Abstract
Low cost, unmodified, commercially available drones can provide an effective platform for the study and characterization of marine megafauna. We present methods which utilize video and flight data to allow for both the continuous tracking of animals and the determination of animal lengths across a range of flight parameters. We also provide a thorough estimation of error in animal position and length measurements while at the same time introducing methods to correct for errors in reported aircraft altitude and heading. Methods are validated using both ground-based markers and tracking data from free swimming white sharks which includes the simultaneous tracking of individual sharks by two drones as the aircraft undergo changes in altitude, gimbal angle, heading and position. The resultant tracks are seen to be highly congruent (mean distance between measured positions: 4.3 m (95% CI 0 to 10)) and length measurements demonstrate a high level of precision (95% CI −8 to 8%) with accuracy confirmed using ground-based markers (mean error: 0.3% (95% CI −4.8 to 4.8%)). Results demonstrate the effectiveness of these methods across a range of flight conditions encountered in the field. The methods introduced allow flexibility in data capture while still providing accurate information, with the potential to both expand the use of and enhance the value of drone-based data for the quantification of animal behaviors and characteristics.
Data availability
All processed data reported in this study as well as aligned flight and image sensor data for reported shark tracks and ground targets, along with the code used to generate figures and conduct analyses will be made available via Code Ocean upon publication. Raw video and flight log data are not publicly archived at this time. However, reasonable requests for access to specific subsets of the raw data that support the findings of this study will be considered by the corresponding author.
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Acknowledgements
The authors would like to thank Mark McCormick, David Sexton, Taylor Lamme, David Mizrahi, Malcolm Stewart, Josh Stewart, Geoff Reuland, John Reuland, and Jackson R. Davis for their assistance with field experiments and piloting. They also wish to thank Max Kullberg and scientists at OCEARCH who provided valuable insight in the preparation of this manuscript. The authors would like to further acknowledge support from the Kay Fellowship through the ‘Iolani School. Finally, they would like to thank the Cape Cod Ocean Community and the Dorr Foundation for their support.
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KJS planned the study and developed the computational methods used in data analysis. KJS and EFD conceived, designed and carried out experiments. RH and EFD provided conceptual input throughout planning, testing and data analysis. KJS, EFD and RH performed data analysis. KJS wrote the initial draft of the manuscript. EFD, RH, NC, AED, and AB provided feedback, editing and numerous other contributions to the further development of the manuscript.
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KJS declares the following competing interests: KJS has filed a patent entitled ‘Method to correct for reported drone altitude errors over duration of a flight based on determination of true altitude error from at least one point within the flight’ with the US Patent and Trademark Office, which covers the landing altitude correction method described in this paper. KJS has filed a patent entitled “System and Method for Correcting Drone Heading and Data Misalignment Using Path-Based and Correlation-Based Optimization” with the US Patent and Trademark Office, which covers the flight and image data alignment method described in this paper. KJS has also filed a provisional patent entitled ‘Method for correcting reported UAS heading errors using single or multiple offset corrections’ with the US Patent and Trademark Office, which covers the heading error correction methods described in this paper. The remaining authors declare no competing interests.
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Sexton, K.J., Danko, E.F., Boesch, A. et al. The use of multi-sensor drone data for the development and validation of methods to track and characterize marine animals.
Sci Rep (2026). https://doi.org/10.1038/s41598-025-31975-2
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DOI: https://doi.org/10.1038/s41598-025-31975-2
Keywords
- Animal tracking
- Drone-based measurements
- Drone accuracy
- Marine megafauna
- Quantitative imaging
- White shark
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