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首页> 外文期刊>Current Science: A Fortnightly Journal of Research >Regression equations for estimating tree volume and biomass of important timber species in Meghalaya, India
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Regression equations for estimating tree volume and biomass of important timber species in Meghalaya, India

机译：回归方程，用于估算梅吉拉亚州的重要木材种类的树木体积和生物量

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摘要

Linear regression models were developed for four ecologically and economically important tree species of Meghalaya, India, viz. Betula alnoides, Duabanga grandiflora, Magnolia champaca and Toona ciliata. In the present study a non-destructive approach has been used for measurement of required variables, i.e. diameter at breast height (DBH), basal diameter, tree height, end-diameters and length of frustum. Comparison of various models of relationship on the basis of adj. R-2 values showed that the value for linear function (V = f (d(2)h)) was more than 0.90 for all the four tree species, except lowest diameter class of T. ciliata (10-30 cm diameter class). Hence this linear regression equation was selected for development of diameter class-wise volume equations. Volume of the stem was taken as the dependent variable, while DBH and tree height were used as independent variables, transformed in the form of d(2)h to develop regression equation. Similarly, linear regression equations for each tree species were also developed using linear function [(V = f (d(2)))], considering tree volume as an dependent variable and DBH as an independent variable, transformed in the form of V = d(2). The present study is among a few attempts to develop regression models without the felling of trees since 1977 and an initial attempt using advanced measurement equipment in North East (NE) India, under the current regime of ban on tree felling. The regression equations developed in this study can be used for estimation of timber yield and carbon content of the selected tree species found in the Meghalaya forests.
机译：线性回归模型是为梅吉拉亚，印度的四种生态和经济上重要的树种而开发的。 Betula alnoides，Duabanga Grandiflora，Magnolia Champaca和Toona Ciliata。在本研究中，非破坏性方法已被用于测量所需的变量，即乳房高度（DBH）的直径，基底直径，树高度，结束直径和截头的长度。基于adj的各种关系模型的比较。 R-2值表明，对于所有四种树种，线性函数的值（V = F（d（2）h））大于0.90，除了T.Ciliata（直径10-30cm直径等级） 。因此，选择该线性回归方程用于开发直径的类方程式方程。茎的体积作为依赖变量，而DBH和树高度用作独立变量，以D（2）H的形式转化为开发回归方程。类似地，使用线性函数（v = f（d（2）））]，考虑到从属变量和dbh作为独立变量的树木卷，以v =的形式转换为v = d（2）。目前的研究是在1977年以来的情况下，在没有树木的砍伐的情况下开发回归模型的尝试，并在当前在禁令的当前禁令政权下使用North East（Ne）印度的高级测量设备。本研究开发的回归方程可用于估计Meghalaya森林中所发现的所选树种的木材产量和碳含量。

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