Browsing by Author "Beaver, Jared T."

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Evaluating the Use of Drones Equipped with Thermal Sensors as an Effective Method for Estimating Wildlife
(2020) Beaver, Jared T.; Baldwin, Robert W.; Messinger, Max; Newbolt, Chad H.; Ditchkoff, Stephen S.; Silman, Miles R.
Drones equipped with thermal sensors have shown ability to overcome some of the limitations often associated with traditional human‐occupied aerial surveys (e.g., low detection, high operational cost, human safety risk). However, their accuracy and reliability as a valid population technique have not been adequately tested. We tested the effectiveness of using a miniaturized thermal sensor equipped to a drone (thermal drone) for surveying white‐tailed deer (Odocoileus virginianus) populations using a captive deer population with a highly constrained (hereafter, known) abundance (151–163 deer, midpoint 157 [87–94 deer/km2, midpoint 90 deer/km2]) at Auburn University's deer research facility, Alabama, USA, 16–17 March 2017. We flew 3 flights beginning 30 minutes prior to sunrise and sunset (1 morning and 2 evening) consisting of 15 nonoverlapping parallel transects (18.8 km) using a small fixed‐wing aircraft equipped with a nonradiometric thermal infrared imager. Deer were identified by 2 separate observers by their contrast against background thermal radiation and body shape. Our average thermal drone density estimate (69.8 deer/km2, 95% CI = 52.2–87.6), was 78% of the mean known value of 90.2 deer/km2, exceeding most sighting probabilities observed with thermal surveys conducted using human‐occupied aircraft. Thermal contrast between animals and background was improved during evening flights and our drone‐based density estimate (82.7 deer/km2) was 92% of the mean known value. This indicates that time of flight, in conjunction with local vegetation types, determines thermal contrast and influences ability to distinguish deer. The method provides the ability to perform accurate and reliable population surveys in a safe and cost‐effective manner compared with traditional aerial surveys and is only expected to continue to improve as sensor technology and machine learning analytics continue to advance. Furthermore, the precise replicability of autonomous flights at future dates results in methodology with superior spatial precision that increases statistical power to detect population trends across surveys.