Assessing Permafrost Degradation and Land Cover Changes (1986–2009) using Remote Sensing Data over Umiujaq, Sub‐Arctic Québec
Bibliographic record
Abstract
Abstract Recent land cover changes in the Umiujaq region of northern Québec, Canada, have been quantified in order to estimate changes in the extent of discontinuous permafrost that strongly affect the forest‐tundra ecotone. Changes in the areas covered by different vegetation types, thermokarst lakes and degradation of lithalsas have been investigated over an area of 60 km2, extending from widespread discontinuous permafrost in the north to areas of scattered permafrost in the south, and from Hudson Bay in the west to the Lac Guillaume‐Delisle graben 10 km further east. We used high‐resolution remote sensing images (QuickBird 2004, GeoEye 2009) and four Landsat scenes (1986, 1990, 2001, 2008) as well as ground‐based data (vegetation, active layer thickness, snow parameters) collected between 2009 and 2011. Two change detection methods applied to estimate the land cover changes between 1986 and 2009 showed an overall increase in vegetation extent between 1986 and 2009, and a 21 per cent increase in tall vegetation (spruce and tall shrubs) between 2004 and 2009 at the expense of low vegetation (lichens, prostrate shrubs, herbaceous vegetation). Thermokarst lakes and lithalsas in ten sub‐areas were mapped manually from satellite imagery. The area covered by water decreased by 24 per cent between 2004 and 2009, often due to vegetation colonising the margins of lakes, and 93 of the observed lakes disappeared completely over that period. The area covered by lithalsas declined by 6 per cent. Our results demonstrate the viability of using high‐resolution satellite imagery to detect changes in the land surface that can serve as indicators of permafrost degradation in the sub‐Arctic. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".