Sustainable forest management practices are receiving renewed attention in the growing effort to make efficient long-term use of natural resources. Sustainable management approaches require accurate and timely measurement of the world’s forests to monitor volume, biomass, and changes in sequestered carbon. It is in this context that remote sensing technologies, which possess the capability to rapidly capture structural data of entire forests, have become a key research area. Laser scanning systems, also known as lidar (light detection and ranging), have reached a maturity level where they may be considered a standard data source for structural measurements of forests; however, airborne lidar mounted on manned aircraft can be cost-prohibitive. The increasing performance capabilities and reduction of cost associated with small unmanned aerial systems (sUAS), coupled with the decreasing size and mass of lidar sensors, provide the potential for a cost-effective alternative. Our objectives for this study were to assess the extensibility of established airborne lidar algorithms to sUAS data and to evaluate the use of more cost-effective structure-from-motion (SfM) point cloud generation techniques from imagery obtained by the sUAS. A data collection was completed by both manned and sUAS lidar and imaging systems in Lebanon, VA and Asheville, NC. Both systems produced adequately dense point clouds with the manned system exceeding 30 pts/m^2 and the sUAS exceeding 400 pts/m^2. A cost analysis, two carbon models and a harvest detection algorithm were explored to test performance. It was found that the sUAS performed similarly on one of the two biomass models, while being competitive at a cost of $8.12/acre, compared to the manned aircraft’s cost of $8.09/acre, excluding mobilization costs of the manned system. On the biomass modeling front, the sUAS effort did not include enough data for training the second model or classifier, due to a lack of samples from data corruption. However, a proxy data set was generated from the manned aircraft, with similar results to the full resolution data, which then was compared to the sUAS data from four overlapping plots. This comparison showed good agreement between the systems when ingested into the trained airborne platform’s data model (RMSE = 1.77 Mg/ha). Producer’s accuracy, User’s accuracy, and the Kappa statistic for detection of harvested plots were 94.1%, 92.2% and 89.8%, respectively. A leave-one-out and holdout cross validation scheme was used to train and test the classifier, using 1000 iterations, with the mean values over all trials presented in this study. In the context of an investigative study, this classifier showed that the detection of harvested and non-harvested forest is possible with simple metrics derived from the vertical structure of the forest. Due to the closed nature of the forest canopy, the SfM data did not contain many ground returns, and thus, was not able to match the airborne lidar’s performance. It did, however, provide fine detail of the forest canopy from the sUAS platform. Overall, we concluded that sUAS is a viable alternative to airborne manned sensing platforms for fine-scale, local forest assessments, but there is a level of system maturity that needs to be attained for larger area applications.

Library of Congress Subject Headings

Sustainable forestry; Drone aircraft; Forest management

Publication Date


Document Type


Student Type


Degree Name

Imaging Science (MS)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)


Jan van Aardt

Advisor/Committee Member

Carl Salvaggio

Advisor/Committee Member

Emmett Ientilucci


RIT – Main Campus

Plan Codes