Improving Wildlife Population Inference Using Aerial Imagery and Entity Resolution

Lu, Xinyi; Hooten, Mevin B.; Kaplan, Andee; Womble, Jamie N.; Bower, Michael R.

doi:10.1007/s13253-021-00484-w

Cited by 1 publication

(2 citation statements)

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“…Lu et al. (2022) developed a hierarchical framework that utilized entity resolution to identify the same individuals in overlapping images and thus avoid double counting when IR images are analyzed instead of video (e.g. Chrétien et al., 2015).…”

Section: Discussionmentioning

confidence: 99%

“…Fortunately, strategies to account for or avoid double counting exist. Lu et al (2022) developed a hierarchical framework that utilized entity resolution to identify the same individuals in overlapping images and thus avoid 4) RGB confirmations of any IR heat signatures where viewers were confident that the object in the RGB video was a deer (Stringent RGB). When assigning IR heat signatures or RGB objects, stringent detections were those with a shape that was clearly defined and unambiguous so that we believed no other object than a deer could be producing such an IR heat signature or RGB object.…”

Section: Discussionmentioning

confidence: 99%

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Imperfect detection and wildlife density estimation using aerial surveys with infrared and visible sensors

Delisle

McGovern

Dillman

2022

Remote Sens Ecol Conserv

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Aerial vehicles equipped with infrared thermal sensors facilitate quick density estimates of wildlife, but detection error can arise from the thermal sensor and viewer of the infrared video. We reviewed published research to determine how commonly these sources of error have been assessed in studies using infrared video from aerial platforms to sample wildlife. The number of annual articles pertaining to aerial sampling using infrared thermography has increased drastically since 2018, but past studies inconsistently assessed sources of imperfect detection. We illustrate the importance of accounting for some of these types of error in a case study on white-tailed deer Odocoileus virginianus in Indiana, USA, using a simple double-observer approach. In our case study, we found evidence of false negatives associated with the viewer of infrared video. Additionally, we found that concordance between the detections of two viewers increased when using a red-green-blue camera paired with the infrared thermal sensor, when altitude decreased and when more stringent criteria were used to classify thermal signatures as deer. We encourage future managers and ecologists recording infrared video from aerial platforms to use double-observer methods to account for viewer-induced false negatives when video is manually viewed by humans. We also recommend combining infrared video with redgreen-blue video to reduce false positives, applying stringent verification standards to detections in infrared and red-green-blue video and collecting data at lower altitudes over snow when needed.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%