More of the Amazon rainforest is disturbed annually than completely deforested, but the impact of disturbances on the carbon cycle remains poorly understood. Recent advances using optical (LandSat) and radar (Sentinel-1) remote sensing have improved detection of small-scale disturbances, but quantifying changes in forest structure and biomass associated with these disturbances has proven challenging.

The Global Ecosystem Dynamics Investigation (GEDI) spaceborne LiDAR provides an opportunity to address this problem. GEDI captured billions of measurements of forest height, leaf area, and understory structure within ~25-meter diameter footprints scattered across the tropics. Though the instrument had no guaranteed repeat cycle, it sometimes sampled nearby locations twice; some of these spatially coincident footprints happened to measure forest structure before and after a detected disturbance.

In this study, we developed an efficient open-source pipeline for identifying spatially coincident footprints and used it to find over 7,100 footprint pairs with intervening disturbance events across the Amazon basin. We also identified ~34,000 spatially coincident footprint pairs that lacked an intervening disturbance but came from regions with similar disturbance threat; these provided a control dataset to evaluate the effectiveness of estimating forest structure and biomass changes with coincident footprints.

Analysis of this Amazon-wide dataset demonstrated that GEDI could detect canopy and biomass losses in non-stand-replacing disturbances as small as 0.09 ha. GEDI’s unique three-dimensional view of forest structure allowed us to identify effects of different intensities of fire disturbance, including areas where the upper canopy retained most of its height but the understory suffered substantial foliage losses. Finally, we identified temporal trends in biomass loss and recovery following disturbance and showed that certain satellite-derived intensity metrics are correlated with increasing biomass loss. This work represents an important step towards the development of a pan-tropical, spatially explicit estimate of carbon losses and structural changes due to forest disturbance.