4.3 Article

Spatial heterogeneity and carbon contribution of aboveground biomass of moso bamboo by using geostatistical theory

期刊

PLANT ECOLOGY
卷 207, 期 1, 页码 131-139

出版社

SPRINGER
DOI: 10.1007/s11258-009-9659-3

关键词

Geostatistics; Moso bamboo; Aboveground biomass; Spatial heterogeneity; Spatial distribution; Carbon contribution

资金

  1. National Natural Science Foundation [30700638]
  2. '948' item of national forestry bureau [2008-4-49]
  3. national '863' program [2006AA12Z104]
  4. items of science and technology department of Zhejiang province [2451012025, 2008C12068]

向作者/读者索取更多资源

Moso bamboo extensively distributes in southeast and south Asia, and plays an important role in global carbon budget. However, its spatial distribution and heterogeneity are poorly understood. This research uses geostatistics theory to examine the spatial heterogeneity of aboveground biomass (AGB) of moso bamboo, and uses a point kriging interpolation method to estimate and map its spatial distribution. Results showed that (1) spatial heterogeneity and spatial pattern of moso bamboo's AGB can be revealed by an exponential semivariance model. The analysis of the model structure indicating that the AGB spatial heterogeneity is mainly composed of spatial autocorrelation components, and spatial autocorrelation range is from 360 to 41,220 m; (2) kriging standard deviation map showing the level of the model errors indicates that the AGB spatial distribution by point kriging interpolation method is reliable; (3) the average AGB of moso bamboo in Anji County is 44.228 Mg hm(-2), and carbon density is 20.297 Mg C hm(-2). The total AGB of moso bamboo accounts for 16.97% of the total forest-stand biomass in Zhejiang province. The total carbon storage of moso bamboo in China is 68.3993 Tg C, accounting for 1.6286% of the total forest carbon storage. This implies the important contribution of moso bamboo in regional or national carbon budget.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.3
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据