Invasive pests cause major ecological and economic damages to forests around the world including reduced carbon sequestration and biodiversity and loss of forest revenue. In this study, we used Random Forest to model forest mortality resulting from a 2015-2017 Spongy moth outbreak in the temperate deciduous forests of Rhode Island (northeastern U.S.). Mortality was modeled with a 100 m spatial resolution based on Landsat-derived defoliation maps and geospatial data representing soil characteristics, drought condition, and forest characteristics as well as proximity to coast, development, and water. Random Forest was used to model forest mortality with two classes (low/high) and three classes (low/med/high). The best models had overall accuracies of 82% and 65% for the two-class and three-class models, respectively. The most important predictors of forest mortality were defoliation, distance to coast, and canopy cover. Model performance improved only slightly with the inclusion of more than three variables. The models classified 35% of forests as having canopy mortality >5 trees/ha and 21% of Rhode Island forests having mortality >11 trees/ha. The study shows the benefit of Random Forest models that use both defoliation maps and geospatial environmental data for classifying forest mortality caused by Spongy moth.