Regression Equations for Estimating Tree Volume and Biomass of Important Timber Species in Meghalaya, India

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. R² values showed that the value for linear function (V = f (d² 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 d² h to develop regression equation. Similarly, linear regression equations for each tree species were also developed using linear function [(V = f (d² ))], considering tree volume as an dependent variable and DBH as an independent variable, transformed in the form of V = d² . 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.