金沟岭林场天然云冷杉林冠幅模型和估计方法比较

【目的】对比不同冠幅预测方法对云冷杉幼树不同方向冠幅(东、西、南、北、东西、南北、平均冠幅)的预测精度的差异,为天然云冷杉林经营提供一定的理论依据。【方法】利用2013年金沟岭云冷杉3块1 hm²固定样地中云冷杉幼树各向冠幅实测数据,以逻辑斯蒂模型为基础模型,以非线性最小二乘法为基础方法进行模型初步拟合。以1/D、1/D^0.5、1/D²作为模型的权函数进行模型异方差的消除。以不加权非线性似乎不相关法、加权非线性似乎不相关法、分位数回归法、非线性最小二乘法分别构建了云冷杉幼树冠幅各组分预测模型。【结果】模型拟合结果显示,分位数回归模型的拟合效果在云冷杉幼树冠幅预测模型中拟合精度最低;相较于分位数回归而言,加权非线性似乎不相关回归模型拟合效果与加权最小二乘模型拟合效果相当。模型拟合效果排序为:加权NSUR≈加权OLS> OLS> QR。以1/D²作为模型的权函数时,模型残差图的异方差趋势被消除最明显,该权函数为最优权函数。【结论】本文中非线性分位数回归模型拟合效果不一定比非线性最小二乘法更好...

Comparison of crown width models and estimation methods of natural spruce fir forest in Jingouling Forest Farm of northeastern China

ObjectiveDifferent crown prediction methods were used to predict varied crown components (east, west, south, north crown width and east-west crown width, south-north crown width, average crown width) of young spruce fir, and the prediction accuracy was compared in order to provide a theoretical basis for the tending of spruce fir management.MethodThe measured data of different crown components in permanent spruce fir sample plots was got from three 1 ha sample plots on Jingouling Forest Farm of northeastern China in 2013, the logistic model was chosen as base model and the ordinary least square method was used to fit crown radii of east, west, south, north and crown width of east-west, south-north, and mean direction. 1/D, 1/D0.5, and 1/D2 were used as weight function to eliminate the heteroscedasticity of model residuals. The unweighted nonlinear seemingly unrelated regression method, weighted nonlinear seemingly unrelated regression method, quantile regression method, and ordinary least square method were applied to develop different crown component prediction model.ResultThe fitting results indicated that, quantile regression model had the lowest fitting accuracy, compared with quantile regression, weighted nonlinear seemingly unrelated regression and weighted ordinary least square regression had nearly same fitting effectiveness. The accuracy order arrangement was weighted NSUR ≈ weighted OLS > OLS > QR, 1/D2 was the best choice to eliminate heteroscedasticity by residuals plot.ConclusionIn this paper, the fitting effect of nonlinear quantile regression model was not necessarily better than that of nonlinear least square method, the weighted nonlinear seemingly unrelated regression model (1/D2 as weight function) developed in this essay can provide some theory basis for different crown components of young spruce fir.