Forest carbon storage accounts for about 45% of terrestrial carbon storage. Accurate assessment of forest carbon storage is of great significance to the scientific management and planning of forests. Based on the data of 77 sampling Larix olgensis trees from Mengjiagang, Shangzhi Maoershan, Xiaojiu Forest Farm and Dongjing, Lin-kou Forestry Bureaus of Jiamusi, Heilongjiang Province from 2015 to 2018, we analyzed the partition of carbon content and variation of carbon concentration for five tree components (i.e., wood, bark, branch, leaf, and root). The mono-element and dual-element additive models of carbon content for each component of L. olgensis were deve-loped. The nonlinear seemly unrelated regression was used to estimate the parameters in the additive models, while the jackknife resampling technique was used to verify and evaluate the developed models. The results showed that the weighted mean carbon concentration of each component differed significantly, branches (49.3%) > bark (48.7%) > foliage (48.5%) > wood (48.2%) > root (47.1%). The aboveground and belowground carbon content accounted for about 80% and 20% of the total carbon content, respectively. The adjusted coefficient of determination (Ra2) of additive models of carbon content was greater than 0.89, the mean absolute error was less than 4.1 kg, and the mean absolute error percentage for most models was less than 30%. Adding tree height in the additive models of carbon content could significantly improve model fitting performance and predicting precision. The additive models of carbon content of total, aboveground, wood and bark were better than that of carbon content of branch, foliage, root and crown.
森林碳储量约占陆地碳储量的45%,准确评估森林碳储量对于森林的科学经营管理及规划具有重要意义。基于2015—2018年黑龙江省佳木斯市孟家岗、尚志帽儿山、小九林场以及东京、林口林业局的77棵人工长白落叶松的解析木数据,分析5种树木成分(即干材、树皮、树枝、树叶和树根)的含碳量分配及含碳率变化,构建了长白落叶松总量及各分项的一元及二元可加性含碳量模型,模型参数估计采用非线性似乎不相关回归模型方法,并采用“刀切法”对模型进行检验,评价其预测能力。结果表明:各分项加权平均含碳率差异显著,树枝(49.3%)>树皮(48.7%)>树叶(48.5%)>干材(48.2%)>树根(47.1%)。地上含碳量约占总含碳量的80%,地下含碳量约占20%。可加性含碳量模型的调整后确定系数Ra2大于0.89,平均绝对误差(MAE)小于4.1 kg,绝大多数模型的平均绝对误差百分比(MAE%)小于30%。引入树高变量,可以有效地提高大部分含碳量模型的拟合效果和预测能力。其中,总量、地上、干材和树皮含碳量模型拟合效果较好,树枝、树叶、树根和树冠含碳量模型拟合效果相对较差。.
Keywords: