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Unmanned Aerial Vehicles (UAVs) for Surveying Marine Fauna: A Dugong Case Study

Overview of attention for article published in PLOS ONE, November 2013
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (92nd percentile)

Mentioned by

news
1 news outlet
blogs
1 blog
policy
1 policy source
twitter
32 X users
facebook
3 Facebook pages

Citations

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306 Dimensions

Readers on

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666 Mendeley
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Title
Unmanned Aerial Vehicles (UAVs) for Surveying Marine Fauna: A Dugong Case Study
Published in
PLOS ONE, November 2013
DOI 10.1371/journal.pone.0079556
Pubmed ID
Authors

Amanda Hodgson, Natalie Kelly, David Peel

Abstract

Aerial surveys of marine mammals are routinely conducted to assess and monitor species' habitat use and population status. In Australia, dugongs (Dugong dugon) are regularly surveyed and long-term datasets have formed the basis for defining habitat of high conservation value and risk assessments of human impacts. Unmanned aerial vehicles (UAVs) may facilitate more accurate, human-risk free, and cheaper aerial surveys. We undertook the first Australian UAV survey trial in Shark Bay, western Australia. We conducted seven flights of the ScanEagle UAV, mounted with a digital SLR camera payload. During each flight, ten transects covering a 1.3 km(2) area frequently used by dugongs, were flown at 500, 750 and 1000 ft. Image (photograph) capture was controlled via the Ground Control Station and the capture rate was scheduled to achieve a prescribed 10% overlap between images along transect lines. Images were manually reviewed post hoc for animals and scored according to sun glitter, Beaufort Sea state and turbidity. We captured 6243 images, 627 containing dugongs. We also identified whales, dolphins, turtles and a range of other fauna. Of all possible dugong sightings, 95% (CI = 90%, 98%) were subjectively classed as 'certain' (unmistakably dugongs). Neither our dugong sighting rate, nor our ability to identify dugongs with certainty, were affected by UAV altitude. Turbidity was the only environmental variable significantly affecting the dugong sighting rate. Our results suggest that UAV systems may not be limited by sea state conditions in the same manner as sightings from manned surveys. The overlap between images proved valuable for detecting animals that were masked by sun glitter in the corners of images, and identifying animals initially captured at awkward body angles. This initial trial of a basic camera system has successfully demonstrated that the ScanEagle UAV has great potential as a tool for marine mammal aerial surveys.

X Demographics

X Demographics

The data shown below were collected from the profiles of 32 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 666 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 <1%
United Kingdom 3 <1%
South Africa 2 <1%
Argentina 2 <1%
Mexico 2 <1%
France 1 <1%
Ecuador 1 <1%
Australia 1 <1%
Italy 1 <1%
Other 8 1%
Unknown 639 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 141 21%
Researcher 116 17%
Student > Ph. D. Student 95 14%
Student > Bachelor 72 11%
Other 43 6%
Other 79 12%
Unknown 120 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 216 32%
Environmental Science 174 26%
Engineering 37 6%
Earth and Planetary Sciences 32 5%
Computer Science 15 2%
Other 45 7%
Unknown 147 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 38. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 July 2021.
All research outputs
#1,101,031
of 25,866,425 outputs
Outputs from PLOS ONE
#14,018
of 225,574 outputs
Outputs of similar age
#9,800
of 228,995 outputs
Outputs of similar age from PLOS ONE
#398
of 5,171 outputs
Altmetric has tracked 25,866,425 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 225,574 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.9. This one has done particularly well, scoring higher than 93% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 228,995 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 5,171 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.