DETECTING UNMARKED MOOSE WITH INFRARED SENSORS VIA AN UNOCCUPIED AERIAL SYSTEM AND CORRECTING FOR SIGHTABILITY

Abstract

Accurate and precise estimates of moose (Alces alces) density are pivotal for understanding population dynamics and informing management decisions. One promising tool for obtaining this information is unoccupied aerial systems (UASs). However, this technology still requires critical evaluation, especially regarding properly accounting for imperfect detection, i.e., the probability that moose are available but not detected and therefore uncounted. A recent review found that less than half of studies estimating moose density adequately accounted for imperfect detection, suggesting that many moose populations might be underestimated. Our objective was to create a sightability model for unmarked moose detected from a UAS equipped with a long-wave infrared sensor that included covariates expected to affect large-scale UAS moose surveys. We conducted 35 UAS flights in northern New Hampshire, USA, during January and February 2023 and completed sightability maneuvers over 59 moose detections to collect images at various relative observation angles. From a Bayesian logistic regression based on a naïve observer analysis, we found that greater conifer cover and sunnier conditions strongly reduced sightability of moose whereas ambient temperature had a weaker but also negative effect. Sightability was near 100% below a threshold of approximately 50% conifer cover, above which sightability declined rapidly which is similar to findings from previous work. This study provides the first successful quantification of sightability for a non-collared moose population, demonstrating a cost-effective approach for calibrating UAS sampling for additional locations and species while paving the way for future applications of this model to correct moose population sampling.