wolf T, Meidinger D, Tart D, Hoagland B, Josse C, Navarro G, Ponomarenko S, Saucier J-P, Weakley A, Comer P (2014) EcoVeg: a new approach to vegetation description and classification. Ecol Monogr 84(4):533–561. CrossRefGoogle Scholar
  40. Flanagan NE, Richardson CJ, Ho M (2015) Connecting differential responses of native and invasive riparian plants to climate change and environmental alteration. Ecol Appl 25(3):753–767. CrossRefPubMedGoogle Scholar
  41. FOREST EUROPE (2011) State of Europe’s Forests 2011. Forest Europe Liaison Unit/UNECE Timber Section/FAO, OsloGoogle Scholar
  42. FOREST EUROPE (2015). State of Europe’s Forests 2015. Ministerial Conference on the Protection of Forests in Europe 2015. Available on-line . Accessed 20 November 2017
  43. Gao J, Chen H, Zhang Y, Zha Y (2004) Knowledge-based approaches to accurate mapping of mangroves from satellite data. Photogramm Eng Remote Sens 70(11):1241–1248.  10.14358/PERS.70.11.1241 CrossRefGoogle Scholar
  44. Greenberg JA (2014) Spatial functions meant to enhance the core functionality of the package ``raster”, including a parallel processing engine for use with rasters. R-CRAN package, Available on-line
  45. Grunwald S (2009) Multi-criteria characterization of recent digital soil mapping and modeling approaches. Geoderma 152(3–4):195–207. CrossRefGoogle Scholar
  46. Hayes-Roth F (1985) Rule-based systems. Commun ACM 28(9):921–932. CrossRefGoogle Scholar
  47. Hédel R (2007) Is sampling subjectivity a distorting factor in surveys for vegetation diversity? Folia Geobot. Phytotaxon 42:191–198Google Scholar
  48. Hiederer R, Durrant T (2010) Evaluation of BioSoil Demonstration Project—preliminary data analysis. Joint Research Centre, report no JRC56739. ISSN 1018–5593Google Scholar
  49. Hijmans RJ (2015) raster: Geographic data analysis and modeling. R-CRAN pakeges,Available on line at
  50. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25(15):1965–1978CrossRefGoogle Scholar
  51. Illyés E, Chytrý M, Botta-Dukàt Z, Jandt U, Škodovà I, Janišova M, Willner W, Hàjek O (2007) Semi-dry grasslands along a climatic gradient across Central Europe: vegetation classification with validation. J Veg Sci 18(6):835–846. CrossRefGoogle Scholar
  52. Ioannis N, Vogiatzakis, Griffiths GH (2006) A GIS-based empirical model for vegetation prediction in Lefka Ori. Crete Plant Ecol 184(2):311–323CrossRefGoogle Scholar
  53. Jiménez-Alfaro B, Chytrý M, Rejmànek M, Mucina L (2014) The number of vegetation types in European countries: major determinants and extrapolation to other regions. J Veg Sci 25(3):863–872. CrossRefGoogle Scholar
  54. JRC (2011) Biosoil biodiversity executive report. Report number: Joint Research Centre report 64509. Durrant, T., San-Miguel-Ayanz, J., Schulte, E., & Suarez-Meyer, A. (Eds). Publications Office of the European Union. ISSN 1018–5593 Available on-line 20 November 2017
  55. Knollovà I, Chytrý M, Tichý L, Hàjek O (2005) Stratified resampling of phytosociological databases: some strategies for obtaining more representative data sets for classification studies. J Veg Sci 16(4):479–486. CrossRefGoogle Scholar
  56. Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2(3):18–22Google Scholar
  57. Mc Roberts RE, Chirici G, Winter S, Barbati A, Corona P, Marchetti M, Hauk E, Brändli U-B, Beranova J, Rondeaux J, Sanchez C, Bertini R, Barsoum N, Alberdi Asencio I, Condéz S, Saura S, Neagu S, Cluzeau C, Hamza N (2011) Prospects for harmonized biodiversity assessments using national forest inventory data. In: Chirici G, Winter S, McRoberts RE (eds) National Forest Inventories: contributions to Forest biodiversity assessments. Springer, Heidelberg, pp 41–97. CrossRefGoogle Scholar
  58. Millington A, Walsh S, Osborne PE (2002) GIS and remote sensing applications in biogeography and ecology. Kluwer Academic PublishersGoogle Scholar
  59. Mucina L (1997) Classification of vegetation: past, present and future. J Veg Sci 8(6):751–760. CrossRefGoogle Scholar
