Allometric Models and Biomass Conversion and Expansion Factors to Predict Total Tree-level Aboveground Biomass for Three Conifers Species in Iran

Accurate estimation of total aboveground biomass (TAGB) is an important challenge in evaluating and monitoring tree biomass. Thus, developing species-specific allometric models is essential. This study aimed to predict tree-level TAGB for Pinus brutia, Pinus pinea, Cupressus sempervirens, and the species-independent case using the most accurate allometric models, biomass conversion and expansion factor (BCEF), and mixed effect models in Golestan Province, Iran. The mean BCEFs for three species were 0.46, 0.47, and 0.86, respectively, and there was no significant difference (p>0.05) between TAGB predictions based on BCEF estimates for this study and observations of TAGB. The results revealed that compared with relative root mean square error (RMSE%) for the Intergovernmental Panel on Climate Change (IPCC) report–based BCEFs, the RMSE% for BCEFs estimated for this study were reduced by 46.91%. The results showed that a diameter at breast height (DBH), height (H), and wood density (ρ)-based model were the most accurate predictors for P. brutia (R2=0.98, RMSE%=14.11), whereas the DBH-based model and the DBH and H-based model were most accurate for P. pinea (R2=0.99, RMSE%=9.04) and C. sempervirens (R2=0.96, RMSE%=17.77), respectively. Compared to the allometric models, mixed-effect models using DBH, H, and ρ improved TAGB prediction for the species-independent case (3% increase in R2 and 6.81% decrease in RMSE%), but not for models for P. brutia, P. pinea, and C. sempervirens. Study Implications: Accurate prediction of total tree-level aboveground biomass (TAGB) requires the most accurate allometric models plus accurate biomass conversion and expansion factors (BCEFs). The relevance of this study is that few allometric models have been developed to predict tree-level TAGB for Pinus brutia, Pinus pinea, and Cupressus sempervirens. We developed allometric models and estimated BCEFs for predicting TAGB for P. brutia, P. pinea, and C. sempervirens in Golestan Province, Iran. We provide accurate allometric models and BCEFs based on available field data. Also, we provide tools to help forest managers predict TAGB.