Quantifying the Impact of Avian Influenza on the Northern Gannet Colony of Bass Rock Using Ultra-High-Resolution Drone Imagery and Deep Learning
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/8/2/40 |
| _version_ | 1797298439512391680 |
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| author | Amy A. Tyndall Caroline J. Nichol Tom Wade Scott Pirrie Michael P. Harris Sarah Wanless Emily Burton |
| author_facet | Amy A. Tyndall Caroline J. Nichol Tom Wade Scott Pirrie Michael P. Harris Sarah Wanless Emily Burton |
| author_sort | Amy A. Tyndall |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T22:35:55Z |
| format | Article |
| id | doaj.art-c7eb7eb735d84331a76591c75ad7b4de |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-07T22:35:55Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-c7eb7eb735d84331a76591c75ad7b4de2024-02-23T15:14:10ZengMDPI AGDrones2504-446X2024-01-01824010.3390/drones8020040Quantifying the Impact of Avian Influenza on the Northern Gannet Colony of Bass Rock Using Ultra-High-Resolution Drone Imagery and Deep LearningAmy A. Tyndall0Caroline J. Nichol1Tom Wade2Scott Pirrie3Michael P. Harris4Sarah Wanless5Emily Burton6School of Geosciences, University of Edinburgh, Alexander Crum Brown Road, Edinburgh EH9 3FF, UKSchool of Geosciences, University of Edinburgh, Alexander Crum Brown Road, Edinburgh EH9 3FF, UKSchool of Geosciences, University of Edinburgh, Alexander Crum Brown Road, Edinburgh EH9 3FF, UKSchool of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB, UKUK Centre for Ecology and Hydrology, Bush Estate, Penicuik EH26 0QB, UKUK Centre for Ecology and Hydrology, Bush Estate, Penicuik EH26 0QB, UKScottish Seabird Centre, North Berwick, East Lothian EH39 4SS, UKDrones 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.https://www.mdpi.com/2504-446X/8/2/40dronehigh-resolution imageryremote sensingphotogrammetrydeep learningneural network |
| spellingShingle | Amy A. Tyndall Caroline J. Nichol Tom Wade Scott Pirrie Michael P. Harris Sarah Wanless Emily Burton Quantifying the Impact of Avian Influenza on the Northern Gannet Colony of Bass Rock Using Ultra-High-Resolution Drone Imagery and Deep Learning Drones drone high-resolution imagery remote sensing photogrammetry deep learning neural network |
| title | Quantifying the Impact of Avian Influenza on the Northern Gannet Colony of Bass Rock Using Ultra-High-Resolution Drone Imagery and Deep Learning |
| title_full | Quantifying the Impact of Avian Influenza on the Northern Gannet Colony of Bass Rock Using Ultra-High-Resolution Drone Imagery and Deep Learning |
| title_fullStr | Quantifying the Impact of Avian Influenza on the Northern Gannet Colony of Bass Rock Using Ultra-High-Resolution Drone Imagery and Deep Learning |
| title_full_unstemmed | Quantifying the Impact of Avian Influenza on the Northern Gannet Colony of Bass Rock Using Ultra-High-Resolution Drone Imagery and Deep Learning |
| title_short | Quantifying the Impact of Avian Influenza on the Northern Gannet Colony of Bass Rock Using Ultra-High-Resolution Drone Imagery and Deep Learning |
| title_sort | quantifying the impact of avian influenza on the northern gannet colony of bass rock using ultra high resolution drone imagery and deep learning |
| topic | drone high-resolution imagery remote sensing photogrammetry deep learning neural network |
| url | https://www.mdpi.com/2504-446X/8/2/40 |
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