The MODIS sensor to be launched on the EOS-AM platform will be the most important sensor for global vegetation mapping. Among the programmatic goals for the MODIS sensor are to assess and track changes in land use/landcover, leaf area index (LAI), and net primary productivity (NPP). For these products to be used in global models, they mast be rigorously validated with site-specific data products. This article presents a review of some of the problems facing a regional- to global-scale validation effort and presents strategies for coordinating the land-cover classification process across multiple sites. We suggest the Enhanced Thematic Mapper (ETM+) as the source of remotely sensed data for validation, and that the IGBP 17-class land-cover classification system be used to provide a link between more complex site-specific systems and global-scale data products. We further recommend that the best site-specific land-cover classifications be obtained, using whatever ancillary data are found to be useful, as a basis for validation. In addition, we propose ways in which ambiguities in translation of classes, from specific to general systems, may be identified. Finally, we stress that even though standardization of methodology among sites may not be appropriate to the goal of obtaining the best possible land-cover products, there should be standardization of error analysis and metadata reporting.
Bibliographical noteFunding Information:
We thank three anonymous reviewers for their helpful comments on an earlier draft of this manuscript. This study was supported in part by grants from the National Aeronautics and Space Administration (NAGW-4880) and the NASA Institutional Research Awards for Minority Universities Program (NAGW-4059), and in part under Grant DEB-9411973 from the National Science Foundation to the Terrestrial Ecology Division, University of Puerto Rico, and the International Institute of Tropical Forestry as part of the Long-Term Ecological Research Program in the Luquillo Experimental Forest. Additional support was provided by the Forest Service (U.S. Department of Agriculture) and the University of Puerto Rico. Part of the work was supported by Grant DEB-9632854 from the National Science Foundation to the University of Minnesota.