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TitleNorth American Landscape Characterization Dataset Development and Data Fusion Issues
AuthorLunetta, R S; Lyon, J G; Guindon, B; Elvidge, C D
SourcePhotogrammetric Engineering and Remote Sensing 64, 8, 1998 p. 821-829
Alt SeriesEarth Sciences Sector, Contribution Series 20042603
AbstractWith the launch of Landsat 1 on 23 July 1972, the United States initiated the capability for land resource monitoring from space. Over a 20-year period, the Landsat Multi-Spectral Scanner (MSS) sensor collected data documenting landcover conditions over the majority of the globe. The global change research community has prioritized reducing the uncertainty associated with land cover-change as a major constituent of importance to balancing the global carbon cycle. The MSS archive currently represents the best available, continuous public source of relatively high resolution imagery for the monitoring of land cover over the 1972-1992 period. The North American Landscape Characterization project was designed to exploit this archive by providing standardized satellite data sets to support land-cover change analysis. Land-cover categorization products derived from MSS data alone provide only a general characterization of land-cover condition and change. A data fusion approach using a post-classification technique is presented as a cost-effective method for delineating land cover at appropriate thematic and spatial resolutions to support a quantitative inventory of forest carbon stocks. Issues related to assessing the accuracy of carbon inventory datasets is presented, and a two-step model is proposed for accuracy assessment.

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