The classification and mapping of land cover provide fundamental information about the characteristics, activities, and status of specific areas on the earth's surface. The quality of the final classification is critical in providing accurate information for ecologists and resource managers in decisionmaking and for developing a landscapelevel understanding of an ecosystem.
The unsupervised classification used an Iterative SelfOrganizing Data Analysis Techniques (ISODATA) algorithm to classify the imagery into 300 spectral classes which then were identified from reference data. Data from 171 field locations were used to assess the accuracy of the final classifications using an error matrix. The supervised classification had an overall accuracy of 74.85% compared to 40.94% for the unsupervised classification. We found that, where greater accuracy is desired, both classification methods should be used and the results integrated to utilize each method's strengths.

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