 This study compared two methods for delineating individual trees from topographically complex, mixed conifer forest in California's Sierra Nevada region. One approach used light detection and ranging, LiDAR, data, and a 3D LiDAR point cloud segmentation algorithm, while the other used raster data and an object-based image analysis, OBIA, of a canopy height model, CHM. Both approaches were then compared to ground reference data. The LiDAR-based approach produced higher accuracy in terms of tree detection, height, and polygon shape than the OBIA-based approach. Additionally, the LiDAR-based approach produced fewer, more complex, and larger polygons that better represented the actual structure of the forest. This article was authored by Maggie Kelly, Qinghua Guo, Wenkai Li, and others.