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. 10.1038/s41467-023-38901-y
Before downloading, please read NORA policies.Preview |
Text
N535680JA.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (9MB) | Preview |
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): | 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: | |
Date made live: | 08 Nov 2023 15:37 +0 (UTC) |
URI: | https://nora.nerc.ac.uk/id/eprint/535680 |
Actions (login required)
View Item |
Document Downloads
Downloads for past 30 days
Downloads per month over past year