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林业科学 ›› 2018, Vol. 54 ›› Issue (8): 99-105.doi: 10.11707/j.1001-7488.20180811

• 论文与研究报告 • 上一篇    下一篇

不同抽样方法对兴安落叶松立木材积方程预测精度的影响

Shahzad Muhammad, 韩斐斐, 姜立春   

  1. 东北林业大学林学院 哈尔滨 150040
  • 收稿日期:2016-10-11 修回日期:2017-01-03 出版日期:2018-08-25 发布日期:2018-08-18
  • 基金资助:
    十三五国家重点研发计划资助项目(2017YFB0502700);国家自然科学基金项目(31570624)。

Effects of Different Sampling Methods on Predict Precision of Individual Tree Volume Equation for Dahurian Larch

Shahzad Muhammad, Khurra Han, Feifei Jiang   

  1. College of Forestry, Northeast Forestry University Harbin 150040
  • Received:2016-10-11 Revised:2017-01-03 Online:2018-08-25 Published:2018-08-18

摘要: [目的]研究不同抽样方法对立木材积方程预测精度的影响,为各地编制不同树种材积表及构建立木材积方程提供基础数据抽样技术依据。[方法]以兴安落叶松立木材积方程为例,设计均匀、正态、右偏和左偏4种抽样方法,根据不同数据类型,利用SAS软件中proc surveyselect模块的简单随机抽样(SRS)并结合条件语句对数据进行分径阶抽样。采用Shapiro-Wilk对不同抽样方法下的胸径统计量进行正态性检验。以异速生长方程为基础材积模型,利用S-PLUS软件的广义非线性GNLS模块对模型进行拟合。采用指数函数、幂函数和常数加幂函数对4种立木材积拟合过程中产生的异方差现象进行校正。利用确定系数(R2)、均方根误差(RMSE)、平均误差绝对值(MAB)和相对误差绝对值(MPB)对立木材积方程精度进行综合比较分析。[结果]1)指数函数、幂函数和常数加幂函数均能消除4种立木材积方程异方差的影响,加入变量为V^的幂函数消除异方差的效果最好。2)拟合结果表明,相对于均匀模型,正态模型的RMSE下降31.6%,右偏模型的RMSE下降23.1%,左偏模型的RMSE下降33.7%。3)分径阶检验表明,径阶分布在12~28 cm、36~40 cm和44~48 cm时,左偏模型的MAB和MPB均小于均匀、正态和右偏模型,即左偏模型在11组径阶中有6组径阶的MAB和MPB均最小;径阶分布在12~32 cm和44~48 cm时,右偏模型的MAB和MPB均小于均匀和正态模型,即右偏模型在11组径阶中有6组径阶的MAB和MPB均最小;径阶分布在12~32 cm和40~44 cm时,正态模型的MAB和MPB均小于均匀模型,即正态模型在11组径阶中有6组径阶的MAB和MPB均最小。[结论]左偏模型的预测精度比均匀、正态和右偏模型高,右偏模型的预测精度比均匀和正态模型高,正态模型的预测精度比均匀模型高,总体模型检验精度顺序为左偏模型>右偏模型>正态模型>均匀模型。

关键词: 兴安落叶松, 抽样方法, 材积, 异方差, 预测精度

Abstract: [Objective] Study the influence of different sampling method on the prediction accuracy of the individual volume equation, and provide the basic data sampling technical basis for the compilation of different tree species volume tables and the establishment of the individual tree volume equation.[Method] Taking the Larix gmelinii volume equation as an example, four different sampling methods are designed for uniform, normal, right and left skewed distribution. According to different distributions, simple random sampling (SRS) of proc surveyselect module in SAS software is combined with conditional statements for sampling at different diameter class. Shapiro-Wilk method is used for normality test. Allometric models are fitted using GNLS in S-PLUS. Variance functions (including exponential function, power function and constant plus power function) were incorporated into generalized allometric models to reduce heteroscedasticity. Coefficient determination (R2), root mean square error (RMSE), mean absolute bias (MAB), and mean percentage of bias (MPB) were employed to evaluate the precision of different individual volume models.[Result] 1) Exponential function, power function and constant power function could reduce heteroscedasticity and power function with weighting factor V^ is the best. 2) Compared with the uniform model, these RMSE of the normal model, right model, and left model decreased by 31.6%, 23.1%, and 33.7% respectively. 3) Diameter class tests of different volume models showed that MAB and MPB of left model were less than those of the uniform model, normal model, and right models at 12-28 cm, 36-40 cm and 44-48 cm diameter classes, i.e. MAB and MPB of 6 groups out of 11 group diameter classes were the smallest; MAB and MPB of right model were less than those of the uniform model and normal model at 12-32 cm and 44-48 cm diameter classes, i.e. MAB and MPB of 6 groups out of 11 group diameter classes were the smallest; MAB and MPB of normal model were less than that of the uniform model at 12-32 cm and 40-44 cm diameter classes, i.e. MAB and MPB of 6 groups out of 11 group diameter classes were the smallest.[Conclusion] The prediction accuracy of the left model is higher than those of uniform model, normal model, and right model, the prediction accuracy of the right model is higher than those of uniform model and normal model, the prediction accuracy of the normal model is better than that of the uniform model. The order of the overall model prediction accuracy is:left model > right model > normal model > uniform model.

Key words: Larix gmelinii, sampling method, volume, heteroscedasticity, prediction accuracy

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