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Volume and error variance estimation using integrated stem taper models

Summary

Published in Forest Science 59(3): 345-358. https://doi.org/10.5849/forsci.11-146

In this study, we propose two volume and error variance estimators based on an integrated nonlinear mixed-effects stem taper model. The estimators rely either on a first- or a second-order Taylor series expansion. They were first tested through Monte Carlo simulations. The accuracy of the volume and error variance estimates was then tested against more than 1,000 observations. Empirical and nominal coverage of the confidence intervals were also compared under the assumption of a Gaussian distribution. For the volume estimators, results showed that the first-order estimator tends to slightly underestimate the volume, mainly because the stem taper model had random effects specified in a nonlinear manner. The second-order estimator was more accurate with neither under- nor overestimations of volume. For both the first- and the second-order variance estimators, the confidence intervals had empirical coverage that closely matches nominal coverage for probability levels >0.9. Although the proposed estimators require the stem taper model to predict the squared diameter of the cross section, they have the benefit of providing a tractable estimate of the variance. The covariances between different stem sections are quickly estimated because there is no need for repeated numerical integrations.

Sector(s): 

Forests

Categorie(s): 

Scientific Article

Theme(s): 

Forest Growth and Yield Modelling, Forestry Research, Forests

Departmental author(s): 

Saucier, Jean-Pierre

Author(s)

FORTIN, Mathieu, Robert SCHNEIDER and Jean-Pierre SAUCIER

Year of publication :

2013

Format :

PDF available upon request

Keywords :

modèle de défilement des tiges, propagation des erreurs, simulations Monte-Carlo, estimation de la variance, internvalles de confiance, développement en série de Taylor, article scientifique de recherche forestière, modélisation de la croissance et du rendement des forêts, forest growth and yield modelling, stem taper model, error propagation, volume estimation, Monte Carlo simulations, variance estimates, confidence intervals, Taylor series expansion

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