Small unmanned aircraft: precise and convenient new tools for surveying wetlands
Bibliographic record
Abstract
Unmanned aircraft systems (UAS) could be of benefit for surveying wetlands, which often have spatially complex habitats that are challenging to navigate and assess at ground level. We used a small UAS to acquire aerial imagery and characterize land cover in a 128 ha wetland impoundment as part of a conservation study of the least bittern (Ixobrychus exilis). The method was successful in gathering sub-decimetre georeferenced imagery that clearly revealed the fine-scale water–vegetation interface and in which several types of vegetation could be distinguished and classified using spectral image analysis software. Simplified three-category land cover classifications obtained in this manner showed strong agreement with manual classification of random points in the imagery, as evidenced by a kappa coefficient of 87.19% (n = 600). Compared to cover estimates made during concurrent ground-based surveys in 30 sampling plots, UAS data yielded overall similar water–vegetation ratios, but proved more effectual for detecting small amounts of highly interspersed water. Significant differences (p = 0.004) in cover estimates of the dominant vegetation, cattail, were likely primarily due to limitations of ground-based surveys. Given the effective and convenient application of a UAS in this study, we recommend their further use in wetland-related research and management.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".