立木生物量模型的误差结构和可加性  

Error Structure and Additivity of Individual Tree Biomass Model

作　　者：董利虎[1] 张连军[2] 李凤日[1] 

机构地区：[1]东北林业大学林学院,哈尔滨150040 [2]美国纽约州立大学环境科学和林业学院,锡拉丘兹13210

出　　处：《林业科学》2015年第2期28-36,共9页Scientia Silvae Sinicae

基　　金：国家科技支撑计划课题(2012BAD22B02);林业公益性行业科研专项(201004026)

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 Ra2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。[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 a

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 

分 类 号：S718.55[农业科学—林学]