North American Landscape Characterization dataset development and data fusion issues. Photogrammetric Engineering and Remote

With 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-Spec-tral Scanner (MSS) sensor collected data documenting land-cover conditions over the majority of the globe. The global change research community has prioritized reducing the un-certainty associated with land cover-change as a major con-stituent of importance to balancing the global carbon cycle. The M s s archive currently represents the best available, con-tinuous 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