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Evaluating the Efficacy and Optimal Deployment of Thermal Infrared and True-Colour Imaging When Using Drones for Monitoring Kangaroos905.57 kB
Published VersionCC BY V4.0, Open Access
Journal article
Evaluating the Efficacy and Optimal Deployment of Thermal Infrared and True-Colour Imaging When Using Drones for Monitoring Kangaroos
Drones, Vol.4(2), pp.1-12
2020
Abstract
Advances in drone technology have given rise to much interest in the use of drone-mounted thermal imagery in wildlife monitoring. This research tested the feasibility of monitoring large mammals in an urban environment and investigated the influence of drone flight parameters and environmental conditions on their successful detection using thermal infrared (TIR) and true-colour (RGB) imagery. We conducted 18 drone flights at different altitudes on the Sunshine Coast, Queensland, Australia. Eastern grey kangaroos (Macropus giganteus) were detected from TIR (n=39) and RGB orthomosaics (n=33) using manual image interpretation. Factors that predicted the detection of kangaroos from drone images were identified using unbiased recursive partitioning. Drone-mounted imagery achieved an overall 73.2% detection success rate using TIR imagery and 67.2% using RGB imagery when compared to on-ground counts of kangaroos. We showed that the successful detection of kangaroos using TIR images was influenced by vegetation type, whereas detection using RGB images was influenced by vegetation type, time of day that the drone was deployed, and weather conditions. Kangaroo detection was highest in grasslands, and kangaroos were not successfully detected in shrublands. Drone-mounted TIR and RGB imagery are effective at detecting large mammals in urban and peri-urban environments.
Details
- Title
- Evaluating the Efficacy and Optimal Deployment of Thermal Infrared and True-Colour Imaging When Using Drones for Monitoring Kangaroos
- Authors
- Elizabeth A Brunton (Author) - University of the Sunshine Coast, Queensland, School of Health and Sport Sciences - LegacyJavier X Leon (Author) - University of the Sunshine Coast, Queensland, School of Science and Engineering - LegacyScott E Burnett (Author) - University of the Sunshine Coast, Queensland, School of Science and Engineering - Legacy
- Publication details
- Drones, Vol.4(2), pp.1-12
- Publisher
- MDPI AG
- Date published
- 2020
- DOI
- 10.3390/drones4020020
- ISSN
- 2504-446X
- Copyright note
- Copyright © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
- Organisation Unit
- School of Science and Engineering - Legacy; Indigenous and Transcultural Research Centre; University of the Sunshine Coast, Queensland; School of Health and Sport Sciences - Legacy; School of Science, Technology and Engineering; Sustainability Research Cluster
- Language
- English
- Record Identifier
- 99462908502621
- Output Type
- Journal article
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