Modelling tree biomasses in Finland

Abstract

Biomass equations for aboveand below-ground tree components of Scots pine (Pinus sylvestris L), Norway spruce (Picea abies [L.] Karst) and birch (Betula pendula Roth and Betula pubescens Ehrh.) were compiled using empirical material from a total of 102 stands. These stands (44 Scots pine, 34 Norway spruce and 24 birch stands) were located mainly on mineral soil sites representing a large part of Finland. The biomass models were based on data measured from 1648 sample trees, comprising 908 pine, 613 spruce and 127 birch trees. Biomass equations were derived for the total above-ground biomass and for the individual tree components: stem wood, stem bark, living and dead branches, needles, the stump, and roots, as dependent variables. Three multivariate models with different numbers of independent variables for above-ground biomass and one for below-ground biomass were constructed. Variables that are normally measured in forest inventories were used as independent variables. The simplest model formulations, multivariate models (1) were mainly based on tree diameter and height as independent variables. In more elaborated multivariate models, (2) and (3), additional commonly measured tree variables such age, crown length, bark thickness and radial growth rate were added. Tree biomass modelling includes consecutive phases, which cause unreliability in the prediction of biomass. First, biomasses of sample trees should be determined reliably to decrease the statistical errors caused by sub-sampling. In this study, methods to improve the accuracy of stem biomass estimates of the sample trees were developed. In addition, the reliability of the method applied to estimate sample-tree crown biomass was tested, and no systematic error was detected. Second, the whole information content of data should be utilized in order to achieve reliable parameter estimates and applicable and flexible model structure. In the modelling approach, the basic assumption was that the biomasses of the tree components on the same site and in the same tree are dependent. This statistical dependency was taken into account when simultaneously estimating parameter estimates for all biomass components, by applying a multivariate procedure. Based on the verified statistical dependence between the biomass components, the multivariate procedure had a number of advantages compared to the traditionally independently estimated equations, by enabling more flexible application of the equations, ensuring better biomass additivity, and giving the more reliable parameter estimates. The generalization and applicability of the models may be restricted by the fact that the study material was not an objective, representative sample, and some tree components were poorly represented. Despite these shortcomings, the models provided logical biomass predictions for individual tree components and were comparable with other functions used in Finland and Sweden.