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.
Highlights
Among the studies on global climate change and the carbon cycle, research on the quantity, distribution, and dynamics of forest carbon stocks is popular and remains a high priority for predicting the growth and yield of forests [1,2,3]
The stand biomass can be estimated using either stand biomass models or biomass expansion and conversion factors (BCEFs), which represent the ratio of the stand biomass to stand volume
We previously developed species-specific tree biomass allometric equations with only tree D as the predictor for the tree total and component biomass (i.e., the stand total biomass (Wt ), the stand root biomass (Wr ), the stand stem biomass (Ws ), the stand branch biomass (Wb ), and the stand foliage biomass (W f )) [12,32,33], and they were applied to each tree within the permanent sample plots
Summary
Among the studies on global climate change and the carbon cycle, research on the quantity, distribution, and dynamics of forest carbon stocks is popular and remains a high priority for predicting the growth and yield of forests [1,2,3]. Since the carbon concentrations in a tree or stand components are relatively constant (approximately 50%), most studies focus on forest biomass estimations rather than carbon storage estimations. The calculation of accurate forest biomass estimations has become one of the most crucial steps for successfully implementing the Reducing Emissions from Deforestation and Forest Degradation (REDD+) project as well as for the conservation and enhancement of forest carbon stocks and the sustainable management of forests. These initiatives provide a framework that benefits developing countries by rewarding them financially to reduce carbon emissions [4,5]. Most biomass studies in the literature have focused on tree biomass models, while efforts related to stand biomass models have been limited or lacking [6,7,9,10,15]
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