Title: Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests
Authors: Aertsen, Wim ×
Kint, Vincent
Van Orshoven, Jos
Ozkan, Kuersad
Muys, Bart #
Issue Date: Apr-2010
Publisher: Elsevier science bv
Series Title: Ecological modelling vol:221 issue:8 pages:1119-1130
Abstract: Forestry science has a long tradition of studying the relationship between stand productivity and abiotic and biotic site characteristics, such as climate, topography, soil and vegetation. Many of the early site quality modelling studies related site index to environmental variables using basic statistical methods such as linear regression. Because most ecological variables show a typical non-linear course and a non-constant variance distribution, a large fraction of the variation remained unexplained by these linear models. More recently, the development of more advanced non-parametric and machine learning methods provided opportunities to overcome these limitations. Nevertheless, these methods also have drawbacks. Due to their increasing complexity they are not only more difficult to implement and interpret, but also more vulnerable to overfitting. Especially in a context of regionalisation, this may prove to be problematic. Although many non-parametric and machine learning methods are increasingly used in applications related to forest site quality assessment, their predictive performance has only been assessed for a limited number of methods and ecosystems.
ISSN: 0304-3800
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Division Forest, Nature and Landscape Research
× corresponding author
# (joint) last author

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