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首页> 外文期刊>Journal of Forest Planning >Comparison of Regression Methods for Fitting Allometric Equations to Biomass of Mizunara Oak (Quercus crispula)
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Comparison of Regression Methods for Fitting Allometric Equations to Biomass of Mizunara Oak (Quercus crispula)

机译：拟合等速方程到水izu橡树（栎栎）生物量的回归方法的比较

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This study examined the use of linear and non-linear regression techniques for estimating parameters of allometric equations for the biomass of mizunara oak (Quercus crispula Blume) trees growing in deciduous secondary forests that are dominated by the species. Four plots were sampled in secondary forests and 31 mizunara oak trees were sampled outside the experimental plots to measure biomass. Three typical allometric equations used for biomass estimation were fitted to the mass of each component andthe sum of some components using three least-squares regression methods: non-linear regression without weighting observations, generalized non-linear regression assuming that the variance of each observation was expressed as a power function of the corresponding mean value, and linear regression after fitting a logarithmically transformed function to logarithms of the data. Errors in the predictions were compared among the three regression models. The fitted allometric equations were applied to tree census data from the experimental plots to examine variation in stand biomass estimates among the three regression methods. In terms of errors, generalized non-linear regression was slightly preferable to logarithmic linear regression and unweighted non-linear regression. Branch mass and foliage mass had different values of variance parameter (half of the exponent of the power function), even though both showed large variation around their regression lines. When the variance parameter was greater than 1.0, as occurred with branch mass, logarithmic linear regression was slightly better than generalized non-linear regression. When applying fitted allometric equations to other data, the three regression methods may produce only slightly different estimatesof stem mass; however, estimates for branch mass and foliage mass may differ largely according to the regression method used.
机译：这项研究研究了使用线性和非线性回归技术来估算在以该树种为主的落叶次生林中生长的Mizunara栎（Quercus crispula Blume）树的生物量的异速方程的参数。在次生林中取样了四个样地，在试验样地外取样了31棵mizunara橡树，以测量生物量。使用三种最小二乘回归方法，将用于生物量估计的三个典型的异速方程拟合为每种成分的质量以及某些成分的总和：无权重观测值的非线性回归，假设各观测值的方差为的广义非线性回归表示为相应平均值的幂函数，然后将对数转换后的函数拟合为数据的对数后进行线性回归。在三个回归模型之间比较了预测中的错误。将拟合的等速方程式应用于来自实验地块的树木普查数据，以检验三种回归方法之间林分生物量估计值的变化。就误差而言，广义非线性回归要好于对数线性回归和非加权非线性回归。分支质量和叶子质量具有不同的方差参数值（幂函数指数的一半），即使它们的回归线附近都存在较大的变化。当方差参数大于1.0时（与分支质量相同），对数线性回归略优于广义非线性回归。将拟合的等速方程应用于其他数据时，这三种回归方法可能只能得出略有不同的茎质量估计值;但是，根据所使用的回归方法，分支质量和枝叶质量的估计值可能会有很大差异。

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