Abstract
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∘. 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∘ 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.
Highlights
Above-ground biomass (AGB) is the total mass of material stored in the living stems, branches and leaves of vegetation, and is often described as a biomass density, with units of mass per unit area
AGB is recognised by the Global Climate Observing System (GCOS) as an Essential Climate Variable (ECV), primarily because it is intimately related to both emissions of CO2 to the atmosphere arising from land use change and fire, and uptake of CO2 from the atmosphere due to vege tation growth (GCOS, 2016)
Plot estimates of AGB may be biased if an incorrect allometric model is used, this bias will tend to be small if local allometric models are used, as is often the case for National Forest Inventories (NFIs) and research plots (Chave et al, 2014)
Summary
Above-ground biomass (AGB) is the total mass of material stored in the living stems, branches and leaves of vegetation, and is often described as a biomass density, with units of mass per unit area. AGB is recognised by the Global Climate Observing System (GCOS) as an Essential Climate Variable (ECV), primarily because it is intimately related to both emissions of CO2 to the atmosphere arising from land use change and fire, and uptake of CO2 from the atmosphere due to vege tation growth (GCOS, 2016). It has much wider significance because of its value to human societies for energy, materials and other ecosystem services, and is important in forest management and for policy initiatives such as Reducing Emissions from Deforestation and forest Degradation (REDD+). The maps have specific individual error properties, rendering them unreliable for biomass change analysis, despite representing different epochs (Herold et al, 2019)
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