Zhang Yue, Xie Long-Fei, Dong Li-Hu
Ministry of Education Key Laboratory of Sustainable Forest Ecosystem Management, School of Forestry, Northeast Forestry University, Harbin 150040, China.
Ying Yong Sheng Tai Xue Bao. 2022 May;33(5):1166-1174. doi: 10.13287/j.1001-9332.202205.009.
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 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 ( wood, bark, branch, leaf, and root). The mono-element and dual-element additive models of carbon content for each component of 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 () 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管理和规划具有重要意义。基于2015年至2018年黑龙江省佳木斯市林口林业局孟家岗、尚志帽儿山、小九林场和东京城77株采样树木的数据,我们分析了五个树木组成部分(木材、树皮、树枝、树叶和树根)的碳含量分配和碳浓度变化。建立了各组成部分碳含量的单元素和双元素相加模型。采用非线性看似不相关回归估计相加模型中的参数,同时采用刀切重抽样技术对所建立的模型进行验证和评估。结果表明,各组成部分的加权平均碳浓度差异显著,树枝(49.3%)>树皮(48.7%)>树叶(48.5%)>木材(48.2%)>树根(47.1%)。地上和地下碳含量分别约占总碳含量的80%和20%。碳含量相加模型的调整决定系数()大于0.89,平均绝对误差小于4.1 kg,大多数模型的平均绝对误差百分比小于30%。在碳含量相加模型中加入树高可以显著提高模型的拟合性能和预测精度。总碳、地上碳、木材碳和树皮碳含量的相加模型优于树枝、树叶、树根和树冠碳含量的相加模型。