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Drones paper

Optimizing observing strategies for monitoring animals using drone-mounted thermal infrared cameras

ORCID Icon, , , , &
Pages 439-467 | Received 26 Apr 2018, Accepted 08 Nov 2018, Published online: 17 Jan 2019
 

ABSTRACT

The proliferation of relatively affordable off-the-shelf drones offers great opportunities for wildlife monitoring and conservation. Similarly the recent reduction in the cost of thermal infrared cameras also offers new promise in this field, as they have the advantage over conventional RGB cameras of being able to distinguish animals based on their body heat and being able to detect animals at night. However, the use of drone-mounted thermal infrared cameras comes with several technical challenges. In this article, we address some of these issues, namely thermal contrast problems due to heat from the ground, absorption and emission of thermal infrared radiation by the atmosphere, obscuration by vegetation, and optimizing the flying height of drones for a best balance between covering a large area and being able to accurately image and identify animals of interest. We demonstrate the application of these methods with a case study using field data and make the first ever detection of the critically endangered riverine rabbit (Bunolagus monticularis) in thermal infrared data. We provide a web-tool so that the community can easily apply these techniques to other studies (http://www.astro.ljmu.ac.uk/aricburk/uav_calc/).

Acknowledgments

We thank the staff of the Drylands Conservation Programme of the Endangered Wildlife Trust, Lourens Leewner, Bonnie Schumann, Matt Pretorius, Este Matthew, and Dr. Ian Little for their assistance with data gathering and providing invaluable expertise on the riverine rabbit. We thank Mara Mulero-Pazmany for her advice and input on the style and content of this article. We acknowledge Lizzie Good, Rob King, and Colin Morice at the UK Met Office for advice on using land surface temperature data and MODIS data. We thank Andy Goodwin for producing drone mounts and providing technical expertise. This research was funded by a ISTAT Foundation Humanitarian Grant. C.B. is funded by STFC Global Challenges grant.

Disclosure statement

No potential conflict of interest was reported by the authors.

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