DIRS Laboratory 76-3215
July 18, 2018 at 9:00am
Michael P. McClelland II
MS Thesis Defense
Abstract: 

Abstract

 

            Sustainable forest management practices are receiving renewed attention in the growing effort to make efficient use of natural resources. Sustainable management approaches require accurate and timely measurement of the world’s forests to monitor 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) provide the potential for a cost effective alternative. Our objectives were to assess the extensibility of lidar algorithms to sUAS data and to evaluate the use of more cost-effective structure-from-motion (SfM) point cloud generation techniques. A data collection was completed by both manned and sUAS sensing systems in Lebanon, VA and Asheville, NC. 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 with competitive costs of $8.12/acre, compared to the manned aircraft’s cost of $8.09/acre, excluding mobilization costs of the manned system. The sUAS effort did not include enough data for training the second model. However, a proxy data set was generated from the manned aircraft, with similar results to the full resolution data, which then was compared to four overlapping plots of each data set, noting good agreement (RMSE = 4.33 Mg/ha). Producer’s accuracy, User’s accuracy, and the Kappa statistic for detection of harvested plots were 94%, 87% and 87%, respectively. A leave-one-out cross validation scheme was applied to the classifier, using 1000 iterations, with the mean values presented in this 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 points, and thus, was not able to match the airborne lidar’s performance. It did 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.