Flight speed and time of day heavily influence rainforest canopy wildlife counts from drone-mounted thermal camera surveys
Bibliographic record
Abstract
Abstract The payload size and commercial availability of thermal infrared cameras mounted on drones has initiated a new wave in the potential for conservationists and researchers to survey, count and detect wildlife, even the most complex of habitats such as forest canopies. However, several fundamental design and methodological questions remain to be tested before standardized monitoring approaches can be broadly adopted. We test the impact of both the speed of drone flights and diel flight period on tropical rainforest canopy wildlife detections. Detection and identification rates differ between both flight speeds and diel time. Overall ~ 36% more detections were made during slower flight speeds, along with a greater ability to categorize taxonomic groups. Flights conducted at 3am resulted in ~ 67% more detections compared to flights conducted at 7am (the diel period with the lowest detection rate). However, 112% more detections could be identified to taxonomic group in 7am flights compared with 3am flights – due to the types of wildlife being identified and the assistance of the RGB camera. Although, this technology holds great promise for carrying out surveys in structurally complex and poorly known ecosystems like forest canopies, there is more to do in further methodological testing, and building automated post-processing systems. Our results suggest that drone studies in the same habitat types, with the same animal densities, could be off by multiples if flown during different times and/or at different speeds. The difference could be an alarming 5-6x variation in animal detections or identification depending on changes in these two factors alone.
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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 | 0.000 | 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) | 0.002 | 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".