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Estimating individual tree growth with nonparametric methods

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The aim of the study was to demonstrate the use of nonparametric methods in estimating tree-level growth models. In the nonparametric methods the growth of a tree is predicted as a weighted mean of the values of neighboring observations. The selection of the nearest neighbors is based on the similarities between tree- and stand-level characteristics of the target tree and the neighbors. The data for the models were collected from Kuusamo in northeastern Finland. Models for the 5-year diameter growth were constructed for Scots pine (Pinus sylvestris L.) with three different nonparametric methods: the k-nearest neighbor regression, k-most-similar neighbor, and generalized additive model.

L'étude illustre l'application des méthodes non paramétriques pour estimer les modèles de croissance d'arbre individuel. Dans les méthodes non paramétriques, la croissance d'un arbre est prédite comme une moyenne pondérée des valeurs observées sur ses voisins. La sélection des voisins les plus proches est basée sur les similarités entre les caractéristiques de l'arbre cible et de ses voisins à l'échelle de l'arbre et du peuplement. Les données utilisées pour estimer les modèles viennent de Kuusamo dans le Nord-Est de la Finlande. Les modèles de croissance en diamètre par pas de temps de 5 ans ont été construits pour le pin sylvestre (Pinus sylvestris L.) à l'aide de trois méthodes non paramétriques différentes : la régression des k plus proches voisins, les k voisins les plus similaires et le modèle additif généralisé.[Traduit par la Rédaction]

Document Type: Research Article

Publication date: March 1, 2003

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  • Published since 1971, this monthly journal features articles, reviews, notes and commentaries on all aspects of forest science, including biometrics and mensuration, conservation, disturbance, ecology, economics, entomology, fire, genetics, management, operations, pathology, physiology, policy, remote sensing, social science, soil, silviculture, wildlife and wood science, contributed by internationally respected scientists. It also publishes special issues dedicated to a topic of current interest.
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