Alessandro Baccini

Reforestation is an important strategy for nature-based climate solutions and identifying carbon storage potential of different locations is critical to its success. Applying average carbon values from forest inventories ignores the spatial heterogeneity in forest carbon and the effects of forest edges on carbon storage degradation. Here we show how spatially-explicit, predictive carbon modeling, that leverages satellite, social and biogeophysical datasets, can be used to identify more efficient restoration opportunities for climate mitigation than area-based carbon stock averages. Accounting for regeneration of forest edges, in addition to reforestation, boosts estimates of potential carbon gains by more than 20%. The total potential carbon gain that could be achieved through reforestation at the level indicated by the Bonn Challenge (350Mha) is 51 Gt CO ₂ -eq, but the “missing carbon” in our current forests accounts for 64.6 Gt CO ₂ -eq globally; the greatest potential carbon gains are found in areas of high fragmentation.

This work is focused on characterizing and understanding the aboveground biomass of Caatinga in a semiarid region in northeastern Brazil. The quantification of Caatinga biomass is limited by the small number of field plots, which are inadequate for addressing the biome's extreme heterogeneity. Satellite-derived biomass products can address spatial and temporal changes but they have not been validated for seasonally dry tropical forests. Here we combine a compilation of published field phytosociological observations with a new 30m spatial resolution satellite biomass product. Both data were significantly correlated, satellite estimates consistently captured the wide variability of the biomass across the different physiognomies (2-272 Mg/ha). Based on the satellite product we show that in year 2000 about 50 percent of the region had very low biomass (<2 Mg/ha) and that the majority of the biomass (86%) is concentrated in only 27% of the area. Our work confirm other estimates of biomass 39 Mg/ha (9-61 Mg/ha) and carbon 0.79 PgC. The satellite products together with ground based estimates has the potential to improve forest management in Caatinga and other seasonally dry tropical forests through improved approximation of spatial variability, how they relate to climate, and support numerical modeling experiments in semiarid regions.

Assessment of socioenvironmental benefits and feasibility identifies priority areas for restoring global tropical rainforests.

The Global Deal for Nature sets an ambitious agenda to protect our biosphere through ecosystem conservation and land restoration.

The Hansen et al . critique centers on the lack of spatial agreement between two very different datasets. Nonetheless, properly constructed comparisons designed to reconcile the two datasets yield up to 90% agreement (e.g., in South America).

The apparent accumulation of carbon on land necessary to balance the global carbon budget has perplexed scientists since the first carbon budgets were constructed more than 40 years ago. The magnitude of the sink over the decade 2006–2015 averaged 3.1 ± 0.9 PgC/year, but neither the factors causing it nor its location are known. Here, based on results from a recent analysis by Baccini et al. (), we estimate that about a third (1.0 PgC/year) of the residual sink is in the tropics, and about two‐thirds is outside the tropics (2.1 PgC/year).

Forests out of balance: Are tropical forests a net source or net sink of atmospheric carbon dioxide? As fundamental a question as that is, there still is no agreement about the answer, with different studies suggesting that it is anything from a sizable sink to a modest source. Baccini et al. used 12 years of MODIS satellite data to determine how the aboveground carbon density of woody, live vegetation has changed throughout the entire tropics on an annual basis. They find that the tropics are a net carbon source, with losses owing to deforestation and reductions in carbon density within standing forests being double that of gains resulting from forest growth. Science , this issue p. 230

Mapping and monitoring of forest carbon stocks across large areas in the tropics will necessarily rely on remote sensing approaches, which in turn depend on field estimates of biomass for calibration and validation purposes. Here, we used field plot data collected in a tropical moist forest in the central Amazon to gain a better understanding of the uncertainty associated with plot-level biomass estimates obtained specifically for the calibration of remote sensing measurements. In addition to accounting for sources of error that would be normally expected in conventional biomass estimates (e.g., measurement and allometric errors), we examined two sources of uncertainty that are specific to the calibration process and should be taken into account in most remote sensing studies: the error resulting from spatial disagreement between field and remote sensing measurements (i.e., co-location error), and the error introduced when accounting for temporal differences in data acquisition. We found that the overall uncertainty in the field biomass was typically 25% for both secondary and primary forests, but ranged from 16 to 53%. Co-location and temporal errors accounted for a large fraction of the total variance (>65%) and were identified as important targets for reducing uncertainty in studies relating tropical forest biomass to remotely sensed data. Although measurement and allometric errors were relatively unimportant when considered alone, combined they accounted for roughly 30% of the total variance on average and should not be ignored. Our results suggest that a thorough understanding of the sources of error associated with field-measured plot-level biomass estimates in tropical forests is critical to determine confidence in remote sensing estimates of carbon stocks and fluxes, and to develop strategies for reducing the overall uncertainty of remote sensing approaches.