Keller, Michael: USDA Forest Service (Project Lead)
Duffy, Paul: Neptune, Inc. (Co-Investigator)
Morton, Douglas (Doug): NASA GSFC (Institution Lead)
Schimel, David (Dave): JPL (Participant)
Souza, Carlos: IMAZON (Participant)
Project Funding:
2013 - 2016
NRA: 2013 NASA: Carbon Monitoring System
Funded by NASA
Abstract:
Brazilian tropical forests contain approximately one-third of the global carbon stock in above-ground tropical forest biomass. Deforestation has cleared about 15% of the extensive forest on the Brazilian Amazon frontier. Logging, and understory forest fires may have degraded a similar area of forest. In response to the potential climatic effects of deforestation, policy makers have suggested reductions in emissions through deforestation and forest degradation and enhanced forest carbon stocks (REDD+). Carbon accounting for REDD+ requires knowledge of deforestation, degradation, and associated changes in forest carbon stocks. Degradation is more difficult to detect than deforestation so SilvaCarbon, an US inter-agency effort, has set a priority to better characterize forest degradation effects on carbon loss.
We propose to quantify carbon stocks and changes and associated uncertainties in Paragominas, a jurisdiction in the eastern Brazilian Amazon with a high proportion of
logged and burned degraded forests where political change has opened the way for REDD+. We will build on a long history of research including our extensive studies of logging damage. In addition, we will use recent forest inventories and airborne lidar supported by USAID and managed by the US Forest Service and the Brazilian Corporation for Agricultural Research (EMBRAPA) under the Sustainable Landscapes Brazil program. Existing data will allow us to start analysis immediately and will also permit REDD+ relevant multi-temporal measurements of change during the brief three-year study period.
We plan to supplement the existing data by collection of additional ground-based forest inventory data contemporary with commercial airborne lidar (supported by USAID) and Landsat remote sensing data that will incorporate a novel use of time series data to estimate the structural properties of degraded forests using bidirectional reflectance information. We identify two objectives for forest carbon accounting at the jurisdictional level:
- Quantify spatially explicit above-ground carbon stocks and the changes in carbon stocks;
- Quantify spatially explicit uncertainties in above-ground carbon stocks and changes in carbon stocks
We will meet these objectives by employing innovative data assimilation methods. Our approach employs a hierarchical Bayesian modeling (HBM) framework where the assimilation of information from multiple sources is accomplished using a change of support (COS) technique. The COS problem formulation allows data from several spatial resolutions to be assimilated into an intermediate resolution. This approach provides a mechanism to assimilate information from multiple sources to produce spatially-explicit maps of carbon stocks and changes with corresponding spatially explicit maps of uncertainty. Importantly, this approach also provides a mechanism that can be used to assess the value of information from specific data products. Hence future data collection can be optimized in the context of the reduction of uncertainty. The spatially explicit quantification of uncertainties naturally provides insights into effective sampling designs. Members of the team used this statistical approach previously as part of prototyping efforts for the National Ecological Observatory Network.
The proposed work will add a new research dimension to the Sustainable Landscapes Brazil program, a direct outcome of the US-Brazil Memorandum of Understanding on climate change. Through that program, we have already successfully acquired airborne remote sensing data in Brazil and all requirements for international data collection have already been met. Because the proposed research is closely linked to an active program of international cooperation and capacity building, we will be in a unique position to transfer the results of our research to practitioners in the Brazilian government and in Brazilian civil society.
Publications:
Morton, D. C., Rubio, J., Cook, B. D., Gastellu-Etchegorry, J., Longo, M., Choi, H., Hunter, M., Keller, M. 2016. Amazon forest structure generates diurnal and seasonal variability in light utilization. Biogeosciences. 13(7), 2195-2206. DOI: 10.5194/bg-13-2195-2016
Rangel Pinage, E., Keller, M., Duffy, P., Longo, M., dos-Santos, M., Morton, D. 2019. Long-Term Impacts of Selective Logging on Amazon Forest Dynamics from Multi-Temporal Airborne LiDAR. Remote Sensing. 11(6), 709. DOI: 10.3390/rs11060709
Longo, M., Keller, M., dos-Santos, M. N., Leitold, V., Pinage, E. R., Baccini, A., Saatchi, S., Nogueira, E. M., Batistella, M., Morton, D. C. 2016. Aboveground biomass variability across intact and degraded forests in the Brazilian Amazon. Global Biogeochemical Cycles. 30(11), 1639-1660. DOI: 10.1002/2016GB005465
More details may be found in the following project profile(s):
