改进栓皮栎树高预测混合效应模型研究

An Improved Hight Prediction Mixed Effect Model for Quercus variabilis

树高与胸径是森林资源调查中2个重要的测树因子。鉴于树高测量相对不易实现的问题,以金华市永康市西溪镇的94株栓皮栎树为研究对象,在比较林业上常用的10个树高曲线模型拟合效果的基础上,改进了传统的Gompertz树高曲线模型,提出并构建了含有立地因子的Gompertz混合效应树高预测改进模型。实验表明:1)当对Gompertz混合效应模型拟合时,引入随机参数b_1,b_3时模型拟合最好;当对Gompertz混合效应改进模型拟合时,引入随机参数b_1,b4时模型拟合最好。2)构建的Gompertz混合效应改进模型决定系数达到0. 779,Gompertz混合效应模型决定系数为0. 553,Gompertz模型决定系数为0. 542,即仅凭混合效应方法构建模型对提高模型预测精度并不明显。实验证明了本文构建的Gompertz混合效应改进模型大大提高了栓皮栎的树高预测精度,为研究树种树高-胸径关系模型提供了一种新方法。

Tree height and DBH are two important tree-building factors in the survey of forest resources.In view of the relatively difficulty for height measurement,in this article we took 94 cork oaks from Xixi Town,Jinhua City as the research object and compared the fitting effects of 10 tree height curve models commonly used in forestry,improved the traditional Gompertz tree height curve model,proposed and constructed a Gompertz mixed effect tree high prediction model with site factors.The results show that:(1)The model fits best when the random parameters b 1 and b 3 are introduced for Gompertz mixed effect model.The model fits best when the random parameters b1 and b4 are introduced for Gompertz mixed effect improvement model.(2)The Gompertz mixed effect improvement model constructed in this paper has a coefficient of determination of 0.779 with 0.553 Gompertz mixed effect model,and with 0.542 for Gompertz model.That is building the model only by the mixed effect method is not obvious for improving the accuracy of the prediction model.(3)Experiments show that the Gompertz mixed effect improvement model constructed in this paper greatly improves the prediction accuracy of the height of Quercus variabilis,and provides a new method for studying the relationship between tree height and diameter at breast height.