Drivers of permafrost degradation along the Inuvik to Tuktoyaktuk Highway (ITH)
Bibliographic record
Abstract
Infrastructure construction on permafrost is challenging. Not only are northern regions undergoing a faster and more intense global warming than the rest of the world, inducing thawing of the permafrost at a worldwide scale. In addition, linear infrastructures such as gravel highways, built on embankments to protect the underlying permafrost, change environmental conditions in various ways, enhancing permafrost degradation. This work aims to utilize remote sensing data and explore the physical parameters that drive permafrost degradation in the regions adjacent to the Inuvik to Tuktoyaktuk Highway (ITH) in Northwest Territories, Canada. Within the work, snow accumulation along the embankment toe, vegetation moisture increase, surface water increase in poorly drained areas, earlier snowmelt and vegetation increase along the road are defined as factors that (I) enhance permafrost degradation and (II) are observable using remote sensing techniques. The analysis is conducted using cloud computing services, open-source software packages, and primarily freely available datasets. Snow accumulation conditions are derived using Digital Elevation Models (DEM) as baseline data. The cardinal direction of the road and the predominating wind direction significantly impact the snow accumulation. Moreover, the results indicate that the enhanced snow accumulation generally reaches further distances from the road than previous studies suggest. The impact from the road on vegetation moisture and vegetation conditions, indicated by the Normalized Difference Moisture Index (NDMI) and the Normalized Difference Vegetation Index (NDVI), respectively, demonstrated significant decreases within the first 25 m from the road edge. This is in line with previous studies. However, whether the observed effect reflects the field conditions or if the spectral signal is affected by other factors like dust is critically discussed. Furthermore, my study revealed that by normalizing the median NDMI and NDVI values on an undisturbed reference area, an additional effect is observed reaching up to 200 m from the road. The analysis of the NIR band indicates that the downstream side became wetter throughout the years compared to the upstream side. The snowmelt pattern indicated by the Normalized Difference Snow Index (NDSI), derived from Landsat images, shows that the areas next to the road are snow-free earlier in spring than the areas further away. The result indicates that the road affects the snowmelt up to 600 m from the road. The findings of this work highlight the importance of future research into the impact of dust on satellite-derived indices. Furthermore, the findings contribute to a better understanding of the spatial scale of altered permafrost drivers following the construction of the ITH.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".