中文摘要：

【目的】选择适合的单木地上生物量异速生长模型形式,获得区域尺度马尾松林生物量及其误差在不同立地等级下的估计,为精准估计不同立地质量的森林生物量提供技术支持,进而为森林立地生产力估计提供参考。【方法】在马尾松林3种单木生物量模型gi=aDi^b＋ε[式（1）]、gi=a（Di^2Hi）^b＋ε[式（2）]、gi=aDi^bHi^c＋ε[式（3）]形式下（式中：gi为单木生物量,Di为单木胸径,Hi为单木树高,a、b、c为估计参数,ε为残差）,运用优势木树高分级法对我国江西省马尾松林占优势的样地进行立地质量分级,采用蒙特卡洛模拟法估计3种模型形式下不同立地质量的单位面积生物量均值和不确定性。【结果】1）3种生物量模型形式的决定系数（R^2）及调整决定系数（R（adj）^2）均达到0.95以上,拟合效果良好。从综合平均偏差、平均绝对偏差及均方根误差来看,式（3）模型较优。2）用优势木树高等级代替立地等级,利用树高分级法建立优势木树高-胸径模型,曲线的R2为0.907,平均偏差为0.001,平均绝对偏差为0.559,均方根误差为0.027,模型拟合效果良好。相同立地等级的样地成片分布,相对集中,每一立地等级的样地在江西省全境范围内均有分布。3）采用蒙特卡洛法对马尾松不同立地等级下的3种单木地上生物量模型估计结果及误差进行10 000次模拟后,马尾松地上生物量均值和误差的估计结果均达到稳定。在同一单木生物量模型形式下,不同立地等级的地上生物量均值估计结果随着立地等级的升高而增大;相对误差估计值在中间立地等级（3级）时最小,并有随着立地等级升高或降低而增大的趋势。相同立地等级下,3种模型地上生物量均值估计结果为式（1）〉式（3）〉式（2）;绝对误差和相对误差估计结果为式（2）〈式（3）〈式（1）。【结论】1）区域尺度下的3种马尾松单木

英文摘要：

【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） gi= a Di^b＋ ε,（2） gi=a （Di^2Hi）~b＋ ε,and （3） gi= aDi^bHi^c＋ ε,where gi is the individual biomass of the ith sample tree,Di and Hi are the diameter at breast height （DBH） and tree height for the ith sample tree,respectively; a,b and c are model parameters; εis the error term.【Result】1） The coefficient of determination （R^2） 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 R^2= 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