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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

1
Department of Forest Management, School of Forestry, Northeast Forestry University, Harbin 150040, China
2
Department of Forest and Natural Resources Management, State University of New York, College of Environmental Science and Forestry (SUNY-ESF), One Forestry Drive, Syracuse, NY 13210, USA
*
Author to whom correspondence should be addressed.
Forests 2018, 9(5), 261; https://doi.org/10.3390/f9050261
Received: 16 April 2018 / Revised: 1 May 2018 / Accepted: 6 May 2018 / Published: 10 May 2018
(This article belongs to the Section Forest Ecology and Management)
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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 Ra2 > 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. View Full-Text
Keywords: seemingly unrelated regression (SUR); additive biomass equations; biomass partitioning; Dahurian larch and white birch seemingly unrelated regression (SUR); additive biomass equations; biomass partitioning; Dahurian larch and white birch
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Dong, L.; Zhang, L.; Li, F. 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. Forests 2018, 9, 261.

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