10 November 2016
Airborne lidar and field inventory data quantify carbon losses from logging and fire in Amazon forests.
The scientists integrated forest inventory plots and high-density airborne lidar data from 18 regions across the Brazilian Amazon, to build a statistical model relating aboveground biomass to lidar metrics across a broad range of degraded forests. Relatively simple models captured the variation of biomass, including persistent and significant carbon losses at the most degraded areas. The authors also found that pantropical maps overestimate carbon stocks in many areas with active logging and burning, and underestimate biomass at intact forests.
The impacts of land use and land cover on the carbon cycle are not restricted to deforestation, and this work identified that carbon losses from logging and fire can be large and persistent: in the most extreme cases, biomass was reduced by more than 90% and remains with 40% less biomass than intact forests even after 15 since last disturbance. The pantropical biomass maps did not capture these patterns and consistently overestimated biomass in degraded forests. These maps need frequent updates to capture the rapid changes in biomass in frontier forests.
The role of tropical forest degradation in the the carbon cycle is highly uncertain. The scientists used 359 forest inventory plots co-located with 18,000 hectares (ha) of airborne lidar data in the Brazilian Amazon and developed statistical models to predict biomass based on airborne lidar metrics of forest structure. Degraded forest areas lost significant portions of their original biomass. The degree of carbon loss was influenced by the intensity of disturbance with a range of more than 90% carbon loss for forests subject to multiple fires to only 4% to 20% for reduced impact logging. The scientists compared lidar biomass estimates with pantropical maps; they found that these maps consistently overestimated biomass at the most degraded forests and underestimated biomass at intact forests, and failed to capture the fine-scale variability of carbon stocks. The differences in carbon stocks indicate that the use of such maps in frontier forests leads to significant biases in estimates of baseline carbon stocks, and they should be improved and updated more frequently to better characterize the effects of forest degradation in the carbon cycle.
BER Program Manager
Terrestrial Ecosystem Science, SC-23.1
International Institute of Tropical Forestry
U.S. Department of Agriculture's Forest Service
Airborne lidar and forest inventory data were acquired by the Sustainable Landscapes Brazil, supported by The Brazilian Agricultural Research Corporation (Embrapa); the U.S. Forest Service; U.S. Agency for International Development (USAID); and the U.S. Department of State, the Brazilian National Council for Scientific and Technological Development (CNPq grants 407366/2013-0, 457927/2013-5), and the National Aeronautics and Space Administration (NASA) Carbon Monitoring System Program (NASA CMSNNH13AW64I). ML was supported by CNPq (grant 151409/2014-5) and the São Paulo State Research Foundation (FAPESP, grant 2015/07227-6). MK was supported as part of the Next Generation Ecosystem Experiments (NGEE)–Tropics, funded by the Office of Biological and Environmental Research within the U.S. Department of Energy Office of Science.
Longo M., Keller M., dos-Santos M.N., Leitold V., et al. "Aboveground biomass variability across intact and degraded forests in the Brazilian Amazon." Global Biogeochemical Cycles 30(11), 1639–1660 (2016). [DOI:10.1002/2016GB005465]