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Articles containing the keyword 'local estimates'
Category : Research article
article id 580,
category
Research article
(2001).
Estimating individual tree growth with the k-nearest neighbour and k-Most Similar Neighbour methods.
Silva Fennica
vol.
35
no.
4
article id 580.
https://doi.org/10.14214/sf.580
Abstract |
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Author Info
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.
-
Sironen,
University of Joensuu, Faculty of Forestry, P.O. Box 111, FIN-80101 Joensuu, Finland
E-mail:
susanna.sironen@forest.joensuu.fi
- Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: ak@nn.fi
- Maltamo, University of Joensuu, Faculty of Forestry, P.O. Box 111, FIN-80101 Joensuu, Finland E-mail: mm@nn.fi
- Kangas, Finnish Forest Research Institute, Kannus Research Station, P.O. Box 44, FIN-69101 Kannus, Finland E-mail: jk@nn.fi
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