Automated SAR Image Thresholds for Water Mask Production in Alberta’s Boreal Region
Bibliographic record
Abstract
Mapping and monitoring surface water features is important for sustainably managing this critical natural resource that is in decline due to numerous natural and anthropogenic pressures. Satellite Synthetic Aperture Radar is a popular and inexpensive solution for such exercises over large scales through the application of thresholds to distinguish water from non-water. Despite improvements to threshold methods, threshold selection is traditionally manual, which introduces subjectivity and inconsistency over large scales. This study presents a novel method for objectively determining and applying a threshold to determine water masks from Synthetic Aperture Radar (SAR) imagery on a scene-by-scene basis. The method was applied to Radarsat-2 and simulated Radarsat Constellation Mission scenes, and validated against two independent validation sources with high accuracy (Kappa ranging from 0.85 to 0.93). Expectedly, greatest misclassification occurs near shorelines, which are often ecologically important zones. Comparisons between Radarsat-2 and Radarsat Constellation Mission thresholds and outputs suggest that the latter is a capable successor for surface water applications. This work represents a foundational step toward objectivity and consistency in large-scale water mapping and monitoring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".