A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps
A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps
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DOI：
10.1016/j.rse.2022.112917
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发表时间：
2022-02-09
影响因子：
13.5
通讯作者：
Lucas, Richard
中科院分区：
文献类型：
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作者：
Araza, Arnan;de Bruin, Sytze;Lucas, Richard
在过去十年中，已经制作了几张全球地上生物量（AGB）地图，但是它们表现出显著的差异，这降低了它们在气候和碳循环建模以及国家森林碳储量及其变化估计中的价值。由于新的卫星任务专门用于测量AGB，这种地图的数量预计会增加。因此，迫切需要客观一致的方法来评估AGB地图的准确性和不确定性。本文开发并演示了一个旨在实现这一目标的框架。该框架提供了一种方法，将AGB地图与来自全球国家森林清单和研究地块的AGB估计值进行比较，这些数据说明了地块AGB误差的不确定性。这种不确定性在很大程度上取决于地块大小，并由树木测量和异速生长模型的综合误差（其标准差（SD）的四分位数范围= 30-151 Mg ha(-1)）所主导。抽样误差的估计也很重要，特别是在最常见的情况下，当地块小于地图像素时（SD = 16-44 Mg ha(-1)）。样地不确定性估计用于计算平均到0.1度时平均森林AGB的最小方差线性无偏估计。这些用于评估四种AGB地图：Baccini（2000）、GEOCARBON（2008）、GlobBiomass（2010）和CCI Biomass（2017）。地图偏差是利用地块和0.1度地图平均值之间的差异来估计的，使用随机森林回归建模，由显示影响地图估计值的变量驱动。偏差模型对AGB和树盖度的地图估计特别敏感，并表现出强烈的区域偏差。方差图表明，AGB地图误差在50-104 km范围内具有地图特定的空间相关性，这增加了空间聚合AGB地图估计的方差。经偏置调整后，得到了4个图期的总泛热带AGB及其相关SD。在每一个历元之后，这一总数越来越接近森林资源评估的估计值，并显示出类似的减少。该框架适用于本地和全球范围的分析，可在https://github.com/arnanaraza/PlotToMap上获得。因此，我们的研究是改进AGB地图验证和改进的重要一步。
Over the past decade, several global maps of above-ground biomass (AGB) have been produced, but they exhibit significant differences that reduce their value for climate and carbon cycle modelling, and also for national estimates of forest carbon stocks and their changes. The number of such maps is anticipated to increase because of new satellite missions dedicated to measuring AGB. Objective and consistent methods to estimate the accuracy and uncertainty of AGB maps are therefore urgently needed. This paper develops and demonstrates a framework aimed at achieving this. The framework provides a means to compare AGB maps with AGB estimates from a global collection of National Forest Inventories and research plots that accounts for the uncertainty of plot AGB errors. This uncertainty depends strongly on plot size, and is dominated by the combined errors from tree measurements and allometric models (inter-quartile range of their standard deviation (SD) = 30-151 Mg ha(-1)). Estimates of sampling errors are also important, especially in the most common case where plots are smaller than map pixels (SD = 16-44 Mg ha(-1)). Plot uncertainty estimates are used to calculate the minimum-variance linear unbiased estimates of the mean forest AGB when averaged to 0.1 degrees. These are used to assess four AGB maps: Baccini (2000), GEOCARBON (2008), GlobBiomass (2010) and CCI Biomass (2017). Map bias, estimated using the differences between the plot and 0.1 degrees map averages, is modelled using random forest regression driven by variables shown to affect the map estimates. The bias model is particularly sensitive to the map estimate of AGB and tree cover, and exhibits strong regional biases. Variograms indicate that AGB map errors have map-specific spatial correlation up to a range of 50-104 km, which increases the variance of spatially aggregated AGB map estimates compared to when pixel errors are independent. After bias adjustment, total pantropical AGB and its associated SD are derived for the four map epochs. This total becomes closer to the value estimated by the Forest Resources Assessment after every epoch and shows a similar decrease. The framework is applicable to both local and global-scale analysis, and is available at https://github.com/arnanaraza/PlotToMap. Our study therefore constitutes a major step towards improved AGB map validation and improvement.