Evaluating Unmanned Aerial Systems for the Detection and Monitoring of Moose in Northeastern Minnesota

Michael C. McMahon; Mark A Ditmer; Edmund J. Isaac; Seth A. Moore; James D. Forester

doi:10.1002/wsb.1167

Evaluating Unmanned Aerial Systems for the Detection and Monitoring of Moose in Northeastern Minnesota

Michael C. McMahon, Mark A Ditmer, Edmund J. Isaac, Seth A. Moore, James D. Forester

Fisheries, Wildlife, and Conservation Biology

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

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 ((Formula presented.) = 1.13, SE = 0.43), whereas increased forest canopy ((Formula presented.) = −1.10, SE = 0.38), and vegetative greenness (enhanced vegetation index, EVI; (Formula presented.) = −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.

Original language	English (US)
Pages (from-to)	312-324
Number of pages	13
Journal	Wildlife Society Bulletin
Volume	45
Issue number	2
DOIs	https://doi.org/10.1002/wsb.1167
State	Published - Jun 2021

Bibliographical note

Funding Information:
We thank the Grand Portage Band of Lake Superior Chippewa for their collaboration on this project. We appreciate Y. C. Ibrahim's support with accessing and managing data on study animals and R. Deschampe Jr.'s technical support in the field. We thank J. Tillery for his assistance as a field technician. We also recognize the University of Minnesota Department of Fisheries, Wildlife, and Conservation Biology for its technical support on this project. Funding was provided by the Minnesota Agricultural Experiment Station (Project# MIN‐41‐020), the University of Minnesota Graduate School (fellowship support for M. McMahon), and the Environment and Natural Resources Trust Fund as recommended by the Legislative‐Citizen Commission on Minnesota Resources. This work could not have been completed without funding from the Great Lakes Restoration Initiative for monitoring moose on the Grand Portage Indian Reservation. We thank A. Rodgers (Associate Editor), A. Knipps (Editorial Assistant), and the anonymous reviewers who provided helpful comments that improved the manuscript.

Funding Information:
We thank the Grand Portage Band of Lake Superior Chippewa for their collaboration on this project. We appreciate Y. C. Ibrahim's support with accessing and managing data on study animals and R. Deschampe Jr.'s technical support in the field. We thank J. Tillery for his assistance as a field technician. We also recognize the University of Minnesota Department of Fisheries, Wildlife, and Conservation Biology for its technical support on this project. Funding was provided by the Minnesota Agricultural Experiment Station (Project# MIN-41-020), the University of Minnesota Graduate School (fellowship support for M. McMahon), and the Environment and Natural Resources Trust Fund as recommended by the Legislative-Citizen Commission on Minnesota Resources. This work could not have been completed without funding from the Great Lakes Restoration Initiative for monitoring moose on the Grand Portage Indian Reservation. We thank A. Rodgers (Associate Editor), A. Knipps (Editorial Assistant), and the anonymous reviewers who provided helpful comments that improved the manuscript.

Publisher Copyright:
© 2021 The Wildlife Society

Keywords

Alces alces
FLIR
Minnesota
aerial survey
calf survival
detection modeling
drones
thermal infrared
unmanned aerial systems (UAS)
unmanned aerial vehicles (UAV)

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title = "Evaluating Unmanned Aerial Systems for the Detection and Monitoring of Moose in Northeastern Minnesota",

abstract = "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 ((Formula presented.) = 1.13, SE = 0.43), whereas increased forest canopy ((Formula presented.) = −1.10, SE = 0.38), and vegetative greenness (enhanced vegetation index, EVI; (Formula presented.) = −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.",

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note = "Funding Information: We thank the Grand Portage Band of Lake Superior Chippewa for their collaboration on this project. We appreciate Y. C. Ibrahim's support with accessing and managing data on study animals and R. Deschampe Jr.'s technical support in the field. We thank J. Tillery for his assistance as a field technician. We also recognize the University of Minnesota Department of Fisheries, Wildlife, and Conservation Biology for its technical support on this project. Funding was provided by the Minnesota Agricultural Experiment Station (Project# MIN‐41‐020), the University of Minnesota Graduate School (fellowship support for M. McMahon), and the Environment and Natural Resources Trust Fund as recommended by the Legislative‐Citizen Commission on Minnesota Resources. This work could not have been completed without funding from the Great Lakes Restoration Initiative for monitoring moose on the Grand Portage Indian Reservation. We thank A. Rodgers (Associate Editor), A. Knipps (Editorial Assistant), and the anonymous reviewers who provided helpful comments that improved the manuscript. Funding Information: We thank the Grand Portage Band of Lake Superior Chippewa for their collaboration on this project. We appreciate Y. C. Ibrahim's support with accessing and managing data on study animals and R. Deschampe Jr.'s technical support in the field. We thank J. Tillery for his assistance as a field technician. We also recognize the University of Minnesota Department of Fisheries, Wildlife, and Conservation Biology for its technical support on this project. Funding was provided by the Minnesota Agricultural Experiment Station (Project# MIN-41-020), the University of Minnesota Graduate School (fellowship support for M. McMahon), and the Environment and Natural Resources Trust Fund as recommended by the Legislative-Citizen Commission on Minnesota Resources. This work could not have been completed without funding from the Great Lakes Restoration Initiative for monitoring moose on the Grand Portage Indian Reservation. We thank A. Rodgers (Associate Editor), A. Knipps (Editorial Assistant), and the anonymous reviewers who provided helpful comments that improved the manuscript. Publisher Copyright: {\textcopyright} 2021 The Wildlife Society",

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N2 - 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 ((Formula presented.) = 1.13, SE = 0.43), whereas increased forest canopy ((Formula presented.) = −1.10, SE = 0.38), and vegetative greenness (enhanced vegetation index, EVI; (Formula presented.) = −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.

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Evaluating Unmanned Aerial Systems for the Detection and Monitoring of Moose in Northeastern Minnesota

Abstract

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Keywords

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