Evaluation of Stand Biomass Estimation Methods for Major Forest Types in the Eastern Da Xing’an Mountains, Northeast China

Abstract

Currently, forest biomass estimation methods at the regional scale have attracted the greatest attention from researchers, and the development of stand biomass models has become popular a trend. In this study, a total of 5074 measurements on 1053 permanent sample plots were obtained in the Eastern Da Xing’an Mountains, and three additive systems of stand biomass equations were developed. The first additive system (M-1) used stand variables as the predictors (i.e., stand basal area and average height), the second additive system (M-2) utilized stand volume as the sole predictor, and the third additive system (M-3) included both stand volume and biomass expansion and conversion factors (BCEFs) as the predictors. The coefficients of the three model systems were estimated with nonlinear seemingly unrelated regression (NSUR), while the heteroscedasticity of the model residuals was solved with the weight function. The jackknifing technique was used on the residuals, and several statistics were used to assess the prediction performance of each model. We comprehensively evaluated four stand biomass estimation methods (i.e., M-1, M-2, M-3 and a constant BCEF (M-4)). Here, we showed that the (1) three additive systems of stand biomass equations showed good model fitting and prediction performance, (2) M-3 significantly improved the model fitting and performance and provided the most accurate predictions for most stand biomass components, and (3) the ranking of the four stand biomass estimation methods followed the order of M-3 > M-2 > M-4 > M-1. Our results demonstrated these additive stand biomass models could be used to estimate the stand aboveground and belowground biomass for the major forest types in the Eastern Da Xing’an Mountains, although the most appropriate method depends on the available data and forest type.

Additive predictions of aboveground stand biomass in commercial logs and harvest residues for rotation age Pinus radiata plantations in New South Wales, Australia

“…Each coefficient in the model is shared between two equations as cross-equation constraints are placed on the structural parameters to ensure additivity of biomass estimates, i.e., to eliminate the inconsistency between the sum of predicted values for biomass components and the prediction for the total tree biomass. The four stand variables are the most common predictors in stand level biomass equations (Bi et al 2010;Castedo-Dorado et al 2012;Paré et al 2013;Dong et al 2019;Jagodziński et al 2019), but the inverse transformation of stand age T in our model (2) was based on the model specification of Bi et al (2010) for additive prediction of aboveground biomass for P. radiata plantations.…”

“…Such an approach was suggested by LeMay and Kurz (2008) for estimating biomass, carbon stocks and stock changes in forests. To serve as an essential linkage of this kind, stand level biomass equations have been developed and adopted for plantations as well as natural forests (Bi et al 2010;Castedo-Dorado et al 2012;Paré et al 2013;Jagodziński et al 2019;Dong et al 2019). These equations predict total aboveground stand biomass in terms of the structural components such as stemwood, bark, branches and foliage of all trees in a stand.…”

“…These equations predict total aboveground stand biomass in terms of the structural components such as stemwood, bark, branches and foliage of all trees in a stand. To ensure the logical consistency of biomass additivity and the statistical efficiency in parameter estimation at the same time, stand biomass equations have increasingly been specified as a system of additive equations with cross-equational parameter constraints and estimated by taking into account the inherent error correlation among the system equations for the biomass components (Bi et al 2010;Castedo-Dorado et al 2012;Jagodziński et al 2019;Dong et al 2019). However, stand-level systems of biomass equations for commercial logs and harvest residuals have so far not been developed for rotation age stands of P. radiata or any other species.…”

Simultaneous Compatible System of Models of Height, Crown Length, and Height to Crown Base for Natural Secondary Forests of Northeast China

“…Moreover, AP (adjustment in proportion) is also a common method to estimate biomass systems and crown radii systems. These methods [31,[52][53][54][55][56] all ensure the additivity and compatibility of models. i.e., Fu et al [8] developed a system of nonlinear additive crown width models utilizing seemingly unrelated regression for Prince Rupprecht larch in northern China.…”

A modified pipe model improved the accuracy of crown biomass estimation in a larch (Larix olgensis) plantation forest in northeast China

“…Heteroscedasticity is common in biomass and volume data (Dong et al 2019). Measures should therefore be taken to eliminate the in uence of heteroscedasticity before constructing biomass models.…”