Digital Landsat cover-type classifications have often proved less accurate than hoped for, or have been less detailed than needed. Recent research efforts have used additional data to supplement the four bands of Landsat MSS data in an attempt to increase the accuracies of computer classifications. The goal of this study was to evaluate the use of vegetation-related ancillary variables for improving the performance of a Landsat classification of the Great Dismal Swamp. Ancillary data considered to be related to the distribution of vegetation types in the swamp were registered with Landsat multispectral scanner data to a 50 meter UTM grid. The ancillary variables were peat depths and elevations from field surveys, and spectral texture values from the Landsat data. Discriminant analyses of a sample of pixels were performed to investigate the ability of spectral and ancillary data, separately and in combination, to discriminate between vegetation cover types. A layered classification procedure was developed that used discriminant analysis of ancillary data after a previous unsupervised spectral classification. This was compared to a spectral stratification classification and a straightforward unsupervised classification of spectral data alone. The layered procedure resulted in an accuracy of 21.46% for level III classes and 41.71% for level II classes. The accuracies for level III and level II classifications using the unsupervised procedure were 41.58% and 63.77%, respectively. Some possible explanations of the seemingly contradictory results were posed, and alternative procedures suggested. / Master of Science