Tropical secondary forests are ecosystems of critical importance for protecting biodiversity, buffering primary forest loss, and sequestering atmospheric carbon. Monitoring the growth and sequestration patterns of secondary forests has historically been difficult at scale, but the recent launch of the Global Ecosystem Dynamics Investigation (GEDI), a space-borne LiDAR sampler, provides accurate aboveground biomass density (AGBD) estimates across the tropics. However, fusing GEDI data (25 m circular samples with geolocation uncertainty) with historical forest change maps derived from LandSat (30 m x 30 m square wall-to-wall pixels) remains a challenge. In this work, we propose a generalizable Monte Carlo-based method for fusing GEDI and LandSat-based maps while robustly propagating uncertainty. The method also allows flexible filtering for high-confidence data points, and we provide open-source code for distributing the computation. Using this novel approach, we estimate the carbon sequestration rate of regrowth forest across the Amazon.
