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This study investigated the usability of hyperspectral remote sensing for characterizing vegetation at hazardous waste sites. The specific objectives of this study were to: (1) estimate leaf-area-index (LAI) of the vegetation using three different methods (i.e., vegetation indices, red-edge positioning (REP), and machine learning regression trees), and (2) map the vegetation cover using machine learning decision trees based on either the scaled reflectance data or mixture tuned matched filtering (MTMF)-derived metrics and vegetation indices. HyMap airborne data (126 bands at 2.3 × 2.3 m spatial resolution), collected over the U.S. Department of Energy uranium processing sites near Monticello, Utah and Monument Valley, Arizona, were used. Grass and shrub species were mixed on an engineered disposal cell cover at the Monticello site while shrub species were dominant in the phytoremediation plantings at the Monument Valley site. Regression trees resulted in the best calibration performance of LAI estimation (R2 > 0.80. The use of REPs failed to accurately predict LAI (R2 < 0.2). The use of the MTMF-derived metrics (matched filter scores and infeasibility) and a range of vegetation indices in decision trees improved the vegetation mapping when compared to the decision tree classification using just the scaled reflectance. Results suggest that hyperspectral imagery are useful for characterizing biophysical characteristics (LAI) and vegetation cover on capped hazardous waste sites. However, it is believed that the vegetation mapping would benefit from the use of higher spatial resolution hyperspectral data due to the small size of many of the vegetation patches ( < 1 m) found on the sites.