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Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape

Author

Listed:
  • Zijing Wu

    (University of Twente)

  • Ce Zhang

    (Lancaster University
    UK Centre for Ecology & Hydrology)

  • Xiaowei Gu

    (University of Kent)

  • Isla Duporge

    (Princeton University
    Army Research Office
    The National Academies of Sciences)

  • Lacey F. Hughey

    (Smithsonian National Zoo and Conservation Biology Institute)

  • Jared A. Stabach

    (Smithsonian National Zoo and Conservation Biology Institute)

  • Andrew K. Skidmore

    (University of Twente
    Macquarie University)

  • J. Grant C. Hopcraft

    (University of Glasgow)

  • Stephen J. Lee

    (Army Research Office)

  • Peter M. Atkinson

    (Lancaster University
    University of Southampton)

  • Douglas J. McCauley

    (University of California)

  • Richard Lamprey

    (University of Twente)

  • Shadrack Ngene

    (Wildlife Research and Training Institute)

  • Tiejun Wang

    (University of Twente)

Abstract

New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global biodiversity with unprecedented speed and precision. These efficiencies promise to reveal novel ecological insights at spatial scales which are germane to the management of populations and entire ecosystems. Here, we present a robust transferable deep learning pipeline to automatically locate and count large herds of migratory ungulates (wildebeest and zebra) in the Serengeti-Mara ecosystem using fine-resolution (38-50 cm) satellite imagery. The results achieve accurate detection of nearly 500,000 individuals across thousands of square kilometers and multiple habitat types, with an overall F1-score of 84.75% (Precision: 87.85%, Recall: 81.86%). This research demonstrates the capability of satellite remote sensing and machine learning techniques to automatically and accurately count very large populations of terrestrial mammals across a highly heterogeneous landscape. We also discuss the potential for satellite-derived species detections to advance basic understanding of animal behavior and ecology.

Suggested Citation

  • Zijing Wu & Ce Zhang & Xiaowei Gu & Isla Duporge & Lacey F. Hughey & Jared A. Stabach & Andrew K. Skidmore & J. Grant C. Hopcraft & Stephen J. Lee & Peter M. Atkinson & Douglas J. McCauley & Richard L, 2023. "Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38901-y
    DOI: 10.1038/s41467-023-38901-y
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    References listed on IDEAS

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    1. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Correction: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 546(7660), pages 686-686, June.
    2. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
    3. Eunji Chong & Elysha Clark-Whitney & Audrey Southerland & Elizabeth Stubbs & Chanel Miller & Eliana L. Ajodan & Melanie R. Silverman & Catherine Lord & Agata Rozga & Rebecca M. Jones & James M. Rehg, 2020. "Detection of eye contact with deep neural networks is as accurate as human experts," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    4. Colin J Torney & Andrew P Dobson & Felix Borner & David J Lloyd-Jones & David Moyer & Honori T Maliti & Machoke Mwita & Howard Fredrick & Markus Borner & J Grant C Hopcraft, 2016. "Assessing Rotation-Invariant Feature Classification for Automated Wildebeest Population Counts," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-10, May.
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