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The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations

机译:2010年全球森林地上的地上生物量池估计来自高分辨率卫星观察

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The terrestrial forest carbon pool is poorly quantified, in particular in regions with low forest inventory capacity. By combining multiple satellite observations of synthetic aperture radar (SAR) backscatter around the year 2010, we generated a global, spatially explicit dataset of above-ground live biomass (AGB; dry mass) stored in forests with a spatial resolution of 1? ha . Using an extensive database of 110?897 AGB measurements from field inventory plots, we show that the spatial patterns and magnitude of?AGB are well captured in our map with the exception of regional uncertainties in high-carbon-stock forests with AGB? 250 ? Mg?ha ?1 , where the retrieval was effectively based on a single radar observation. With a total global AGB of 522? Pg , our estimate of the terrestrial biomass pool in forests is lower than most estimates published in the literature (426–571? Pg ). Nonetheless, our dataset increases knowledge on the spatial distribution of AGB compared to the Global Forest Resources Assessment (FRA) by the Food and Agriculture Organization (FAO) and highlights the impact of a country's national inventory capacity on the accuracy of the biomass statistics reported to the FRA. We also reassessed previous remote sensing AGB maps and identified major biases compared to inventory data, up to 120? % of the inventory value in dry tropical forests, in the subtropics and temperate zone. Because of the high level of detail and the overall reliability of the AGB spatial patterns, our global dataset of AGB is likely to have significant impacts on climate, carbon, and socio-economic modelling schemes and provides a crucial baseline in future carbon stock change estimates. The dataset is available at https://doi.org/10.1594/PANGAEA.894711 (Santoro, 2018).
机译:陆地森林碳库量化差,特别是森林库存能力低的地区。通过将多个卫星观察雷达(SAR)反向散射在2010年左右的多个卫星观察,我们在具有1个空间分辨率的森林中储存在森林中的地上活生物量(AGB;干块)的全球性空间显式数据集?哈 。使用来自现场库存图的广泛数据库,我们表明,在我们的地图中占据了空间模式和幅度的空间模式和幅度,除了具有agb的高碳森林中的区域不确定性& 250? Mg?HA?1,其中检索是基于单个雷达观察的有效性。全球全球AGB为522年? PG,我们对森林中陆地生物量池的估计低于文献中公布的大多数估计(426-571〜PG)。尽管如此,我们的数据集与粮食和农业组织(粮农组织)(粮农组织)的全球森林资源评估(FRA)相比增加了AGB的空间分配知识,并突出了国家的国家库存能力对据报道的生物量统计数据准确性的影响Fra。我们还重新评估了以前的遥感AGB地图,并与库存数据相比,最多120次识别主要偏见?干热带林中库存价值的百分比,在亚波质和温带区。由于细节水平和AGB空间模式的总体可靠性,我们的AGB的全球数据集可能对气候,碳和社会经济建模计划产生重大影响,并为未来的碳股票变化估算提供了一个关键的基线。数据集可在HTTPS://doi.org/10.1594/pangaea.894711(Santoro,2018)中获得。

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