Mapping aboveground tree biomass at the stand level from inventory information: test cases in Newfoundland and Quebec
Bibliographic record
Abstract
A method of estimating and mapping aboveground tree biomass (AGTB) was developed using provincially available forest inventory databases. More specifically, AGTB conversion tables were devised to estimate biomass for stand attributes that are commonly mapped in provincial inventories over the Canadian landscape, i.e., species composition, projected crown density, and dominant tree height. AGTB is first estimated at the tree level using allometric relationships and measured stem distributions that are subsequently summed to estimate plot-level biomass. AGTB conversion tables are then computed from regression models that relate the plot-level biomass values to stand attributes. AGTB can then be mapped over the landscape by assigning the plot-level biomass values to the mapped stands. The method was developed using two provinces, Newfoundland and Labrador (N.L.) and Quebec, as test cases to assess the adaptation required between different management units. Models used to develop conversion tables from the test areas provided estimates of biomass with R2 ranging from 0.22 to 0.35 and from 0.31 to 0.64 and root mean square errors of 38 to 47 t/ha and 21 to 41 t/ha for N.L. and Quebec, respectively, based on an independent validation data set not used in the development of the models. Mapping errors and potential improvements to the models are discussed. To extend the methods developed in this study to a national map of forest AGTB will require significant adjustments to account for differences in regional inventory specifications. While the method for AGTB mapping can fulfil an important monitoring requirement in forestry, applying it to all provinces, as well as including alternate data sources for areas where inventories do not exist, such as satellite remotely sensed images, requires further research, some of which is currently in progress.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".