Accurate and precise monitoring of species abundance is essential for determining population trends and responses to environmental change. However, traditional population survey methods can be unreliable and labour-intensive, which complicates the effective conservation and management of many threatened species. We developed a method of using drone-acquired thermal orthomosaics to monitor the abundance of grey-headed flying-foxes (Pteropus poliocephalus) within tree roosts, an IUCN Red Listed species of bat. We assessed the accuracy and precision of this new method and evaluated the performance of four semi-automated methods for counting flying-foxes in thermal orthomosaics, including machine learning and Computer Vision (CV) methods. We found a high concordance between the number of flying-foxes manually counted in drone-acquired thermal imagery and the true abundance of flying-foxes in single roost trees, as obtained from direct on-ground observation. This indicated that the number of flying-foxes observed in thermal imagery accurately reflected the true abundance of flying-foxes. In addition, for thermal orthomosaics of whole roost sites, the number of flying-foxes manually counted was highly repeatable between the same-day drone surveys and human counters, indicating that this method produced highly precise abundance estimates independent of the identity/experience of human counters. Finally, the number of flying-foxes manually counted in drone-acquired thermal orthomosaics was highly concordant with the counts derived from CV and machine learning-enabled classification techniques. This indicated that accurate and precise measures of colony abundance can be obtained semi-automatically, thus greatly reducing the amount of human effort involved for obtaining abundance estimates. Our method is thus valuable for reliably monitoring the abundance of individuals in flying-fox roosts and will aid in the conservation and management of this globally threatened group of flying-mammals, as well as other homeothermic arboreal-roosting species.
CITATION STYLE
McCarthy, E. D., Martin, J. M., Boer, M. M., & Welbergen, J. A. (2021). Drone-based thermal remote sensing provides an effective new tool for monitoring the abundance of roosting fruit bats. Remote Sensing in Ecology and Conservation, 7(3), 461–474. https://doi.org/10.1002/rse2.202
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