Evaluating Unmanned Aerial Systems For The Detection And Monitoring Of Moose In Northeastern Minnesota

The use of unmanned aerial systems (UAS) for wildlife surveying and research has widely expanded in the past decade, but with varying levels of success. Applying UAS paired with Forward Looking Infrared (FLIR) technology to survey forest-dwelling species has been particularly challenging because of unreliable animal detection. We describe our application of UAS and FLIR technology to detect GPS-collared moose (Alces alces) and their calves in the heavily-forested region of northeastern Minnesota, USA, during 2018 and 2019. We conducted grid-pattern UAS thermal surveys over GPS-collared cows during the calving seasons (April to June) of 2018 and 2019 to determine the feasibility of using a FLIR-equipped UAS for detecting cow moose, and for quantifying the number of calves. We also collected data on environmental and flight characteristic variables to model moose detection. Our best fitting model of moose detection showed increased detection with more cloud cover at the survey site ((beta) over cap = 1.13, SE = 0.43), whereas increased forest canopy ((beta) over cap = -1.10, SE = 0.38), and vegetative greenness (enhanced vegetation index, EVI; (beta) over cap = -1.37, SE = 0.32) both reduced detection success. By adjusting our methodology based on our detection model findings, we increased our adult moose detection success from 25% during our first season, to 85% during our second season, and calf detection from 27% to 79%, respectively. We report on our methodological improvements and identify limitations to UAS-based wildlife research in forested systems. Overall, we found that UAS with FLIR sensing is a promising tool for quantifying moose calving success, twinning rate, and calf survival, and may be effective for monitoring the reproductive success and survival of other wildlife species in densely forested regions. (c) 2021 The Wildlife Society.