An integrated pan-tropical biomass map using multiple reference datasets
An integrated pan-tropical biomass map using multiple reference datasets
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DOI：
10.1111/gcb.13139
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发表时间：
2016-04-01
影响因子：
11.6
通讯作者：
Willcock, Simon
中科院分区：
文献类型：
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作者：
Avitabile, Valerio;Herold, Martin;Willcock, Simon
我们结合了两个现有的植被地上生物量（AGB）数据集，（美国国家科学院院刊，108，2011，9899; Nature Climate Change，2，2012，182）转化为1 km分辨率的泛热带AGB地图，使用独立的实地观测参考数据集和当地校准的高分辨率生物量地图，统一和升级到14477 1公里AGB估计。我们的数据融合方法使用偏差去除和加权线性平均，结合和空间化的生物量模式所指示的参考数据。该方法被独立地应用于输入（萨奇和Baccini）地图，估计从参考数据和额外的协变量的均匀错误模式的地区（地层）。基于融合的地图，我们估计热带（23.4 N-23.4 S）的AGB存量为375 Pg干物质，比Saatchi和Baccini的估计低9-18%。融合后的地图还显示了大面积AGB的不同空间模式，其中刚果盆地、亚马逊东部和东南亚的茂密森林地区的AGB密度较高，而中美洲和非洲大部分干旱植被地区的AGB密度较低。与任何一个输入地图相比。基于融合过程中未使用的参考数据集的2118个估计值的验证练习表明，融合图的RMSE比输入图低15-21%，最重要的是，几乎无偏估计值（平均偏差5 Mg干物质（-1）与输入图的21和28 Mgha（-1））。融合方法可以在任何规模上应用，包括与政策相关的国家一级，通过将现有的区域生物量地图作为输入地图和额外的、针对具体国家的参考数据集相结合，可以提供更好的生物量估计。
We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan-tropical AGB map at 1-km resolution using an independent reference dataset of field observations and locally calibrated high-resolution biomass maps, harmonized and upscaled to 14477 1-km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N-23.4 S) of 375 Pg dry mass, 9-18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South-East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15-21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5Mg dry massha(-1) vs. 21 and 28Mgha(-1) for the input maps). The fusion method can be applied at any scale including the policy-relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country-specific reference datasets.