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Improving the accuracy of tree biomass estimations for three coniferous tree species in Northeast China

Abstract

Key message

Seemingly unrelated mixed-effects models were used to model biomass models for Larix olgensis, Pinus koraiensis, and Pinus sylvestris, and the developed models considered multiple variables to enhance the practicality of the models.

Abstract

Korean larch (Larix olgensis), Korean pine (Pinus koraiensis), and Mongolian pine (Pinus sylvestris) are important coniferous species in the northeastern area of the P.R. China; hence, accurate biomass estimates of these species are meaningful for evaluating forest health and calculating carbon storage. Based on the tree variables of diameter at breast height (\(DBH\)), tree height (\(H\)), age, live crown length and crown width, three log-transformed species-specific biomass functions were developed using linear seemingly unrelated regression (SUR) since the error structures of the component biomass model (stem, branches, foliage and roots) were proven to be multiplicative. Furthermore, the plot-level random effect was introduced into the SUR model, namely the SURM model, to achieve more accurate biomass estimates. The results showed that the SURM model has a better fitting performance than the SUR models for three species and model types. The determination coefficient, \({R}^{2}\), was always larger for the SURM models than for the corresponding SUR models, while the root mean square error, \({\text{RMSE}}\), was always smaller for the SURM models. By leave-one-out validation, the SURM models were proven to provide more accurate predictions because the mean prediction percent error (\({\text{MPE}}\)) was close to 0 and the mean absolute percentage error (\({\text{MAPE}}\)) was smaller than that of the corresponding SUR models across species and model types. In addition, the size and design of the sample used to calibrate for random effects were assessed using the \({\text{MAPE}}\) statistical index, \({\text{MAPE}}\). The calibration of the SURM showed a decreasing pattern of bias as the sample size increased, indicating that more available sampled trees improved the prediction accuracy of SURM models, while only slight differences existed across sampling designs. Overall, the newly developed biomass models have advantages in various data structures, and will be meaningful and useful in terms of accurately predicting the biomass of three important species in Northeast China.

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The data underlying this article cannot be shared publicly because the data are also being used in ongoing studies. The data will be shared upon reasonable request to the corresponding authors.

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Acknowledgements

The authors would like to thank the faculty and students of the Department of Forest Management, Northeast Forestry University (NEFU), China, who collected and provided the data for this study.

Funding

This research was financially supported by the National Key R&D Program of China (No. 2017YFD0600402), the Natural Science Foundation of China (No. 31971649), the Fundamental Research Funds for the Central Universities (No. 2572020DR03), the Provincial Funding for National Key R&D Program of China in Heilongjiang Province (No. GX18B041), and the Heilongjiang Touyan Innovation Team Program (Technology Development Team for High-efficient Silviculture of Forest Resources).

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Xie, L., Fu, L., Widagdo, F.R.A. et al. Improving the accuracy of tree biomass estimations for three coniferous tree species in Northeast China. Trees (2021). https://doi.org/10.1007/s00468-021-02220-w

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Keywords

  • Seemingly unrelated regression
  • Mixed effects
  • Biomass
  • Logarithmic transformation
  • Calibration