Detection of Coniferous Seedlings in UAV Imagery
Bibliographic record
Abstract
Rapid assessment of forest regeneration using unmanned aerial vehicles (UAVs) is likely to decrease the cost of establishment surveys in a variety of resource industries. This research tests the feasibility of using UAVs to rapidly identify coniferous seedlings in replanted forest-harvest areas in Alberta, Canada. In developing our protocols, we gave special consideration to creating a workflow that could perform in an operational context, avoiding comprehensive wall-to-wall surveys and complex photogrammetric processing in favor of an efficient sampling-based approach, consumer-grade cameras, and straightforward image handling. Using simple spectral decision rules from a red, green, and blue (RGB) camera, we documented a seedling detection rate of 75.8 % (n = 149), on the basis of independent test data. While moderate imbalances between the omission and commission errors suggest that our workflow has a tendency to underestimate the seedling density in a harvest block, the plot-level associations with ground surveys were very high (Pearson’s r = 0.98; n = 14). Our results were promising enough to suggest that UAVs can be used to detect coniferous seedlings in an operational capacity with standard RGB cameras alone, although our workflow relies on seasonal leaf-off windows where seedlings are visible and spectrally distinct from their surroundings. In addition, the differential errors between the pine seedlings and spruce seedlings suggest that operational workflows could benefit from multiple decision rules designed to handle diversity in species and other sources of spectral variability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".