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Improving automated disturbance maps using snow-covered landsat time series stacksAuthor(s): Kirk M. Stueve; Ian W. Housman; Patrick L. Zimmerman; Mark D. Nelson; Jeremy Webb; Charles H. Perry; Robert A. Chastain; Dale D. Gormanson; Chengquan Huang; Sean P. Healey; Warren B. Cohen
Source: In: Morin, Randall S.; Liknes, Greg C., comps. Moving from status to trends: Forest Inventory and Analysis (FIA) symposium 2012; 2012 December 4-6; Baltimore, MD. Gen. Tech. Rep. NRS-P-105. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. [CD-ROM]: 137-142.
Publication Series: Paper (invited, offered, keynote)
Station: Northern Research Station
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DescriptionSnow-covered winter Landsat time series stacks are used to develop a nonforest mask to enhance automated disturbance maps produced by the Vegetation Change Tracker (VCT). This method exploits the enhanced spectral separability between forested and nonforested areas that occurs with sufficient snow cover. This method resulted in significant improvements in Vegetation Change Tracker outputs at the 95 percent confidence interval. An estimated 34 percent of the world's forests receive sufficient snowfall to use this method.
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CitationStueve, Kirk M.; Housman, Ian W.; Zimmerman, Patrick L.; Nelson, Mark D.; Webb, Jeremy; Perry, Charles H.; Chastain, Robert A.; Gormanson, Dale D.; Huang, Chengquan; Healey, Sean P.; Cohen, Warren B. 2012. Improving automated disturbance maps using snow-covered landsat time series stacks. In: Morin, Randall S.; Liknes, Greg C., comps. Moving from status to trends: Forest Inventory and Analysis (FIA) symposium 2012; 2012 December 4-6; Baltimore, MD. Gen. Tech. Rep. NRS-P-105. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. [CD-ROM]: 137-142.
Keywordsstatistics, estimation, sampling, modeling, remote sensing, forest health, data integrity, environmental monitoring, cover estimation, international forest monitoring
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