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Characterising prediction error as a function of scale in spatial surfaces of tree productivity

Characterising prediction error as a function of scale in spatial surfaces of tree productivity Background: Two indices, the 300 Index and Site Index, are commonly used to quantify productivity of Pinus radiata D.Don within New Zealand. Although maps of these indices exist, availability of new data and modifications to underlying models makes a refit of these prediction surfaces desirable. Prediction errors of such surfaces have only been reported at a plot-level scale, but their application is invariably at a larger scale where prediction accuracy should be better. The objectives of this study were to: (i) develop updated predictive surfaces for the 300 Index and Site Index; and (ii) characterise the relationship between prediction error and spatial scale for both surfaces. Methods: Models were developed using a dataset of 4108 permanent sample plots from throughout New Zealand. Productivity indices were estimated from plot measurements and environmental variables extracted for each plot. Data were randomly split into fitting and validation datasets and surfaces developed from the fitting dataset for the 300 Index and Site Index using partial least squares regression, ordinary kriging and regression kriging. Prediction accuracy across a range of scales from 0.2 to 200 km was evaluated using the validation dataset. Results: Regression kriging was found to be the most accurate method for http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png New Zealand Journal of Forestry Science Springer Journals

Characterising prediction error as a function of scale in spatial surfaces of tree productivity

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References (50)

Publisher
Springer Journals
Copyright
Copyright © 2017 by The Author(s).
Subject
Life Sciences; Forestry
eISSN
1179-5395
DOI
10.1186/s40490-017-0100-8
Publisher site
See Article on Publisher Site

Abstract

Background: Two indices, the 300 Index and Site Index, are commonly used to quantify productivity of Pinus radiata D.Don within New Zealand. Although maps of these indices exist, availability of new data and modifications to underlying models makes a refit of these prediction surfaces desirable. Prediction errors of such surfaces have only been reported at a plot-level scale, but their application is invariably at a larger scale where prediction accuracy should be better. The objectives of this study were to: (i) develop updated predictive surfaces for the 300 Index and Site Index; and (ii) characterise the relationship between prediction error and spatial scale for both surfaces. Methods: Models were developed using a dataset of 4108 permanent sample plots from throughout New Zealand. Productivity indices were estimated from plot measurements and environmental variables extracted for each plot. Data were randomly split into fitting and validation datasets and surfaces developed from the fitting dataset for the 300 Index and Site Index using partial least squares regression, ordinary kriging and regression kriging. Prediction accuracy across a range of scales from 0.2 to 200 km was evaluated using the validation dataset. Results: Regression kriging was found to be the most accurate method for

Journal

New Zealand Journal of Forestry ScienceSpringer Journals

Published: Oct 2, 2017

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