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油松针叶面积估计模型及比叶面积的研究
引用本文:刁军,国红,卢军,雷相东,唐守正.油松针叶面积估计模型及比叶面积的研究[J].林业科学研究,2013,26(2):174-180.
作者姓名:刁军  国红  卢军  雷相东  唐守正
作者单位:中国林业科学研究院资源信息研究所,北京,100091
基金项目:国家自然科学基金(No.31100474、30872022)
摘    要:叶面积和比叶面积是植物生长过程中的重要参数.本研究基于河北木兰围场实测油松数据,通过winSEEDLE种子和针叶图像分析系统获得油松522个单个针叶的表面积LA、针叶长度L、针叶宽W、针叶周长P,分别建立了以针叶长、针叶宽、针叶周长等形状属性为自变量的叶面积估计模型和以针叶干质量为自变量的叶面积估计模型.用总相对误差、平均相对误差、平均相对误差绝对值、均方根误差、预估精度5个统计量来检验模型的误差和拟合优度,经检验模型LA=-2.761 +0.464 L +6.608W和LA =1.345 +0.501X分别为这两种模型中最好,X为针叶干质量.通过对算术平均法、比估计法、最小二乘法3种方法的比较,得到油松的比叶面积为7.08 m2 ·kg-1.本研究为油松叶面积的估计提供了一个简单可靠的方法.

关 键 词:油松  叶面积  回归模型  比叶面积
收稿时间:2012/7/16 0:00:00

Leaf Area Estimation Model and Specific Leaf Area of Chinese Pine
DIAO Jun,GUO Hong,LU Jun,LEI Xiang-dong and TANG Shou-zheng.Leaf Area Estimation Model and Specific Leaf Area of Chinese Pine[J].Forest Research,2013,26(2):174-180.
Authors:DIAO Jun  GUO Hong  LU Jun  LEI Xiang-dong and TANG Shou-zheng
Institution:Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China;Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China;Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China;Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China;Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Abstract:Leaf area and specific leaf area are important parameters in the process of plant growth. 522 needles of Chinese pine (Pinus tabulaeformis) in Mulanweichang were sampled and the needle surface area LA, needle length L, needle width W and needle perimeter P were obtained by winSEEDLE software. The models for leaf area and shape attributes including leaf length, leaf width, leaf perimeter and models for leaf area and leaf dry weight were developed, respectively. The total relative error, average relative error, average absolute relative error, root mean square error and predicted precision were used to verify the errors and the goodness of fit of the models. The models LA=-2.761+0.464 L+6.608 W and LA=1.345+0.501 X were proved to be the best, where X is the leaf dry weight. The specific leaf area of Chinese pine is 7.08 m2·kg-1 derived from the comparison among the arithmetic average method, ratio estimation method, and the least square method. It provides a simple and reliable method for estimating leaf area of Chinese pine.
Keywords:Pinus tabulaeformis  leaf area  regression model  specific leaf area
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