Computer-aided techniques for interpreting multispectral data acquired by Landsat offer economies in the mapping of land cover. Even so, the actual establishment of the statistical classes, or 'signatures,' is one of the relatively more costly operations involved. Analysts have therefore been seeking cost-saving signature extension techniques that would accept training data acquired for one time or place and apply them to another. Opportunities to extend signatures occur in preprocessing steps and in the classification steps that follow. In the present example, land cover classes were derived by the simplest and most direct form of signature extension: Classes statistically derived from a Landsat scene for the Puget Sound area, Nash., were applied to the Portland area, Oreg., using data for the next Landsat scene acquired less than 25 seconds down orbit. Many features can be recognized on the reduced-scale version of the Portland land cover map shown in this report, although no statistical assessment of its accuracy is available.
The cost of classifying 5,607 square kilometers (2,165 sq. mi.) in the Portland area was less than 8 cents per square kilometer ($0.0788, or $0.2041 per square mile). Besides saving in costs, this and other signature extension techniques may be useful in completing land use and land cover mapping in other large areas where multispectral and multi- temporal Landsat data are available in digital form but other source materials are generally lacking.
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Low-cost computer classification of land cover in the Portland area, Oregon, by signature extension techniques