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

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape. / Wu, Zijing; Zhang, Ce; Gu, Xiaowei et al.
In: Nature Communications, Vol. 14, No. 1, 3072, 27.05.2023, p. 1-15.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Wu, Z, Zhang, C, Gu, X, Duporge, I, Hughey, L, Stabach, J, Skidmore, A, Hopcraft, G, Lee, S, Atkinson, P, McCauley, D, Lamprey, R, Ngene, S & Wang, T 2023, 'Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape', Nature Communications, vol. 14, no. 1, 3072, pp. 1-15. https://doi.org/10.1038/s41467-023-38901-y

APA

Wu, Z., Zhang, C., Gu, X., Duporge, I., Hughey, L., Stabach, J., Skidmore, A., Hopcraft, G., Lee, S., Atkinson, P., McCauley, D., Lamprey, R., Ngene, S., & Wang, T. (2023). Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape. Nature Communications, 14(1), 1-15. Article 3072. https://doi.org/10.1038/s41467-023-38901-y

Vancouver

Wu Z, Zhang C, Gu X, Duporge I, Hughey L, Stabach J et al. Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape. Nature Communications. 2023 May 27;14(1):1-15. 3072. doi: 10.1038/s41467-023-38901-y

Author

Wu, Zijing ; Zhang, Ce ; Gu, Xiaowei et al. / Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape. In: Nature Communications. 2023 ; Vol. 14, No. 1. pp. 1-15.

Bibtex

@article{215e484757144e398b49af475184db77,
title = "Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape",
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.",
author = "Zijing Wu and Ce Zhang and Xiaowei Gu and Isla Duporge and Lacey Hughey and Jared Stabach and Andrew Skidmore and Grant Hopcraft and Stephen Lee and Peter Atkinson and Douglas McCauley and Richard Lamprey and Shadrack Ngene and Tiejun Wang",
year = "2023",
month = may,
day = "27",
doi = "10.1038/s41467-023-38901-y",
language = "English",
volume = "14",
pages = "1--15",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "Nature Publishing Group",
number = "1",

}

RIS

TY - JOUR

T1 - Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape

AU - Wu, Zijing

AU - Zhang, Ce

AU - Gu, Xiaowei

AU - Duporge, Isla

AU - Hughey, Lacey

AU - Stabach, Jared

AU - Skidmore, Andrew

AU - Hopcraft, Grant

AU - Lee, Stephen

AU - Atkinson, Peter

AU - McCauley, Douglas

AU - Lamprey, Richard

AU - Ngene, Shadrack

AU - Wang, Tiejun

PY - 2023/5/27

Y1 - 2023/5/27

N2 - 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.

AB - 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.

U2 - 10.1038/s41467-023-38901-y

DO - 10.1038/s41467-023-38901-y

M3 - Journal article

C2 - 37244940

VL - 14

SP - 1

EP - 15

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

IS - 1

M1 - 3072

ER -