Decades‐Long Evolution of Post‐Fire Permafrost Deformation Detected by InSAR: Insights From Chronosequence in North Yukon
Bibliographic record
Abstract
Abstract Permafrost, a critical global cryospheric component, supports subarctic boreal forests but is frequently disturbed by wildfires, an important driver of permafrost degradation. Wildfires reduce vegetation, organic layers, and surface albedo, leading to active layer thickening and ground subsidence. Recent studies using interferometric synthetic aperture radar (InSAR) have confirmed the rapid and extensive post‐fire permafrost degradation, and have largely focused on short‐term impacts. However, the longer‐term post‐fire permafrost deformation, potentially persisting for decades, remains poorly understood due to limited data. Here, we applied InSAR in North Yukon to detect deformation signals across multiple fire scars in the past five decades. Using a chronosequence (space‐for‐time substitution) approach, we summarize a continuous trajectory of post‐fire permafrost evolution: (a) an initial degradation stage, characterized by abrupt subsidence up to 50 mm/year and gradually slowing over the first decade, with cumulative subsidence exceeding 100 mm locally; (b) an aggradation stage from approximately 15 to 30 years after fire, marked by ground uplift reaching 25 mm/year before gradually declining, compensating for the earlier subsidence; and (c) a stabilization stage beyond three to four decades, where permafrost nearly recovers to pre‐fire conditions with indistinguishable deformation between burned and unburned areas. Notably, the rarely‐reported uplift phase appears closely related to vegetation regeneration and fire‐greening feedback that provide thermal protection, suggesting a critical mechanism of permafrost recovery. These findings provide new insights into the resilience of boreal‐permafrost systems to wildfires and also underscore the importance of long‐term InSAR monitoring in understanding permafrost responses to wildfires under climate change.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".