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“A Multivariate Comparison of Drone-Based Structure from Motion and Drone-Based Lidar for Dense Topographic Mapping Applications.”
Major advisor: Christopher Parrish, associate professor of geomatics. Committee members: Michael Olsen, associate professor of geomatics; Daniel Gillins, National Geodetic Survey; Michael Wing, associate professor of geomatics, Forestry Engineering (GCR).
Open to the public.
Abstract: Unmanned aircraft systems (UAS), also known as drones, are becoming an increasingly popular method of collecting surveying and mapping data. Two common drone-based mapping techniques are lidar and structure from motion (SfM) photogrammetry. Surveyors and mapping professionals currently need information on how these two techniques compare. The most common metric for comparison is spatial accuracy, but the two techniques also vary in other key aspects, such as cost, complexity, learning curve, payload requirements, acquisition and processing speeds, and abilities to map under canopy or in vegetation. While there is no “one-size-fits-all” technology or technique, comparisons of drone-based lidar and SfM photogrammetry along all of these different dimensions and in different settings can help surveyors and mappers make informed decisions in purchasing and operating UAS-based systems. This study makes these comparisons using data acquired with two remote aircraft at a project site with a robust control network and high-accuracy reference data located in Stevenson, Washington. The results shed light on the relative strengths and weaknesses of UAS-SfM and UAS-lidar. Since the two techniques typically provide comparable data accuracies (with some differences, as a function of terrain, ground cover type and surface texture), yet UAS-SfM is generally less expensive, imposes less stringent requirements for the remote aircraft, requires less expert knowledge and training, and yields higher data densities, an overarching recommendation from this research is that UAS-SfM be considered the default technique for many mapping projects. However, there are a number of specific scenarios in which UAS-lidar is preferable to UAS-SfM, and it is critical to understand these cases. Additionally, while the combination of UAS-SfM and UAS-lidar would typically be unnecessarily expensive and complex for most projects, the synergistic use of both techniques could provide an optimal solution for the most demanding projects.
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