Trialling a real-time drone detection and validation protocol for the koala (Phascolarctos cinereus)
Chad T. Beranek A B C D , Adam Roff A B , Bob Denholm A , Lachlan G. Howell B C and Ryan R. Witt B CA Science Division, Department of Planning and Environment, Newcastle, NSW 2300, Australia.
B School of Environmental and Life Sciences, Biology Building, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia.
C FAUNA Research Alliance, PO Box 5092, Kahibah, NSW 2290, Australia.
D Corresponding author. Email: chad.beranek@uon.edu.au
Australian Mammalogy 43(2) 260-264 https://doi.org/10.1071/AM20043
Submitted: 26 May 2020 Accepted: 11 June 2020 Published: 7 July 2020
Abstract
Remotely piloted aircraft system (RPAS), or drone, technology has emerged as a promising survey method for the cryptic koala. We demonstrate an in-field protocol for wild koala RPAS surveys which provides real-time validation of thermal signatures. During 15 trial flights using a quadcopter drone (DJI Matrice 200 v2) we successfully detected and validated koala thermal signatures (n = 12) using two in-field approaches: validation by on-ground observer (n = 10) and validation using 4K footage captured and reviewed directly after the survey (n = 2). We also provide detectability considerations relative to survey time, temperature, wildlife–RPAS interactions and detection of non-target species, which can be used to further inform RPAS survey protocols.
Additional keywords: drone, koala, monitoring, protocol, RPAS, thermal signature validation.
References
Beyer, H. L., de Villiers, D., Loader, J., Robbins, A., Stigner, M., Forbes, N., and Hanger, J. (2018). Management of multiple threats achieves meaningful koala conservation outcomes. Journal of Applied Ecology 55, 1966–1975.| Management of multiple threats achieves meaningful koala conservation outcomes.Crossref | GoogleScholarGoogle Scholar |
Corcoran, E., Denman, S., Hanger, J., Wilson, B., and Hamilton, G. (2019). Automated detection of koalas using low-level aerial surveillance and machine learning. Scientific Reports 9, 3208.
| Automated detection of koalas using low-level aerial surveillance and machine learning.Crossref | GoogleScholarGoogle Scholar | 30824795PubMed |
Cristescu, R. H., Goethals, K., Banks, P. B., Carrick, F. N., and Frere, C. (2012). Experimental evaluation of koala scat persistence and detectability with implications for pellet-based fauna census. International Journal of Zoology 2012, 631856.
| Experimental evaluation of koala scat persistence and detectability with implications for pellet-based fauna census.Crossref | GoogleScholarGoogle Scholar |
Dique, D. S., de Villiers, D. L., and Preece, H. J. (2003). Evaluation of line-transect sampling for estimating koala abundance in the Pine Rivers Shire, south-east Queensland. Wildlife Research 30, 127–133.
| Evaluation of line-transect sampling for estimating koala abundance in the Pine Rivers Shire, south-east Queensland.Crossref | GoogleScholarGoogle Scholar |
Ditmer, M. A., Vincent, J. B., Werden, L. K., Tanner, J. C., Laske, T. G., Laizzo, P. A., Garshelis, D. L., and Fieberg, J. R. (2015). Bears show a physiological but limited behavioral response to unmanned aerial vehicles. Current Biology 25, 2278–2283.
| Bears show a physiological but limited behavioral response to unmanned aerial vehicles.Crossref | GoogleScholarGoogle Scholar | 26279232PubMed |
Ditmer, M. A., Werden, L. K., Tanner, J. C., Vincent, J. B., Callahan, P., Iaizzo, P. A., Laske, T. G., and Garshelis, D. L. (2019). Bears habituate to the repeated exposure of a novel stimulus, unmanned aircraft systems. Conservation Physiology 7, coy067.
| Bears habituate to the repeated exposure of a novel stimulus, unmanned aircraft systems.Crossref | GoogleScholarGoogle Scholar | 30680216PubMed |
Ellis, W., FitzGibbon, S., Melzer, A., Wilson, R., Johnston, S., Bercovitch, F., Dique, D., and Carrick, F. (2013). Koala habitat use and population density: using field data to test the assumptions of ecological models. Australian Mammalogy 35, 160–165.
