title: Estimating distribution of hidden objects with drones: from tennis balls to manatees.
abstract: Unmanned aerial vehicles (UAV), or drones, have been used widely in military applications, but more recently civilian applications have emerged (e.g., wildlife population monitoring, traffic monitoring, law enforcement, oil and gas pipeline threat detection). UAV can have several advantages over manned aircraft for wildlife surveys, including reduced ecological footprint, increased safety, and the ability to collect high-resolution geo-referenced imagery that can document the presence of species without the use of a human observer. We illustrate how geo-referenced data collected with UAV technology in combination with recently developed statistical models can improve our ability to estimate the distribution of organisms. To demonstrate the efficacy of this methodology, we conducted an experiment in which tennis balls were used as surrogates of organisms to be surveyed. We used a UAV to collect images of an experimental field with a known number of tennis balls, each of which had a certain probability of being hidden. We then applied spatially explicit occupancy models to estimate the number of balls and created precise distribution maps. We conducted three consecutive surveys over the experimental field and estimated the total number of balls to be 328 (95%CI: 312, 348). The true number was 329 balls, but simple counts based on the UAV pictures would have led to a total maximum count of 284. The distribution of the balls in the field followed a simulated environmental gradient. We also were able to accurately estimate the relationship between the gradient and the distribution of balls. Our experiment demonstrates how this technology can be used to create precise distribution maps in which discrete regions of the study area are assigned a probability of presence of an object. Finally, we discuss the applicability and relevance of this experimental study to the case study of Florida manatee distribution at power plants.
authors: Florida Fish and Wildlife Conservation Commission, Fish and Wildlife Research Institute, St Petersburg, Florida, United States of America. Julien.Martin@myfwc.com
authors: Julien
authors: J
authors: Martin
authors: Holly H
authors: HH
authors: Edwards
authors: Matthew A
authors: MA
authors: Burgess
authors: H Franklin
authors: HF
authors: Percival
authors: Daniel E
authors: DE
authors: Fagan
authors: Beth E
authors: BE
authors: Gardner
authors: Joel G
authors: JG
authors: Ortega-Ortiz
authors: Peter G
authors: PG
authors: Ifju
authors: Brandon S
authors: BS
authors: Evers
authors: Thomas J
authors: TJ
authors: Rambo
mesh_terms: D000401:Aircraft; D000818:Animals; D003198:Computer Simulation; D004777:Environment; D006801:Humans; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D019876:Sports Equipment; D013715:Tennis; D020455:Trichechus