Drone thermal imaging technology provides a cost-effective tool for landscape-scale monitoring of a cryptic forest-dwelling species across all population densities
Lachlan G. Howell
A B C G , John Clulow A C , Neil R. Jordan D E , Chad T. Beranek A C , Shelby A. Ryan A C , Adam Roff A F and Ryan R. Witt A C
+ Author Affiliations
- Author Affiliations
A School of Environmental and Life Sciences, Biology Building, University of Newcastle, University Drive, Callaghan, NSW 2308, Australia.
B Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University Geelong, Melbourne Burwood Campus, 221 Burwood Highway, Burwood, Vic. 3125, Australia
C FAUNA Research Alliance, PO Box 5092, Kahibah, NSW 2290, Australia.
D Centre for Ecosystem Science, School of BEES, University of New South Wales (UNSW Sydney), Sydney, NSW 2052, Australia.
E Taronga Institute of Science and Learning, Taronga Conservation Society Australia, Taronga Western Plains Zoo, Dubbo, NSW 2830, Australia.
F Science Division, Department of Planning, Industry and Environment, Newcastle, NSW 2300, Australia.
G Corresponding author. Email: lachlan.howell@newcastle.edu.au
Wildlife Research 49(1) 66-78 https://doi.org/10.1071/WR21034
Submitted: 11 February 2021 Accepted: 17 September 2021 Published: 20 December 2021
Journal Compilation © CSIRO 2022 Open Access CC BY-NC
Abstract
Context: Drones, or remotely piloted aircraft systems, equipped with thermal imaging technology (RPAS thermal imaging) have recently emerged as a powerful monitoring tool for koala populations. Before wide uptake of novel technologies by government, conservation practitioners and researchers, evidence of greater efficiency and cost-effectiveness than with other available methods is required.
Aims: We aimed to provide the first comprehensive analysis of the cost-effectiveness of RPAS thermal imaging for koala detection against two field-based methods, systematic spotlighting (Spotlight) and the refined diurnal radial search component of the spot-assessment technique (SAT).
Methods: We conducted various economic comparisons, particularly comparative cost-effectiveness of RPAS thermal imaging, Spotlight and SAT for repeat surveys of a low-density koala population. We compared methods on cost-effectiveness as well as long-term costs by using accumulating cost models. We also compared detection costs across population density using a predictive cost model.
Key results: Despite substantial hardware, training and licensing costs at the outset (>A$49 900), RPAS thermal imaging surveys were cost-effective, detecting the highest number of koalas per dollar spent. Modelling also suggested that RPAS thermal imaging requires the lowest survey effort to detect koalas within the range of publicly available koala population densities (~0.006–18 koalas ha−1) and would provide long-term cost reductions across longitudinal monitoring programs. RPAS thermal imaging would also require the lowest average survey effort costs at a landscape scale (A$3.84 ha−1), providing a cost-effective tool across large spatial areas.
Conclusions: Our analyses demonstrated drone thermal imaging technology as a cost-effective tool for conservation practitioners monitoring koala populations. Our analyses may also form the basis of decision-making tools to estimate survey effort or total program costs across any koala population density.
Implications: Our novel approach offers a means to perform various economic comparisons of available survey techniques and guide investment decisions towards developing standardised koala monitoring approaches. Our results may assist stakeholders and policymakers to confidently invest in RPAS thermal imaging technology and achieve optimal conservation outcomes for koala populations, with standardised data collection delivered through evidence-based and cost-effective monitoring programs.
Keywords: cost-effectiveness, density, drones, koala, population monitoring, Phascolarctos cinereus, survey methods.
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