Drones count wildlife more accurately and precisely than humans
By Jarrod C. Hodgson, Rowan Mott, Shane Baylis, Trung Pham, Simon Wotherspoon, A. Kilpatrick, R. R. Segaran, Ian Reid, A. Terauds, Lian Pin Koh. bioRxiv, 2017
379 citations recorded by the source corpus.
Ecologists are increasingly using technology to improve the quality of data collected on wildlife, particularly for assessing the environmental impacts of human activities. Remotely Piloted Aircraft Systems (RPAS; commonly known as ‘drones’) are widely touted as a cost-effective way to collect high quality wildlife population data, however, the validity of these claims is unclear. Using life-sized seabird colonies containing a known number of replica birds, we show that RPAS-derived data are, on average, between 43% and 96% more accurate than data from the traditional ground-based collection method. We also demonstrate that counts from this remotely sensed imagery can be semi-automated with a high degree of accuracy. The increased accuracy and precision of RPAS-derived wildlife monitoring data provides greater statistical power to detect fine-scale population fluctuations allowing for more informed and proactive ecological management.
Subfields: Ecological Modeling, Ecology
View Paper: https://www.semanticscholar.org/paper/166605aba5fbd7876ebb4f6b8252aa813937a43c
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In Ecological Modeling
In Ecology
- Margaret C. Crofoot
- Martin Wikelski
- R. Kays
- Sara Beery
- Devis Tuia
- Holger Klinck
- Grant Van Horn
- Iain D. Couzin
Citations in the corpus
- Perspectives in machine learning for wildlife conservation (2021, background): “For example, humans conducting counts of seabird colonies (12) and bats emerging from cave roosts (13) tend to significantly underestimate the number of individuals present.”