Use of automated remote sensing techniques to map forest cover and other vegetation types is important when modeling environmental quality, and tick and wildlife habitats; however identifying forest cover with multispectral imagery (MSI) often results in confusion caused by similar spectral profiles between forest and other vegetation. Previous research in forest mapping has included integration of hyperspectral imagery and LiDAR data for tree detection, and use of MSI to distinguish tree crowns from non-vegetated features. Since these data sources are not widely available to most remote sensing practitioners, an innovative method was created to discriminate between forest and other land covers using only commercial MSI. This research will discuss two vegetation indices, the Forest Cover Index 1 and Forest Cover Index 2, which were developed to model forest cover from WorldView-2 satellite imagery at the Beltsville Agricultural Research Center in Beltsville, MD. The study site included conifer and deciduous forest cover, a range of agricultural and other vegetation cover types, urban features, bare soil, and water. The tree cover indices exploited the product of either reflectance in red (0.630-0.690 µm) and red edge (0.705-0.745 µm) bands or the product of reflectance in red and near infrared (0.770-0.895 µm) bands. For two classes (trees vs. all other), overall classification accuracy was >85% for 11 of 13 images that were acquired throughout the year. Additional research is required to evaluate these indices for other scenes and other sensors.