The Amazon Forest, the largest contiguous tropical forest in the world, holds an estimated 123 billion tons of terrestrial carbon, making it a vital carbon sink that absorbs more greenhouse gasses than any other tropical forest. Monitoring changes in its carbon stocks due to climate and land use is crucial. However, traditional forest inventory data cover only a small portion of the Amazon, resulting in insufficient data for reliable interpolation, validation, and science-based policy making.

Jean Pierre Ometto et al. used the largest airborne light detection and ranging (LiDAR) database ever collected in the Amazon, mapping 360,000 km2 through transects distributed in all vegetation categories in the region to present a new forest above-ground biomass map for the Brazilian Amazon. The study uses airborne laser scanning (ALS) data calibrated by field forest inventories that are extrapolated to the region using a machine learning approach with inputs from Synthetic Aperture Radar (PALSAR), vegetation indices obtained from the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite, and precipitation information from the Tropical Rainfall Measuring Mission (TRMM). Study outputs include several tangible products that can be accessed through the Zenodo repository:

• LiDAR transects boundaries, location and attributes summarized in a shapefile format.
• An above ground biomass (AGB) map presented a maximum AGB value of 518 Mg ha−1, a mean AGB of 174 Mg ha−1, and a standard deviation of 102 Mg ha−1.
• Map of the uncertainty of AGB estimates (Mg ha−1) across the Amazon biome.

This unique dataset has definitively moved the needle as it allows for a better representation of the regional distribution of forest biomass and structure. It provides crucial information for further research and informs decision-making on forest conservation, planning, carbon emissions estimates, and designing strategies for reducing carbon emissions.