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The use of unmanned aerial vehicles (UAVs) equipped with multispectral and thermal sensors provides a promising approach to wildlife monitoring, especially in the dynamic environment of Komodo National Park. This study explores the effectiveness of UAVs in tracking Komodo dragons and other wildlife using thermal imaging, which distinguishes animals based on body temperature contrasts with the surrounding environment. Thermal sensors detect wildlife more effectively in the afternoon, as animals like the Komodo dragon exhibit higher body temperatures compared to the cooler surroundings. Challenges, however, arise in the morning when animals body temperatures are closer to the environment, making them harder to detect. Factors such as fog, animal movement, and sensor limitations also impact detection accuracy. The study highlights the advantages of combining UAV thermal imaging with multispectral data to enhance monitoring accuracy. Despite the challenges, this method proves to be an efficient tool for wildlife management and conservation in remote, vast areas like Komodo National Park.
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Tom
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315--329
Opis fizyczny
Bibliogr. 36 poz., rys., tab.
Twórcy
autor
- Tropical Biodiversity Conservation Study Program, Faculty of Forestry and Environment, IPB University, Bogor, Indonesia
autor
- Department of Forest Conservation and Ecotorism, Faculty of Forestry and Environment, IPB University, Bogor, Indonesia
autor
- Department of Forest Conservation and Ecotorism, Faculty of Forestry and Environment, IPB University, Bogor, Indonesia
autor
- Department of Forest Conservation and Ecotorism, Faculty of Forestry and Environment, IPB University, Bogor, Indonesia
Bibliografia
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- 10. Harlow, HJ, Purwandana, D., Jessop, TS, Phillips, J.A. (2010). Body temperature and thermoregulation of Komodo dragons in the Field. Thermal Biology. 35, 338–347. https://doi.org/10.1016/j. jtherbio.2010.07.002
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- 23. Novianto. (2024). Unmanned aerial vehicle technology for quantitative morphometry and geomorphic process – study case in rotational landslide deposited area. Ecological Engineering & Environmental Technology, 25(8), 89–95. https://doi.org/10.12912/27197050/189284
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- 25. Paredes, H.H.R., Suazo W.C.M., Calderon J.G.A., Quijano, S.A.C. (2023). Design of a drone that applies multisensor information for the early detection of forest fires. IEEE. https://doi.org/10.1109/ RAAI59955.2023.10601205
- 26. Povlsen, P., Bhrun, D., Durdevic, P., Arroyo, DO, Pertoldi, C. (2023). Using yolo object detection to identify hare and roe deer in thermal aerial video footage-possible future applications in real-time automatic drone surveillance and wildlife monitoring. Drones, 8(2). https://doi.org/10.3390/ drones8010002
- 27. Price, S.J., Leung, W.T.M., Owen, C., Sergeant, C., Cunningham, AA, Balloux, F., Garner, T.W.J., Nichols, R.A. (2018). Temperature is a key driver of a wildlife epidemic and future warming will increase impacts. bioRxiv. https://doi.org/10.1101/272369
- 28. Purwandana, D., Ariefiandy, A., Azmi, M., Nasu, S.A., Sahudin., Dos, A.A., Jessop, T.S. (2022). Turning ghosts into dragons improving camera monitoring outcomes for a cryptic low-density Komodo dragon population in Eastern Indonesia. Csiro, 49, 295–302. https://doi.org/10.1071/WR21057
- 29. Rietz, J., Calkoen, STS, Ferry, N., Schluter, J., Wehner, H., Schindlatz, K.H., Lackner, T., Hoermann, C.V., Conraths, F.J., Muller J., Heurich, M. (2023). Drone-based thermal imaging in the detection of wildlife carcasses and disease Management. Transboundary and Emerging Diseases. 2023. https://doi.org/10.1155/2023/5517000
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- 32. Warwick, C. (2003). Observations on disease associated preferred body temperatures in reptiles. Applied Animal Behaviour Science, 28(4), 375–380. https://doi.org/10.1016/0168-1591(91)90169-X
- 33. Verfuss, UK, Aniceto, AS, Harris, DV, Gillespie, D., Fielding, S., Jimenez, G., Johnston, P., Sinclair, RR, Sivertsen, A., Solbo, SA, Storvold, R., Biuw, M., Wyatt, R. (2019). A review of unmanned vehicles for the detection and monitoring of marine fauna. Elsevier B.V, 140, 17–29. https://doi.org/10.1016/j. marpolbul.2019.01.009
- 34. Wijayanto, A.K., Prasetyo, L.B., Hudjimartsu, S.A., Sigit, G., Hongo, C. (2024). Textural features for BLB disease damage assessment in paddy fields using drone data and machine learning: Enhancing disease detection accuracy. Elsevier B.V. https://doi.org/10.1016/j.atech.2024.100498
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- 36. Gonzalez F., Johnson, S. (2017). Standard operating procedures for UAV or drone based monitoring of wildlife. In Turner, D (Ed.) Proceedings of the UAS4RS 2017 (Unmanned Aircraft Systems for Remote Sensing) Conference. TerraLuma Research Group UAS Remote Sensing, University of Tasmania, Australia, 1–7. https://eprints.qut.edu.au/108859/
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8fdb0dd8-e7c3-4c8d-95ee-705b2e5df21a
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