Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1016/j.foreco.2018.07.052 |
Plot-level variability in biomass for tropical forest inventory designs | |
Picard, Nicolas; Gamarra, Javier G. P.; Birigazzi, Luca; Branthomme, Anne | |
2018-12-15 | |
发表期刊 | FOREST ECOLOGY AND MANAGEMENT
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ISSN | 0378-1127 |
EISSN | 1872-7042 |
出版年 | 2018 |
卷号 | 430页码:10-20 |
文章类型 | Article |
语种 | 英语 |
国家 | Italy |
英文摘要 | The spatial distribution of biomass is key to optimize forest inventory designs to estimate forest aboveground biomass. Point process theory sets an appropriate mathematical framework to model the spatial distribution of trees, then to derive analytical expressions for the relationship between the variance of biomass in plots and the characteristics (size and shape) of plots, possibly accounting also for plot autocorrelation in biomass. Models derived from point process theory provided a better fit to data from twenty spatially homogeneous sites in tropical rain forests than the commonly used Taylor power model for biomass variance. The model CV = root omega + kappa/vertical bar A vertical bar with CV the coefficient of variation of biomass, vertical bar A vertical bar the plot area, and omega and kappa parameters to estimate, provided in particular a better fit than the power model when the range of autocorrelation in biomass was greater than the plot width. The twenty tropical forest sites greatly differed in the observed relationship between biomass variance and plot size, reflecting differences in the spatial pattern of biomass according to the fitted point process. Accordingly, optimized forest inventory designs also greatly differed between forest sites, with positive biomass autocorrelation favouring cluster sampling design with a distance between subplots in the order of the range of the biomass autocorrelation. In a spatially heterogeneous context consisting of different homogeneous forest strata, large-scale heterogeneity prevailed upon local biomass autocorrelation in determining the optimized plot size and shape. If uncontrolled through stratification, large-scale heterogeneity resulted in much smaller (approximately 0.1-0.2 ha) optimized plot sizes than the homogeneous case (approximately 1-2 ha). |
英文关键词 | Aboveground biomass Cluster sampling Forest inventory Point process Sampling design Tropical rain forest |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000449137300002 |
WOS关键词 | PATTERNS ; SIZE ; LAW |
WOS类目 | Forestry |
WOS研究方向 | Forestry |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/22615 |
专题 | 气候变化 |
作者单位 | Food & Agr Org United Nations, Forestry Dept, I-00153 Rome, Italy |
推荐引用方式 GB/T 7714 | Picard, Nicolas,Gamarra, Javier G. P.,Birigazzi, Luca,et al. Plot-level variability in biomass for tropical forest inventory designs[J]. FOREST ECOLOGY AND MANAGEMENT,2018,430:10-20. |
APA | Picard, Nicolas,Gamarra, Javier G. P.,Birigazzi, Luca,&Branthomme, Anne.(2018).Plot-level variability in biomass for tropical forest inventory designs.FOREST ECOLOGY AND MANAGEMENT,430,10-20. |
MLA | Picard, Nicolas,et al."Plot-level variability in biomass for tropical forest inventory designs".FOREST ECOLOGY AND MANAGEMENT 430(2018):10-20. |
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