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UCSB IBM ERP Related Publications Abstracts

Map-guided classification of regional land-cover with multi-temporal AVHRR data

David M. Stoms, Michael J. Bueno, Frank W. Davis, Kelly M. Cassidy, Ken L. Driese, and James S. Kagan

Photogrammetric Engineering and Remote Sensing 64: 831-838.

Cartographers often need to use information in existing land-cover maps when compiling regional or global maps, but there are no standardized techniques for using such data effectively. An iterative, map-guided classification approach was developed to compile a spatially and thematically consistent, seamless land-cover map of the entire Intermountain Semi-Desert ecoregion from a set of semi-independent subregional maps derived by various methods. A multi-temporal dataset derived from AVHRR data was classified using the subregional maps as training data. The resulting regional map attempted to meet the guidelines of the proposed National Vegetation Classification Standards for classification at the alliance level. The approach generally improved the spatial properties of the regional mapping, while maintaining the thematic detail of the source maps. The methods described may be useful in many situations where mapped information exists but is incomplete, compiled by different methods, or is based on inconsistent classification systems.

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Email stoms@bren.ucsb.edu