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Zoometric data extraction from drone imagery: the Arabian oryx (Oryx leucoryx)

Published online by Cambridge University Press:  22 July 2021

Meyer E de Kock Open the ORCID record for Meyer E de Kock [Opens in a new window] ,
Declan O’Donovan ,
Tamer Khafaga Open the ORCID record for Tamer Khafaga [Opens in a new window]  and
Pavla Hejcmanová Open the ORCID record for Pavla Hejcmanová [Opens in a new window]
Meyer E de Kock*
Affiliation:
Faculty of Tropical AgriSciences, Czech University of Life Sciences Prague, Kamýcká 129, 165 00 Praha-Suchdol, Czechia
Declan O’Donovan
Affiliation:
Wadi Al Safa Wildlife Centre, Dubai, United Arab Emirates Fota Wildlife Park, Carrigtwohill, Co. Cork, Ireland
Tamer Khafaga
Affiliation:
Diversidad Biologica y Medio Ambiete, Facultad de Ciencias, Universidad Malaga, Malaga, Spain Dubai Desert Conservation Reserve, Dubai, United Arab Emirates
Pavla Hejcmanová
Affiliation:
Faculty of Tropical AgriSciences, Czech University of Life Sciences Prague, Kamýcká 129, 165 00 Praha-Suchdol, Czechia
*
Author for correspondence: Meyer E de Kock, Email: [email protected]
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Summary

Data extraction from unmanned aerial vehicle (UAV) imagery has proved effective in animal surveys and monitoring, but to date has scarcely been used for detailed population analysis and individual animal feature extraction. We assessed the zoometric and feature extraction of the Arabian oryx (Oryx leucoryx) using data acquired from a captive population for comparison with reintroduced populations monitored by UAVs. Highly accurate scaled and geo-rectified imagery derived from UAV surveys allowed precise morphometric measurements of the oryx. The scaled top-view imagery combined with baseline data from known sex, age, weight and pregnancy status of captive individuals were used to develop predictive models. A bracketed index developed from the predictive models showed high accuracy for classifying the age group ≤16 months, animals with a weight >80 kg and pregnancy. The pregnancy classification decision tree model performed with 91.7% accuracy. The polynomial weight predictive model performed well with relatively high accuracy when using the total top-view surface measurement. Photogrammetrically processed UAV-acquired imagery can yield valuable zoometric data, feature extraction and modelling; it is a tool with a practical application for field biologists that can assist in the decision-making process for species conservation management.

Keywords

aerial imageryantelope conservationdronelarge mammal monitoringphotogrammetrywildlife management
Type
Research Paper
Information
Environmental Conservation , Volume 48 , Issue 4 , December 2021 , pp. 295 - 300
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Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of Foundation for Environmental Conservation

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