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ORIGINAL RESEARCH article
Front. For. Glob. Change
Sec. Forest Management
Volume 8 - 2025 | doi: 10.3389/ffgc.2025.1549531
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Accurate biomass estimation is crucial for quantifying forest carbon storage and guiding sustainable management. In this study, we developed four biomass modeling systems for natural white birch (Betula platyphylla) in northeastern China using field data from 148 trees. The data included diameter at breast height (DBH), tree height (H), crown dimensions, and biomass components (stem, branch, foliage, and root biomass), as well as soil and climate variables. We employed Seemingly Unrelated Regression (SUR) and mixed-effects models (SURM) to account for component correlations and spatial variability. The base model (SURba), using only the DBH variable, explained 89-96% of the biomass variance (RMSE%: 1.34-19.94%). The second model (SURbio) incorporated H for stem/branch biomass and crown length (CL) for foliage, improving the predictions of stem, branch, and foliage biomass (R² increased by 1.69-4.86%; RMSE% decreased by 10.76-59.04%). Next, the SURba-abio and SURbio-abio models integrated abiotic factors, including soil organic carbon content (SOC), mean annual precipitation (MAP), degree-days above 18°C (DD18), and soil bulk density (BD). Both models showed improvement, with the abiotic factor model SURba-abio performing similarly to the biotic factor model SURbio (ΔR² < 4.36%), while the SURbio-abio model performed the best. Subsequently, random effects were introduced at the sampling point (Forestry Bureau) level, developing seemingly unrelated mixed-effects models (SURMba, SURMbio, SURMba-abio, SURMbio-abio), which improved model fitting and prediction accuracy. The gap between the SURMba-abio model (with abiotic factors) and the SURMbio-abio model (including both biotic and abiotic factors) was minimal (ΔR² < 2.80%). The random effects model stabilized when calibrated with aboveground biomass measurements from four trees. In conclusion, these models provide an effective approach for estimating the biomass of natural white birch in northeastern China. In the absence of biotic factors, the SURba-abio and SURMba-abio models serve as reliable alternatives, emphasizing the importance of abiotic factors in biomass estimation and offering a practical solution for predicting birch biomass.
Keywords: Biomass estimation, SUR mixed-effects models, Biotic and Abiotic Factors, Natural white birch, SUR
Received: 21 Dec 2024; Accepted: 27 Feb 2025.
Copyright: © 2025 Ma, Miao, Xie, Tian, Zhao and Dong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Lihu Dong, Northeast Forestry University, Harbin, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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