Rapid condition monitoring of an endangered marine vertebrate using precise, non-invasive morphometrics

Hodgson, Jarrod C.; Holman, Dirk; Terauds, Aleks; Koh, Lian Pin; Goldsworthy, Simon

doi:10.1016/j.biocon.2019.108402

Cited by 20 publications

(41 citation statements)

References 59 publications

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“…In this study, the MAE for the handheld SVC was 6.31 cm (percent error 1.34% ± 0.16 SE) and Siegfried et al [ 30 ] measured percent error to be 0.98% (±0.01). In addition, Hodgson et al [ 57 ] reported the estimated slope for a linear regression comparing standard length from physical and photogrammetric measurements was 1.02 (95% CI 0.98—1.07) when measuring Australian sea lions ( Neophoca cinerea ), which is similar to our slope estimate for green sea turtles of 1.0855 (±0.099 SE). Dawson et al [ 23 ] tested the accuracy of photogrammetry using a UAV equipped with a gimballed micro4/3 camera and 25 mm lens with the objective of measuring southern right whales ( Eubalaena australis ).…”

Section: Discussionsupporting

confidence: 87%

Design and fabrication of a stereo-video camera equipped unoccupied aerial vehicle for measuring sea turtles, sharks, and other marine fauna

Piacenza

Faller

et al. 2022

PLoS ONE

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The recent commercialization of unoccupied aerial vehicles (UAVs) has facilitated their incorporation into a variety of ecological studies. While UAVs are able to provide accurate visual data of marine species from an aerial perspective, these devices have some limitations that make measuring marine animals below the surface challenging. Many marine organisms are often visible from the air, but are deeper in the water column, and current methods cannot measure animals below the surface. Here, we developed and tested a stereo-video camera (SVC) system that was mounted onto a commercially-available UAV. We used the SVC-UAV to conduct remote body-size measurements for two marine species: the green sea turtle (Chelonia mydas) and the nurse shark (Ginglymostoma cirratum). When comparing SVC measurements to those taken by hand, the SVC-UAV had a mean absolute error (MAE) of 4.44 cm (n = 6; mean percent error (MPE) = 10.6%) for green sea turtles and 7.16 cm absolute error (n = 1; PE = 3.6%) for the nurse shark. Using a linear model, we estimated the slope of the SVC versus hand measurements for green sea turtles to be 1.085 (±0.099 SE), and accounting for the standard error, a measurement bias was not apparent. Using model selection, based on a global model predicting MAE from animal distance to the SVC and body size, the top ranked model was the intercept-only model. This indicates that neither animal distance nor body size strongly influenced measurement error. Incorporating SVC systems into UAVs can allow for relatively accurate measurements of near surface-dwelling marine species. To our knowledge, there is no other stand-alone SVC for UAVs available that offers similar accuracy and utility.

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Section: Discussionsupporting

confidence: 87%

Design and fabrication of a stereo-video camera equipped unoccupied aerial vehicle for measuring sea turtles, sharks, and other marine fauna

Piacenza

Faller

et al. 2022

PLoS ONE

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“…In addition to reducing disturbance, drones also offer several advantages for data capture and research outcomes. The imagery captured creates a permanent record of the data, which reduces observer bias [97], it is faster than observer counts, so there is less time for individuals to move, and correctly captured data can also be used to create spatially accurate orthomosaics, digital surface models (DSMs), and 3-dimensional models for animal health metrics and condition assessment [98,99]. UAV pup counts also detected up to 32% more pups than observer counts [100].…”

Section: Pinniped Traitsmentioning

confidence: 99%

Operational Protocols for the Use of Drones in Marine Animal Research

Raoult

Colefax²,

Allan

et al. 2020

Drones

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The use of drones to study marine animals shows promise for the examination of numerous aspects of their ecology, behaviour, health and movement patterns. However, the responses of some marine phyla to the presence of drones varies broadly, as do the general operational protocols used to study them. Inconsistent methodological approaches could lead to difficulties comparing studies and can call into question the repeatability of research. This review draws on current literature and researchers with a wealth of practical experience to outline the idiosyncrasies of studying various marine taxa with drones. We also outline current best practice for drone operation in marine environments based on the literature and our practical experience in the field. The protocols outlined herein will be of use to researchers interested in incorporating drones as a tool into their research on marine animals and will help form consistent approaches for drone-based studies in the future.

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“…An additional consideration for UAS photogrammetry is selecting an approach for scaling each photograph. Previous studies have used a scale bar (Haley et al, 1991), custom calibration board (Hodgson, Holman, Terauds, Koh, & Goldsworthy, 2020), laser distance meter (Meise et al, 2014), or highly accurate altimeter (Krause et al, 2017). In our study, resolution of the built‐in UAS GPS was insufficient for altitude measurements, which required us to use a scale bar.…”

Section: Figurementioning

confidence: 99%

“…Integration of real‐time kinematic positioning, a mode of measurement used to increase the precision of GPS signals (Lambiel & Delaloye, 2004), can increase the accuracy of altitude measurements. Newer studies have used UAS photogrammetry to obtain accurate 3‐dimensional measurements; however, this method requires animals to remain stationary, which may not be ideal for active, free‐ranging animals (Hodgson et al, 2020). Further evaluations could determine whether combinations of UAS photogrammetry metrics such as length to width ratios (Hodgson et al, 2020; Krause et al, 2017; Perryman & Lynn, 2002) and height to width ratios (Christiansen et al, 2019) could be used to estimate body condition (i.e., percent fat), which is a more valuable indicator of foraging success and predictor of reproductive success than mass alone (McDonald, Crocker, Burns, & Costa, 2008).…”

Section: Figurementioning

confidence: 99%