U.S. Department of Energy Office of Biological and Environmental Research

BER Research Highlights

Aboveground Biomass Variability Across Intact and Degraded Forests in the Brazilian Amazon
Published: November 10, 2016
Posted: July 19, 2017

Airborne lidar and field inventory data quantify carbon losses from logging and fire in Amazon forests.

The Science  
The authors 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 Impact
The impacts of land use and land cover on the carbon cycle are not restricted to deforestation, and this paper 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 remain with 40% less biomass than intact forests even 15 year since the 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 carbon cycle is highly uncertain.  The authors used 359 forest inventory plots co-located with 18,000 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 4-20% for reduced impact logging.  The authors compared lidar biomass estimates with pantropical maps, and 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.

Contacts (BER PM)
Daniel Stover
Daniel.Stover@science.doe.gov (301-903-0289)

(PI Contact)
Michael Keller
International Institute of Tropical Forestry, USDA Forest Service

Airborne lidar and forest inventory data were acquired by the Sustainable Landscapes Brazil, supported by The Brazilian Agricultural Research Corporation (Embrapa), the US Forest Service, USAID, and the US Department of State, the Brazilian National Council for Scientific and Technological Development (CNPq grants 407366/2013-0, 457927/2013-5), and by 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 Experiment-Tropics, funded by the US Department of Energy, Office of Science, Office of Biological and Environmental Research. 

Longo M, Keller M, dos-Santos MN, Leitold V, et al. (2016) Aboveground biomass variability across intact and degraded forests in the Brazilian Amazon. Global Biogeochem. Cycles. 30, 1639-1660. DOI:10.1002/2016GB005465. (Reference link)

Topic Areas:

  • Research Area: Terrestrial Ecosystem Science

Division: SC-23.1 Climate and Environmental Sciences Division, BER


BER supports basic research and scientific user facilities to advance DOE missions in energy and environment. More about BER

Recent Highlights

May 10, 2019
Quantifying Decision Uncertainty in Water Management via a Coupled Agent-Based Model
Considering risk perception can improve the representation of human decision-making processes in age [more...]

May 09, 2019
Projecting Global Urban Area Growth Through 2100 Based on Historical Time Series Data and Future Scenarios
Study provides country-specific urban area growth models and the first dataset on country-level urba [more...]

May 05, 2019
Calibrating Building Energy Demand Models to Refine Long-Term Energy Planning
A new, flexible calibration approach improved model accuracy in capturing year-to-year changes in bu [more...]

May 03, 2019
Calibration and Uncertainty Analysis of Demeter for Better Downscaling of Global Land Use and Land Cover Projections
Researchers improved the Demeter model’s performance by calibrating key parameters and establi [more...]

Apr 22, 2019
Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles
Representation of warm temperature events varies considerably among global climate models, which has [more...]

List all highlights (possible long download time)