Polarimetric L-band PALSAR2 for Discontinuous Permafrost Mapping In Peatland Regions
Bibliographic record
Abstract
Cost-effective permafrost characterization and monitoring should be possible due to advances in the technology of earth observation satellites. In particular, the long-penetration capabilities of L-band ALOS2-PALSAR2 should permit large scale mapping of discontinuous permafrost in peatland areas. Recently, it has been shown that the long penetrating polarimetric L-band ALOS is very promising for boreal and subractic peatland mapping and monitoring [1], [2]. The unique information provided by the Touzi decomposition [3], [4], and the Touzi scattering phase in particular, on peatland subsurface water flow permits enhanced discrimination of bogs from fens; two peat- land classes that can hardly be discriminated using conventional optical remote sensing. In this study, the Touzi scattering phase is investigated for mapping discontinuous permafrost in peatland regions Northern Alberta. Polarimetric ALOS-2 (FP6-4) and field data were collected in August 2014 over discontinuous distributed within wooded palsa bogs and peat plateaus near the Namur Lake (Northern Alberta). The ALOS2 image is re-calibrated to reduce the residual error from -33 dB down to -43 dB. This permits full exploiting the excellent ALOS2 performance in term of low noise floor (NESZ about -38 dB) to increase the sensitivity of the Touzi phase to deep permafrost. It is shown that the information provided by the scattering type phase permits enhanced mapping of discontinuous permafrost. The results obtained with the long penetrating L-band polarimetric PALSAR2 are much better than the ones obtained with conventional discontinuous permafrost mapping methods based on Lidar and optical (Landsat and Spot) images.
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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 | 0.001 |
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".