Quantifying the impact of avian influenza on the northern gannet colony of Bass Rock using ultra-high-resolution drone imagery and deep learning

Tyndall, Amy A.; Nichol, Caroline J.; Wade, Tom; Pirrie, Scott; Harris, Michael P. ORCID: https://orcid.org/0000-0002-9559-5830; Wanless, Sarah ORCID: https://orcid.org/0000-0002-2788-4606; Burton, Emily. 2024 Quantifying the impact of avian influenza on the northern gannet colony of Bass Rock using ultra-high-resolution drone imagery and deep learning. Drones, 8 (2), 40. 23, pp. https://doi.org/10.3390/drones8020040

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Official URL: http://dx.doi.org/10.3390/drones8020040

Abstract/Summary

Drones are an increasingly popular choice for wildlife surveys due to their versatility, quick response capabilities, and ability to access remote areas while covering large regions. A novel application presented here is to combine drone imagery with neural networks to assess mortality within a bird colony. Since 2021, Highly Pathogenic Avian Influenza (HPAI) has caused significant bird mortality in the UK, mainly affecting aquatic bird species. The world’s largest northern gannet colony on Scotland’s Bass Rock experienced substantial losses in 2022 due to the outbreak. To assess the impact, RGB imagery of Bass Rock was acquired in both 2022 and 2023 by deploying a drone over the island for the first time. A deep learning neural network was subsequently applied to the data to automatically detect and count live and dead gannets, providing population estimates for both years. The model was trained on the 2022 dataset and achieved a mean average precision (mAP) of 37%. Application of the model predicted 18,220 live and 3761 dead gannets for 2022, consistent with NatureScot’s manual count of 21,277 live and 5035 dead gannets. For 2023, the model predicted 48,455 live and 43 dead gannets, and the manual count carried out by the Scottish Seabird Centre and UK Centre for Ecology and Hydrology (UKCEH) of the same area gave 51,428 live and 23 dead gannets. This marks a promising start to the colony’s recovery with a population increase of 166% determined by the model. The results presented here are the first known application of deep learning to detect dead birds from drone imagery, showcasing the methodology’s swift and adaptable nature to not only provide ongoing monitoring of seabird colonies and other wildlife species but also to conduct mortality assessments. As such, it could prove to be a valuable tool for conservation purposes.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.3390/drones8020040
UKCEH and CEH Sections/Science Areas:	UKCEH Fellows
ISSN:	2504-446X
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	drone, high-resolution imagery, remote sensing, photogrammetry, deep learning, neural network, conservation, wildlife, gannets, avian influenza
NORA Subject Terms:	Ecology and Environment Computer Science Data and Information
Date made live:	02 Feb 2024 09:14 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/536833

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.3390/drones8020040

UKCEH and CEH Sections/Science Areas:

UKCEH Fellows

ISSN:

2504-446X

Additional Information. Not used in RCUK Gateway to Research.:

Open Access paper - full text available via Official URL link.

Additional Keywords:

drone, high-resolution imagery, remote sensing, photogrammetry, deep learning, neural network, conservation, wildlife, gannets, avian influenza