A major challenge in forest management is the inaccessibility of large swaths of land, which makes accurate monitoring of forest change difficult. Remote sensing methods can help address this issue by allowing investigators to monitor remote or inaccessible regions using aerial or satellite-based platforms. However, most remote sensing methods do not provide a full three-dimensional (3D) description of the area. Rather, they return only a single elevation point or landcover description. Multiple-return LiDAR (Light Detection and Ranging) gathers data in a 3D point cloud, which allows forest managers to more accurately characterize and monitor changes in canopy structure and vegetation-type distribution. Our project used high-resolution aerial multiple-return LiDAR data to determine vegetation canopy structures and their spatial distribution in Great Smoky Mountains National Park. To ensure sufficient data density and to match LANDSAT resolution, we gridded the data into 30m x 30m cells. The LiDAR data points within each cell were then used to generate the vertical canopy structure for that cell. After vertical profiles had been created, we used a k-means cluster analysis algorithm to classify the landscape based on the canopy structure. The spatial distribution of distinct and unique canopy structures was mapped across the park and compared to a vegetation-type map to determine the correlation of canopy structure to vegetation types. Preliminary analysis conducted at a number of phenology sites maintained by the Great Smoky Mountains Institute at Tremont shows strong correspondence between canopy structure and vegetation type. However, more validation is needed in other regions of the park to establish this method as a reliable tool. LiDAR data has a unique ability to map full 3D structures of vegetation and the methods developed in this project offer an extensible tool for forest mapping and monitoring.