A review of biomass equations for China's tree species
A review of biomass equations for China's tree species
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DOI：
10.5194/essd-12-21-2020
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发表时间：
2020-01-03
影响因子：
11.4
通讯作者：
Tao, Jun
中科院分区：
文献类型：
--
作者：
Luo, Yunjian;Wang, Xiaoke;Tao, Jun
树木生物量方程是在不同时空尺度上估算树木和森林生物量最常用的方法，具有精度高、效率高、简明等优点。几十年来，许多树木生物量方程在不同类型的文献(如期刊、书籍和报告)中都有报道。正在汇编这些零散的方程式，目前许多地理区域(例如欧洲、北美和撒哈拉以南非洲)和国家(例如澳大利亚、印度尼西亚和墨西哥)都有树木生物量方程式数据集。然而，有一个重要的国家脱颖而出，因为大量的生物量方程尚未得到广泛的审查和编目：中国。因此，在本研究中，我们对1978年至2013年中国关于生物量方程的文献进行了广泛的调查和批判性的回顾，并编制了适合中国的归一化树木生物量方程数据集。该数据集包括近200个树种的5924个生物量方程及其相关的背景信息(如地理位置、气候和林分描述)，显示了中国全境良好的地理、气候和森林植被覆盖。该数据集在https://doi.org/10.1594/PANGAEA.895244(罗等人，2018年)上免费提供，用于非商业科学应用，该数据集填补了全球生物量方程中的一个重要区域空白，并为中国森林资源清查和碳核算研究中的生物量估计提供了关键参数。
Tree biomass equations are the most commonly used method to estimate tree and forest biomasses at various spatial and temporal scales because of their high accuracy, efficiency and conciseness. For decades, many tree biomass equations have been reported in diverse types of literature (e.g., journals, books and reports). These scattered equations are being compiled, and tree biomass equation datasets are currently available for many geographical regions (e.g., Europe, North America and sub-Saharan Africa) and countries (e.g., Australia, Indonesia and Mexico). However, one important country stands out as an area where a large number of biomass equations have not yet been reviewed and inventoried extensively: China. Therefore, in this study, we carried out a broad survey and critical review of the literature (from 1978 to 2013) on biomass equations in China and compiled a normalized tree biomass equation dataset for China. This dataset consists of 5924 biomass equations for nearly 200 tree species and their associated background information (e.g., geographical location, climate and stand description), showing sound geographical, climatic and forest vegetation coverage across China. The dataset is freely available at https://doi.org/10.1594/PANGAEA.895244 (Luo et al., 2018) for noncommercial scientific applications, and this dataset fills an important regional gap in global biomass equations and provides key parameters for biomass estimation in forest inventory and carbon accounting studies in China.