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Investigation of Polarimetric ALOS2 for Peatland and Permafrost Monitoring in the Wapusk National Park

2024· article· en· W4402260687 on OpenAlexaffabout
R. Touzi, P. A. Wilson, Gabriel Hould Gosselin, Richard Brook

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of SaskatchewanNatural Resources Canada
Fundersnot available
KeywordsPeatPermafrostNational parkRemote sensingEnvironmental sciencePolarimetryForestryPhysical geographyHydrology (agriculture)GeologyGeographyArchaeologyGeotechnical engineering

Abstract

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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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.325

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.082
GPT teacher head0.280
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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