The Effects of Combining the Variables in Allometric Biomass Models on Biomass Estimates over Large Forest Areas: A European Beech Case Study

Erick O Osewe,Ioan Dutcă

doi:10.3390/f12101428

Abstract

Effective initiatives for forest-based mitigation of climate change rely on continuous efforts to improve the estimation of forest biomass. Allometric biomass models, which are nonlinear models that predict aboveground biomass (AGB) as a function of diameter at breast height (D) and tree height (H), are typically used in forest biomass estimations. A combined variable D2H may be used instead of two separate predictors. The Q-ratio (i.e., the ratio between the parameter estimates of D and parameter estimates of H, in a separate variable model) was proposed recently as a measure to guide the decision on whether D and H can be safely combined into D2H, being shown that the two model forms are similar when Q = 2.0. Here, using five European beech (Fagus sylvatica L.) biomass datasets (of different Q-ratios ranging from 1.50 to 5.05) and an inventory dataset for the same species, we investigated the effects of combining the variables in allometric models on biomass estimation over large forest areas. The results showed that using a combined variable model instead of a separate variable model to predict biomass of European beech trees resulted in overestimation of mean AGB per hectare for Q > 2.0 (i.e., by 6.3% for Q = 5.05), underestimation for Q < 2.0 (i.e., by –3.9% for Q = 1.50), whereas for Q = 2.03, the differences were minimum (0.1%). The standard errors of mean AGB per hectare were similar for Q = 2.03 (differences up to 0.2%), and the differences increased with the Q-ratio, by up to 10.2% for Q = 5.05. Therefore, we demonstrated for European beech that combining the variables in allometric biomass models when Q ≠ 2.0 resulted in biased estimates of mean AGB per hectare and of uncertainty.

Highlights

The policies on forest-based mitigation of climate change cannot be successfully implemented without a rigorous quantification of the forest carbon stock and/or stock change [1,2,3,4,5]
The results showed that using a combined variable model instead of a separate variable model to predict biomass of European beech trees resulted in overestimation of mean aboveground biomass (AGB) per hectare for Q > 2.0, underestimation for Q < 2.0, whereas for Q = 2.03, the differences were minimum (0.1%)
Dataset Q2 (Q = 2.03), the difference in mean AGB per hectare between estimates based on combined vs. separate variable models was the lowest, by approximately

Summary

Introduction

The policies on forest-based mitigation of climate change cannot be successfully implemented without a rigorous quantification of the forest carbon stock and/or stock change [1,2,3,4,5]. In many cases, on the use of allometric biomass models [7], which are nonlinear regression models predicting tree aboveground biomass (AGB) as a function of diameter at breast height (D) and/or tree height (H) [8,9]. These models are applied to a sample of plots, which may involve remote sensing data, to obtain a statistical parameter of mean biomass per unit of forest area [3,10]. The dependence on tree species was only marginally reduced when including H [13], ‘wood density’ as an additional predictor of D and H was shown to significantly reduce dependence on species [14]

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