Landsat ETM+ classificaiton of Fluxnet Canada flux stations
Bibliographic record
Abstract
Changes in land use and cover and natural disturbance are thought to be major controls of the dynamic sink/source balance for the immense boreal terrestrial carbon stock. Fluxnet-Canada is a national research network developed to study the influence of climate and disturbance on terrestrial carbon cycling along an east-west transect of Canadian forest and peatlands. The purpose of the present work was to create large-area land cover classifications from satellite imagery to support Fluxnet scaling and modeling studies. The methodology presented in [9] was implemented on seven Fluxnet monitoring sites across Canada. A Landsat ETM+ image covering each site was clustered to 150 classes using unsupervised K-Means classification prior to 50-cluster merging through classification by generalization (CPG). The resulting clusters were merged to 16-class landcover maps through interactive labeling using cluster bitmaps to create spatial context for the 50 clusters. The bitmaps aided the analyst when ground data was scarce or nonexistent. The products were sent to Fluxnet-Canada site managers for ground validation and the classifications were refined according to the feedback provided. The final landcover maps were used in the calculation of leaf area index (LAI) for the Landsat ETM+ scenes, thus allowing for upscaling of carbon flux measurements based on the correlation of LAI to carbon flux.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".