UAV photogrammetry for mapping vegetation in the low-Arctic
Why this work is in the frame
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Bibliographic record
Abstract
Plot-scale field measurements are necessary to monitor changes to tundra vegetation, which has a small stature and high spatial heterogeneity, while satellite remote sensing can be used to track coarser changes over larger regions. In this study, we explored the potential of unmanned aerial vehicle (UAV) photographic surveys to map low-Arctic vegetation at an intermediate scale. A multicopter was used to capture highly overlapping, subcentimetre photographs over a 2 ha site near Tuktoyaktuk, Northwest Territories. Images were processed into ultradense 3D point clouds and 1 cm resolution orthomosaics and vegetation height models using Structure-from-Motion (SfM) methods. Shrub vegetation heights measured on the ground were accurately represented using SfM point cloud data (r 2 = 0.96, SE = 8 cm, n = 31) and a combination of spectral and height predictor variables yielded an 11-class classification with 82% overall accuracy. Differencing repeat UAV surveys before and after manually trimming shrub patches showed that vegetation height decreases in trimmed areas (− 6.5 cm, SD = 21 cm). Based on these findings, we conclude that UAV photogrammetry provides a promising, cost-efficient method for high-resolution mapping and monitoring of tundra vegetation that can be used to bridge the gap between plot and satellite remote sensing measurements.
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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 | 0.001 |
| 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) | 0.000 | 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 it