Browsing by Subject "Drones"

During my PhD journey, I have lived at the intersection of a previous military career, leadership as an elected official, and a student passionate about robotics and protecting our beautiful coastal ecosystem. As a non-traditional student, Duke University has presented me with experiences I could not have imagined. With the Duke Marine Robotics and Remote Sensing (MaRRS) Lab drones, I have had the opportunity to survey the mass nesting of thousands of olive ridley sea turtles in Costa Rica, hundreds of gray seals in Massachusetts, endangered right whales off the coast of Florida, dozens of World War I shipwrecks in Maryland, Etruscan and Roman archeological sites in Italy, and hundreds of seals in the Bering Sea. And there have been many more multi-domain surveys of our glorious coastal ecosystem in Carteret County. There have been more than our fair share of challenges during this time frame to include preparing and responding to Hurricanes Florence, Dorian, and Isaias, plus the COVID-19 pandemic. These events took a toll on many fronts, but also presented leadership opportunities. With our drones, we have been able to survey before and after storms, and we’re watching barrier islands move at centimeter scale. The increasing effects of climate change are very personal for those of us living in eastern North Carolina, but in the MaRRS Lab we are well postured with our robotics to air-, sea-, and ground-truth these effects. Perhaps most importantly, the knowledge I gained during my PhD program informed my policy positions during my tenure as the Mayor of the Town of Beaufort, NC. I am very proud of the progress that we made to include a massive clean-up of our waterways following Hurricane Florence, a Harbor Management Ordinance to better manage our waterways, expanded municipal jurisdiction to further manage our ecosystem, unprecedented repairs of infrastructure that were neglected for decades and have negatively affected our water quality, investment in the community, and a five-year budgeting plan to provide greater stability for Beaufort.

This dissertation is a summation of some of the work performed during my Duke PhD experience. In Chapter 1, I describe the evolution of autonomous drones, define distinct generations of this technology, and articulate the negative impacts of a regulatory system that is stifling critical research. For Chapter 2, I discuss the lexicon, taxonomy, and ontology of small autonomous drones, the critical importance of situational awareness, and a framework of considerations and best practices for those interested in pursuing autonomous mobile robots to enhance their research. With Chapters 1 and 2 as a foundation, I next highlight my expansion to the marine domain for water quality research with autonomous surface vessels (Chapter 3) and multi-disciplinary archeological drone surveys in Vulci, Italy (Chapter 4). Finally in Chapter 5, I address scientific research that informed policy successes during my time as a mayor and PhD student. What a great journey!

Increasingly, drone-based photogrammetry has been used to measure size and body condition changes in marine megafauna. A broad range of platforms, sensors, and altimeters are being applied for these purposes, but there is no unified way to predict photogrammetric uncertainty across this methodological spectrum. As such, it is difficult to make robust comparisons across studies, disrupting collaborations amongst researchers using platforms with varying levels of measurement accuracy.

In this dissertation, I evaluate the major drivers of photogrammetric error and develop a framework to easily quantify and incorporate uncertainty associated with different UAS platforms. To do this, I take an experimental approach to train a Bayesian statistical model using a known-sized object floating at the water’s surface to quantify how measurement error scales with altitude for several different drones equipped with different cameras, focal length lenses, and altimeters. I then use the fitted model to predict the length distributions of unknown-sized humpback whales and assess how predicted uncertainty can affect quantities derived from photogrammetric measurements such as the age class of an animal (Chapter 1). I also use the fitted model to predict body condition of blue whales, humpback whales, and Antarctic minke whales, providing the first comparison of how uncertainty scales across commonly used 1-, 2-, and 3-dimensional (1D, 2D, and 3D, respectively) body condition measurements (Chapter 2). This statistical framework jointly estimates errors from altitude and length measurements and accounts for altitudes measured with both barometers and laser altimeters while incorporating errors specific to each. This Bayesian statistical model outputs a posterior predictive distribution of measurement uncertainty around length and body condition measurements and allows for the construction of highest posterior density intervals to define measurement uncertainty, which allows one to make probabilistic statements and stronger inferences pertaining to morphometric features critical for understanding life history patterns and potential impacts from anthropogenically altered habitats. From these studies, I find that altimeters can greatly influence measurement predictions, with measurements using a barometer producing larger and greater uncertainty compared to using a laser altimeter, which can influence age classifications. I also find that while the different body condition measurements are highly correlated with one another, uncertainty does not scale linearly across 1D, 2D, and 3D body condition measurements, with 2D and 3D uncertainty increasing by a factor of 1.44 and 2.14 compared to 1D measurements, respectively. I find that body area index (BAI) accounts for potential variation along the body for each species and was the most precise body condition measurement.

I then use the model to incorporate uncertainty associated with different drone platforms to measure how body condition (as BAI) changes over the course of the foraging season for humpback whales along the Western Antarctic Peninsula (Chapter 3). I find that BAI increases curvilinearly for each reproductive class, with rapid increases in body condition early in the season compared to later in the season. Lactating females had the lowest BAI, reflecting the high energetic costs of reproduction, whereas mature whales had the largest BAI, reflecting their high energy stores for financing the costs of reproduction on the breeding grounds. Calves also increased BAI opposed to strictly increasing length, while immature whales may increase their BAI and commence an early migration by mid-season. These results set a baseline for monitoring this healthy population in the future as they face potential impacts from climate change and anthropogenic stresses. This dissertation concludes with a best practices guide for minimizing, quantifying, and incorporating uncertainty associated with photogrammetry data. This work provides novel insights into how to obtain more accurate morphological measurements to help increase our understanding of how animals perform and function in their environment, as well as better track the health of populations over time and space.