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. © 2020 The Wildlife Society.

中文翻译：

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评估配备有热传感器的无人机作为估计野生生物的有效方法的使用

配备有热传感器的无人机已经显示出能够克服通常与传统的人为航空勘测相关的某些局限性的能力（例如，低探测，高运营成本，人身安全风险）。但是，作为有效的人口技术的准确性和可靠性尚未得到充分测试。我们测试了使用配备有无人机（小型无人机）的小型热传感器来调查白尾鹿（Odocoileus virginianus）种群的有效性，该圈养鹿种群的种群高度受限（以下称为“已知”）（151–163头，中点157 [87–94鹿/ km ²，中点90鹿/ km ²]）在美国阿拉巴马州奥本大学的鹿研究中心，2017年3月16日至17日。我们乘坐3个航班，其中包括15个非重叠的平行样线（18.8公里），分别在日出和日落前30分钟（1个早和2个晚上）开始。装有非辐射热红外成像仪的小型固定翼飞机。鹿是由2位独立的观察员通过与背景热辐射和身体形状的对比来识别的。我们的平均热雄蜂密度估计（69.8鹿/千米²，95％CI = 52.2-87.6），是90.2鹿/ km的平均已知值的78％²，超过了使用人工飞机进行的热调查所观察到的大多数瞄准概率。在夜间飞行期间，动物和背景之间的热对比度得到了改善，我们的无人机密度估计值也有所提高（82.7鹿/ km ²）是平均已知值的92％。这表明飞行时间与当地的植被类型共同决定了热对比度并影响了辨别鹿的能力。与传统的航空调查相比，该方法提供了以安全且经济高效的方式执行准确，可靠的人口调查的能力，并且只有随着传感器技术和机器学习分析技术的不断发展，这种方法才有望继续得到改善。此外，未来日期自动飞行的精确可复制性导致了具有卓越空间精度的方法，从而提高了统计能力，可检测整个调查的人口趋势。©2020野生动物协会。