ALBERT

Description: One of the most fundamental questions in ecology is how many species inhabit the Earth. However, due to massive logistical and financial challenges and taxonomic difficulties connected to the species concept definition, the global numbers of species, including those of important and well-studied life forms such as trees, still remain largely unknown. Here, based on global ground-sourced data, we estimate the total tree species richness at global, continental, and biome levels. Our results indicate that there are ∼73,000 tree species globally, among which ∼9,000 tree species are yet to be discovered. Roughly 40% of undiscovered tree species are in South America. Moreover, almost one-third of all tree species to be discovered may be rare, with very low populations and limited spatial distribution (likely in remote tropical lowlands and mountains). These findings highlight the vulnerability of global forest biodiversity to anthropogenic changes in land use and climate, which disproportionately threaten rare species and thus, global tree richness.

Description: The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025° × 0.025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers.

Description: 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).

Description: 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.

Description: The backscattered power recorded by a spaceborne scatterometer operating at C-band is sensitive to land surface parameters and is operationally used by some global remote sensing services, e.g., to estimate soil moisture. The estimation of forest variables, in particular above-ground biomass (AGB), from scatterometer data instead was seldom explored. Given the availability of multi-decadal sets of scatterometer observations from space, it is of interest to address the contribution of C-band scatterometer data to the quantification of carbon stocks stored in forests even if the spatial resolution of spaceborne scatterometers is very coarse. In this paper, we investigated the prospects of AGB estimation using backscatter observations by the MetOp Advanced SCATterometer (ASCAT) with a spatial resolution of 0.25°. For this study, ASCAT observations acquired in 2010 were used to be contemporary with AGB datasets selected to benchmark the performance of the estimation. A Water Cloud Model that integrates two allometric equations derived from spaceborne LiDAR data reproduced the relationship between observations of radar backscatter as a function of AGB. Estimates of AGB from individual observations were then combined with a weighted average to reduce uncertainties. Finally, a correction was introduced to compensate for the offset introduced by sloped terrain and surfaces not covered by woody vegetation on the AGB estimate of a pixel. Uncertainties associated with the scatterometer observations, and the modelling framework were propagated to obtain per-pixel values of the standard deviation of an AGB estimate. The proposed method explains much of the variance in AGB estimates when compared to measurements from inventory data (R2 = 0.72) and generated unbiased estimates globally (bias: −3.3 Mg⋅ha−1). Nonetheless, the discrepancy between estimated and plot-based AGB values tended to increase for decreasing biomass level from 20% to 60% of the reference AGB level. A further assessment related to global stocks indicated that the value estimated from the scatterometer dataset (596 Pg, 95% of which 563 Pg stored in forest land) was in line with two published estimates based on forest inventory data only (571 Pg and 600 Pg, respectively). Despite the coarse spatial resolution, our results indicate that C-band scatterometer observations from space can contribute to the characterization of terrestrial biomass pools. The record of observations starting in the early 1990s may provide an unprecedented way to look at long-term forest dynamics as well as to constrain the strength of carbon-climate cycle feedback simulated by Earth System models.