NAFD : North American Forest Dynamics

Related Research Areas
Carbon Cycle & Ecosystems
Project Description
The results from this study will not only be published in the scientific literature but a set of national map products, including annual disturbance history, national variations in forest recovery trajectories and nationwide analysis of the spatio-temporal patterns of underlying forest disturbance causal factors will be made available to interested users. This information is of vital importance to understanding the carbon balance of the US and the North American continent. North American forests are thought to be a long-term sink for atmospheric carbon, with much of the sink attributed to either forest regrowth from past agricultural clearing or to woody encroachment. However, the magnitude of the North American forest sink is uncertain, because disturbance and regrowth dynamics are not well characterized or understood. Disturbance events (including harvest, fire, insect and storm damage, and disease) strongly affect carbon dynamics in many ways, including biomass removal, emissions from decaying biomass, and changes in productivity. This proposed research should help reduce these uncertainties through integration of forest inventory data and satellite data. In addition, our approach of automating the processing and analysis steps in this study will serve as a necessary precursor to the implementation of operational national carbon monitoring system and the development of more aggressive systematic land survey missions. This project continues an effort that exploits the combination of two key data sets, the Landsat historical record and plotrecords from the US Forest Service (USFS) Forest Inventory and Analysis (FIA) program, for the purpose of developing a quantitative understanding of forest disturbance patterns in North America. The primary goals of this study are: 1) Wall-to-all Annual Assessment of US forest disturbance history between 1985 and 2010. This approach not only reduces the errors encountered in early sampling efforts but also tests automation of processing and analysis procedures that have previously been carried out in a handcrafted fashion. 2) The products of this comprehensive analysis, maps and statistics, will be subjected to a rigorous validation to provide quantitative assessments of the accuracy of these products. This will support interested users in understanding the reliability of our analysis. 3) An investigation of the satellite-observed forest recovery trajectories will be carried out in comparison to USFS Forest Inventory Analysis measurement. This work supports extended use of forest disturbance history analysis by linking the observed forest recovery with field measured growth dynamics. 4) The team will evaluate various attributes of the disturbance analyses, including the recovery trajectories and spatial patterns of the disturbed forest areas, to evaluate how successfully the causal factors that led to the observed disturbances may be extracted from the Landsat observations. The North American Forest Dynamics (NAFD) project team from the University of Maryland, USFS, and NASA has been working to accomplish this science goal as a contribution to the North American Carbon Program since 2003, when the team first began to explore whether Landsat time series stacks (LTSS) could be combined with FIA data for this purpose. In earlier phases of this work a national sampling approach has been used, mostly because of data cost and processing complexity. In this study we will transform our previous data processing and analysis to a more highly automated approach, exploiting the newly developed NASA Ames Research Center NEX computing environment. Whereas in previous efforts we have examined no more that 2000 Landsat scene, in this study we will process and analyze over 15,000 scenes to produce this annual, nationwide assessment of forest disturbance history.
Project Administrator(s):
Karen Schleeweis,


Karen Schleeweis
zhiqiang yang
Andrew Michaelis
Samuel Goward
Louis Keddell
Feng Zhao
Elaine Denning
Stephen Stehman
Chris Toney
Christopher Neigh
gretchen moisen
Peder Nelson
Mary Lindsey
Khaldoun Rishmawi
Chengquan Huang
Warren Cohen
Todd Schroeder
Zhiqiang Yang
Chris Toney
Marc Cotnoir