Height curve of natural <i>Larix gmelinii</i> in the Daxing’anling Mountains of northeastern China based on forest classification

Dong Lingbo; Shao Weiwei; Tian Dongyuan; Liu Zhaogang

doi:10.12171/j.1000-1522.20210513

Journal of Beijing Forestry University > 2023 > 45(5): 88-96. > DOI: 10.12171/j.1000-1522.20210513

Dong Lingbo, Shao Weiwei, Tian Dongyuan, Liu Zhaogang. Height curve of natural Larix gmelinii in the Daxing’anling Mountains of northeastern China based on forest classification[J]. Journal of Beijing Forestry University, 2023, 45(5): 88-96. DOI: 10.12171/j.1000-1522.20210513

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Height curve of natural Larix gmelinii in the Daxing’anling Mountains of northeastern China based on forest classification

Key Laboratory of Sustainable Forest Ecosystem Management of Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, Heilongjiang, China

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Received Date: May 12, 2021
Revised Date: March 24, 2023
Accepted Date: March 29, 2023
Available Online: March 31, 2023
Published Date: May 24, 2023

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Abstract

Abstract

Objective The tree height curve of main tree species was established based on tree classification, which provided reference for studying the growth law of Larix gmelinii, and provided technical support for forest sustainable management in Daxing’anling Mountains of northeastern China.
Method Based on the data of 56 fixed sample plots in Cuigang Forest farm of Daxing’anling Mountains, trees were divided into three grades of dominant, average and crushed trees according to the relative diameter (d) of individual trees. Based on the maximum adjusted coefficient (R² _adj), minimum root mean square error (RMSE) and the minimum red pool information (AIC), the optimal tree height curve basic model of different grades of natural Larix gmelinii was screened out, and the effects of quantile regression and dummy variable regression on the simulation accuracy of tree height curve models of different grades of Larix gmelinii were further evaluated and compared.
Result The optimal basic model of Larix gmelinii height curves was Wykoff equation. When the dumb variables of stand classification were added to parameters a and b of Wykoff equation, the model had the best fitting effect. R² _adj, RMSE and AIC of Larix gmelinii tree species curve model were 0.858 8, 1.642 4 and 2 081.902, respectively. There was no difference between the optimal quantile model and the whole stand of Larix gmelinii, and the median model was optimal (τ = 0.5). The three statistics of height curve of the deciduous pine were 0.849 8, 1.693 8 and 2 211.037, respectively. Through comparative analysis, the tree height curve model with tree classification as dummy variable had the best fitting effect.
Conclusion The height curve model of Larix gmelinii in the Daxing’anling Mountains, which contains dummy variables for tree classification, has better fitting performance than the basic model, and has good prediction accuracy and adaptability. It can reflect the growth differences of tree height and DBH under different tree grades, and can provide a theoretical basis for the management and growth prediction of Larix gmelinii in the Daxing’anling Mountains region.
- tree classification,
- quantile regression,
- dummy variable,
- tree height curve

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References

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Height curve of natural Larix gmelinii in the Daxing’anling Mountains of northeastern China based on forest classification

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