Applying drones and developing novel tools to assess whale behavior, morphology, and body condition
Investigators: Dr. Leigh Torres, Dr. KC Bierlich, Clara Bird, Dr. Dawn Barlow, Todd Chandler
From our traditional boat-based horizontal perspective, cetacean behavioral observations are typically limited to when the animal is at the surface, and health assessment is constrained to photographs captured of this limited body view. Previously, achieving an aerial perspective has been restricted to brief helicopter- or plane-based observations that are costly, noisy, and risky. The emergence of commercial drones (also called Unoccupied Aircraft Systems, UAS) has significantly reduced these constraints, and provide a stable, relatively quiet, and inexpensive platform that enables replicate cetacean observations for prolonged periods with minimal disturbance. With the imminent proliferation of using drones in cetacean studies comes the need for robust quantitative methods of image analysis.
The GEMM Lab has been pioneering the use of UAS technology to study marine mammal health and behavior. Since 2015 we have conducted drone flights over gray whales in Oregon and Alaska and pygmy blue whales in New Zealand to document behavior and assess body condition through photogrammetry. Through these efforts we have developed new analytical methods that allow robust quantification and comparability of metrics. This also includes developing open-source hardware and software packages designed to help researchers obtain accurate measurements. We continue to employ these methods across projects, compare methodological approaches, and evaluate sources contributing to photogrammetric error. We also link these datasets with multiple habitat quality measurements to gain a better understanding of the impacts due to disturbance events and environmental change. As new technologies emerge, we look for new applications to help us non-invasively study multiple marine megafauna species to better assess their health in changing oceans.
Current projects:
Publications:
Bierlich, K.C., Hewitt, J., Bird, C.N., Schick R.S., Friedlaender, A.S., Torres, L.G., Dale, J., Goldbogen, J.A., Read, A., Calambokidis J., Johnston, D.W., (2021). Comparing uncertainty associated with 1-, 2-, and 3D aerial photogrammetry-based body condition measurements of baleen whales. Frontiers in Marine Science. 8:749943.
Savoca, M. S. Czapanskiy, M. F., Kahane-Rapport, S. R., Gough, W. T., Falhbusch, J. A., Bierlich, K. C., Segre, P. S., Di Clemente, J., Penry G. S., Wiley, D. N., Calambokids, J., Nowacek, D. P., Johnston, D. W., Pyenson, N. D., Friedlaender, A. S., Hazen, E. L., & Goldbogen, J.A. (2021). Baleen whale prey consumption based on high-resolution foraging measurements. Nature, 599, 85–90.
Bierlich, K.C., Schick, R.S., Hewitt, J., Dale, J., Goldbogen, J.A., Friedlaender, A.S., Johnston D.J. (2021). A Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from UAS. Marine Ecology Progress Series.
Lemos, L. S., Olsen, A., Smith, A., Burnett, J. D., Chandler, T. E., Larson, S., Hunt, K. E., Torres, L. G. (2021). Stressed and slim or relaxed and chubby? A simultaneous assessment of gray whale body condition and hormone variability. Marine Mammal Science.
Bird, C.N., and Bierlich, K.C. (2020). CollatriX: A GUI to collate MorphoMetriX outputs. Journal of Open Source Software, 5(51), 2328.
Torres, W.I., & Bierlich, K.C. (2020). MorphoMetriX: a photogrammetric measurement GUI for morphometric analysis of megafauna. Journal of Open Source Software, 5(45), 1825.
Blogs:
Media:
Software:
Source: Torres, W.I., and Bierlich, K.C (2020). MorphoMetriX: a photogrammetric measurement GUI for morphometric analysis of megafauna.. Journal of Open Source Software, 4(44), 1825. https://doi.org/10.21105/joss.01825
Source: Bird, C.N., and Bierlich, K.C. (2020). CollatriX: A GUI to collate MorphoMetriX outputs. Journal of Open Source Software, 5(51), 2328. https://doi:10.21105/joss.02328
Incorporating Photogrammetric Uncertainty
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. V2 https://doi.org/10.7924/r4sj1jj6s
Uncertainty associated with photogrammetry-based body condition
Bierlich, K.C., Hewitt, J., Bird, C.N., Schick R.S., Friedlaender, A.S., Torres, L.G., Dale, J., Goldbogen, J.A., Read, A., Calambokidis J., Johnston, D.W., (2021). Comparing uncertainty associated with 1-, 2-, and 3D aerial photogrammetry-based body condition measurements of baleen whales. Frontiers in Marine Science. 8:749943. doi: 10.3389/fmars.2021.749943
Whale photogrammetry analysis code
Source: Appendix S2 from Burnett, J. D., L. Lemos, D. Barlow, M. G. Wing, T. Chandler, and L. G. Torres. Estimating morphometric attributes of baleen whales with photogrammetry from small UASs: A case study with blue and gray whales. Marine Mammal Science. https://doi.org/10.1111/mms.12527
Whale photogrammetry tutorial video
Project Collaborators:
Innovation Lab (iLab)
Dr. Josh Hewitt
Dr. Leila Lemos
Dr. Jon Burnett
Duke University Marine Robotics and Remote Sensing Lab (MaRRS) Lab
We are using drones and developing new tools and methods to help obtain accurate morphological measurements of marine megafauna to better monitor the health of populations in changing oceans.
Oregon State University Marine Mammal Institute
Hatfield Marine Science Center
2030 SE Marine Science Dr
Newport, Oregon 97365
Phone: (541) 867-0202
Fax: (541) 867-0128
Email: mmi_web@oregonstate.edu
Information Sheet