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

Abstract

Abstract. 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 forest biomass (dry mass, AGB) 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 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 identify major biases compared to inventory data, up to 120 % of the inventory value in dry tropical forests, in the sub-tropics 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 changes estimates. The dataset is available at: https://doi.pangaea.de/10.1594/PANGAEA.894711 (Santoro, 2018).

Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach

“…While this study and Avitabile et al [22] presented probability distribution functions skewed towards low biomass ranges, other studies [21,23,81] showed distributions tending towards much higher AGB levels, with averages two to three times larger than those found in this study and by Avitabile et al [22] (Figure 7). Figure 8 shows that the datasets from [21,23,81] did not adequately represent all the variations that exist in the area due to the lower spatial resolution and, also, generally showed a greater overestimation of the AGB. Although Santoro et al [81] provided more details in terms of the distribution of AGB in comparison with the other three maps, their map still overestimated the AGB compared to our results.…”

“…In the map developed in this study, one can observe the AGB variation in detail, which is corroborated by the observed high coefficient of determination (R 2 = 0.89) between the AGB reference datasets and the final AGB map. Figures 7 and 8 show the comparisons between the AGB estimates produced in our study and four other studies [21][22][23]81]. Although these maps were estimated using different methods and at different resolutions over the pantropical area, it is interesting to observe how the different data products compared to each other.…”

“…Aside from the overall assessment, we also analysed the errors by biomass ranges. Finally, we also compared our AGB estimates to four other studies [21][22][23]81] (Table 2). Baccini et al [23] used multi-sensor satellite data to estimate the aboveground live woody vegetation carbon density for pantropical ecosystems with a 500-m resolution.…”

“…Saatchi et al [21] mapped the total carbon stock in live biomasses (above-and belowground) in the tropics using a combination of data from in situ inventory plots and satellite LiDAR samples of the forest structure to estimate the carbon storage, plus optical and microwave imagery (1 km resolution), to extrapolate over the landscape. Santoro et al [81] estimated AGB globally at a 100-m resolution by combining spaceborne SAR, LiDAR, and optical observations for the year 2010, with auxiliary datasets from forest inventories-namely, additional remote-sensing observations, climatological variables, and ecosystems classifications. Avitabile et al [22] combined two existing datasets of vegetation aboveground biomasses (AGB) into a pantropical AGB map at a 1-km resolution using an independent reference dataset of field observations and locally calibrated it using high-resolution, harmonised, and upscaled biomass maps.…”

“…Figure 8 shows that the datasets from [21,23,81] did not adequately represent all the variations that exist in the area due to the lower spatial resolution and, also, generally showed a greater overestimation of the AGB. Although Santoro et al [81] provided more details in terms of the distribution of AGB in comparison with the other three maps, their map still overestimated the AGB compared to our results. In the map developed in this study, one can observe the AGB variation in detail, which is corroborated by the observed high coefficient of determination (R 2 = 0.89) between the AGB reference datasets and the final AGB map.…”