Potential for Individual Tree Monitoring in Ponderosa Pine-Dominated Forests Using Unmanned Aerial System Structure from Motion Point Clouds

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 5,000 stem-mapped trees in ponderosa pine-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 to 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.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author