Hot monkey, cold reality: surveying rainforest canopy mammals using drone-mounted thermal infrared sensors
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Issue Date
2018Author
Kays, RolandSheppard, James
Mclean, Kevin
Welch, Charlie
Paunescu, Cris
Wang, Victor
Kravit, Greg
Crofoot, Meg
Journal title
International Journal of Remote SensingVolume
40Begin page
407End page
419
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Show full item recordAlternative link
https://doi.org/10.1080/01431161.2018.1523580Abstract
Animals of the rainforest canopies are often endangered by deforestation or hunting but are difficult to survey and study because of the inaccessibility of the treetops, combined with the visual camouflage of many species. Drone-based thermal sensors have the potential to overcome these hurdles by rapidly scanning large forested areas from above, detecting and mapping wildlife based on the contrast between their warm body temperatures and the cool tree canopies....Description
Summary and promo video link, from James Sheppard: We explore the promise and pitfalls of deploying drone-mounted thermal infrared sensors to detect and monitor animals in the upper canopy of forests. Traditional methods for surveying canopy animal populations are restricted to the ground and involve trained biologists hiking forest paths with binoculars and a clipboard. Drones can cover a much larger area in a short period of time than a biologist and can survey remote areas inaccessible to people. Thermal sensors can also detect the heat signatures of warm blooded animals that can be identified based on their movement patterns and morphometry. Although drones have great potential as an exciting new conservation research tool they do have limitations that must be factored into any drone-based wildlife study, including an inability to detect animals lower in the canopy, range limitations, and the potential for wildlife disturbance. Here is a link to a short YouTube promotional video describing our findings: https://youtu.be/4PvsI_NTu2AType
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Copyright Taylor & FrancisYouTube
https://youtu.be/4PvsI_NTu2AEmbedded videos
ae974a485f413a2113503eed53cd6c53
10.1080/01431161.2018.1523580
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