Literature DB >> 28178755

Unmanned aerial vehicles for surveying marine fauna: assessing detection probability.

Amanda Hodgson1, David Peel2, Natalie Kelly2.   

Abstract

Aerial surveys are conducted for various fauna to assess abundance, distribution, and habitat use over large spatial scales. They are traditionally conducted using light aircraft with observers recording sightings in real time. Unmanned Aerial Vehicles (UAVs) offer an alternative with many potential advantages, including eliminating human risk. To be effective, this emerging platform needs to provide detection rates of animals comparable to traditional methods. UAVs can also acquire new types of information, and this new data requires a reevaluation of traditional analyses used in aerial surveys; including estimating the probability of detecting animals. We conducted 17 replicate UAV surveys of humpback whales (Megaptera novaeangliae) while simultaneously obtaining a 'census' of the population from land-based observations, to assess UAV detection probability. The ScanEagle UAV, carrying a digital SLR camera, continuously captured images (with 75% overlap) along transects covering the visual range of land-based observers. We also used ScanEagle to conduct focal follows of whale pods (n = 12, mean duration = 40 min), to assess a new method of estimating availability. A comparison of the whale detections from the UAV to the land-based census provided an estimated UAV detection probability of 0.33 (CV = 0.25; incorporating both availability and perception biases), which was not affected by environmental covariates (Beaufort sea state, glare, and cloud cover). According to our focal follows, the mean availability was 0.63 (CV = 0.37), with pods including mother/calf pairs having a higher availability (0.86, CV = 0.20) than those without (0.59, CV = 0.38). The follows also revealed (and provided a potential correction for) a downward bias in group size estimates from the UAV surveys, which resulted from asynchronous diving within whale pods, and a relatively short observation window of 9 s. We have shown that UAVs are an effective alternative to traditional methods, providing a detection probability that is within the range of previous studies for our target species. We also describe a method of assessing availability bias that represents spatial and temporal characteristics of a survey, from the same perspective as the survey platform, is benign, and provides additional data on animal behavior.
© 2017 by the Ecological Society of America.

Entities:  

Keywords:  abundance; aerial survey; availability; behavior; cetaceans; digital photography; drones; images; unmanned aerial systems; unmanned aircraft systems

Mesh:

Year:  2017        PMID: 28178755     DOI: 10.1002/eap.1519

Source DB:  PubMed          Journal:  Ecol Appl        ISSN: 1051-0761            Impact factor:   4.657


  8 in total

1.  First Unmanned Aerial Vehicle Observation of Epimeletic Behavior in Indo-Pacific Humpback Dolphins.

Authors:  Tabris Yik-To Chung; Heysen Hei-Nam Ho; Henry Chun-Lok Tsui; Brian Chin-Wing Kot
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2.  Measuring behavioral responses of sea turtles, saltwater crocodiles, and crested terns to drone disturbance to define ethical operating thresholds.

Authors:  Elizabeth Bevan; Scott Whiting; Tony Tucker; Michael Guinea; Andrew Raith; Ryan Douglas
Journal:  PLoS One       Date:  2018-03-21       Impact factor: 3.240

3.  The use of Unmanned Aerial Vehicles (UAVs) to sample the blow microbiome of small cetaceans.

Authors:  Cinzia Centelleghe; Lisa Carraro; Joan Gonzalvo; Massimiliano Rosso; Erika Esposti; Claudia Gili; Marco Bonato; Davide Pedrotti; Barbara Cardazzo; Michele Povinelli; Sandro Mazzariol
Journal:  PLoS One       Date:  2020-07-02       Impact factor: 3.240

4.  A new method to control error rates in automated species identification with deep learning algorithms.

Authors:  Sébastien Villon; David Mouillot; Marc Chaumont; Gérard Subsol; Thomas Claverie; Sébastien Villéger
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5.  Remote sensing techniques for automated marine mammals detection: a review of methods and current challenges.

Authors:  Esteban N Rodofili; Vincent Lecours; Michelle LaRue
Journal:  PeerJ       Date:  2022-06-20       Impact factor: 3.061

6.  Estimating uncertainty in density surface models.

Authors:  David L Miller; Elizabeth A Becker; Karin A Forney; Jason J Roberts; Ana Cañadas; Robert S Schick
Journal:  PeerJ       Date:  2022-08-23       Impact factor: 3.061

7.  Monitoring abundance of aggregated animals (Florida manatees) using an unmanned aerial system (UAS).

Authors:  Holly H Edwards; Jeffrey A Hostetler; Bradley M Stith; Julien Martin
Journal:  Sci Rep       Date:  2021-06-21       Impact factor: 4.379

8.  A comparison of baleen whale density estimates derived from overlapping satellite imagery and a shipborne survey.

Authors:  C C G Bamford; N Kelly; L Dalla Rosa; D E Cade; P T Fretwell; P N Trathan; H C Cubaynes; A F C Mesquita; L Gerrish; A S Friedlaender; J A Jackson
Journal:  Sci Rep       Date:  2020-07-31       Impact factor: 4.379

  8 in total

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