UAV Data Upscaling for Soil Erosion Monitoring in High-Latitude Rangelands, Northeastern Iceland
Bibliographic record
Abstract
Vegetation cover exerts a strong influence on the rate and severity of soil erosion. In Iceland, soil erosion is a major land management issue, with accelerating rates of degradation since human occupation. Current methods for erosion mapping and monitoring are costly and difficult to employ frequently over large regions. Satellite remote sensing can offer synoptic and systematic information on vegetation conditions useful in environmental monitoring. However, fine-scaled erosive features, such as small deflation patches, may not be easily identifiable in moderate resolution imagery (10-30 m). Here the integration of Unoccupied Aerial Vehicle (UAV), Sentinel-2, and field data is examined to bridge the gap between ground-based and spaceborne monitoring. High resolution (< 5 cm) UAV-based land cover maps are produced for six sites, achieving high overall accuracy (> 90%) compared to ground measurements. These data are upscaled via a regression model estimating bare soil cover, yielding good results (R2 = 0.81). Using land-monitoring data from the Icelandic National monitoring program GróLind, erosion severity classes are defined and mapped. This study highlights the potential of multiscale remote sensing for estimating sub-pixel landscape information and improving automated soil erosion mapping.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".