Date of Award:
Doctor of Philosophy (PhD)
Electrical and Computer Engineering
Remote sensing enables the acquisition of large amounts of data, over a small period of time, in support of many ecological applications (i.e. precision agriculture, vegetation mapping, etc.) commonly from satellite or manned aircraft platforms. This dissertation focuses on using small unmanned aerial systems (UAS) as a remote sensing platform to collect aerial imagery from commercial-grade cameras and as a radio localization platform to track radio-tagged sh. The small, low-cost nature of small UAS enables remotely sensed data to be captured at a lower cost, higher spatial and temporal resolution, and in a more timely manner than conventional platforms. However, these same attributes limit the types of cameras and sensors that can be used on small UAS and introduce challenges in calibrating the imagery and converting it into actionable information for end users. A major contribution of this dissertation addresses this issue and includes a complete description on how to calibrate imagery from commercial-grade visual, near-infrared, and thermal cameras. This includes the presentation of novel surface temperature sampling methods, which can be used during the ight, to help calibrate thermal imagery. Landsat imagery is used to help evaluate these methods for accuracy; one of the methods performs very well and is logistically feasible for regular use. Another major contribution of this dissertation includes novel, simple methods to estimate the location of radio-tagged fish using multiple unmanned aircraft (UA). A simulation is created to test these methods, and Monte Carlo analysis is used to predict their performance in real-world scenarios. This analysis shows that the methods are able to locate the radio-tagged fish with good accuracy. When multiple UAs are used, the accuracy does not improve; however the fish is located much quicker than when one UA is used.
Jensen, Austin M., "Innovative Payloads for Small Unmanned Aerial System-Based Personal Remote Sensing and Applications" (2014). All Graduate Theses and Dissertations. 2192.
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