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林业科学 ›› 2011, Vol. 47 ›› Issue (6): 70-76.doi: 10.11707/j.1001-7488.20110611

• 论文 • 上一篇    下一篇

帽儿山天然次生林主要树种冠长率模型

卢军1, 李凤日2, 张会儒1, 张守攻3   

  1. 1. 中国林业科学研究院资源信息研究所 北京100091;2. 东北林业大学林学院 哈尔滨 150040;3. 中国林业科学研究院林业研究所 北京 100091
  • 收稿日期:2009-07-29 修回日期:2010-10-25 出版日期:2011-06-25 发布日期:2011-06-25

A Crown Ratio Model for Dominant Species in Secondary Forests in Mao'er Mountain

Lu Jun1, Li Fengri2, Zhang Huiru1, Zhang Shougong3   

  1. 1. Research Institute of Resources Information Techniques, CAF Beijing 100091;2. Forestry College, Northeast Forestry University Harbin 150040;3. Research Institute of Forestry, CAF Beijing 100091
  • Received:2009-07-29 Revised:2010-10-25 Online:2011-06-25 Published:2011-06-25

摘要:

对黑龙江省帽儿山实验林场天然次生林内的10个主要阔叶树种建立冠长率模型。采用2007年设置的30块固定标准地中获取的4 237株样木,使用其中的3 628株建立冠长率模型。从大小、竞争和立地3个角度来解释冠长率,利用Logistic方程的形式来构造冠长率模型,并且使用多重决定系数来评价3个方面因子所解释变量的百分比。模型拟合结果表明:变量解释的百分比从12.881 3%(色木)到42.116 8%(白桦),而对于帽儿山次生林区分布最多的树种紫椴的解释变量百分比是17.403 2%。对所建立的冠长率模型进行检验,获得较高的预估精度和较低的各种误差百分比。

关键词: 冠长率, 次生林, Logistic方程

Abstract:

Crown ratio models for 10 dominant species in Mao'er Mountain experimental forest station located in Heilongjiang Province were developed and the study is part of a project to develop the protection demonstration and sustainable management techniques for natural forest in northeast China. Thirty permanent sample plots were established and 3 628 of 4 237 sample trees were used to develop the crown ratio models in 2007. The models developed with Logistic function were designed from size, competition and site to explain the crown ratio and unadjusted multiple coefficient of determination was used to evaluate the percentage of variation explained by three variable groups. The estimation results of the models indicated that the total percentage of variation explained by variable groups ranged from 12.881 3%(Acer mono) to 42.116 8%(Betula platyphylla), and the percentage was 17.403 2% for Tilia spp, a widest-distributed species in this area. Validation for the crown ratio models showed relatively high prediction precision and low errors.

Key words: crown ratio, secondary forest, Logistic equation

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