王永刚, 舒清态, 李圣娇, 徐云栋, 张焱. 香格里拉高山松天然林林分蓄积混合效应模型构建[J]. 西南林业大学学报, 2016, 36(3): 121-125. DOI: 10.11929/j.issn.2095-1914.2016.03.021
引用本文: 王永刚, 舒清态, 李圣娇, 徐云栋, 张焱. 香格里拉高山松天然林林分蓄积混合效应模型构建[J]. 西南林业大学学报, 2016, 36(3): 121-125. DOI: 10.11929/j.issn.2095-1914.2016.03.021
Wang Yonggang, Shu Qingtai, Li Shengjiao, Xu Yundong, Zhang Yan. Building Mixed Effects Model of Stand Volume of Nature Pinus densata Forest in Shangri La[J]. Journal of Southwest Forestry University, 2016, 36(3): 121-125. DOI: 10.11929/j.issn.2095-1914.2016.03.021
Citation: Wang Yonggang, Shu Qingtai, Li Shengjiao, Xu Yundong, Zhang Yan. Building Mixed Effects Model of Stand Volume of Nature Pinus densata Forest in Shangri La[J]. Journal of Southwest Forestry University, 2016, 36(3): 121-125. DOI: 10.11929/j.issn.2095-1914.2016.03.021

香格里拉高山松天然林林分蓄积混合效应模型构建

Building Mixed Effects Model of Stand Volume of Nature Pinus densata Forest in Shangri La

  • 摘要: 以云南省香格里拉的高山松天然林为研究对象,构建林分蓄积的混合效应模型;引入林分、海拔等环境因子的影响,构建含环境因子的林分蓄积混合效应模型,所有模型均采用拟合指标和独立样本检验进行评价。结果表明:混合效应模型相对于一般回归模型在林分蓄积拟合中有较高较好的拟合效果;引入环境因子的混合效应模型的拟合效果比一般混合效应模型要好;其中,引入林分因子的混合效应模型拟合效果最好;从模型独立性检验来看,一般混合效应模型的预估精度最高,绝对平均相对误差最小;引入环境因子后,混合效应模型的总相对误差以及平均相对误差有所减小,其中又以引入林分因子的混合效应模型的误差最小,表现最佳;一般回归模型无论在误差方面还是在精度方面都与混合效应模型有很大差距。

     

    Abstract: In this paper, we took natural Pinus densata forest of ShangriLa City of Yunnan Province as the research object to establish a mixed effects model stand volume. At the same time, we construct the mixed effects models of stand volume which contain the environment factors, including stand factors and elevation etc. All models were estimated by indexes of fitting and Independent Samples Test. The results showed that compared with general regression model, the result of mixed effects model was better in stand volume fitting; The mixed effect model which introduced environment factors was better than the model which did not. And the best model was that used the stand factors. In view of the independence test of model, the prediction precision of general fixed effects model was the highest, its absolute mean relative error was least. The sum relative error and mean relative error decreased by introducing environment factors, and the one that stand factors introduced was the best. General regression model was bad at error and precision aspect compared with mixed effects model.

     

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