A strategy for mapping Canada's Forest biomass with Landsat TM imagery
Bibliographic record
Abstract
Estimates of forest biomass are needed to meet Canada's international reporting requirements and to provide important inputs for global change, carbon accounting, and forest productivity models. The Canadian Forest Service, in cooperation with the Canadian Space Agency, has developed a strategy for mapping Canada's forest biomass as part of the Earth Observation for Sustainable Development of Forests (EOSD) Project. The strategy includes: (i) development of a biomass mapping method, (ii) regional expansion of the method, and (iii) national implementation. The method estimates forest biomass at the forest management stand level using forest cover type and structure information extracted from Landsat Thematic Mapper (TM) data. Regional expansion of the method has occurred over several pilot regions that represent a range of forest ecosystems across Canada. Validation of regional products provides an indication of the precision of the method, defines the data requirements and limits to regional expansion, and has led to the development of research themes. Specific research themes address known limitations of the method by (i) improving the extraction of cover type and structure information from satellite imagery, (ii) defining the role of environmental variables and other factors for biomass estimation, and (iii) separating understorey and overstorey biomass.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".