长白落叶松人工林单木和林分水平的相容性生物量模型研究
Compatible Biomass Models for Artificial Larix olgensis Base on Tree-Level and Stand-Level
投稿时间:2018-11-15  修订日期:2019-01-21
DOI:
中文关键词:  长白落叶松  非线性似然无关回归法  哑变量  相容性  生物量模型
英文关键词:Larix olgensis  nonlinear seemingly unrelated regression  dummy variable  compatible  biomass model  
基金项目:国家自然基金重点项目(31430017)
作者单位E-mail
洪奕丰 中国林业科学研究院林业研究所 killrro@126.com 
陈东升 中国林业科学研究院林业研究所  
申佳朋 中国林业科学研究院林业研究所  
项伟波 中国林业科学研究院林业研究所  
张守攻 中国林科院林业研究所 larch_rif@163.com 
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中文摘要:
      [目的]构建落叶松人工林单木和林分水平的相容性生物量模型,使之既在数据采集区域内能够表征不同水平下的差异程度,又具有较强的通用性。[方法] 基于64株长白落叶松人工林样木生物量实测数据和40个每木检尺样地数据,在考虑和未考虑林龄2种情形下,利用哑变量和非线性似然无关回归方法相结合,构建单木和林分水平的一元相容性生物量模型。[结果]表明:(1)地上及全株生物量模型单木水平下的R_adj^2均大于0.95,林分水平下的R_adj^2均大于0.78,(2)利用哑变量考虑林龄因素后,单木水平下各评价指标总体稳定,参数b值范围从0.9055~2.5125减小为1.0470~2.2028。林分水平下R2提升0.2019,参数b值范围从0.0711~1.5607减小为0.7811~1.0551;且具有更小的TRE、MPE和MSE。(3)利用对数转换的线性回归模型,全株及各组分生物量模型残差的分布趋势均平行于横轴。[结论] 非线性似然无关回归和哑变量相结合的方法灵活、建模过程简单、模型稳定性好,适用于不同因素下落叶松人工林相容性生物量模型构建。林龄因素对林分模型拟合效果的改善更显著,在建模过程中,单木模型可以不考虑林龄的影响,而林分模型需要考虑林龄的影响。
英文摘要:
      [Objective] Compatible models for the single tree biomass and stand biomass of Larix olgensis were established to represent different levels of variations and to improve generalization capability of models. [Methods] Based on the biomass data of 64 trees in 40 sample plots of L.olgensis plantation, compatible models were established by combining dummy variable and nonlinear seemingly unrelated regression under the conditions of considering or not considering stand age. [Results] (1) The models had good estimation precision with R_adj^2 >0.95 and R_adj^2>0.78 for single tree biomass and stand biomass, respectively. (2) Under the consideration of stand age with dummy variable, the fitting goodness of model was improved with smaller TRE, MPE and MSE, the evaluation statistics were stable overall and the range of parameter b reduced form 0.9055~2.5125 to 1.0470~2.2028 for single level and R_adj^2 increased by 0.2019 and the range of parameter b reduced form 0.0711~1.5607 to 0.7811~1.0551 for stand level. (3) Using the linear regression model of logarithmic transformation, the distribution trends of model residual error of the whole plant and its components were parallel to the transverse axis. [Conclusion] The method of combing dummy variable and nonlinear seemingly unrelated regression was flexible, simple and applicable to the establishment of single tree biomass and stand biomass models. The fitting goodness of biomass model was improved with the consideration of stand age, especially in stand biomass model. Thus, the influence of stand age should be considered in the process of stand biomass modeling.
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