基于北京市1 400个森林资源连续清查样地数据,采用混合模型方法,建立了森林蓄积量和生物量与优势树种、龄组、平均胸径、株数、郁闭度等因子之间的多元回归模型。结果显示:所建北京市森林蓄积量和生物量多元混合模型,确定系数R²都在0.8以上,平均预估误差MPE均在3%以下,平均百分标准误差MPSE均在25%以下;对10种森林类型的蓄积量和生物量估计,MPE均在15%以下,MPSE均在30%以下。基于混合模型方法建立森林蓄积量和生物量与林分定量因子、定性因子之间的多元回归模型,在实践中是完全可行的;各省可利用第九次清查的乔木林样地数据,建立森林蓄积量和生物量多元混合模型,为实现将全省总量数据分解落实到市、县及每一个图斑提供重要依据。

Based on the data of 1400 sample plots from continuous forest inventory in Beijing,the multivariate regression models between forest volume,biomass and factors including dominant species,age group,mean diameter,stem number,and canopy closure were developed using the mixed model approach. The results showed that the determination coefficients (R²) of the multivariate mixed models for forest volume and biomass were more than 0.8,the mean prediction errors (MPEs) were less than 3%,and the mean percent standard errors (MPSEs) were less than 25%;and for the forest volume and biomass estimates of 10 forest types,the MPEs were less than 15%,and the MPSEs were less than 30%. It is totally feasible in practice to develop multivariate regression models between forest volume,biomass and quantitative,qualitative/indicative factors of forest stands;the data of sample plots in arboreal forest from 9^th national forest inventory can be used to develop multivariate mixed forest volume and biomass models,which would provide important basis for realizing the decomposition of total provincial data to city,county and each sub-compartment on forest map.