Accuracy and precision of citizen scientist animal counts from drone imagery

Repeated counts of animal abundance can reveal changes in local ecosystem health and inform conservation strategies. Unmanned aircraft systems (UAS), also known as drones, are commonly used to photograph animals in remote locations; however, counting animals in images is a laborious task. Crowd-sour...

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Bibliographic Details
Published in:PloS one 2021-02, Vol.16 (2), p.e0244040-e0244040
Main Authors: Wood, Sarah A, Robinson, Patrick W, Costa, Daniel P, Beltran, Roxanne S
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Animal behavior
Animal populations
Animals
Aquatic mammals
Biology and Life Sciences
Breeding seasons
Cameras
Censuses
Citizen scientists
Conservation
Crowdsourcing
Drone aircraft
Drones
Earth Sciences
Environmental changes
Error analysis
Error reduction
Evaluation
Evolutionary biology
Geospatial data
Imagery
Marine mammals
Methods
Observations
Otariidae
People and Places
Project design
Remote sensing
Research and Analysis Methods
Scholarships & fellowships
Science Policy
Scientists
Sea lions
Seals
Statistics
Supervision
Unmanned aerial vehicles
Unmanned aircraft
Websites
Wildlife conservation
Zoological research
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Summary:Repeated counts of animal abundance can reveal changes in local ecosystem health and inform conservation strategies. Unmanned aircraft systems (UAS), also known as drones, are commonly used to photograph animals in remote locations; however, counting animals in images is a laborious task. Crowd-sourcing can reduce the time required to conduct these censuses considerably, but must first be validated against expert counts to measure sources of error. Our objectives were to assess the accuracy and precision of citizen science counts and make recommendations for future citizen science projects. We uploaded drone imagery from Año Nuevo Island (California, USA) to a curated Zooniverse website that instructed citizen scientists to count seals and sea lions. Across 212 days, over 1,500 volunteers counted animals in 90,000 photographs. We quantified the error associated with several descriptive statistics to extract a single citizen science count per photograph from the 15 repeat counts and then compared the resulting citizen science counts to expert counts. Although proportional error was relatively low (9% for sea lions and 5% for seals during the breeding seasons) and improved with repeat sampling, the 12+ volunteers required to reduce error was prohibitively slow, taking on average 6 weeks to estimate animals from a single drone flight covering 25 acres, despite strong public outreach efforts. The single best algorithm was 'Median without the lowest two values', demonstrating that citizen scientists tended to under-estimate the number of animals present. Citizen scientists accurately counted adult seals, but accuracy was lower when sea lions were present during the summer and could be confused for seals. We underscore the importance of validation efforts and careful project design for researchers hoping to combine citizen science with imagery from drones, occupied aircraft, and/or remote cameras.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0244040