Characterizing the tundra taiga interface using Radarsat-2 (Mealy Mountains, Labrador)
Bibliographic record
Abstract
The transition zone between the boreal forest and Arctic tundra, also known as the tundra taiga interface (TTl), is a unique and sensitive ecosystem. A convenient way to monitor and understand TTl changes is through the interpretation and analysis of earth observation satellite images, or remote sensing. Ecosystem monitoring provides useful information about vegetation distribution and global climate. Currently, vegetation is monitored at both global and regional scales through the use of multispectral, light detection and ranging and synthetic aperture radar imagery. Each remote sensing technology offers unique spatial, spectral and radiometric resolution sets. This thesis investigates the use of synthetic aperture radar images from the Canadian Space Agency's RADARSAT-2 satellite to derive an image product discriminating different types of vegetation cover within the TTl region of Labrador. A selection of texture measures was applied to a dataset consisting of six RADARSAT-1 and fourteen RADARSAT-2 images. Statistical parameters were utilized to measure how strongly the radar derived vegetation product correlated with the well established normalized difference vegetation index (NDVI). The analysis was guided and validated by field data describing forest and non-forest land cover types. The results indicate that a mean texture measure with a window size relating to a ground area of 330x330 m (fine mode} and 450x450 m (standard mode) applied to an R-2 HV-polarized image is able to inform on the location of the TTl and also complements the vegetation cover found in NDVl 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 | 0.002 | 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".