Plausible scenarios of future land use derived from model projections may differ substantially from what is actually desired by society, and identifying such mismatches is important for identifying
This paper tested the ability of machine learning techniques, namely artificial neural networks and random forests, to predict the individual trees within a forest most at risk of damage in storms.
More information Recommended citation: A. Pussinen, M.J. Schelhaas, E. Verkaik, E. Heikkinen, J. Liski, T. Karjalainen, R. Päivinen and G.J. Nabuurs. 2001. Manual for the European Forest Information
More information Recommended citation: Andrei P. Kirilenko. 2002. Simulation of long-term dynamics of forest wildfires in Russia with Marcovian model. Internal Report 10. European Forest Institute
More information Recommended citation: Jeannette Meyer. 2005. Fire Effects on Forest Resource Development in the French Mediterranean Region – Projections with a Large-Scale Forest Scenario Model. EFI
More information Recommended citation: Mart-Jan Schelhaas, Jeannette Eggers, Marcus Lindner, Gert-Jan Nabuurs, Ari Pussinen, Risto Päivinen, Andreas Schuck, P.J. Verkerk, D.C. van der Werf and Sergey
This report is a deliverable from the EU FP6 Integrated Project EFORWOOD – Tools for Sustainability Impact Assessment of the Forestry-Wood Chain. More information Recommended citation: Filip Aggestam
