National remote sensing-derived aboveground biomass yield curves for Canada
Bibliographic record
Abstract
Abstract Accurate and current estimates of aboveground biomass (AGB) are essential outputs of forest inventories and are critical for carbon accounting. To obtain current estimates and provide projections into the future, growth simulators or yield curves are applied. Remotely sensed time series of AGB estimates across large areas provide a novel opportunity to generate representative AGB yield curves for a broader range of tree species, age classes, and regions, including noncommercial and unmanaged forests using a single, consistent data source and approach. In this study, remote sensing yield curves (RSYC) are demonstrated as a means to extend AGB modeling across Canada’s forested ecosystems. To ensure national coverage while maintaining regional representativeness, yield curves were developed at a 150 × 150 km tile extent for 27 individual species, along with three additional multi-species models (generic, coniferous, and broadleaf). Evaluation against an independent set of plot data showed that among the multispecies models, the coniferous model achieved the lowest relative bias (2.06%) and root mean square error (RMSE) (41.03 t/ha), while species-specific models exhibited variable performance. Comparison to existing national species-specific yield curves indicated that the RSYC models generally achieved lower bias and RMSE than existing national models, with particularly notable improvements for key coniferous species such as black spruce and jack pine (e.g. RMSE% reductions from 55.89% to 34.69% for black spruce, and from 112.79% to 35.41% for jack pine), demonstrating enhanced accuracy and consistency for national-scale applications. Remotely sensed time series data enabled the development of a pool of species-specific and generic yield curves for Canada that are generated from a nationally consistent (and freely available) data source and approach—regardless of the management, ownership, or protection status of the forest.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".