Forest inventory and analysis data monitor the presence and extent of certain non-native invasive species. On forestland, non-native species are considered part of the understory vegetation and can be found near canopy openings as well as and the forest edge. The objective of this study is to incorporate the presence of select nonnative species into forest classification modeling procedures and determine the accuracy for producing non-native species spatial distribution classifications. A secondary objective is to compare classification accuracies of the different spatial resolution data (Landsat-TM and MODIS), which suggests that an increase in resolution provides an increase in overall accuracy. The classification results provide forest distribution combined with non-native species occurrence for honeysuckle (Lonicera spp.) and privet (Ligustrum spp.). Subsets of the plot data are used in a decision tree modeling process (See5) applied to the satellite data (Landsat-TM, MODIS), and ancillary data to classify the land cover and model privet/honeysuckle spatial distribution. Classification results show that overall classification accuracy for the percent of pixels correctly classified (%PCC) increased from 67.5% to 72.5% (privet) and from 67.5% to 70.0% (honeysuckle) when privet and honeysuckle are coded as present in the plot. Comparisons between overall classification accuracy show a 5.0% increase for privet, and 2.5% for honeysuckle. A comparison between MODIS and Landsat-TM classifications shows a 3.7% increase in accuracy when both privet and honeysuckle are coded in several categories based on their percent of participation on the plot. Classifications from Landsat-TM and MODIS models show a higher confusion in the privet/honeysuckle categories (percent privet or honeysuckle on plot), however the Landsat-TM model performed slightly better.