This study used hyperspectral imagery from NASA's G-LiHT airborne sensor to map land cover types at Brookhaven National Laboratory. Researchers classified imagery into six cover classes - pine, oak, grass - using spectral angle mapper and maximum likelihood classification in ENVI. Field data on plant species and characteristics were collected and used to validate the classifications. Spectral angle mapper produced a more accurate land cover map than maximum likelihood for this high resolution hyperspectral data. Future work could combine hyperspectral and LiDAR data to better distinguish vegetation types.