Allometric biomass models are efficient tools to estimate biomass of trees and forest stands in a non-destructive way. Development of species-specific allometric biomass models requires extensive fieldwork and time. O...Allometric biomass models are efficient tools to estimate biomass of trees and forest stands in a non-destructive way. Development of species-specific allometric biomass models requires extensive fieldwork and time. Our study aimed to generate species-specific allometric biomass models for the most common fuelwood and timber species of Bangladesh. We also wanted to evaluate the performances of our models relative to the performances of regional and commonly used pan-tropical biomass models. We used semi-destructive method that incorporates tree-level volume, species-specific biomass expansion factor (BEF), and wood density. We considered four base models, 1) Ln (biomass) = a + bLn (D);2) Ln (biomass) = a + bLn (H);3) Ln (Biomass) = a + bLn (D^2H);4) Ln (Biomass) = a + bLn (D) + cLn (H) to develop species-specific best-fitted models for Total Above-Ground Biomass (TAGB) and stem biomass. The best-fitted model for each species was selected by the lowest value of Akaike Information Criterion (AIC), Residual Standard Error (RSE) and Root Mean Square Error (RMSE). The derived best-fitted models were then evaluated with respect to regional and pan-tropical models using a separate set of observed data. This evaluation was conducted by computing ME (Model Efficiency) and MPE (Model Prediction Error). The best-fitted allometric biomass models have shown higher model efficiency (0.85 to 0.99 at scale 1) and the lowest model prediction error (-8.94% to 5.27%) compared to the regional and pan-tropical models. All the examined regional and pan-tropical biomass models showed different magnitude of ME and MPE. Some models showed higher level (>0.90 at scale 1) of ME compared to the best-fitted specific species biomass model.展开更多
文摘Allometric biomass models are efficient tools to estimate biomass of trees and forest stands in a non-destructive way. Development of species-specific allometric biomass models requires extensive fieldwork and time. Our study aimed to generate species-specific allometric biomass models for the most common fuelwood and timber species of Bangladesh. We also wanted to evaluate the performances of our models relative to the performances of regional and commonly used pan-tropical biomass models. We used semi-destructive method that incorporates tree-level volume, species-specific biomass expansion factor (BEF), and wood density. We considered four base models, 1) Ln (biomass) = a + bLn (D);2) Ln (biomass) = a + bLn (H);3) Ln (Biomass) = a + bLn (D^2H);4) Ln (Biomass) = a + bLn (D) + cLn (H) to develop species-specific best-fitted models for Total Above-Ground Biomass (TAGB) and stem biomass. The best-fitted model for each species was selected by the lowest value of Akaike Information Criterion (AIC), Residual Standard Error (RSE) and Root Mean Square Error (RMSE). The derived best-fitted models were then evaluated with respect to regional and pan-tropical models using a separate set of observed data. This evaluation was conducted by computing ME (Model Efficiency) and MPE (Model Prediction Error). The best-fitted allometric biomass models have shown higher model efficiency (0.85 to 0.99 at scale 1) and the lowest model prediction error (-8.94% to 5.27%) compared to the regional and pan-tropical models. All the examined regional and pan-tropical biomass models showed different magnitude of ME and MPE. Some models showed higher level (>0.90 at scale 1) of ME compared to the best-fitted specific species biomass model.
