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Unmanned Aerial Survey of Elephants

Overview of attention for article published in PLOS ONE, February 2013
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Title
Unmanned Aerial Survey of Elephants
Published in
PLOS ONE, February 2013
DOI 10.1371/journal.pone.0054700
Pubmed ID
Authors

Cédric Vermeulen, Philippe Lejeune, Jonathan Lisein, Prosper Sawadogo, Philippe Bouché

Abstract

The use of a UAS (Unmanned Aircraft System) was tested to survey large mammals in the Nazinga Game Ranch in the south of Burkina Faso. The Gatewing ×100™ equipped with a Ricoh GR III camera was used to test animal reaction as the UAS passed, and visibility on the images. No reaction was recorded as the UAS passed at a height of 100 m. Observations, made on a set of more than 7000 images, revealed that only elephants (Loxodonta africana) were easily visible while medium and small sized mammals were not. The easy observation of elephants allows experts to enumerate them on images acquired at a height of 100 m. We, therefore, implemented an aerial strip sample count along transects used for the annual wildlife foot count. A total of 34 elephants were recorded on 4 transects, each overflown twice. The elephant density was estimated at 2.47 elephants/km(2) with a coefficient of variation (CV%) of 36.10%. The main drawback of our UAS was its low autonomy (45 min). Increased endurance of small UAS is required to replace manned aircraft survey of large areas (about 1000 km of transect per day vs 40 km for our UAS). The monitoring strategy should be adapted according to the sampling plan. Also, the UAS is as expensive as a second-hand light aircraft. However the logistic and flight implementation are easier, the running costs are lower and its use is safer. Technological evolution will make civil UAS more efficient, allowing them to compete with light aircraft for aerial wildlife surveys.

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Geographical breakdown

Country Count As %
Malaysia 1 <1%
France 1 <1%
Botswana 1 <1%
Brazil 1 <1%
South Africa 1 <1%
Czechia 1 <1%
United Kingdom 1 <1%
Mexico 1 <1%
Argentina 1 <1%
Other 2 <1%
Unknown 292 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 67 22%
Student > Ph. D. Student 49 16%
Researcher 43 14%
Student > Bachelor 39 13%
Student > Postgraduate 15 5%
Other 41 14%
Unknown 49 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 103 34%
Environmental Science 80 26%
Engineering 16 5%
Earth and Planetary Sciences 13 4%
Computer Science 10 3%
Other 27 9%
Unknown 54 18%