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林业资源管理 ›› 2010›› Issue (3): 16-23.

• 科学技术 • 上一篇    下一篇

全国立木生物量建模总体划分与样本构成研究

曾伟生1, 唐守正1, 黄国胜2, 张敏3   

  1. 1.中国林业科学研究院资源信息研究所,北京 100091;
    2.国家林业局调查规划设计院,北京 100714;
    3.国家林业局森林资源管理司,北京 100714
  • 收稿日期:2010-03-08 修回日期:2010-05-18 发布日期:2020-12-14
  • 作者简介:曾伟生(1966-),男,湖南涟源人,教授级高工,在读博士,主要从事森林资源监测和林业数表研制工作。Email:zengweisheng@sohu.com
  • 基金资助:
    国家林业局“基于清查资料的中国森林植被生物量和碳储量评估”

Population Classification and Sample Structure on Modeling of Single- Tree Biomass Equations for National Biomass Estimation in China

ZENG Weisheng1, TANG Shouzheng1, HUANG Guosheng2, ZHANG Min3   

  1. 1. Institute of Forest Resources Information, Chinese Academy of Forestry, Beijing 100091, China;
    2. Academy of Forest Inventory and Planning of SFA, Beijing 100714, China;
    3. Department of Forest Resources, State Forestry Administration, Beijing 100714, China
  • Received:2010-03-08 Revised:2010-05-18 Published:2020-12-14

摘要: 结合全国生态地理区域和行政区域,并兼顾立木材积表的建模总体划分,提出了全国立木生物量建模总体划分方案,将全部树种分为34个树种组,全国分为6大地理区域,共划分70个建模总体;通过对已有立木生物量数据进行建模分析,将立木生物量模型的预估精度确定为95%以上,同时根据变动系数分析结果提出合适的建模样本单元数应该在150以上,且要按划定的10个径阶均匀分配,保证每个径阶的样本单元数不少于15个;以第七次全国森林资源清查数据为基础,确定了每一个建模总体的样本结构,将样本单元数全部落实到了各省和各级径阶。研究成果可为推进全国森林生物量调查建模工作提供参考依据。

关键词: 立木生物量建模, 总体划分, 样本构成, 生态地理区域, 变动系数, 预估精度

Abstract: The population classification and sample structure on modeling of single-tree biomass equations for national biomass estimation in China was studied in this paper. Firstly, combining the classification of eco-geographic region and administrative region, a scheme of population classification on modeling of single-tree biomass equations was presented, which is mostly compatible with the population classification on modeling of single-tree volume equations, including 34 tree species or species groups, 6 large-scale geographic regions, and a total of 70 populations. Secondly, based on the analysis of variation coefficients derived from one-and two-variable tree biomass models fitted with available tree biomass data of several tree species, it was concluded that prediction precision of tree biomass models could be defined as more than 95%, and at least 150 sample trees are needed for a regional model which should be selected in equal numbers for each diameter class, i.e., not less than 15 trees in each of 10 diameter classes. Finally, by using the 7th National Forest Inventory data, the sample structure of each population was determined, that is, all the sample tree numbers of 70 populations were specified by province and by diameter class. The results will provide a practical reference for the establishment of generalized single-tree biomass models in China.

Key words: single-tree biomass equation modeling, population classification, sample structure, eco-geographic region, coefficient of variation, prediction precision

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