Aerial photography and machine learning for estimating extremely high flamingo numbers on the Makgadikgadi Pans, Botswana

Sophie Yang, Roxane J. Francis, Mike Holding, Richard T. Kingsford, Aerial photography and machine learning for estimating extremely high flamingo numbers on the Makgadikgadi Pans, Botswana, Global Ecology and Conservation, 2024, e03011, ISSN 2351-9894, https://doi.org/10.1016/j.gecco.2024.e03011. (...

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description Sophie Yang, Roxane J. Francis, Mike Holding, Richard T. Kingsford, Aerial photography and machine learning for estimating extremely high flamingo numbers on the Makgadikgadi Pans, Botswana, Global Ecology and Conservation, 2024, e03011, ISSN 2351-9894, https://doi.org/10.1016/j.gecco.2024.e03011. (https://www.sciencedirect.com/science/article/pii/S2351989424002154) We developed a semi-supervised machine learning method for counting a large feeding concentration of flamingos (2 June 2019) in aerial photographs from northern Sua Pan of the Makgadikgadi Pans. We also analysed rainfall and flooding frequency and extent, using satellite imagery, estimating likely frequency of these flamingo concentrations. Our analysis successfully provided an estimate of 372,172 to 689,473 flamingos, with methods producing over 97% test accuracy. Uncertainty related primarily to data coverage and collection rather than methodology. Code from Google Earth Engine has been provided in a JavaScript file, along with an example collage. To use the code, please copy and paste into Google Earth Engine scripts and upload relevant images as assets to be analysed.
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Francis, Mike Holding, Richard T. Kingsford, Aerial photography and machine learning for estimating extremely high flamingo numbers on the Makgadikgadi Pans, Botswana, Global Ecology and Conservation, 2024, e03011, ISSN 2351-9894, https://doi.org/10.1016/j.gecco.2024.e03011. (https://www.sciencedirect.com/science/article/pii/S2351989424002154) We developed a semi-supervised machine learning method for counting a large feeding concentration of flamingos (2 June 2019) in aerial photographs from northern Sua Pan of the Makgadikgadi Pans. We also analysed rainfall and flooding frequency and extent, using satellite imagery, estimating likely frequency of these flamingo concentrations. Our analysis successfully provided an estimate of 372,172 to 689,473 flamingos, with methods producing over 97% test accuracy. Uncertainty related primarily to data coverage and collection rather than methodology. 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Francis, Mike Holding, Richard T. Kingsford, Aerial photography and machine learning for estimating extremely high flamingo numbers on the Makgadikgadi Pans, Botswana, Global Ecology and Conservation, 2024, e03011, ISSN 2351-9894, https://doi.org/10.1016/j.gecco.2024.e03011. (https://www.sciencedirect.com/science/article/pii/S2351989424002154) We developed a semi-supervised machine learning method for counting a large feeding concentration of flamingos (2 June 2019) in aerial photographs from northern Sua Pan of the Makgadikgadi Pans. We also analysed rainfall and flooding frequency and extent, using satellite imagery, estimating likely frequency of these flamingo concentrations. Our analysis successfully provided an estimate of 372,172 to 689,473 flamingos, with methods producing over 97% test accuracy. Uncertainty related primarily to data coverage and collection rather than methodology. 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Francis, Mike Holding, Richard T. Kingsford, Aerial photography and machine learning for estimating extremely high flamingo numbers on the Makgadikgadi Pans, Botswana, Global Ecology and Conservation, 2024, e03011, ISSN 2351-9894, https://doi.org/10.1016/j.gecco.2024.e03011. (https://www.sciencedirect.com/science/article/pii/S2351989424002154) We developed a semi-supervised machine learning method for counting a large feeding concentration of flamingos (2 June 2019) in aerial photographs from northern Sua Pan of the Makgadikgadi Pans. We also analysed rainfall and flooding frequency and extent, using satellite imagery, estimating likely frequency of these flamingo concentrations. Our analysis successfully provided an estimate of 372,172 to 689,473 flamingos, with methods producing over 97% test accuracy. Uncertainty related primarily to data coverage and collection rather than methodology. Code from Google Earth Engine has been provided in a JavaScript file, along with an example collage. To use the code, please copy and paste into Google Earth Engine scripts and upload relevant images as assets to be analysed.</abstract><pub>Mendeley Data</pub><doi>10.17632/5wvcc54xpx.1</doi><oa>free_for_read</oa></addata></record>
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identifier DOI: 10.17632/5wvcc54xpx.1
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subjects Aquatic Ecology
Botswana
Conservation Ecology
Flamingo
Machine Learning
Southern Africa
title Aerial photography and machine learning for estimating extremely high flamingo numbers on the Makgadikgadi Pans, Botswana
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