Gao Huilin, Dong Lihu, Li Fengri, Zhang Lianjun
Department of Forest Management, School of Forestry, Northeast Forestry University, Harbin, Heilongjiang, People's Republic of China.
Department of Forest and Natural Resources Management, College of Environmental Science and Forestry, State University of New York, Old Westbury, NY, United States of America.
PLoS One. 2015 Dec 14;10(12):e0145017. doi: 10.1371/journal.pone.0145017. eCollection 2015.
A total of 89 trees of Korean pine (Pinus koraiensis) were destructively sampled from the plantations in Heilongjiang Province, P.R. China. The sample trees were measured and calculated for the biomass and carbon stocks of tree components (i.e., stem, branch, foliage and root). Both compatible biomass and carbon stock models were developed with the total biomass and total carbon stocks as the constraints, respectively. Four methods were used to evaluate the carbon stocks of tree components. The first method predicted carbon stocks directly by the compatible carbon stocks models (Method 1). The other three methods indirectly predicted the carbon stocks in two steps: (1) estimating the biomass by the compatible biomass models, and (2) multiplying the estimated biomass by three different carbon conversion factors (i.e., carbon conversion factor 0.5 (Method 2), average carbon concentration of the sample trees (Method 3), and average carbon concentration of each tree component (Method 4)). The prediction errors of estimating the carbon stocks were compared and tested for the differences between the four methods. The results showed that the compatible biomass and carbon models with tree diameter (D) as the sole independent variable performed well so that Method 1 was the best method for predicting the carbon stocks of tree components and total. There were significant differences among the four methods for the carbon stock of stem. Method 2 produced the largest error, especially for stem and total. Methods 3 and Method 4 were slightly worse than Method 1, but the differences were not statistically significant. In practice, the indirect method using the mean carbon concentration of individual trees was sufficient to obtain accurate carbon stocks estimation if carbon stocks models are not available.
在中国黑龙江省的人工林中,对89株红松(Pinus koraiensis)进行了破坏性采样。对采样树木进行了测量,并计算了树木各组成部分(即树干、树枝、树叶和根系)的生物量和碳储量。分别以总生物量和总碳储量为约束条件,建立了兼容的生物量和碳储量模型。采用四种方法评估树木各组成部分的碳储量。第一种方法是直接通过兼容的碳储量模型预测碳储量(方法1)。其他三种方法分两步间接预测碳储量:(1)通过兼容的生物量模型估算生物量,(2)将估算的生物量乘以三个不同的碳转换因子(即碳转换因子0.5(方法2)、采样树木的平均碳浓度(方法3)和各树木组成部分的平均碳浓度(方法4))。比较了四种方法估算碳储量的预测误差,并检验了它们之间的差异。结果表明,以树径(D)为唯一自变量的兼容生物量和碳模型表现良好,因此方法1是预测树木各组成部分和总体碳储量的最佳方法。四种方法在树干碳储量上存在显著差异。方法2产生的误差最大,尤其是在树干和总体碳储量方面。方法3和方法4略逊于方法1,但差异无统计学意义。在实际应用中,如果没有碳储量模型,使用单株树木平均碳浓度的间接方法足以获得准确的碳储量估算。