Investigation of Polarimetric ALOS2 for Peatland and Permafrost Monitoring in the Wapusk National Park
Bibliographic record
Abstract
Wetlands with at least 30-40 cm of peat accumulated on the surface represent an important class of wetlands named peatland. Although peatlands globally only cover 3% of the land surface they store 30% of the terrestrial carbon. Therefore, it is important to maintain and protect peatlands to prevent greenhouse gas release. Unfortunately, major peatland transformations have been detected in the boreal and subarctic peatland regions. Permafrost degradation across Canada’s vast peatlands threatens Canada’s existing energy infrastructure network and the relative vulnerability of peatlands is a critical knowledge gap that represents a substantial risk in new infrastructure development projects. Uninformed management of, and thus subsequent loss of, vulnerable peatlands over the next decade represents one of the most significant impacts on Canada’s carbon balance resulting from land management and resource development. Cost-effective peatland & permafrost mapping and monitoring should be possible due to advances in the technology of earth observation satellites. In particular, the sensitivity of polarimetric L-band ALOS, ALOS2 and upcoming ALOS4, to peatland subsurface water flow, should permit accurate discrimination of bogs from fens; two important wetland classes of similar vegetation that can hardly be discriminated by Visible near-infrared satellites, C-band dual and polarimetric satellite SAR (Radarsat2, RCM, Sentinel), and conventional L-band (single- & dual-pol) SARs. Recently, we have shown that polarimetric long penetrating L-band ALOS permits a clear discrimination of bogs and fens using their different hydrological properties [1], [2]. This has been demonstrated for boreal peatlands (in La Baie des Mines, the Athabasca oil sand exploration region), and subarctic peatlands located at the Wapusk National Park ([3], [4], [2]). it is shown that among all the parameters generated by the Touzi, Cloude-Pottier [5] and Freeman [6] ICTDs, only the dominant scattering type phase ϕs1generated by the Touzi decomposition [7], [8] is sensitive to peatland subsurface water flow. The complementary information provided by the Touzi discriminators [9], which exploits the extrema of the degree of polarization (DoP), permits an enhanced separation of treed peatlands from upland forests [4]. Recently, we have shown that the excellent performances of polarimetric ALOS2 in term of NESZ (-37 dB) permits the demonstration of the unique long penetration L-band SAR capabilities for enhanced detection and mapping of discontinuous permafrost (up to 50cm) in the vicinity of Namur Lake, in Northern Alberta [10]. The use of ALOS2 images collected at 27°incidence angle and the medium scattering type phase ϕs2provided by the Touzi decomposition leads to more accurate mapping of relatively deep permafrost (up to 50 cm under the peatland surface) than the Lidar-Landsat permafrost map obtained by Alberta Geological Survey [10]. In this study, polarimetric ALOS2 images collected at 25° are used for characterization of peatlands and permafrost in the Wapusk National Park. The results obtained permit the confirmation of the unique information provided by the dominant scattering type ϕs1and the medium scattering type phase ϕs2for enhanced mapping of peatlands, and charcterization of relatively deep subsurface permafrost (up to 50cm).
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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) | 0.000 | 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 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".