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Height-Diameter Allometry for Tree Species in Tanzania Mainland

机译:坦桑尼亚大陆树种的高度直径分数

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Total tree height (H) and diameter at beast height (D) are important independent variables in predicting volume, biomass, and other forest stand attributes. However, unlike D measurement, which is easy to measure with high accuracy, H measurement is laborious. This study, therefore, developed H-D relationships for ten different forest types in Tanzania Mainland. Extents in which climate and forest stand variables explain the variation in H-D allometry were also assessed. A total of 31782 sample trees covering miombo woodlands, humid montane, lowland forests, bushlands, grasslands, mangroves, cultivated land, wetlands forests, and pines and Eucalyptus species plantations were used for model development. The H estimating model without climate and forest stand variables referred herein as “base model” was first developed followed by “generalized model” which included climate and stand variables. All the data were fitted using nonlinear mixed effect modelling approach. Results indicated that generalized H estimating models had better fit than the base models. We therefore confirm a significant contribution of climate and forest structure variables in improving H-D allometry. Among the forest structure variables, basal area (BA) was far more important explanatory variable than other variables. In addition, it was found that the mean tree H tends to increase with the increase of mean precipitation (PRA). We therefore conclude that forest specific generalized H model is to be applied when predicting H. When forest type information is not available, generalized regional model may be applied. Base model may be applied when forest stand or climate information are missing.
机译:野兽高度(d)的总树高度(h)和直径是预测体积,生物量和其他森林支架属性的重要独立变量。然而,与D测量不同,这易于以高精度测量,H测量是费力的。因此,本研究开发了坦桑尼亚大陆的十种不同森林类型的H-D关系。还评估了气候和森林间变量解释H-D amvometry的变化的范围。共有31782年覆盖Miombo林地,潮湿的山地,低地森林,丛林,草原,红树林,耕地,湿地森林和松树和桉树种植园的模型开发。在没有气候和森林间变量的H估计模型首先开发出作为“基础模型”,然后包括“广义模型”,其包括气候和架构变量。所有数据都配合使用非线性混合效果建模方法。结果表明,广泛性的H估计模型比基础模型更好。因此,我们确认了气候和森林结构变量在改善H-D amvometry中的重大贡献。在森林结构变量中,基础区域(BA)比其他变量更重要的解释变量。此外,发现平均树H随着平均沉淀(PRA)的增加而趋于增加。因此,我们得出结论,在预测H时要应用森林特定的广义H模型。当森林类型信息不可用时,可以应用广义区域模型。缺少森林或气候信息时,可以应用基础模型。

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