Modelling and mapping the suitability of European forest formations at 1-km resolution |
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Authors: | Stefano Casalegno Giuseppe Amatulli Annemarie Bastrup-Birk Tracy Houston Durrant Anssi Pekkarinen |
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Institution: | (1) Institute for Environment and Sustainability, Joint Research Centre of the European Commission, TP260, 21020 Ispra, VA, Italy;(2) Predictive Models for Biomedicine and Environment, Fondazione Bruno Kessler, via Sommarive 18, 38123 Trento, Italy;(3) Life Sciences, Forest and Landscape, University of Copenhagen, 1958 Frederiksberg, Denmark;(4) Finnish Forest Research Institute (Metla), PO Box 18, 01301 Vantaa, Finland |
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Abstract: | Proactive forest conservation planning requires spatially accurate information about the potential distribution of tree species.
The most cost-efficient way to obtain this information is habitat suitability modelling i.e. predicting the potential distribution
of biota as a function of environmental factors. Here, we used the bootstrap-aggregating machine-learning ensemble classifier
Random Forest (RF) to derive a 1-km resolution European forest formation suitability map. The statistical model use as inputs
more than 6,000 field data forest inventory plots and a large set of environmental variables. The field data plots were classified
into different forest formations using the forest category classification scheme of the European Environmental Agency. The
ten most dominant forest categories excluding plantations were chosen for the analysis. Model results have an overall accuracy
of 76%. Between categories scores were unbalanced and Mesophitic deciduous forests were found to be the least correctly classified
forest category. The model’s variable ranking scores are used to discuss relationship between forest category/environmental
factors and to gain insight into the model’s limits and strengths for map applicability. The European forest suitability map
is now available for further applications in forest conservation and climate change issues. |
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