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    小兴安岭兴安落叶松人工林冠幅模型构建

    Construction of crown width model of Larix gmelinii plantation in Xiaoxing’an Mountains of northeastern China

      摘要
    • 摘要:
        目的  使用非线性回归、混合效应模型、分位数回归以及分位数组合构建兴安落叶松冠幅模型，为小兴安岭落叶松冠幅的准确预测提供参考。
        方法  利用2019年马永顺林场的60块兴安落叶松人工林实测样地数据，分别构建了广义非线性模型、分位数回归模型以及混合效应模型。使用10折交叉检验，在每块样地分别随机抽取1 ~ 8株样木对两种分位数组合模型QRc-1（τ = 0.1，0.5，0.9）和QRc-2（τ = 0.3，0.5，0.7），以及混合效应模型进行校正，确定分位数组合与混合效应模型的最佳抽样方案并进行不同方法的对比分析。
        结果  （1）模型拟合结果表明：混合效应模型拟合效果最好；中位数回归为最优的分位数回归模型，中位数回归与非线性模型的拟合统计量比较差异不大，但略优于非线性回归模型。（2）抽样校正的结果表明：当抽样数量大于2株时，模型的排序为：分位数组合QRc-2 > 混合效应模型 > 分位数组合QRc-1。（3）交叉检验结果显著性检验表明：两种分位数组合的最佳抽样方案均为4株，混合效应模型的最佳抽样方案为5株。
        结论  本研究中混合效应模型和分位数组合都能提升冠幅模型的预测精度，在最佳抽样方案下，分位数组合QRc-2（τ = 0.3，0.5，0.7）时的检验统计量略高于混合效应模型的检验统计量，且抽样数更少，更加节约时间和成本，因此选择抽样数为4株的分位数组合QRc-2（τ = 0.3，0.5，0.7）作为最终的冠幅预测模型。

       

      Abstract:
        Objective  This paper uses nonlinear regression, mixed effect model, quantile regression and quantile regression combination to construct the crown width model of Larix gmelinii, which provides a reference for the accurate prediction of the crown width of Larix gmelinii in Xiaoxing’an Mountains of northeastern China.
        Method  In this study, the data were collected from sample plot of 60 Larix gmelinii plantations in Mayongshun Forest Farm, the generalized nonlinear model, quantile regression model and mixed effect model were constructed, respectively. 10-fold cross validation was used to compare the prediction. The number of 1 to 8 sample trees were randomly selected from each sample plot to calibrate the two kinds of quantile regression combination models, including QRc-1 (τ = 0.1, 0.5, 0.9) and QRc-2 (τ = 0.3, 0.5, 0.7), and mixed effect model to determine the best sampling scheme for quantile regression combination and mixed effect model, and comparison was carried out and analyzed for different methods.
        Result  (1) The model fitting results showed that the mixed effect model had the best fitting statistics. Median regression was the best quantile regression model. There was little difference between the fitting statistics of median regression and nonlinear regression model, but it was slightly better than nonlinear regression model. (2) The results of sampling calibration showed that, when the number of samples was greater than 2 trees, the order of the models was QRc-2 > the mixed effect model > QRc-1. (3) The significance test of cross validation showed that the best sampling scheme of the two kinds quantile regression combinations was 4 trees, and the best sampling scheme of the mixed effect model was 5 trees.
        Conclusion  In this study, both mixed effect model and quantile regression combination can improve the prediction accuracy of crown width model. Quantile regression combination (τ = 0.3, 0.5, 0.7) is slightly higher than the mixed effect model in the validation statistics when using the best sampling scheme. Due to the sample number of quantile regression combination is less, which saves more time and cost, therefore, the quantile regression combination (τ = 0.3, 0.5, 0.7) of 4 sample trees is selected as the best model to predict the crown width.

       

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