%0 Research article
%T Estimating individual tree growth with the k-nearest neighbour and k-Most Similar Neighbour methods
%A Sironen, Susanna
%A Kangas, Annika
%A Maltamo, Matti
%A Kangas, Jyrki
%D 2001
%J Silva Fennica
%V 35
%N 4
%R doi:10.14214/sf.580
%U https://silvafennica.fi/article/580
%X The purpose of this study was to examine the use of non-parametric  methods in estimating tree level growth models. In non-parametric  methods the growth of a tree is predicted as a weighted average of the  values of neighbouring observations. The selection of the nearest  neighbours is based on the differences between tree and stand level  characteristics of the target tree and the neighbours. The data for the  models were collected from the areas owned by Kuusamo Common Forest in  Northeast Finland. The whole data consisted of 4051 tally trees and 1308  Scots pines (Pinus sylvestris L.) and 367 Norway spruces (Picea abies  Karst.). Models for 5-year diameter growth and bark thickness at the end  of the growing period were constructed with two different  non-parametric methods: the k-nearest neighbour regression and k-Most  Similar Neighbour method. Diameter at breast height, tree height, mean  age of the stand and basal area of the trees larger than the subject  tree were found to predict the diameter growth most accurately. The  non-parametric methods were compared to traditional regression growth  models and were found to be quite competitive and reliable growth  estimators.