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Site-Specific Allometric Models for Prediction of Above-and Belowground Biomass of Subtropical Forests in Guangzhou, Southern China

机译:中国南方广州亚热带林下和地下生物量的预测特定的特定模式模型

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摘要

Tree allometric models that are used to predict the biomass of individual tree are critical to forest carbon accounting and ecosystem service modeling. To enhance the accuracy of such predictions, the development of site-specific, rather than generalized, allometric models is advised whenever possible. Subtropical forests are important carbon sinks and have a huge potential for mitigating climate change. However, few biomass models compared to the diversity of forest ecosystems are currently available for the subtropical forests of China. This study developed site-specific allometric models to estimate the aboveground and the belowground biomass for south subtropical humid forest in Guangzhou, Southern China. Destructive methods were used to measure the aboveground biomass with a sample of 144 trees from 26 species, and the belowground biomass was measured with a subsample of 116 of them. Linear regression with logarithmic transformation was used to model biomass according to dendrometric parameters. The mixed-species regressions with diameter at breast height (DBH) as a single predictor were able to adequately estimate aboveground, belowground and total biomass. The coefficients of determination (R2) were 0.955, 0.914 and 0.954, respectively, and the mean prediction errors were −1.96, −5.84 and 2.26%, respectively. Adding tree height (H) compounded with DBH as one variable (DBH2H) did not improve model performance. Using H as a second variable in the equation can improve the model fitness in estimation of belowground biomass, but there are collinearity effects, resulting in an increased standard error of regression coefficients. Therefore, it is not recommended to add H in the allometric models. Adding wood density (WD) compounded with DBH as one variable (DBH2WD) slightly improved model fitness for prediction of belowground biomass, but there was no positive effect on the prediction of aboveground and total biomass. Using WD as a second variable in the equation, the best-fitting allometric relationship for biomass estimation of the aboveground, belowground, and total biomass was given, indicating that WD is a crucial factor in biomass models of subtropical forest. Root-shoot ratio of subtropical forest in this study varies with species and tree size, and it is not suitable to apply it to estimate belowground biomass. These findings are of great significance for accurately measuring regional forest carbon sinks, and having reference value for forest management.
机译:被用来预测单株生物量异速生长树模型对森林碳核算和生态系统服务建模的关键。为了提高这种预测的准确性,特定地点的发展,而不是一概而论,异速生长模型建议只要有可能。亚热带森林是重要的碳汇,对减缓气候变化的巨大潜力。不过,相比于森林生态系统的多样性几个生物质车型目前可用于中国的亚热带森林。本研究制定了具体的站点异速生长模型来估计地上和南亚热带湿润森林在广州,中国南方的地下生物量。破坏性的方法被用来测量与来自26种树木144的样品的地上生物量,和地下生物量用的他们的116的子样本进行测定。线性回归用对数转换是根据dendrometric参数用于模型的生物质。与胸高(DBH),为单一的预测直径的混合物种回归能够适当地估计地上,地下和总生物量。判定(R2)的系数分别为0.955,0.914和0.954,分别,平均预测误差为-1.96,-5.84和2.26%之间。添加胸径复合为一个变量(DBH2H)树高(H)没有提高模型的性能。使用H作为方程中的第二变量可以提高在地下生物量的估计模型健身,但也有共线性的影响,导致增加的回归系数的标准误差。因此,不建议在异速生长模型添加小时。添加具有DBH作为一个变量(DBH2WD),用于地下生物量的预测略有改善模型健身复合木材密度(WD),但对地上和总​​生物量的预测没有积极的影响。使用WD如等式的第二变量,地上生物量估计,地下和总生物量的最佳拟合异速生长关系给出,表明WD是在亚热带森林的生物量估算模式的关键因素。亚热带森林在这项研究中的根冠比与品种和树的大小不同而不同,它不适合运用它来估算地下生物量。这些发现的用于精确测量区域森林碳汇,并具有用于森林管理基准值具有重要意义。

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