Open AccessDrone‐based thermal remote sensing provides an effective new tool for monitoring the abundance of roosting fruit bats
Author(s) -
McCarthy Eliane D.,
Martin John M.,
Boer Matthias M.,
Welbergen Justin A.
Publication year - 2021
Publication title -
remote sensing in ecology and conservation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.191
H-Index - 21
ISSN - 2056-3485
DOI - 10.1002/rse2.202
Subject(s) - abundance (ecology) , drone , aerial survey , threatened species , population , remote sensing , ecology , biology , geography , habitat , genetics , demography , sociology
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.