CANADA'S FOREST COVER INDICATOR: DEFINITION, METHODOLOGY AND RESULTS
Bibliographic record
Abstract
ABSTRACT. The Environment and Sustainable Development Indicators (ESDI) Initiative was introduced to track Canada's overall wealth in the form of natural and human capital, in additionto familiar economic data such as the gross domestic product (GDP). One of the six ESDIs is the Forest Cover Indicator (FCI). In this paper we define FCI, outline the overall method for deriving FCI, and report results for addressing four key technical issues in carrying out this overall method. The FCI is defined as interannual variations of Canada's forest area with the middle-summer crown closure (CC) ? 10%. Crown closure is the percentage of the ground surface covered by a downward vertical projection of the tree crowns. Theoverall monitoring method is mainly based on coarse resolution remote sensing data because of the need to cover Canada's extensive landmass during the middle-summer months and toupdate the results annually. Medium resolution satellite data, field measurements, and modeling approaches were used for calibration, correction, validation, and down-scaling, with a focus on the following 4 key technical issues: (1) correcting understory non-tree vegetation effect on CC, (2) downscaling forest cover area from 1-km to 100-m spatial resolution as required by the FCI definition, (3) detecting the changes of CC caused by disturbances, and (4) detecting changes in CC caused by forest regrowth. Methods and results for addressing these technical issues are described in the paper. While these results indicate that the key technical issues can be solved by integrating satellite remote sensing data/products and other data, there are clear needs for further development, especially testing against field measurements.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".