Investigation of Polarimetric ALOS-2 for Discontinuous Permafrost Mapping in Northern Alberta
Bibliographic record
Abstract
In this study, the dominant and medium scattering phases generated by the Touzi decomposition are investigated for discontinuous permafrost mapping in peatland regions. Polarimetric ALOS2, LIDAR and field data were collected in the middle of August 2014, at the maximum permafrost thaw conditions, over discontinuous permafrost distributed within wooded palsa bogs and peat plateaus near the Namur Lake (Northern Alberta). The ALOS2 image, which was miscellaneously calibrated with antenna cross-talk (-33dB), much higher than the actual ones, is recalibrated. This leads to a reduction of the residual calibration error (down to -43 dB), and permit a significant improvement of the dominant and medium scattering type phase (20°-to-30°) over peatlands underlain by discontinuous permafrost. The Touzi decomposition, Cloude-Pottier α-H incoherent target scattering decomposition, and the HH-VV phase difference are investigated, in addition to the conventional multi-polarization (HH, HV, and VV) channels, for discontinuous permafrost mapping using the recalibrated ALOS2 image. A LiDAR-based permafrost classification developed by Alberta Geological Survey (AGS) is used, in conjunction with the field data collected during the ALOS2 image acquisition, for the validation of the results. It is shown that the dominant and scattering type phases are the only polarimetric parameters which can detect peatland subsurface discontinuous permafrost. The medium scattering type phase, ϕs2, performs better than the dominant scattering type phase, ϕs1, and permits a better detection of subsurface discontinuous permafrost in peatland regions. ϕs2also allows for better discrimination of areas underlain by permafrost from the non-permafrost areas. The medium Huynen maximum polarisation return (m2) and the minimum degree of polarisation (DoP), pmin, can be used to remove the scattering type phase ambiguities that might occur in areas with deep permafrost (more than 50cm depth). The excellent performances of polarimetric PALSAR2 in term of NESZ (-37 dB) permit the demonstration of the very promising L-band long penetration SAR capabilities for enhanced detection and mapping of relatively deep (up to 50 cm) discontinuous permafrost in peatlands regions [1].
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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 | 0.001 | 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".