Detecting recent disturbance on Montane blanket bogs in the Wicklow Mountains, Ireland using the MODIS enhanced vegetation index
Bibliographic record
Abstract
Irish peat soils are extensive, covering approximately 14–20% of the national land area. They contain between 53% and 62% of the national soil organic carbon stock. Montane blanket bog covers approximately 25% or 242 650 ha of the total peatland area in Ireland and is the dominant peatland type covering the upland area of Wicklow. Blanket bogs are very sensitive systems and have experienced much disturbance in Ireland due to overgrazing, burning, drainage, forestry and turf cutting. It has been estimated that disturbance of blanket bog, on a national area basis, ranges from 74% to 82% and in Wicklow is 57%. Disturbance can be detrimental to stocks of soil organic carbon in peatlands. Monitoring disturbance in peatlands, which tend to cover large, remote areas, is difficult and expensive using conventional surveying methods. Satellite remote sensing offers a way to gather data for these areas. In this paper a method of determining the probability of disturbance is presented. This method uses the Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) in combination with univariate image differencing along with thresholding and binary logistic regression. A probability map was produced depicting the geospatial patterns and pressures on the peatland soil organic carbon stock in Wicklow. Peat soils in higher and steeper areas were more disturbed and the primary disturbance in between 2000 and 2005 was fire. Lower, flatter areas did not experience as much disturbance probably because they are wetter. The consumer's and producer's accuracy for the map was 76% and 42%, respectively.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".