The use of drones and thermal camera imaging technology for avian nest searching
| dc.contributor.author | Stander, Roald | |
| dc.contributor.examiningcommittee | Baydack, Richard (Environment and Geography) | en_US |
| dc.contributor.examiningcommittee | Rowher, Frank (Delta Waterfowl) | en_US |
| dc.contributor.supervisor | Walker, David | |
| dc.date.accessioned | 2023-05-18T20:50:41Z | |
| dc.date.available | 2023-05-18T20:50:41Z | |
| dc.date.copyright | 2023-04-29 | |
| dc.date.issued | 2023-04 | |
| dc.date.submitted | 2023-04-29T05:10:31Z | en_US |
| dc.degree.discipline | Environment and Geography | en_US |
| dc.degree.level | Master of Environment (M.Env.) | en_US |
| dc.description.abstract | In the rapidly evolving field of drone wildlife surveys, we test a commercially available drone and thermal camera as a nest searching tool for various upland nesting gamebird and non-gamebird species by evaluating the method in a multi-species, multi-habitat comparison across North America. Furthermore, the study compares the efficacy of a thermal drone system to the chain-drag method to search for upland nesting waterfowl in the Prairie Pothole Region. We also examine how factors, such as meteorology, influence thermal drone detection. This study searches for nests in four U.S. states and one Canadian province between 15 May–17 June 2017 and 8 May to 15 June 2018. We used a modified point count and automated flight line transect sampling methodologies for drone nest searching with real-time visual detection of thermal targets. We found 18 out of 22 known nests in the multi-species, multi-habitat component and identified several challenges associated with the method, such as high commission errors (60 – 95%) that impede detection. In comparing nest searching methods, we find 150 upland waterfowl nests and use a Huggins Closed Capture model to compare detection rates. We found that drone and chain drag methods have detection rates of 0.36±0.04 and 0.55±0.06, respectively. In addition, we found that the drone method has challenges detecting laying stage nests and hypothesize that the survey timing impedes their detection due to biological reasons. Finally, we considered various factors influencing drone detection in a binary logistic regression model, which shows that the difference between dew point and temperature significantly influences detection and hypothesize that it is a proxy for other macro weather trends or patterns not measured. Based on our findings, we make recommendations that future nest searching drone research should consider. | en_US |
| dc.description.note | October 2023 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/37345 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | drone | en_US |
| dc.subject | methodology | en_US |
| dc.subject | Prairie Pothole Region | en_US |
| dc.subject | thermal camera | en_US |
| dc.subject | upland nest searching | en_US |
| dc.subject | waterfowl | en_US |
| dc.title | The use of drones and thermal camera imaging technology for avian nest searching | en_US |
| dc.type | master thesis | en_US |
| local.subject.manitoba | no | en_US |
| project.funder.name | Delta Waterfowl Foundation | en_US |