Characterization of forest structure is important for management-related decision making, monitoring, and adaptive management. Increasingly, observations of forest structure are needed at both finer resolutions and across greater extents to support spatially explicit management planning. Unmanned aerial system (UAS) based photogrammetry provides an airborne method of forest structure data acquisition at a significantly lower cost and time commitment than existing methods such as airborne laser scanning (LiDAR). This study utilizes nearly 5000 stem-mapped trees in ponderosa pine (Pinus ponderosa Lawson & C. Lawson) dominated forests to evaluate several algorithms for detecting individual tree locations and characterizing crown area across tree sizes. Our results indicate that adaptive variable window detection methods with UAS-based canopy height models have greater tree detection rates compared with fixed window analysis across a range of tree sizes. Using the UAS approach, probability of detecting individual trees decreases from 97% for dominant overstory to 67% for suppressed understory trees. Additionally, crown radii were correctly determined within 0.5 m for approximately two-thirds of sampled trees. These findings highlight the potential for UAS photogrammetry to characterize forest structure through the detection of trees and tree groups in open-canopy ponderosa pine forests. Further work should investigate how these methods transfer to more diverse species compositions and forest structures.