A practical measure for determining if diameter (D) and height (H) should be combined into D2H in allometric biomass models. (24th August 2019)

• Record Type: Journal Article
• Authors: Dutcă, I; McRoberts, R E; Næsset, E; Blujdea, V N B
• Is Part Of: Forestry. Volume 92:Number 5(2019)
• Page Start: 627
• Page End: 634
• Publication Date: 2019-08-24
• Subjects: combined variable -- diameter at breast height -- tree height -- biomass -- allometric model -- prediction; Forests and forestry -- Periodicals; Forests and forestry -- Great Britain -- Periodicals; 634.9
• DOI: 10.1093/forestry/cpz041
• Languages: English
• ISSNs: 0015-752X

Abstract

Abstract: Tree diameter at breast height (D) and tree height (H) are often used as predictors of individual tree biomass. Because D and H are correlated, the combined variable D 2 H is frequently used in regression models instead of two separate independent variables, to avoid collinearity related issues. The justification for D 2 H is that aboveground biomass is proportional to the volume of a cylinder of diameter, D, and height, H. However, the D 2 H predictor constrains the model to produce parameter estimates for D and H that have a fixed ratio, in this case, 2.0. In this paper we investigate the degree to which the D 2 H predictor reduces prediction accuracy relative to D and H separately and propose a practical measure, Q-ratio, to guide the decision as to whether D and H should or should not be combined into D 2 H. Using five training biomass datasets and two fitting approaches, weighted nonlinear regression and linear regression following logarithmic transformations, we showed that the D 2 H predictor becomes less efficient in predicting aboveground biomass as the Q-ratio deviates from 2.0. Because of the model constraint, the D 2 H-based model performed less well than the separate variable model by as much as 12 per cent with regard to mean absolute percentage residual and as much as 18 per cent with regard to sum of squares of log accuracy ratios. For the analysed datasets, we observed a wide variation in Q-ratios, ranging from 2.5 to 5.1, and a large decrease in efficiency for the combined variable model. Therefore, we recommend using the Q-ratio as a measure to guide the decision as to whether D and H may be combined further into D 2 H without the adverse effects of loss in biomass prediction accuracy.