The use of image classification to estimate flamingo abundance from aerial, drone and satellite imagery-Reference-Cited by-同舟云学术

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The use of image classification to estimate flamingo abundance from aerial, drone and satellite imagery

Published:2024-07-10 Issue: Volume: Page:1-12
ISSN:0030-6525
Container-title:Ostrich
language:en
Short-container-title:Ostrich

Author:

Colyn RB¹²^ORCID,Anderson TA²,Anderson MD²,Retief EF²,Van der Westhuizen-Coetzer EJ³,Smit-Robinson H²⁴

Affiliation:

1. FitzPatrick Institute of African Ornithology, DST-NRF Centre of Excellence, University of Cape Town, South Africa

2. BirdLife South Africa, Johannesburg, South Africa

3. Ekapa, Kimberley, South Africa

4. Applied Behavioural Ecology and Ecosystem Research Unit (ABEERU), UNISA, Florida, South Africa

Publisher

National Inquiry Services Center (NISC)

Link

https://www.tandfonline.com/doi/pdf/10.2989/00306525.2024.2325674

Reference29 articles.

1. Automated Bird Counting with Deep Learning for Regional Bird Distribution Mapping

2. Anderson M. 2015. Lesser Flamingo. In: Taylor MR, Peacock F, Wanless RM (eds), The 2015 Eskom Red Data Book of Birds of South Africa, Lesotho and Swaziland. Johannesburg: BirdLife South Africa. pp 305–307.

3. Anderson TA, Anderson MD. 2017. Man’s impact on flamingos. In: Anderson MJ (ed.), Flamingos: behavior, biology and relationship with humans. Hauppauge: Nova Science Publishers. pp 199–225.

4. Estimating the relative abundance of emperor penguins at inaccessible colonies using satellite imagery

5. BirdLife International. 2021. Flamingos. BirdLife International. Available at www.birdlife.org/birds/flamingo [accessed 24 October 2021].

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