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In this paper, we compare the classification effectiveness of two relatively new techniques on data consisting of leaf-level reflectance from five species of salt marsh and two species of crop plants (in four experiments) that have been exposed to varying levels of different heavy metal or petroleum toxicity, with a control treatment for each experiment. If these methodologies work well on leaf-level data, then there is hope that they will also work well on data from air- and spaceborne platforms. The classification methods compared were support vector classification (SVC) of exposed and nonexposed plants based on the spectral reflectance data, and partial least squares compression of the spectral reflectance data followed by classification using logistic discrimination (PLS/LD). The statistic we used to compare the effectiveness of the methodologies was the leave-one-out cross-validation estimate of the prediction error. Our results suggest that both techniques perform reasonably well, but that SVC was superior to PLS/LD for use on hyperspectral data and it is worth exploring as a technique for classifying heavy-metal or petroleum exposed plants for the more complicated data from air- and spaceborne sensors.