Simulation of structural timber properties based on geographical data and stand-level forest inventory data

ABSTRACT

A method for simulation of distributions of structural timber properties based on geographical data and stand-level forest inventory data is presented. The method is based on Monte Carlo simulation of randomly selected sites from volume distributions of standing timber, in our case from the Norwegian National Forest Inventory. Simulation models of density, modulus of elasticity and bending strength were estimated from data from 2369 boards of Norway spruce from 27 sites in Norway. The models were estimated as mixed models including covariate models with fixed effects of latitude, altitude, site index and stand age, random site effects and residual variances, representing the within-site variances. Correlations between the simulated properties were obtained by transformation of the random effects in the simulations. The transformations were based on Cholesky decomposition of the correlation matrices of the random site-effects and the residuals of the simulation models. The simulations provide distributions for timber from each selected site and from all selected sites within defined areas, with different limitations on geographical data and stand-level forest inventory data. Simulations of Norway spruce from Eastern Norway are presented for demonstration, comparing timber from maturity classes, altitudes and site indices.

KEYWORDS:

• Bending strength
• density
• modulus of elasticity
• Monte Carlo simulation
• Norway spruce
• Picea abies

Acknowledgements

This article is based partly on data obtained from projects carried out by the Norwegian University of Life Sciences, the Norwegian Institute of Wood Technology and Silvinova AS, with financial support from the Research Council of Norway. Rune Eriksen, Norwegian Institute of Bioeconomic Research, has provided data from the Norwegian National Forest Inventory. The testing of new data, the data analyses and the writing have been supported by the Norwegian University of Life Sciences and the Forest Research Institute Baden-Württemberg. The authors are grateful to all who have contributed to this article.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.