| Koala habitat use and population density: using field data to test the assumptions of ecological models.Crossref | GoogleScholarGoogle Scholar |
Hamilton, G., Corcoran, E., Denman, S., Hennekam, M. E., and Koh, L. P. (2020). When you can’t see the koalas for the trees: using drones and machine learning in complex environments. Biological Conservation 247, 108598.
| When you can’t see the koalas for the trees: using drones and machine learning in complex environments.Crossref | GoogleScholarGoogle Scholar |
Kays, R., Sheppard, J., McClean, K., Welch, C., Paunescu, C., Wang, V., Kravit, G., and Crofoot, M. (2019). Hot monkey, cold reality: surveying rainforest canopy mammals using drone-mounted thermal infrared sensors. International Journal of Remote Sensing 40, 407–419.
| Hot monkey, cold reality: surveying rainforest canopy mammals using drone-mounted thermal infrared sensors.Crossref | GoogleScholarGoogle Scholar |
Koh, L. P., and Wich, S. A. (2012). Dawn of drone ecology: low-cost autonomous aerial vehicles for conservation. Tropical Conservation Science 5, 121–132.
| Dawn of drone ecology: low-cost autonomous aerial vehicles for conservation.Crossref | GoogleScholarGoogle Scholar |
Law, B. S., Brassil, T., Gonsalves, L., Roe, P., Truskinger, A., and McConville, A. (2018). Passive acoustics and sound recognition provide new insights on status and resilience of an iconic endangered marsupial (koala Phascolarctos cinereus) to timber harvesting. PLoS One 13, e0205075.
| Passive acoustics and sound recognition provide new insights on status and resilience of an iconic endangered marsupial (koala Phascolarctos cinereus) to timber harvesting.Crossref | GoogleScholarGoogle Scholar | 30379836PubMed |
Legge, S., Lindenmayer, D. B., Robinson, N. M., Scheele, B. C., Southwell, D. M., and Wintle, B. A. (2018). ‘Monitoring Threatened Species and Ecological Communities.’ (CSIRO Publishing: Melbourne.)
Lyons, M., Brandis, K., Callaghan, C., McCann, J., Mills, C., Ryall, S., and Kingsford, R. (2018). Bird interactions with drones, from individuals to large colonies. Australian Field Ornithology 35, 51–56.
| Bird interactions with drones, from individuals to large colonies.Crossref | GoogleScholarGoogle Scholar |
McGowan, N. E., Scantlebury, D. M., Maule, A. G., and Marks, N. J. (2018). Measuring the emissivity of mammal pelage. Quantitative Infrared Thermography Journal 15, 214–222.
Melzer, A., Carrick, F., Menkhorst, P., Lunney, D., and John, B. S. (2000). Overview, critical assessment, and conservation implications of koala distribution and abundance. Conservation Biology 14, 619–628.
| Overview, critical assessment, and conservation implications of koala distribution and abundance.Crossref | GoogleScholarGoogle Scholar |
Rhodes, J. R., Tyre, A. J., Jonzen, N., McAlpine, C. A., and Possingham, H. P. (2006). Optimizing presence–absence surveys for detecting population trends. Journal of Wildlife Management 70, 8–18.
| Optimizing presence–absence surveys for detecting population trends.Crossref | GoogleScholarGoogle Scholar |
Vinson, S. G., Johnson, A. P., and Mikac, K. M. (2020). Thermal cameras as a survey method for Australian arboreal mammals: a focus on the greater glider. Australian Mammalogy , .
| Thermal cameras as a survey method for Australian arboreal mammals: a focus on the greater glider.Crossref | GoogleScholarGoogle Scholar |
Weimerskirch, H., Prudor, A., and Schull, Q. (2018). Flights of drones over sub-Antarctic seabirds show species- and status-specific behavioural and physiological responses. Polar Biology 41, 259–266.
| Flights of drones over sub-Antarctic seabirds show species- and status-specific behavioural and physiological responses.Crossref | GoogleScholarGoogle Scholar |
Wilmott, L., Cullen, D., Madani, G., Krogh, M., and Madden, K. (2019). Are koalas detected more effectively by systematic spotlighting or diurnal searches? Australian Mammalogy 41, 157–160.
| Are koalas detected more effectively by systematic spotlighting or diurnal searches?Crossref | GoogleScholarGoogle Scholar |