  60. Ohman J, Gregory M (2002) Predictive mapping of forest composition and structure with direct gradient analysis and nearest-neighbour imputation in coastal Oregon, USA. Can J For Res 32(4):725–741. CrossRefGoogle Scholar
  61. Openshaw S, Openshaw C (1997) Artificial intelligence in geography. Wiley, LondonGoogle Scholar
  62. Panagos P, Jones A, Bosco C, Senthil Kumar PS (2011) European digital archive on soil maps (EuDASM): preserving important soil data for public free access. You can download the article in press for your documentation. Int J Digital Earth 4(5):434–443. CrossRefGoogle Scholar
  63. Peet R, Harris J, Grossman D, JenningsM, WalkerMD (2001) An information infrastructure for vegetation science: project overview and progress report. Available on-ine at Accessed 20 November 2017
  64. Pérez-Ortiz M, Peña JM, Gutiérrez PA, Torres-Sánchez J, Hervás-Martínez C, López-Granados F (2016) Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery. Expert Syst Appl 47:85–94. CrossRefGoogle Scholar
  65. Pividori M, Giannetti F, Barbati A, Chirici G (2016) European Forest Types: tree species matrix in San-Miguel. In: Ayanz J, de Rigo D, Caudullo G, Houston Durrant T, Mauri A (eds) European Atlas of Forest Tree Species. Publication Office of the European Union, Luxembourg, pp 34–35Google Scholar
  66. Puletti N, Giannetti F, Chirici G, Canullo R (2017) Deadwood distribution in European forests. Journal of Maps 13(2):733–736. CrossRefGoogle Scholar
  67. Robinove CJ (1986) Principles of logic and the use of digital geographic systems. Department of Interior, US Geological Survay, Reston, VAGoogle Scholar
  68. Rodwell JS, Schaminée JHJ, Mucina L, Pignatti S, Dring J, Moss D (2002) The diversity of European vegetation—an overview of phytosociological alliances and their relationships to EUNIS habitats. Rapport EC-LNV 2002(054):1–168Google Scholar
  69. San-Miguel-Ayanz J, de Rigo D, Caudullo G, Houston DT, Mauri A (2016) European Atlas of Forest Tree Species. Publication Office of the European Union, Luxembourg, pp 32–33. Google Scholar
  70. Shumchenia EJ, King JW (2010) Comparison of methods for integrating biological land physical data for marine habitat classification. Cont Shelf Res 30(16):1717–1729. CrossRefGoogle Scholar
  71. Song M, Zhou C, Ouyang H (2005) Simulated distribution of vegetation types in response to climate change on the Tibetan Plateau. J Veg Sci 16(3):341–350. CrossRefGoogle Scholar
  72. Vaz AS, Marcos B, Gonçalves J, Monteiro A, Alves P, Civantos E et al (2015) Can we predict habitat quality from space? A multi-indicator assessment based on an automated knowledge-driven system. Int J Appl Earth Obs Geoinf 37:106–113. CrossRefGoogle Scholar
  73. Wang ZY, Leung KS, Klir GJ (2005) Applying fuzzy measures and nonlinear integrals in data mining. Fuzzy Sets Syst 156(2005):371–380. CrossRefGoogle Scholar
  74. Working Group on Forest Biodiversity (2007) The BioSoil Forest Biodiversity field manual. In: JRC 2011, Evaluation of BioSoil Demonstration Project: Forest Biodiversity. Publications Office of the European Union, Luxembourg. Google Scholar
  75. Zimmermann NE, Kienast F (1999) Predictive mapping of alpine grasslands in Switzerland: species versus community approach. J Veg Sci 10(4):469–482. CrossRefGoogle Scholar

Copyright information

© INRA and Springer-Verlag France SAS, part of Springer Nature 2017

Authors and Affiliations

  • Francesca Giannetti
    • 1
  • Anna Barbati
    • 2
  • Leone Davide Mancini
    • 2
  • Davide Travaglini
    • 1
  • Annemarie Bastrup-Birk
    • 3
  • Roberto Canullo
    • 4
  • Susanna Nocentini
    • 1
  • Gherardo Chirici
    • 1
  1. 1.Department of Agricultural, Food and Forestry SystemUniversità degli Studi di FirenzeFlorenceItaly
  2. 2.Department for Innovation in Biological, Agro-Food and Forest SystemUniversità degli Studi della TusciaViterboItaly
  3. 3.European Environmental AgencyCopenhagenDenmark
  4. 4.Plant Diversity and Ecosystems Management Unit, School of Biosciences and Veterinary MedicineUniversity of CamerinoCamerinoItaly

Personalised recommendations