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林业科学 ›› 2013, Vol. 49 ›› Issue (10): 74-79.doi: 10.11707/j.1001-7488.20131012

• 论文与研究报告 • 上一篇    下一篇

中国杉木相容性立木材积和地上生物量方程

曾鸣1, 聂祥永2, 曾伟生3   

  1. 1. 杭州电子科技大学管理科学与信息工程研究所 杭州 310018;
    2. 国家林业局华东林业调查规划设计院 杭州 310019;
    3. 国家林业局调查规划设计院 北京 100714
  • 收稿日期:2012-11-26 修回日期:2013-04-15 出版日期:2013-10-25 发布日期:2013-11-05
  • 通讯作者: 曾伟生

Compatible Tree Volume and Aboveground Biomass Equations of Chinese Fir in China

Zeng Ming1, Nie Xiangyong2, Zeng Weisheng3   

  1. 1. Research Institute of Management Science & Information Engineering, Hangzhou Dianzi University Hangzhou 310018;
    2. East China Forest Inventory and Planning Institute, State Forestry Administration Hangzhou 310019;
    3. Academy of Forest Inventory and Planning, State Forestry Administration Beijing 100714
  • Received:2012-11-26 Revised:2013-04-15 Online:2013-10-25 Published:2013-11-05

摘要:

以我国南方地区的最重要针叶树种——杉木为研究对象,采用误差变量联立方程组和哑变量模型方法,建立适合不同杉木生长区域(总体)应用的相容性立木材积方程、地上生物量方程及生物量转换因子函数。结果表明: 二元立木材积方程和地上生物量方程均优于其相应的一元模型; 不同总体的模型之间存在显著差异,总体A的模型估计值要大于总体B; 一元和二元地上生物量方程的平均预估误差均在3%以内,可应用于不同区域的杉木林生物量估计。

关键词: 材积方程, 生物量方程, 生物量转换因子, 误差变量联立方程组, 哑变量, 杉木

Abstract:

Taking the most important coniferous species of southern China, Chinese fir (Cunninghamia lanceolata), as the study object, the compatible tree volume equations, aboveground biomass equations and biomass conversion factor functions suitable for two regions (population areas) were constructed using the error-in-variable simultaneous equation and dummy variable model approach. The results showed that two-variable models are better than one-variable models whether tree volume equation or aboveground biomass equation; the models for two populations are significantly different and the projected estimates for population A are larger than those for population B; the mean prediction errors (MPE's) of one-and two-variable aboveground biomass equations are both less than 3%, which means the aboveground biomass equations could be applied for estimation of Chinese fir forest biomass in the regions.

Key words: volume equation, biomass equation, biomass conversion factor, error-in-variable simultaneous equation, dummy variable, Cunninghamia lanceolata

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