基于树干不同形率的樟子松立木材积方程研建  

Equation construction on standing tree volume of Pinus sylvestris var. mongolica based on different form quotients of trunk

作　　者：靳晓东 姜立春[1] Jin Xiaodong;Jiang Lichun(Key Laboratory of Sustainable Forest Ecosystem Management of Ministry of Education,School of Forestry,Northeast Forestry University,Harbin 150040,Heilongjiang,China)

机构地区：[1]东北林业大学林学院森林生态系统可持续经营教育部重点实验室,黑龙江哈尔滨150040

出　　处：《北京林业大学学报》2020年第3期78-86,共9页Journal of Beijing Forestry University

基　　金：国家自然科学基金项目(31570624);黑龙江省应用技术研究与开发计划项目(GA19C006);中央高校基本科研业务费专项(2572019CP15)。

摘　　要：【目的】立木材积方程在森林生产力、生物量和碳储量等林业问题方面都有着广泛的应用。因此,提高立木材积的预测精度一直是林业模型研究者的重要任务。本研究以大兴安岭樟子松为研究对象,构建含有不同形率的二元和三元材积方程,并对比检验其预测效果,旨在把传统立木材积的预测精度提高到一个新的水平。【方法】利用15个树干不同形率,基于传统的一元和二元立木材积方程分别建立二元和三元立木材积方程,并与传统的一元和二元材积方程比较。通过对各模型进行拟合选出最优形率模型,具体选用统计软件S-PLUS中的广义非线性模块(GNLS)进行拟合。并利用幂函数、指数函数以及常数加幂函数校正在拟合过程中各立木材积模型表现的异方差现象。选择确定系数(R2)、均方根误差(RMSE)、平均误差绝对值(MAB)和相对误差绝对值(MPB)4个指标对模型进行评价。最终采用分径阶比较法比较不同径阶范围内4种方程的预测精度。【结果】基于相对树高70%处形率的二元模型拟合效果最好,基于相对树高50%处形率的三元模型拟合效果最好。模型检验结果表明:基于传统的一元模型,加入形率后模型的RMSE、MAB、MPB分别降低了33.7%、30.7%、29.9%;基于传统的二元模型,加入形率后的模型RMSE、MAB、MPB分别降低了70.5%、70.9%、71.2%。不同径阶的检验表明:对于小径阶和中等径阶的树木,模型的检验精度顺序为模型(13)>模型(2)>模型(12)>模型(1);对于大径阶的树木,模型的检验精度顺序为模型(13)>模型(12)>模型(2)>模型(1)。【结论】形率因子是干形的重要指标。在传统立木材积模型中引入形率因子可以提高材积的预测精度,因此,对于樟子松立木材积的估算,尤其是中大径阶林分,推荐使用带有形率的三元立木材积模型。[Objective] The tree volume equation has been widely used in studying forest productivity,biomass and carbon storage. Therefore, improving the prediction precision of tree volume has always been an important task for forestry model researchers. Two-variable and three-variable volume equations were established based on different form quotients of the trunk, and compared and tested the predictive effects for Pinus sylvestris var. mongolica in Daxing,anling Mountains of northeastern China to improve the prediction precision of traditional tree volume to a new level. [Method] Using the 15 different form quotients of the trunk, two-variable and three-variable volume equations were established based on the traditional onevariable and two-variable volume equations, respectively, and compared with the traditional one-variable and two-variable volume equations. All models were fitted using GNLS in S-PLUS. And the optimal form quotient model was selected. Variance functions(power function, constant plus power function and exponential function) were incorporated into generalized models to decrease heteroscedasticity. And the precision of different individual volume models was evaluated using four factors: mean absolute bias(MAB), mean percentage of bias(MPB), root mean square error(RMSE), and coefficient determination(R2).Finally, the prediction precision of four kinds of equations in different diameter classes was compared by the method of different diameter classes testing. [Result] The results showed that the two-variable model based on 70% of the relative height had the best fitting effect, and the three-variable model based on the relative height of 50% was the best. The model test results showed that based on the traditional one-variable model,the RMSE, MAB and MPB of the model after adding form quotient were reduced by 33.7%, 30.7%, and29.9%, respectively. Based on the traditional two-variable model, the RMSE, MAB and MPB of the model after adding form quotient, were reduced by 70.5%, 70.9% and 71.2%, respectively. F

关 键 词：樟子松 形率 异方差 材积 预测精度 

分 类 号：S791.253[农业科学—林木遗传育种] S758.1[农业科学—林学]