Evaluating drone-mounted thermal imaging for population surveys of wild ungulates in boreal and montane habitats of Western Canada
Bibliographic record
Abstract
Accurate population estimates of wild ungulates underpin harvest management, habitat conservation, and predator-prey modelling, yet conventional aerial surveys conducted from manned helicopters suffer from high cost, observer fatigue, variable sightability, and safety risks in mountainous terrain. Thermal-imaging drones have been proposed as an alternative, but their performance across the range of species, habitats, and climatic conditions encountered in western Canada has not been systematically evaluated. This research assessed the detection performance of drone-mounted thermal imaging relative to drone-mounted RGB (visible-light) cameras and traditional ground-based line transects for four ungulate species—moose (Alces alces), white-tailed deer (Odocoileus virginianus), elk (Cervus canadensis), and caribou (Rangifer tarandus)—across five habitat types and four seasons in British Columbia between January 2023 and December 2023. A total of 312 survey flights covering 4,680 hectares were conducted using a DJI Matrice 30T equipped with a 640 × 512 pixel radiometric thermal sensor. Thermal drone surveys achieved an overall detection rate of 88.9%, compared with 65.7% for RGB drone surveys and 49.7% for ground transects (p<0.001 for all pairwise comparisons). Detection rates were highest for moose (94.2%) and lowest for caribou (82.4%), reflecting body-size-dependent thermal signatures. Ambient temperature was the strongest environmental predictor of detection success: thermal detection rates exceeded 90% at temperatures below-5 °C but declined to 67.2% above 15 °C as the thermal contrast between animals and the environment diminished. Coniferous forest canopy reduced detection by 12-18% compared with open meadow. These results demonstrate that thermal drone surveys substantially outperform conventional methods for ungulate population monitoring in western Canadian landscapes, with optimal deployment during cold-season windows when thermal contrast is maximal.
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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 | 0.001 | 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 | 0.000 | 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) | 0.000 | 0.000 |
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; a candidate call from one teacher head, not a consensus.
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".