VOLUME ESTIMATION MODELS FOR QUERCUS SERRATA AND PINUS KESIYA USING NON-DESTRUCTIVE TECHNIQUES. A CASE STUDY FROM NORTHEAST INDIA
The accurate, non-destructive estimation of tree volume is essential for sustainable forest management, particularly in data-deficient regions like Manipur, Northeast India. This study developed species-specific regression models to estimate the over-bark stem volume of Quercus serrata and Pinus kesiya, based on the measurements from 108 trees per species across 29 sites. Diameter at breast height (DBH), total height, and sectional diameters were obtained using a Nikon Rangefinder and Criterion™ RD 1000 dendrometer. Smalian’s method was used to calculate the stem volume. Models using DBH alone and in combination with height were evaluated via model efficiency (EF), root mean square error (RMSE), and Akaike’s information criterion (AIC). For Q. serrata, the best models were an inverse quadratic (DBH only) and a third-degree polynomial (DBH and height). For P. kesiya, the best fits were logarithmic and quadratic, respectively. QQ plots confirmed the fit after logarithmic transformation. Residuals were normally distributed, and high EF confirmed model accuracy.