Modelling and mapping permafrost at high spatial resolution using Landsat and Radarsat-2 images in Northern Ontario, Canada: Part 2 – regional mapping
Bibliographic record
Abstract
Permafrost occurs mainly at high latitudes and is currently subjected to widespread thawing due to global warming, which has significant ecological and socio-economic impacts. One way to map permafrost condition and its change at high spatial resolution is to calculate soil temperatures using a process-based permafrost model, such as the Northern Ecosystem Soil Temperature (NEST) model, based on remote-sensing data. In a previous paper (Ou et al. 2015), we showed that the NEST model can be used to model soil temperatures, frozen depth, and active layer thickness in a warm and discontinuous permafrost area that is located in the Hudson Bay Lowlands in northern Ontario, Canada. In this article, we applied the model over the whole study area to map the distribution of permafrost conditions and their changes from the 1960s to the 2000s at high spatial resolution. Some of the model inputs were estimated based on land-cover and surficial material maps derived from Landsat-5 TM and Radarsat-2 SAR C-HH and C-HV images. By comparison to field observations, we were able to achieve an average mapping accuracy of 99% for both the land-cover and surficial material maps, when both Landsat and Radarsat-2 images were used. In addition, all the 49 field-observed treed and shrub permafrost landform sites found in the study area were correctly identified on the classified image. This spatial modelling study shows that permafrost currently underlies about 9.4% of the land in this area, and varies significantly among land-cover types. With the increases in mean annual air temperature (1.9°C) and precipitation (2.4%) from the 1960s to the 2000s, the model results show a deepening of active layer by 20.5%. However, the permafrost has persisted in most of the area due to the protection of thick peat. The modelled permafrost extent reduced only from 9.8% to 9.4%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".