辽宁省落叶松人工林生物量碳库特征

Biomass Carbon Pool of Larch Forests in Liaoning Province

利用辽宁省东部山区落叶松人工林典型分布区样地清查资料,构建了不同林龄落叶松生物量与蓄积量转换模型,结合辽宁省2006年森林资源二类调查资料,估算全省落叶松林生物量碳密度和碳储量。研究表明,干碳量比、根碳量比和根冠碳量比随着林龄的增加而增大,叶碳量比、枝碳量比随着林龄的增加而降低,方差结果显示不同林龄落叶松单木碳分配模式存在显著的差异。对不同林龄阶段生物量与蓄积量进行曲线回归得到,除中龄林生物量与蓄积量转换模型为CVD模型拟合效果最好外(决定系数R2=0.925),幼龄林、近熟林和成熟林均为线性模型拟合效果最好,而且决定系数R2分别达到了0.843、0.967和0.927。不同林龄落叶松林生物量碳密度随着林龄的增加而增大。2006年辽宁省落叶松林生物量碳储量为19.63M t,其中乔木层占总生物量碳储量的94.15%,灌木层占2.21%,草本层占0.37%,枯落物层占3.27%。不同林龄落叶松林生物量碳储量呈现出中龄林>幼龄林>近熟林>成熟林。

Volume-to-biomass conversion models of larch of different ages were established by means of plot-level inventory data from a typical distribution area of larch plantations in eastern Liaoning Province.Then the optimal volume-to-biomass model and the forest inventory data of Liaoning Province for 2006 were used to evaluate biomass carbon density and carbon storage of larch forests in Liaoning Province.Results show that ratios of carbon density in stem,root and A/B（aboveground/belowground） increase with increasing forest age;however,ratios of carbon density in leave and branch decrease.The variance analysis results reveal that carbon allocation patterns of individual larch trees have significant differences.The regression curve of biomass and volume of larch trees of different ages shows that the CVD model is the best model to fit the middle-aged forest,and the coefficient of determination R2 is 0.925.Linear model is the best-fit model for the young forest,near-mature forest and mature forest,and the coefficients of determination R2 are 0.843,0.967 and 0.927,respectively.Biomass carbon density of the larch forests of different ages increases with increasing forest age.Total biomass carbon storage of larch forests in Liaoning Province was 19.63 Mt in 2006,among which tree layer,shrub layer,herb layer and litter layer accounted for 94.15 percent,2.21 percent,0.37 percent and 3.27 percent of the total,respectively.The biomass carbon storage of the middle-aged larch forests is the largest,followed by the young forest,near-mature forest and mature forest.