Dr. Samuel Goward and his research group are working on a study that exploits the combination of two key datasets, the Landsat historical record and plot records from the U.S. 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-Wall Annual Assessment of U.S. 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 which 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. Recent publications relating to this study include:
Goward, S. N., J. G. Masek, W. Cohen, G. Moisen, G. J. Collatz, S. Healey, R. Houghton, C. Huang, R. Kennedy, B. Law, S. Powell, D. Turner, and M. A. Wulder. 2008. Forest disturbance and North American carbon flux. Eos Transactions89 (11):105-116.
Huang, C., S. N. Goward, J. G. Masek, N. Thomas, Z. Zhu, and J. E. Vogelmann. 2010. An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sensing of Environment114 (1):183-198.