Contemporary losses of early-successional young forest and shrubland habitat have resulted in range-wide population declines of numerous wildlife species, establishing shrubland conservation as a high priority in the eastern United States. However, the extent and spatial distribution of shrubland habitat is lacking for many locations. The Landsat program is an important resource for quantifying vegetation and land cover, but recent applications indicate that divergent structural characteristics between certain feature classes, such as vegetation height, are not readily captured by spectral response alone. The fusion of Landsat imagery with light detection and ranging (LiDAR) data at the pixel level into existing supervised classification schemes may offset this discrepancy. We used multispectral images from Landsat 8′s Operational Land Imager (OLI) to compare Landsat OLI and Landsat OLI-LiDAR fusion data in the classification of 10 vegetation and land cover types across a ∼5000 km² area in southeastern Ohio at the native 30-m Landsat resolution. Fusion data produced a 12% increase in overall classification accuracy from the Landsat OLI model and met minimum mapping accuracy standards in remote sensing. Importantly, LiDAR textural bands in the fusion model improved discrimination of forest and shrubland classes with the most dramatic difference manifesting as a 75% increase in shrubland user's accuracy. We demonstrate that Landsat OLI–LiDAR fusions are valuable for accurate shrubland mapping in forestlands. Additionally, we show that vegetation and land cover modeling can be easily integrated into existing wildlife monitoring programs by simultaneously sampling vegetation classes and wildlife communities from similar locations.