Land Cover Mapping with MERIS at the BOREAS Study Area
Bibliographic record
Abstract
The objective of this study is to validate the MERIS vegetation land cover classification product. The full resolution MERIS radiance data sets obtained over the BOREAS (Boreal Ecosystem-Atmosphere Study) South Study Area in May and August 2003 were used. The MERIS radiance data were first converted to at-canopy reflectance data, which were compared to CASI data obtained during BOREAS (1994), followed by unsupervised classification performed based on seasonal variation of pigments as inferred from visible and near-infrared spectral bands. Three modified normalized Difference Vegetation Indices (mNDVI), sensitive to relative proportions among pigments and pigment content, and a red-edge spectral parameter, the wavelength at the reflectance minimum (λ0) were used in the unsupervised classification. Accuracy assessments of the derived vegetation classification maps were performed using a forest inventory map provided by the Saskatchewan Environment and Resource Management Forestry Branch-Inventory Unit (SERM-FBIU). The forest vegetation classification using seasonal changes in optical indices (mNDVIs and λ0), derived from the MERIS imagery in May and August revealed a reasonably high overall classification accuracy for all vegetation cover types identified: conifer, mixed stands, and fen. The classification results also demonstrated that classification using reflectance parameters sensitive to pigment absorption outperformed that using reflectance itself and the classification using seasonal information was better than that using information obtained in a single MERIS image,
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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) | 0.002 | 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".