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

Authors :
Wu Z
Zhang C
Gu X
Duporge I
Hughey LF
Stabach JA
Skidmore AK
Hopcraft JGC
Lee SJ
Atkinson PM
McCauley DJ
Lamprey R
Ngene S
Wang T
Source :
Nature communications [Nat Commun] 2023 May 27; Vol. 14 (1), pp. 3072. Date of Electronic Publication: 2023 May 27.
Publication Year :
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.<br /> (© 2023. The Author(s).)

Details

Language :
English
ISSN :
2041-1723
Volume :
14
Issue :
1
Database :
MEDLINE
Journal :
Nature communications
Publication Type :
Academic Journal
Accession number :
37244940
Full Text :
https://doi.org/10.1038/s41467-023-38901-y