[1]王冬至,胡雪娇,李大勇,等.基于非线性混合效应模型的针阔混交林地位指数研究[J].南京林业大学学报(自然科学版),2020,44(4):159-166.[doi:10.3969/j.issn.1000-2006.201907010]
 WANG Dongzhi,HU Xuejiao,LI Dayong,et al.Creating site indexes for needle and broadleaved mixed forest using the nonlinear mixed effect model[J].Journal of Nanjing Forestry University(Natural Science Edition),2020,44(4):159-166.[doi:10.3969/j.issn.1000-2006.201907010]
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基于非线性混合效应模型的针阔混交林地位指数研究/HTML
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《南京林业大学学报(自然科学版)》[ISSN:1000-2006/CN:32-1161/S]

卷:
44
期数:
2020年4期
页码:
159-166
栏目:
研究论文
出版日期:
2020-09-01

文章信息/Info

Title:
Creating site indexes for needle and broadleaved mixed forest using the nonlinear mixed effect model
文章编号:
1000-2006(2020)04-0159-08
作者:
王冬至 胡雪娇 李大勇 高雨珊 李天宇
作者单位:河北农业大学林学院,河北 保定 071000; 河北省木兰围场国有林场管理局,河北 围场 068450
Author(s):
WANG Dongzhi1 HU Xuejiao1 LI Dayong2 GAO Yushan1 LI Tianyu1
(1.College of Forestry, Agricultural University of Hebei, Baoding 071000, China; 2.Mulan Weichang State-Owned Forest Farm Administration Bureau of Hebei Province, Weichang 068450, China)
关键词:
非线性混合效应模型 地位指数 转换方程 混交林
Keywords:
nonlinear mixed effects model site index conversion equation mixed forest
分类号:
S758.5
DOI:
10.3969/j.issn.1000-2006.201907010
文献标志码:
A
摘要:
目的 基于非线性混合效应模型和树种间地位指数转换方程,建立混交林中不同树种地位指数混合效应模型,为多树种混交林立地生产力评价提供科学依据。 方法 利用塞罕坝机械林场83块华北落叶松与白桦针阔混交林标准地(30 m×30 m)调查数据,首先基于6个具有生物学意义的基础地位指数模型,利用最小二乘法分别拟合与评价不同树种基础模型,确定构建各树种地位指数混合效应模型的基础模型;然后通过几何线性回归方法构建不同树种间地位指数转换方程。 结果 在6个候选地位指数基础模型中,确定Richards模型和Logistic模型分别为华北落叶松与白桦最优的基础模型,并构建了包含随机效应参数的混合效应地位指数预测模型。当随机效应参数分别作用于渐近线参数和形状参数时,华北落叶松与白桦非线性混合效应地位指数模型拟合精度较高。在构建的华北落叶松与白桦地位指数转换方程中,不同树种间转换方程的决定系数分别为0.88和0.91,表明不同树种地位指数转换方程预测精度较高。 结论 在混交林中利用混合效应理论建立单树种非线性混合效应地位指数模型,并进一步构建不同树种地位指数转换方程,为混交林立地质量评价及生产潜力预测提供科学依据。
Abstract:
Objective Based on the nonlinear mixed effect model and inter-species site index conversion equation, a mixed effect model for the site index of the different tree species in a mixed forest was established to provide a scientific basis for productivity evaluations of mixed tree species forest sites. Method Based on data from 83 sample plots (30 m × 30 m) of Larix principis?rupprechtii and Betula platyphylla in the Saihanba mechanical forest farm, the optimal position indices of the different tree species were determined based on six basic site index models (Richards, Log-Logistic, Logistic, Power, Weibull and Korf models) with biological significance. The basic models for the different tree species were fitted and evaluated using the least square method. The best basic model of the different tree species was selected based on the model evaluation index. A nonlinear mixed effect position index model for each tree species was constructed using the Gauss-Newton method. Then, the geometrical linear regression method was used to construct the position index conversion equation between the different tree species based on a nonlinear mixed effect model. Result Of the six candidate basic site index models, the Richards and Logistic models were the optimal models for Larix principis?rupprechtii and Betula platyphylla, respectively. In the study, when a site index model with two random effects was constructed, the nonlinear mixed effect model of the different species could not converge, so only one random effect parameter was inclu?ded in the site index model of the different species. When the asymptotic and shape parameters were applied, the nonlinear mixed effect position index model of Larix principis?rupprechtii and Betula platyphylla had a higher fitting precision. Based on the analysis of the prediction of the residuals of the nonlinear mixed effect model, it was determined that there was no heteroscedasticity in the distribution of the residuals of the different tree species, which shows that the nonlinear mixed effect site index model of the different tree species has better prediction and practicability accuracies.The parameters and evaluation indices of the site index conversion equation for Larix principis?rupprechtii and Betula platyphylla were constructed using a geometric linear regression algorithm. The transformation coefficients of the different tree species were determined to be 0.88 and 0.91, respectively, indicating that the prediction accuracy of the site index conversion equations for the different tree species was higher. Conclusion In this study, based on the biological significance of the site index prediction model, Richards and Logistic models were determined as the optimal site index models of Larixprincipis?rupprechtii and Betula platyphylla, respectively. Based on the optimal model, the nonlinear mixed effect site index prediction model of the different tree species was constructed using nonlinear mixed effect modeling technology. When the random effect parameters act on the asymptote and shape parameters, the fitting accuracy of the parameters is high. In addition, the transformation equation for the site index of the different species was established using a geometric regression algorithm, which could provide a scientific basis for site quality evaluations and production potential predictions of mixed forests.

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备注/Memo

备注/Memo:
收稿日期:2019-07-08
更新日期/Last Update: 2020-08-13