High‐Resolution Mapping of Wet Terrain within Discontinuous Permafrost using LiDAR Intensity
Bibliographic record
Abstract
ABSTRACT Surface hydrology is an important aspect of northern environments on account of the thermal influence of water on permafrost. In this study, we demonstrate the ability of light detection and ranging (LiDAR) to map wet terrain within an area of discontinuous permafrost adjacent to the Northwest Territories Highway 3, located west of Yellowknife, Canada. Wet terrain was identified from LiDAR intensity measurements beneath forest canopies and across vegetated surfaces, including peatlands, fens, flooded black spruce and birch forests, and terrain adjacent to the highway embankment. Surface water pathways representing hydrological connections between water bodies and wet terrain were also identified at locations otherwise indiscernible from optical imagery. Statistical separability between terrain types, and thus the ability to map them, was improved by integrating LiDAR all‐return and bare‐earth intensity with colour orthophotos. The average classification accuracy for wet terrain was 93 per cent. These results indicate that LiDAR intensity can be used for local‐scale mapping of wet terrain, as required by northern engineers and scientists. Future integration of LiDAR intensity and elevation measurements may be used to assess changes in surface hydrological conditions impacting permafrost. Copyright © Her Majesty the Queen in Right of Canada 2012.
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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 | 0.000 | 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) | 0.001 | 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".