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Scientia Silvae Sinicae ›› 2015, Vol. 51 ›› Issue (2): 28-36.doi: 10.11707/j.1001-7488.20150204

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Error Structure and Additivity of Individual Tree Biomass Model

Dong Lihu1, Zhang Lianjun2, Li Fengri1   

  1. 1. Forestry College, Northeast Forestry University Harbin 150040;
    2. State University of New York, College of Environmental Science and Forestry Syracuse 13210
  • Received:2014-04-29 Revised:2014-09-29 Online:2015-02-25 Published:2015-03-11

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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 Ra2 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

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