Can drones count gulls? Minimal disturbance and semiautomated image processing with an unmanned aerial vehicle for colony-nesting seabirds

Rush, Graham ORCID: 0000-0002-8858-6987, Clarke, Lucy E ORCID: 0000-0002-8174-3839, Stone, Meg and Wood, Matthew J ORCID: 0000-0003-0920-8396 (2018) Can drones count gulls? Minimal disturbance and semiautomated image processing with an unmanned aerial vehicle for colony-nesting seabirds. Ecology and Evolution. ISSN 2045-7758 (In Press)

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Abstract

Accurate counts of wild populations are essential to monitor change through time, but some techniques demand specialist surveyors and may result in unacceptable disturbance or inaccurate counts. Recent technological developments in unmanned aerial vehicles (UAVs) offer great potential for a range of survey and monitoring approaches. They literally offer a bird’s-eye view, but this increased power of observation presents the challenge of translating large amounts of imagery into accurate survey data. Seabirds, in particular, present the particular challenges of nesting in large, often inaccessible colonies that are difficult to view for ground observers, which are commonly susceptible to disturbance. We develop a protocol for carrying out UAV surveys of a breeding seabird colony (Lesser Black-backed Gulls, Larus fuscus) and subsequent image processing to provide a semiautomated classification for counting the number of birds. Behavioral analysis of the gull colonies demonstrated that minimal disturbance occurred during UAV survey flights at an altitude of 15 m above ground level, which provided high-resolution imagery for analysis. A protocol of best practice was developed using the expertise from both a UAV perspective and that of a dedicated observer. A GIS-based semiautomated classification process successfully counted the gulls, with a mean agreement of 98% and a correlation of 99% with manual counts of imagery. We also propose a method to differentiate between the different gull species captured by our survey. Our UAV survey and analysis approach provide accurate counts (when comparing manual vs. semi-automated counts taken from the UAV imagery) of a wild seabird population with minimal disturbance, with the potential to expand this to include species differentiation. The continued development of analytical and survey tools whilst minimizing the disturbance to wild populations is both key to unlocking the future of the rapid advances in UAV technology for ecological survey.

Item Type: Article
Article Type: Article
Additional Information: Link to data model (see p12 of manuscript). RJ 4/12/18
Uncontrolled Keywords: Aerial survey; Image classification; Laridae; Monitoring seabird; Population ecology
Subjects: G Geography. Anthropology. Recreation > G Geography (General)
G Geography. Anthropology. Recreation > GB Physical geography
Q Science > QL Zoology > QL605 Chordates. Vertebrates > QL671-699 Birds
Divisions: Schools and Research Institutes > School of Natural & Social Sciences > Environmental Sciences
Research Priority Areas: Environmental Dynamics & Governance
Depositing User: Lucy Clarke
Date Deposited: 05 Dec 2018 11:16
Last Modified: 11 Dec 2018 12:15
URI: http://eprints.glos.ac.uk/id/eprint/6278

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