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

Wu, Zijing and Zhang, Ce and Gu, Xiaowei and Duporge, Isla and Hughey, Lacey and Stabach, Jared and Skidmore, Andrew and Hopcraft, Grant and Lee, Stephen and Atkinson, Peter and McCauley, Douglas and Lamprey, Richard and Ngene, Shadrack and Wang, Tiejun (2023) Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape. Nature Communications, 14 (1): 3072. pp. 1-15. ISSN 2041-1723

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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.