Monitoring subarctic environments using x- and c-band radar imagery.
Bibliographic record
Abstract
Subarctic environments are signifi cantly aff ected by \nthe climate warming observed in recent decades. Th ese \nperturbations lead to many environmental changes such as \npermafrost thawing and expansion of shrub vegetation cover. \nSnow, with its insulating properties, also plays an important \nrole in these processes maintaining relatively warm ground \ntemperatures during the winter while protecting the vegetation \nfrom the cold and wind. Th e presence of shrubs may, in turn, \ntrap wind blown snow and creates a positive feedback favoring \nthe expansion of shrub vegetation at the expense of the tundra. \nTh e objective of this study is to develop methods for monitoring \nsimultaneous subarctic environments of these two elements to \nensure eff ective monitoring of subarctic environments. Satellite \nSynthetic Aperture Radar (SAR) allow, among other things, \nprovide information on the vertical structure of the observed \nobjects and are considered here to estimate the height of the \nvegetation and snow cover thickness. \nTh e study area is a 60 km2 \n region situated around the \nUmiujaq community (56.55° N, 76.55° W) in northern \nQuebec, Canada. Th e area can be divided into two distinct \nenvironments: the coastal region to the east and the Lac \nGuillaume-Delisle graben to the west. Th e vegetation in the \ncoastal region is very sporadic and dominated by tundra \nvegetation, while the graben vegetation is mainly shrublands \nwith patches of conifers. A series of polarimetric RADARSAT-2 \nC-band images (HH, VV, HV, VH polarizations) and dualpolarized \nTerraSAR-X X-band images (HH, HV polarizations) \nhave been acquired over the area between October 2011 \nand April 2012 during the fall and winter seasons. Field \nmeasurement campaigns where performed during the summer \nof 2009 and the winter of 2012 to collect data on the vegetation \nand snow characteristics respectively. Temperature and soil \nmoisture sensors were also installed at 6 selected sampling sites. \nIn-situ observations have shown that the height of the shrub \nvegetation infl uences the depth of the snow cover. Preliminary \nresults show an increase in the RADARSAT-2 backscattering \nwith vegetation height while the TerraSAR-X backscattering \nseems to saturate with higher vegetation. Th e fall images, \nrepresenting the baseline without snow cover, are then compared \nwith the winter images to evaluate the eff ect of snow cover on \nthe SAR signal. In the presence of snow, RADARSAT-2 signal \nis attenuated while the TerraSAR-X signal increases slightly. \nTh e relationship between snow depth and radar parameters \nis relatively weak. A fi rst classifi cation with and unsupervised \nWishart method was performed using the polarimetric \nRADARSAT-2 data to delineate vegetated areas and allowed \nto classify three types of vegetation. Given the correlation \nbetween the heights of vegetation and snow cover, improved \nsegmentation based on the height of the shrub cover should \nallow a better estimation of the snow cover parameters.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".