Drone‐based thermal remote sensing provides an effective new tool for monitoring the abundance of roosting fruit bats
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 threat...
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Veröffentlicht in: | Remote sensing in ecology and conservation 2021-09, Vol.7 (3), p.461-474 |
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Zusammenfassung: | 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.
Fruit bats are globally threatened, yet important ecosystem engineers. Population monitoring is difficult, traditionally with significant error. This study shows that the number of flying‐foxes roosting in a colony can be determined by counting individuals in thermal orthomosaics either manually or using semi‐automated methods. A semi‐automated Computer Vision workflow has the best performance, compared with object‐based ima |
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ISSN: | 2056-3485 2056-3485 |
DOI: | 10.1002/rse2.202 |