Adaptation of the SNOWMAP algorithm for snow mapping over eastern Canada using Landsat-TM imagery
Bibliographic record
Abstract
The snow mapping algorithm SNOWMAP was adapted to Landsat-TM data and to the context of eastern Canada. Six Landsat-5 TM scenes were used. It was found that the original version of SNOWMAP greatly underestimates snow cover extent. The modification made to the original algorithm, by cancelling the minimum threshold of 0.1 on the NDVI value, allows gaps to be filled in. In addition, a spatial correction procedure applied to the modified SNOWMAP algorithm results improves snow detection under coniferous forests. Based on a limited data set of ground-based observations (only 40 sites were available), the modified SNOWMAP seems to perform better in snow detection than the original version of the algorithm. An application case is presented in order to demonstrate the relevance of the modified SNOWMAP results as a high spatial-resolution reference for the validation of historical snow maps derived from medium spatial-resolution satellite data. Citation Chokmani, K., Dever, K., Bernier, M., Gauthier, Y. & Paquet, L.-M. (2010) Adaptation of the SNOWMAP algorithm for snow mapping over eastern Canada using Landsat-TM imagery. Hydrol. Sci. J. 55(4), 649–660.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".