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

Wu, Z. et al. (2023) Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape. Nature Communications, 14, 3072. (doi: 10.1038/s41467-023-38901-y) (PMID:37244940) (PMCID:PMC10224963)

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.

• Item Type: Articles
• Additional Information: This collaboration was supported, in part, by grant no. 00138000039 from the Microsoft’s AI for Earth Program (https://www.microsoft.com/en-us/ai/ai-for-earth). Z.W.’s research was supported, in part, by funding from the Department of Natural Resources at the Faculty of Geo-Information Science and Earth Observation (ITC) of the University of Twente. This research was performed while I.D. held an NRC Research Associateship award at United States Army Research Laboratory. A.K.S.’s research was partly supported by funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement no. 834709 BIOSPACE).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Hopcraft, Professor Grant
• Authors: Wu, Z., Zhang, C., Gu, X., Duporge, I., Hughey, L. F., Stabach, J. A., Skidmore, A. K., Hopcraft, J. G. C., Lee, S. J., Atkinson, P. M., McCauley, D. J., Lamprey, R., Ngene, S., and Wang, T.
• College/School: College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine
• Journal Name: Nature Communications
• Publisher: Nature Research
• ISSN: 2041-1723
• ISSN (Online): 2041-1723
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in Nature Communications 14: 3072
• Publisher Policy: Reproduced under a Creative Commons License
• Data DOI: 10.5281/zenodo.7810487