NDLI: Comparison of nonspatial and spatial approaches with parametric and nonparametric methods in prediction of tree height

Content Provider	Springer Nature Link
Author	Kangas, Annika Haara, Arto
Copyright Year	2012
Abstract	Nonparametric modelling has been popular in recent forestry applications. However, nonparametric modelling methods usually assume independent observations, that is, do not acknowledge the spatial relationships of most forest data sets. For these situations, mixed model and kriging approaches have been used. The aim of this paper was to compare accuracy of spatial parametric and nonparametric approaches, namely mixed models and a combination of k-nn method and mixed models, in prediction of tree height. The spatial approaches were compared to a nonspatial parametric model and k-nn method. Tree height was first modelled using either mixed model or k-nn. The residual error was divided into plot and tree effects. A nonspatial prediction was obtained using the fixed part of the models. The spatial prediction was obtained when this prediction was further adjusted using the estimates of within-plot correlation of errors and best linear predictor. The influence of the quality of modelling data was also considered. The adjustment of nonspatial estimates of both parametric and nonparametric approaches markedly improved the predictions in all study cases. For many applications, the combination of the nonparametric k-nn method for the fixed component of the model, along with random effects for spatial correlations to create a mixed model, could be used. This would allow for spatial prediction, which would likely provide improved predictions, as shown for predicting height in this paper. Also, there is the added benefit that the nonparametric k-nn does not require a particular model form.
Starting Page	1771
Ending Page	1782
Page Count	12
File Format	PDF
ISSN	16124669
Journal	Forstwissenschaftliches Centralblatt vereinigt mit Tharandter forstliches Jahrbuch
Volume Number	131
Issue Number	6
e-ISSN	16124677
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-04-01
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Mixed model k-nearest neighbours Linear prediction theory Plant Ecology Plant Sciences Forestry
Content Type	Text
Resource Type	Article
Subject	Plant Science Forestry

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