Crown Prediction Model of Larix principis-rupprechtii Plantation in Saihanba of Hebei Province, Northern China

被引：1
|
作者：
Zhao T. [1 ]
Wang D. [1 ,2 ]
Zhang D. [1 ,3 ]
Guo L. [4 ]
Huang X. [1 ,2 ]
机构：
[1] Forestry College, Hebei Agricultural University, Baoding
[2] Forest Resources Innovation and Protection Laboratory of Hebei, Baoding
[3] College of Economics and Management, Hebei Agricultural University, Baoding
[4] Fengning Qiansongba Forest Farm of Hebei Province, Chengde
来源：
Linye Kexue/Scientia Silvae Sinicae | 2021年 / 57卷 / 05期
关键词：
Crown profile; Larix principis-rupprechtii; Nonlinear mixed effect model; Nonlinear quantile regression model; Saihanba;
D O I：
10.11707/j.1001-7488.20210510
中图分类号：
学科分类号：
摘要：
Objective: The crown of a tree is an important organ for material exchange and energy conversion. The nonlinear mixed effect model and the nonlinear quantile regression model of the maximum crown outline were constructed to provide a scientific basis for accurately predicting the growth and development law of the crown and its productivity. Method: Taking Larix principis-rupprechtii plantation of Saihanba forest farm in Hebei Province as the research object, based on 1 789 branches data of 58 trees, the power equation, modified Kozak and modified Weibull equation were selected as the basic models to construct a mixed effect model and a nonlinear quantile regression model for predicting the crown shape of L. principis-rupprechtii plantation. Result: Among the power function, modified Kozak equation and modified Weibull equation, the power function equation had the best fitting effect of tree crown profile. The power function equation was chosen as the basic model of tree crown profile. Stand age (Age), diameter at breast height(DBH), height of the tree (HT), crown height ratio (CHR), and height to diameter ratio (HDR) had significant effects on fitting crown contour. In the mixed effect model, the two-level mixed effect model considering both plot and tree effects was superior to the single-level mixed effect model. The random effect of the plot was added to the HDR parameter, and the random effect of the sample was added to the RDINC(relative depth into crown) and CHR parameters. The model determination coefficient (R2) was 0.873, the root mean square error (RMSE) was 0.319 m, and the mean relative error (MRE) was 6.642 m. In the quantile regression model, when q=0.90, the model curve model was the closest to the crown maximum profile, R2 was 0.672. Conclusion: The mixed-effect model might be a better fitting accuracy and could accurately describe the average trend of the largest branches of the canopy. The quantile regression model could determine the outermost contour of the canopy and may play an important role in research beyond the prediction of conditioned mean. © 2021, Editorial Department of Scientia Silvae Sinicae. All right reserved.
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页码：108 / 118
页数：10
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