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

Wu, Zijing; Zhang, Ce ORCID: https://orcid.org/0000-0001-5100-3584; Gu, Xiaowei; Duporge, Isla; Hughey, Lacey F.; Stabach, Jared A.; Skidmore, Andrew K.; Hopcraft, J. Grant C.; Lee, Stephen J.; Atkinson, Peter M.; McCauley, Douglas J.; Lamprey, Richard; Ngene, Shadrack; Wang, Tiejun. 2023 Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape. Nature Communications, 14 (1), 3072. 15, pp. https://doi.org/10.1038/s41467-023-38901-y

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Official URL: http://dx.doi.org/10.1038/s41467-023-38901-y

Abstract/Summary

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:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1038/s41467-023-38901-y
UKCEH and CEH Sections/Science Areas:	Soils and Land Use (Science Area 2017-)
ISSN:	2041-1723
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	animal migration, imaging and sensing
NORA Subject Terms:	Ecology and Environment Computer Science Data and Information
Related URLs:	Dataset Other
Date made live:	08 Nov 2023 15:37 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/535680

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1038/s41467-023-38901-y

UKCEH and CEH Sections/Science Areas:

Soils and Land Use (Science Area 2017-)

ISSN:

2041-1723

Additional Information. Not used in RCUK Gateway to Research.:

Open Access paper - full text available via Official URL link.

Additional Keywords:

animal migration, imaging and sensing