Aboveground biomass variability across intact and degraded forests in the Brazilian Amazon. Issue 11 (10th November 2016) - Longo, Marcos; Keller, Michael; dos‐Santos, Maiza N.; Leitold, Veronika; Pinagé, Ekena R.; Baccini, Alessandro; Saatchi, Sassan; Nogueira, Euler M.; Batistella, Mateus; Morton, Douglas C.
Abstract: Deforestation rates have declined in the Brazilian Amazon since 2005, yet degradation from logging, fire, and fragmentation has continued in frontier forests. In this study we quantified the aboveground carbon density (ACD) in intact and degraded forests using the largest data set of integrated forest inventory plots ( n = 359) and airborne lidar data (18, 000 ha) assembled to date for the Brazilian Amazon. We developed statistical models relating inventory ACD estimates to lidar metrics that explained 70% of the variance across forest types. Airborne lidar‐ACD estimates for intact forests ranged between 5.0 ± 2.5 and 31.9 ± 10.8 kg C m −2 . Degradation carbon losses were large and persistent. Sites that burned multiple times within a decade lost up to 15.0 ± 0.7 kg C m −2 (94%) of ACD. Forests that burned nearly 15 years ago had between 4.1 ± 0.5 and 6.8 ± 0.3 kg C m −2 (22–40%) less ACD than intact forests. Even for low‐impact logging disturbances, ACD was between 0.7 ± 0.3 and 4.4 ± 0.4 kg C m −2 (4–21%) lower than unlogged forests. Comparing biomass estimates from airborne lidar to existing biomass maps, we found that regional and pantropical products consistently overestimated ACD in degraded forests, underestimated ACD in intact forests, and showed little sensitivity to fires and logging. Fine‐scale heterogeneity in ACD across intact and degraded forests highlights the benefits of airborne lidar for carbon mapping. Differences between airborne lidar and regional biomass maps underscore the need to improve and update biomass estimates for dynamic land use frontiers, to better characterize deforestation and degradation carbon emissions for regional carbon budgets and Reduce Emissions from Deforestation and forest Degradation (REDD+). Key Points: One lidar model can predict biomass across intact, degraded, and secondary Amazon forests Biomass depletion from degradation is large, persistent, and greater from fires than from logging Pantropical maps overestimate degraded forest biomass and underestimate intact forest biomass