Incorporating Photogrammetric Uncertainty in UAS-based Morphometric Measurements of Baleen Whales
Dissertation 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...
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ftdukeunivdsp:oai:localhost:10161/23123 2023-11-12T04:04:15+01:00 Incorporating Photogrammetric Uncertainty in UAS-based Morphometric Measurements of Baleen Whales Bierlich, Kevin Charles Johnston, David W 2021 application/pdf https://hdl.handle.net/10161/23123 unknown https://hdl.handle.net/10161/23123 Morphology Ecology Biology Bayesian cetaceans drones marine mammals photogrammetry uncertainty Dissertation 2021 ftdukeunivdsp 2023-10-17T09:43:11Z Dissertation 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 ... Doctoral or Postdoctoral Thesis Antarc* Antarctic baleen whales Duke University Libraries: DukeSpace Antarctic |
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Duke University Libraries: DukeSpace |
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Morphology Ecology Biology Bayesian cetaceans drones marine mammals photogrammetry uncertainty |
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Morphology Ecology Biology Bayesian cetaceans drones marine mammals photogrammetry uncertainty Bierlich, Kevin Charles Incorporating Photogrammetric Uncertainty in UAS-based Morphometric Measurements of Baleen Whales |
topic_facet |
Morphology Ecology Biology Bayesian cetaceans drones marine mammals photogrammetry uncertainty |
description |
Dissertation 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 ... |
author2 |
Johnston, David W |
format |
Doctoral or Postdoctoral Thesis |
author |
Bierlich, Kevin Charles |
author_facet |
Bierlich, Kevin Charles |
author_sort |
Bierlich, Kevin Charles |
title |
Incorporating Photogrammetric Uncertainty in UAS-based Morphometric Measurements of Baleen Whales |
title_short |
Incorporating Photogrammetric Uncertainty in UAS-based Morphometric Measurements of Baleen Whales |
title_full |
Incorporating Photogrammetric Uncertainty in UAS-based Morphometric Measurements of Baleen Whales |
title_fullStr |
Incorporating Photogrammetric Uncertainty in UAS-based Morphometric Measurements of Baleen Whales |
title_full_unstemmed |
Incorporating Photogrammetric Uncertainty in UAS-based Morphometric Measurements of Baleen Whales |
title_sort |
incorporating photogrammetric uncertainty in uas-based morphometric measurements of baleen whales |
publishDate |
2021 |
url |
https://hdl.handle.net/10161/23123 |
geographic |
Antarctic |
geographic_facet |
Antarctic |
genre |
Antarc* Antarctic baleen whales |
genre_facet |
Antarc* Antarctic baleen whales |
op_relation |
https://hdl.handle.net/10161/23123 |
_version_ |
1782341337927909376 |