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Burke, C, Rashman, M, Wich, SA, Symons, A, Theron, C and Longmore, SN (2019) Optimising observing strategies for monitoring animals using drone-mounted thermal infrared cameras. International Journal of Remote Sensing, 40 (2). pp. 439-467. ISSN 0143-1161
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1812.05498v1.pdf - Accepted Version Download (5MB) | Preview |
Abstract
The proliferation of relatively affordable off-the-shelf drones offers great opportunities for wildlife monitoring and conservation. Similarly the recent reduction in 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 paper 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/).
| Item Type: | Article |
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| Additional Information: | This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Remote Sensing on 17/1/19, available online: http://www.tandfonline.com/10.1080/01431161.2018.1558372 |
| Uncontrolled Keywords: | eess.SP; eess.SP; astro-ph.IM |
| Subjects: | Q Science > QB Astronomy Q Science > QL Zoology |
| Divisions: | Astrophysics Research Institute Natural Sciences & Psychology (closed 31 Aug 19) |
| Publisher: | Taylor & Francis |
| Related URLs: | |
| Date Deposited: | 07 Jan 2019 09:27 |
| Last Modified: | 04 Sep 2021 09:49 |
| DOI or ID number: | 10.1080/01431161.2018.1558372 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/9872 |
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