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    宋博, 李凤日, 董利虎, 周翼飞. 黑龙江西部地区人工小黑杨立木可加性生物量模型[J]. 北京林业大学学报, 2018, 40(11): 58-68. DOI: 10.13332/j.1000-1522.20180062
    引用本文: 宋博, 李凤日, 董利虎, 周翼飞. 黑龙江西部地区人工小黑杨立木可加性生物量模型[J]. 北京林业大学学报, 2018, 40(11): 58-68. DOI: 10.13332/j.1000-1522.20180062
    Song Bo, Li Fengri, Dong Lihu, Zhou Yifei. Additive system of biomass equations for planted Populus simonii × P. nigra in western Heilongjiang Province of northeastern China[J]. Journal of Beijing Forestry University, 2018, 40(11): 58-68. DOI: 10.13332/j.1000-1522.20180062
    Citation: Song Bo, Li Fengri, Dong Lihu, Zhou Yifei. Additive system of biomass equations for planted Populus simonii × P. nigra in western Heilongjiang Province of northeastern China[J]. Journal of Beijing Forestry University, 2018, 40(11): 58-68. DOI: 10.13332/j.1000-1522.20180062

    黑龙江西部地区人工小黑杨立木可加性生物量模型

    Additive system of biomass equations for planted Populus simonii × P. nigra in western Heilongjiang Province of northeastern China

    • 摘要:
      目的森林生物量和碳储量是研究许多林业问题与生态问题的基础。因此,准确测定生物量和碳储量十分重要。建立生物量模型是生物量和碳储量估测的重要手段。以人工小黑杨为研究对象,进行各分项生物量最优模型的选取,构建3种小黑杨可加性生物量模型系统,即基于胸径变量的一元可加性生物量模型系统、基于胸径和树高变量的二元可加性生物量模型系统以及基于最优变量的多元可加性生物量模型系统,为全国性生物量监测提供可靠的理论与技术支持。
      方法采用聚合型可加性模型来建立生物量模型;模型参数估计采用非线性似乎不相关回归模型方法;采用“刀切法”评价所建立的3种立木可加性生物量模型。
      结果仅含有胸径的异速生长方程是一种最为简单的模型形式,且具有较高的预测精度。包含树高和树冠属性因子(冠幅和冠长)的生物量模型能提高模型的预测能力,尤其能显著提高树枝、树叶和树冠生物量模型的预测能力。所建立的3种小黑杨可加性生物量模型拟合效果较好,其调整后确定系数(Ra2)均大于0.81,平均相对误差(ME)为-1.0%~10.0%,平均相对误差绝对值(MAE)均小于25%,所有模型的平均预测精度在85%以上。多元可加性生物量模型优于一元可加性生物量模型和二元可加性生物量模型。
      结论为了使模型参数估计更有效,所建立的生物量模型需要考虑立木总生物量及各分项生物量的可加性。虽然获取树冠属性因子需要花费大量人力和财力,但随着林地环境的变化,多元可加性生物量模型在结合生长模型精确估计小黑杨生物量方面具有一定的优势。总的来看,所建立的立木生物量模型均可对小黑杨生物量进行很好的估算。

       

      Abstract:
      ObjectiveForest biomass, the foundation of researching many forestry and ecology problems, is a basic quantity character of the forest ecological system. Thus, accurate measurement of biomass and carbon is very important. Developing biomass models is a major way to biomass estimation. Based on the data of biomass for Populus simonii × P. nigra, we established three additive systems of individual tree biomass equations, i.e., the additive system of biomass equations based on one-variable models, the additive system of biomass equations based on two-variable models, the best additive system of biomass equations based on multiple-variable models. These provided technical and theoretical support for accounting and monitoring the Chinese forest biomass and carbon stock.
      MethodThe aggregation system was used to establish the individual tree biomass additive models, and nonlinear seemly unrelated regression was used to estimate the parameters in the additive system of biomass equations. The individual tree biomass model validation was accomplished by Jackknifing technique in this study.
      ResultTree biomass models using diameter at breast height (D) as the sole predictor are simple in model structure, and have higher prediction precision. Adding tree height (H) and crown attributes (crown width (CW) and crown length (CL)) as the additional predictors into biomass equations can significantly improve the model fitting and predictive ability, especially for predicting branch, foliage and crown biomass. The model fitting results showed that three additive systems of individual tree biomass equations fitted the data well, of which the adjusted coefficient of determination (Ra2) of biomass additive systems was all above 0.81, the mean relative error (ME) was between -1.0%-10.0%, the mean absolute relative error (MAE) was less than 25%, and all models for total and component biomass had the good prediction precision (more than 85%). Most biomass equations of the additive system based on multiple-variable models produced better model fitting than those of the additive system based D and the additive system based D and H.
      ConclusionIn order to estimate biomass model parameters more effectively, the additive property of estimating tree total, sub-totals, and component biomass should be taken into account. Although obtaining crown attributes is costly in terms of labor and time, the additive system of biomass equations based on the best models is very useful in conjunction with individual growth models to accurately predict biomass in response to changes in stand condition. Overall, the biomass models would be suitable for predicting individual tree biomass and carbon of planted Populus simonii × P. nigra.

       

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