Data and scripts from: A Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from UAS

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  • 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 uncertainty associated with photogrammetric measurements 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.

    We used 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 UAS platforms equipped with different cameras, focal length lenses, and altimeters. We 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.

    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 model outputs a posterior predictive distribution of measurement uncertainty around length measurements and allows for the construction of credible 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.

    The present study provides a robust and coherent methodology for determining measurement error associated with different UAS and sensor platforms and a model for predicting measurement uncertainty of unknown-sized objects, such as whales and other marine megafauna. This framework can guide researchers in determining the most appropriate UAS and sensor platforms needed to answer specific research questions, as well as facilitate collaboration amongst researchers collecting photogrammetric imagery with different levels of measurement accuracy.
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6 files (537 MB)
Data Citation
  • Bierlich, K. C., Schick, R. S., Hewitt, J., Dale, J., Goldbogen, J. A., Friedlaender, A. S., Johnston, D. W. (2020). Data and scripts from: A Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from UAS. Duke Research Data Repository. https://doi.org/10.7924/r4wd3x28b
DOI
  • 10.7924/r4wd3x28b
Publication Date
ARK
  • ark:/87924/r4wd3x28b
Type
Funding Agency
  • National Science Foundation, Office of Polar Programs Grants
Grant Number
  • Antarctic humpback whale imagery was collected as part of National Science Foundation Office of Polar Programs Grants 1643877 and 1440435 to ASF under NMFS permits 14809 and 23095, ACA Permits 2015-011 and 2016-024, UCSC IACUC Friea1706.
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  • Data and scripts from: A Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from UAS

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