Comparison of forest above‐ground biomass from dynamic global vegetation models with spatially explicit remotely sensed observation‐based estimates

Abstract

Gaps in our current understanding and quantification of biomass carbon stocks, particularly in tropics, lead to large uncertainty in future projections of the terrestrial carbon balance. We use the recently published GlobBiomass data set of forest above‐ground biomass (AGB) density for the year 2010, obtained from multiple remote sensing and in situ observations at 100 m spatial resolution to evaluate AGB estimated by nine dynamic global vegetation models (DGVMs). The global total forest AGB of the nine DGVMs is 365 ± 66 Pg C, the spread corresponding to the standard deviation between models, compared to 275 Pg C with an uncertainty of ~13.5% from GlobBiomass. Model‐data discrepancy in total forest AGB can be attributed to their discrepancies in the AGB density and/or forest area. While DGVMs represent the global spatial gradients of AGB density reasonably well, they only have modest ability to reproduce the regional spatial gradients of AGB density at scales below 1000 km. The 95th percentile of AGB density (AGB95) in tropics can be considered as the potential maximum of AGB density which can be reached for a given annual precipitation. GlobBiomass data show local deficits of AGB density compared to the AGB95, particularly in transitional and/or wet regions in tropics. We hypothesize that local human disturbances cause more AGB density deficits from GlobBiomass than from DGVMs, which rarely represent human disturbances. We then analyse empirical relationships between AGB density deficits and forest cover changes, population density, burned areas and livestock density. Regression analysis indicated that more than 40% of the spatial variance of AGB density deficits in South America and Africa can be explained; in Southeast Asia, these factors explain only ~25%. This result suggests TRENDY v6 DGVMs tend to underestimate biomass loss from diverse and widespread anthropogenic disturbances, and as a result overestimate turnover time in AGB.

Parameter uncertainty dominates C cycle forecast errors over most of Brazil for the 21st Century

“…Forest biomass removal has a significant impact on the Brazilian C-cycle, resulting in losses between 100-450 TgC yr −1 (Table 2; Figure A10) and the subsequent regrowth of secondary forests. Secondary forests across the Brazilian Amazon alone are estimated to cover an area of 22-28 Mha accumulating 1.5-11.25 MgC ha yr −1 but are estimated to be re-cleared every 5-10 years (Poorter et al, 2016;Yang et al, 2020). It is likely that we are missing losses driven by degradation, re-clearance events, and edge effects (e.g., Yang et al, 2020)(e.g.…”

“…Secondary forests across the Brazilian Amazon alone are estimated to cover an area of 22-28 Mha accumulating 1.5-11.25 MgC ha yr −1 but are estimated to be re-cleared every 5-10 years (Poorter et al, 2016;Yang et al, 2020). It is likely that we are missing losses driven by degradation, re-clearance events, and edge effects (e.g., Yang et al, 2020)(e.g. Yang et al, 2020) that are not accounted for in existing EO datasets, such as GFW, that are used to drive disturbance in our models (Milodowski et al, 2017;Silva Junior et al, 2020 these disturbance events would lead to overestimation of long term accumulation of woody carbon, consistent with the likely overestimate of net carbon uptake estimated by the DALEC models already discussed in comparison with CTE.…”

“…It is likely that we are missing losses driven by degradation, re-clearance events, and edge effects (e.g., Yang et al, 2020)(e.g. Yang et al, 2020) that are not accounted for in existing EO datasets, such as GFW, that are used to drive disturbance in our models (Milodowski et al, 2017;Silva Junior et al, 2020 these disturbance events would lead to overestimation of long term accumulation of woody carbon, consistent with the likely overestimate of net carbon uptake estimated by the DALEC models already discussed in comparison with CTE. Moreover, 520 improved disturbance drivers can add additional constraint to the C-cycle, potentially reducing uncertainty and refining our best estimates of key ecosystem traits as has previously been demonstrated due to the inclusion of fire disturbance information (Exbrayat et al, 2018b).…”

Assessment of linear relationships between TanDEM-X coherence and canopy height as well as aboveground biomass in tropical forests

“…This would enable the combined use of the different sensor systems such as LiDAR and InSAR, which would support a spatially and temporally consistent estimation of canopy height and AGB. Such an estimation could be used, at a minimum, to provide information about the canopy height and AGB distribution, but it could be also assimilated into dynamic ecosystem and vegetation models (Joetzjer et al 2017;Yang et al 2020).…”

New Forest Aboveground Biomass Maps of China Integrating Multiple Datasets

“…In general, forest AGB can be estimated by forest inventories or field measurements [2,8,9], remote sensing techniques [10][11][12], or ecosystem models [3,[13][14][15]. Forest inventories or field measurements provide the most direct estimates of forest AGB by using the biomass expansion factors or allometric models.…”