Issue |
E3S Web Conf.
Volume 211, 2020
The 1st JESSD Symposium: International Symposium of Earth, Energy, Environmental Science and Sustainable Development 2020
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Article Number | 04007 | |
Number of page(s) | 13 | |
Section | Digitalization and Sustainability | |
DOI | https://doi.org/10.1051/e3sconf/202021104007 | |
Published online | 25 November 2020 |
Possibility of applying unmanned aerial vehicle and thermal imaging in several canopy cover class for wildlife monitoring – preliminary results
1
Department of Forest Resources Conservation and Ecotourism, Faculty of Forestry and Environment, IPB University (Bogor Agricultural University), Kampus IPB Darmaga Bogor 16680 Indonesia.
2
Tropical Biodiversity Conservation Program, Faculty of Forestry and Environment, IPB University (Bogor Agricultural University), Kampus IPB Darmaga Bogor 16680 Indonesia.
* Corresponding author: dede.auliarahman@gmail.com
Tropical rainforests are one of the important habitats on earth but are rarely explored because they are difficult to access, making their cryptic animals challenging to monitor. Unmanned aerial vehicle (UAV) with thermal infrared imaging (TIR) technology is gaining entry into wildlife research and monitoring. The researcher tested the possibility of applying DJI Mavic 2 Enterprise Dual with FLIR as aerial survey platforms to wildlife in the five tree density classes in the IPB University Campus. To assess the effectiveness of using drones in detecting wildlife, the researcher measured the optimum flying height, sound level, temperature, and optimum flight time in each canopy cover class. The optimum height for animal detection is <50 m HAGL with a sound level that animals can still tolerate. Wildlife detected had body temperatures around 27 °C and were conspicuous in the thermal infrared imagery at night and early morning when the forest canopy was cool (15–27°C), but were difficult to detect by mid-day. By that time, the direct sunshine had heated up canopy vegetation to over 30°C. Species were difficult to identify from thermal infrared imagery alone but could be recognized from synchronized visual images taken during the daytime.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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