立木生物量模型的误差结构和可加性

doi:10.11707/j.1001-7488.20150204

摘要/Abstract

摘要：

【目的】从确定生物量模型误差结构和建立可加性生物量模型2方面进行立木生物量研究,为构建生物量模型提供建议。【方法】以黑龙江西部平原地区人工林小黑杨为例,利用似然分析法判断总生物量及各分项生物量模型的误差结构,在此基础上利用SAS/ETS模块的非线性似乎不相关回归建立其可加性生物量模型,并采用"刀切法"对生物量模型进行评价。【结果】经似然分析法判断,人工林小黑杨生物量模型的误差结构都为相乘的,对数转换的可加性生物量模型应当被选用。所建立的人工林小黑杨可加性生物量模型的调整后确定系数R_a²为0.92~0.99,绝大多数模型的平均相对误差以及平均相对误差绝对值都较小,所有模型的预测精度都在85%以上,且总生物量、地上和树干生物量模型效果较好,树根、树枝、树叶和树冠生物量模型效果较差。总的来说,各分项生物量和总生物量模型的拟合效果和预测能力较好。【结论】模型的误差结构和可加性是构建生物量模型中所存在的2个关键问题,建议在构建生物量模型时考虑并解决这2个问题。

关键词: 人工林小黑杨, 误差结构, 似然分析, 可加性, "刀切法"检验

Abstract:

【Objective】Forest biomass is a basic quantity character of the forest ecological system. Biomass data are the foundation of researching many forestry and ecology problems. Therefore, accurate quantification of biomass is critical for calculating carbon storage, as well as for studying climate change, forest health, forest productivity and nutrient cycling, etc. Directly measuring the actual weight of each component (i.e., stem, branch, foliage and root) is undoubtedly the most accurate method, but it is destructive, time consuming, and costly. Thus, developing biomass models is regarded as a better approach to estimating forest biomass. However, some issues are needed to take care when constructing and applying biomass models, such as: 1) some reported biomass models may not hold the additivity or compatibility among tree component models; 2) which model error structure is appropriate for biomass data, i.e., additive error structure versus multiplicative error structure; 3) few models are available for tree belowground (root) biomass. Researchers have been continuously working and debating on these issues over the last decades. Development of the additive system of biomass equations were reported in the literature. However, how to evaluate the model error structure of the biomass equation in forestry have not been well investigated so far. The present paper mainly deals with two parts: evaluating error structure of the biomass model and developing the additive system of biomass equations.【Method】The P. simonii×P. nigra plantation in the west of Heilongjiang Province of China is selected to ensure error structure by likelihood analysis. Nonlinear seemly unrelated regression (NSUR) of SAS/ETS module is used to estimate the parameters in the additive system of biomass equations. The biomass model validation is accomplished by Jackknifing technique.【Result】The multiplicative error structure was favored for the total and component biomass equations for P. simonii×P. nigra plantation by a likelihood analysis, and the additive system of log-transformed biomass equations should be applied. Overall, the R_a² of all biomass models was between 0.92 and 0.99. The mean relative error and mean absolute relative error were smaller for most biomass models. All models for total and component biomass had the good prediction precision (85 % or more). The effect of total tree, aboveground and stem biomass models are better than root, branch, foliage and crown biomass models. Overall, all models for total and component biomass could be a good predict of the P. simonii×P. nigra biomass.【Conclusion】Although the significance of likelihood analysis is proposed by several studies, it has not been widely applied in forestry. When total biomass is divided into aboveground and belowground biomass, aboveround biomass is divided into stem and crown biomass, crown biomass is divided into branch and foliage biomass, and stem biomass is divided into bark and wood biomass, the additivity of total and component biomass should be taken into account. Overall, the error structure and additivity of biomass models are the two key issues, and should be taken into account when biomass models are constructed. If the two issues are well solved, the constructed biomass models will be more effective.

Key words: P. simonii×, P. nigra plantation, error structure, likelihood analyses, additivity, jackknifing technique

中图分类号:

S718.55

董利虎, 张连军, 李凤日. 立木生物量模型的误差结构和可加性[J]. 林业科学, 2015, 51(2): 28-36.

Dong Lihu, Zhang Lianjun, Li Fengri. Error Structure and Additivity of Individual Tree Biomass Model[J]. Scientia Silvae Sinicae, 2015, 51(2): 28-36.

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