Additive Biomass Equations Based on Different Dendrometric Variables for Two Dominant Species (Larix gmelini Rupr. and Betula platyphylla Suk.) in Natural Forests in the Eastern Daxing’an Mountains, Northeast China

Abstract

A total of 138 Dahurian larch (Larix gmelinii Rupr.) trees and 108 white birch (Betula platyphylla Suk.) trees were harvested in the eastern Daxing'an Mountains, northeast China. We developed four additive systems of biomass equations as follows: the first additive model system (MS-1) used the best combination of tree variables as the predictors; the second additive model system (MS-2) included tree diameter at breast height (D) as the sole predictor; the third additive model system (MS-3) included both D and tree height (H) as the predictors; and the fourth additive model system (MS-4) included D, H, and crown attributes (crown width (CW) and crown length (CL)) as the predictors. The model coefficients were simultaneously estimated using seemingly unrelated regression (SUR). The heteroscedasticity in model residuals was addressed by applying a unique weight function to each equation. The results indicated that: (1) the stem biomass accounted for the largest proportion of the total tree biomass, while the foliage biomass had the smallest proportion for the two species; (2) the four additive systems of biomass equations exhibited good model fitting and prediction performance, of which the model R a 2 > 0.81, the mean prediction error (MPE) was close to 0, and the mean absolute error (MAE) was relatively small (<9 kg); (3) MS-1 and MS-4 significantly improved the model fitting and performance; the ranking of the four additive systems followed the order of MS-1 > MS-4 > MS-3 > MS-2. Overall, the four additive systems can be applied to estimate individual tree biomass of both species in the Chinese National Forest Inventory.

Aggregated Biomass Model Systems and Carbon Concentration Variations for Tree Carbon Quantification of Natural Mongolian Oak in Northeast China

“…Our results noted that the inclusion of H and H and CL in the stem and foliage biomass equations, respectively, could improve the overall model fitting performance. This finding is in accordance with previous literature [6,15,58,59].…”

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

“…The Eastern Da Xing'an Mountains in Northeast China are defined as in our previous study [14], and they cover an area of 83,517 km 2 ( Figure 1). The terrain, soil, and climate of the region were described in Dong et al [14] and, thus, are not detailed here.…”

“…The Eastern Da Xing'an Mountains in Northeast China are defined as in our previous study [14], and they cover an area of 83,517 km 2 ( Figure 1). The terrain, soil, and climate of the region were described in Dong et al [14] and, thus, are not detailed here. According to the classification standard of forest types in the Eastern Da Xing'an Mountains, the forest types in this study were mainly divided as follows: white birch forest, larch forest, poplar-birch forest, deciduous broadleaf mixed forest, coniferous and broadleaf mixed forest, and coniferous mixed forest.…”

“…Forest stand biomass can be estimated at either the tree or stand level. At the tree level, the most commonly used method is the allometric equation, which estimates tree biomass using simple tree variables, such as the tree diameter at breast height (D) and/or tree height (H) [11][12][13][14]. Tree allometric equations require inventories that have high cost and time requirements, although they yield high accuracies for estimating tree biomass.…”

“…Among these methods of parameter estimation, nonlinear seemingly unrelated regression (NSUR) and seemingly unrelated regression (SUR) are the most widely used. An advantage of SUR and NSUR is the low variance of the total stand biomass model because of their own predictor variables and weighting function account for heteroscedasticity, which makes SUR and NSUR popular methods for parameter estimation in nonlinear and linear stand biomass equations [11][12][13][14]30]. Although several researchers have proposed the inclusion of additivity, it has often been ignored in some stand biomass models [8,10].…”