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

Journal: Nature communications
Published Date: May 27, 2023

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.

Authors

  • Zijing Wu
    Department of Endocrinology, Institute of Translational Medicine, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, Guangdong, China.
  • Ce Zhang
    Stanford University.
  • Xiaowei Gu
    CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
  • Isla Duporge
    Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.
  • Lacey F Hughey
    Conservation Ecology Center, Smithsonian National Zoo and Conservation Biology Institute, Front Royal, VA, USA.
  • Jared A Stabach
    Conservation Ecology Center, Smithsonian National Zoo and Conservation Biology Institute, Front Royal, VA, USA.
  • Andrew K Skidmore
    Department of Natural Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, The Netherlands.
  • J Grant C Hopcraft
    Institute of Biodiversity, Animal Health, and Comparative Medicine, University of Glasgow, Glasgow, UK.
  • Stephen J Lee
    U.S. Army Research Laboratory, Army Research Office, Durham, NC, USA.
  • Peter M Atkinson
    Lancaster Environment Center, Lancaster University, Lancaster, UK.
  • Douglas J McCauley
    Department of Ecology, Evolution and Marine Biology, University of California, Santa Barbara, CA, USA.
  • Richard Lamprey
    Department of Natural Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, The Netherlands.
  • Shadrack Ngene
    Wildlife Research and Training Institute, Naivasha, Kenya.
  • Tiejun Wang
    Department of Natural Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, The Netherlands. t.wang@utwente.nl.

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