Abstract: [Objective] To obtain the regional tree aboveground biomass and its uncertainty estimate on different site quality and choose the optimizational model for biomass estimation,this study presented a novel method to obtain more accurate estimates of forest biomass in the forest productivity estimation.[Method] The regional site quality classification in Pinus massoniana forests of Jiangxi Province was determined using the dominant tree height (H)-diameter at breast height (D) model. The aboveground biomass density and its root mean square error (RMSE) in each site class were estimated by the Monte Carolmethod based on the three allometric biomass models including (1) g_i=aD_i^b+ε,(2) g_i=a(D_i²H_i)^b+ε,and (3) g_i=aD_i^bH_i^c+ε,where g_i is the individual biomass of the ith sample tree, D_i and H_i are the diameter at breast height (DBH) and tree height for the ith sample tree, respectively; a,b and c are model parameters; ε is the error term.[Result] 1) The coefficient of determination (R²) obtained from the three biomass equations are more than 0.95, which indicated that the three equations have good fitting abilities. Among the candidate models, Model (3) showed the best performance. 2) The dominant H-D model showed a good fitting ability with R²=0.907, mean error (ME)=0.001, mean absolute error (MAE)=0.559, and RMSE=0.027. Plots classified by site quality were distributed to all the regions of Jiangxi Province and the sample plots in the same site level were relatively concentrated. 3) The simulation studies using Monte Carlo method were achieved stability by 10 000 times repeats. Aboveground biomass estimates calculating by the same individual tree biomass equation increased with increasing level of site. The middle site class level (the third level) represents the mean level of the regional site conditions and has similar biomass estimate with the whole region. Under the same site class, the order of mean aboveground biomass estimate values of the three models was the following:equation (1) > equation (3) > equation (2) and the order of both RMSE and relative RMSE estimates values was the following:equation (2) < equation (3) < equation (1).[Conclusion] 1) The equation (2) is better than equation (3) and then the equation (1) by comparing the relative RMSEs of the mean biomass density estimate. 2) The more similar the site quality is to the mean site quality level, the smaller the relative RMSE of the aboveground biomass density will be. 3) This study put forward a method to estimate the regional tree biomass and uncertainty in different site quality by combining the H-D model and the Monte Carlo simulation, and provides a probability and reference to accurately estimate the site productivity and biomass under different site quality.

Key words: site classification, allometric model, biomass estimates, uncertainty estimates, Monte Carlo simulation