Monitoring the state of a large boreal forest region in eastern Canada through the use of multitemporal classified satellite imagery
Bibliographic record
Abstract
Multitemporal classification of Landsat imagery was used to measure and monitor the state of the forest over a large area (11.6 million ha) of boreal forest in eastern Canada using four criteria for a 20 year period (1985–2005). The Enhancement-Classification Method was used in this study. Forty-eight thematic classes based on Canada's National Forest Inventory were identified, then grouped into 13 indicators, and reorganized within four main criteria: (i) forest versus nonforest land cover, (ii) forest development stage, (iii) forest cover type, and (iv) forest cover density. Validation based on 2973 high-resolution geo-referenced digital aerial colour photos of the 2005 classified images showed an overall accuracy of the four criteria of 83%, 68%, 58%, and 62%, respectively. The change in each indicator between 1985 and 2005 could be summarized as: (i) a decrease in productive forest area of 0.4% (approx. 43 000 ha); (ii) a 4.6% decrease in mature stand area, with a concomitant increase in areas classified as vegetated (1.3%) and regenerated (3.4%); (iii) concentration of harvesting pressure on coniferous and mixed stands with respective reductions of 8.2% and 0.8%, due to their conversion to deciduous stands; and (iv) an increase in low-density stands (3.1%) and a decrease in high-density stands (8.3%). These results demonstrate that medium-resolution (30 m) remote sensing tools can be used both to monitor the state of the boreal forest and to produce key indicators, which were extracted from the multidate Landsat satellite imagery.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 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".