Drone Nest Searching Applications Using a Thermal Camera
Bibliographic record
Abstract
ABSTRACT Unmanned aerial systems (UASs), or drones, are increasingly being used for field sampling in wildlife research. However, to date there have been few studies using drones in avian nest searching, and none where a fixed survey methodology has been applied across multiple habitats spanning a large geographic region to locate avian nests. We tested nest searching using drone‐based thermal imagery for a variety of game (Anseriformes, Galliformes), and nongame bird species (Passerines and Charadriids) at sites located in the Dakotas, North Carolina, and Alberta. We used a manual, point‐transect survey method for all study sites in habitats ranging from marine‐coastal marshlands to upland grasslands. We found that 77–100% of known nests can be detected based on thermal signatures in multiple habitat types and across an array of different avian species. However, detectability varies with a variety of site, weather, atmospheric conditions, and the effectiveness of the method is impeded by a high false positive rate (60–95%). There are several identified challenges to be addressed for future thermal drone research, and our study provides observations and advice for planning studies. Drone technology has the potential for field applications by wildlife managers and conservationists, and as it matures will enhance the field practitioner's toolset. © 2021 The Wildlife Society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".