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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 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. We build off previous studies quantifying uncertainty and use 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. We then apply the fitted model to predict the length distributions and estimate age class of unknown-sized humpback whales (Megaptera novaeangliae), as well as to predict the population-level morphological relationship between rostrum to blowhole distance and total body length of Antarctic minke whales (Balaenoptera bonaerensis). This statistical framework jointly estimates errors from altitude and length measurements from multiple observations 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 highest posterior density (HPD) 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. ... [Read More]
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- Total Size
- 3 files (18 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 drones. Duke Research Data Repository. V2 https://doi.org/10.7924/r4sj1jj6s
- Related Materials
- K. C. Bierlich, R. S. Schick, J. Hewitt, J. Dale, J. A. Goldbogen, A. S. Friedlaender, & D. W. Johnston. (2021). A Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from drones. Marine Ecology Progress Series. https://doi.org/10.3354/meps13814
- Provenance
- Code and data have been updated to compare measurement uncertainty under two Ecological Scenarios: 1) length-based maturity classification of humpback whales, and 2) population level-morphological relationship between rostrum to blowhole distance and total body length for Antarctic minke whales. Both ecological scenarios compare uncertainty when using only a barometer (Model 1) vs. a barometer and laser altimeter (Model 2). Validation results are also automatically generated.
- 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.
- Title
- Data and scripts from: A Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from drones
Versions
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Version
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DOI
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Comment
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Publication Date
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2
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10.7924/r4sj1jj6s
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Code and data have been updated to compare measurement uncertainty under two Ecological Scenarios: 1) length-based maturity classification of humpback whales, and 2) population level-morphological relationship between rostrum to blowhole distance and total body length for Antarctic minke whales. Both ecological scenarios compare uncertainty when using only a barometer (Model 1) vs. a barometer and laser altimeter (Model 2). Validation results are also automatically generated.
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2021-07-06
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1
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10.7924/r4wd3x28b
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2020-11-30
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