Assessment of LiDAR and Spectral Techniques for High-Resolution Mapping of Sporadic Permafrost on the Yukon-Kuskokwim Delta, Alaska
Bibliographic record
Abstract
Western Alaska’s Yukon-Kuskokwim Delta (YKD) spans nearly 67,200 km2 and is among the largest and most productive coastal wetland ecosystems in the pan-Arctic. Permafrost currently forms extensive elevated plateaus on abandoned floodplain deposits of the outer delta, but is vulnerable to disturbance from rising air temperatures, inland storm surges, and salt-kill of vegetation. As pan-Arctic air and ground temperatures rise, accurate baseline maps of permafrost extent are critical for a variety of applications including long-term monitoring, understanding the scale and pace of permafrost degradation processes, and estimating resultant greenhouse gas dynamics. This study assesses novel, high-resolution techniques to map permafrost distribution using LiDAR and IKONOS imagery, in tandem with field-based parameterization and validation. With LiDAR, use of a simple elevation threshold provided a permafrost map with 94.9% overall accuracy; this approach was possible due to the extremely flat coastal plain of the YKD. The addition of high spatial-resolution IKONOS satellite data yielded similar results, but did not increase model performance. The methods and the results of this study enhance high-resolution permafrost mapping efforts in tundra regions in general and deltaic landscapes in particular, and provide a baseline for remote monitoring of permafrost distribution on the YKD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".