Decoding the Landsat Record of Forest Disturbance History
Chengquan
Huang, Department of Geography, University of Maryland, College Park, MD 20742, cqhuang@umd.edu
(Presenting)
Samuel
N.
Goward, Department of Geography, University of Maryland, College Park, MD 20742, sgoward@umd.edu
Jeffrey
G.
Masek, iospheric Sciences Branch, NASA Goddard Space Flight Center, Greenbelt, MD 20771, jeffrey.g.masek@nasa.gov
Zhiliang
Zhu, USDA Forest Service, 1601 North Kent Street, Arlington, VA 22209, zhu@usgs.gov
Nancy
Thomas, Department of Geography, University of Maryland, College Park, MD 20742, nthomas1@umd.edu
Karen
Schleeweis, Department of Geography, University of Maryland, College Park, MD 20742, ska1@umd.edu
Mingshi
Li, Visiting Scientist at USGS Center for Earth Resources Observation and Science,, 47914 252nd Street, Sioux Falls, SD 57198, mli@usgs.gov
Hua
Shi, SAIC, Contractor at USGS Center for Earth Resources Observation and Science,, 47914 252nd Street, Sioux Falls, SD 57198, hshi@usgs.gov
Scott
Powell, USDA Forest Service, Pacific Northwest Research Station, 407 S. 14th Ave., Bozeman, MT 59715, scott.powell@oregonstate.edu
Forest disturbance and recovery processes are significant forces modulating atmospheric carbon balance. Understanding forest change at appropriate spatial and temporal scales is therefore necessary for study of carbon fluxes due to such changes. The spatial resolutions of Landsat instruments allow reliable characterization of most forest disturbances, including those of both natural and anthropogenic origin. A time series collection of Landsat acquisitions can therefore be considered a satellite record of forest disturbance history. Here we describe an approach for reconstruct forest disturbance history using the Landsat record. In this approach, Landsat time series stacks (LTSS) are assembled using images acquired at nominal temporal intervals of roughly one image every year or every two years. An automated change detection algorithm called vegetation change tracker (VCT) is then used to map forest disturbances using the LTSS. Example disturbance products derived using this approach for different places across the United States and validation results will be provided to demonstrate the effectiveness of the developed approach.
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