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Aerial photogrammetry of seabirds from digital aerial video images using relative change in size to estimate flight height

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Abstract

Calculating the height at which birds fly over the sea is a challenging task, but remains important to assessing collision risk in proposed offshore wind farm areas for consenting purposes. This could be done by several methods (e.g. GPS or laser rangefinders), but each have biases that make it difficult to generate site-wide assessments. Digital video aerial surveys, which quickly cover large areas, were used to assess flight heights of northern gannets and black-legged kittiwakes using a photogrammetric technique combined with a semi-automated measurement tool. The lengths of birds known to be at sea surface, as identified by reflection on the water, were compared to lengths of birds at unknown height to generate individual flight height profiles. Validation of the method on kittiwake found that LiDAR produced mean flights that were not significantly different to the photogrammetry approach. Validation of the flight height method using man-made objects of known dimensions and heights suggested a 9–18% error (3–6 m at ~ 30 m height). However, the profiles of mean flight height distribution matched patterns in previous work. This method was able to estimate the flying heights of 65% and 75% of flying gannets and kittiwakes, respectively, in this case study. The annual percentage of gannets at collision risk height for a set of turbines with a 30 m air gap was estimated at 29.8%, and 16.1% for kittiwake. This technique can greatly improve our knowledge of the spatial distribution of flight height patterns in marine ecosystems, but also allows stakeholders to assess collision risk more easily within the sphere of offshore wind for the consenting process.

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Availability of data and materials

The data underlying this analysis are not available online due to agreement with the confidential client. A request for data can be made to the authors, which would be considered by the client.

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Acknowledgements

The authors would like to thank M Cameron for work on the aircraft camera systems, as well as the pilots from DEA and Ravenair. P Rodgers and D Rodriguez Estevez of Createc carried out the coding for the RGB tool, with additional support from R Hexter. We would like to further thank NatureScot, Natural England, BioConsult-SH, Natural Resources Wales, Dr A. Cook, Dr C. Thaxter and Dr P Boersch-Supan, and Dr. V Morera Pujol for comments on drafts of the manuscript. We would finally like to thank our confidential clients for allowing us to utilize these data to test and demonstrate the methodology.

Funding

The authors declare that no funds, grants or other support were received during the preparation of this manuscript; all research was undertaken via internal funding.

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Authors

Contributions

Study concept and design were prepared by GH, MS and AW, while the full version of the manuscript was written by GH with additional comments and revisions by MS, RPG, and AW. MW and TF contributed to data analysis during the Humber River bridge and runway trials by measuring object sizes to help calibrate the methodology. WH coordinated the LiDAR validation flights and data collection, while RT performed the analysis for the LiDAR validation. RPG developed the human-based architecture for the methodology with help from GH and KK. KK contributed to generating figures and tables in the manuscript as well as ideation for validation techniques. All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Grant R. W. Humphries.

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Conflict of interest

All authors were employed by HiDef Aerial Surveying Ltd at the time of submitting this manuscript for publication; the method is employed by HiDef Aerial Surveying.

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This article does not contain work that required ethical approval as no animals were approached.

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Humphries, G.R.W., Fail, T., Watson, M. et al. Aerial photogrammetry of seabirds from digital aerial video images using relative change in size to estimate flight height. Mar Biol 170, 18 (2023). https://doi.org/10.1007/s00227-022-04161-5

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