Modeling the Standing Traits to Estimate Tree Volume and Biomass of Acer monspessulanum Subsp. cinerascens (Boiss.) using Multiple Regression

Predicting the volume and biomass of multi-stem maple trees (Acer monspessulanum Subsp. cinerascens Boiss.) based on standing traits is necessary in forestry. In this research twenty sample trees were selected in four transects randomly in Bagh-Shadi Forest of Yazd province. After measuring the diameter at root collar (DRC), tree height, stems numbers and crown diameter and area all trees were cut down. Trunks and branches were separated, weighted and some sample disks were taken. Dry weight and volume of samples were determined in laboratory and according to dry-wet ratio and wood specific gravity; total dry weight (aboveground biomass) and volume of all trees were calculated. Multiple regression and curve estimation were applied for modeling. Results indicated that there were strong and significant relation between volume and biomass of trees and their height and DRC. Two-variable models were significant and reliable for branch (or crown) wet and dry weight (R2=0.85), total tree wet and dry weight (R2=0.86) and total tree volume (R2=0.87). Prediction capability of two-order models according to tree height increased up to 10 percent. Results showed that R-square change in two-variable models was significant in contrast to one-variable models and coefficients increased from 6 to 44 percent. Also amount of error (NRMSE) decreased from 15 to 41 percent. Finally it can be said that tree height and DRC was able to predict 87 percent of biomass and volume of maple trees with a high precision.