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Arid lands are distinctive ecological zones that require vegetation maps for management and monitoring. The use of remote sensing for mapping desert vegetation is made difficult by the mixing of reflectance spectra of bright desert soils with the relatively weak spectral response of sparse vegetation. To investigate ways to improve desert vegetation mapping, a comparison of the effect of supervised classification using two contrasting measures of field vegetation data as reference data was performed. We took cover- and density-based field vegetation data that had been collected by the US Army on the US Yuma Proving Ground (USYPG) in southwest Arizona, converted them into cover- and density-based reference classification schemes and used them to train both maximum likelihood (ML) and artificial neural net (ANN) classifiers. The impact on the accuracy of cover- and density-based vegetation maps were further analyzed using different combinations of input data (i.e., Landsat Thematic Mapper (TM) imagery, ERS-1 C-band synthetic aperture radar (SAR) imagery, and elevation data). In spite of the fact that a cover-based plot classification is the logical training data for remote sensing classification, both cover- and density-based classified maps had similar accuracies for each data combination. The use of all data combinations gave the highest map classification accuracies, with the radar data improving the accuracy the most where the vegetation is dense. Classification accuracies of maps using the ML classifier were generally higher than those using the ANN classifier. ANN map classification accuracies improved significantly when the sigmoid transfer function was replaced with the hyperbolic tangent transfer function. Using the two contrasting measures for mapping proved complementary: the coverbased map located areas of significant tree presence that were not mapped on the density-based map and the density-based map located areas of significant cacti presence that were not mapped on the cover-based map. Creating both cover- and density-based vegetation maps may therefore better assist and land management than creating only a cover-based vegetation map. (C) 2004 Elsevier Inc. All rights reserved.