Improving estimation of forest aboveground biomass using Landsat 8 imagery by incorporating forest crown density as a dummy variable
Bibliographic record
Abstract
Optical remote sensing data are widely used in estimation of forest aboveground biomass (AGB), and the accuracy of AGB estimations has drawn wide attention. A method to improve the accuracy of remote sensing-based AGB models was developed by combining Landsat 8’s Operational Land Imager (OLI) and forest crown density (FCD). Remote sensing-based AGB models with and without an FCD dummy variable were developed using linear regression based on vegetation type (coniferous forest, broadleaf forest, mixed forest, and total vegetation). The differences between the models with and without an FCD dummy variable were analysed and compared. The models involving stratification of vegetation types provided more accurate estimations than the models of total vegetation. The models with an FCD dummy variable performed better than the models without an FCD dummy variable for each vegetation type. In each FCD class, the models with an FCD dummy variable provided more accurate estimations than the models without an FCD dummy variable, and the over- and underestimation problems associated with the models without an FCD dummy variable in thin and dense stands were significantly alleviated by the models with an FCD dummy variable. Therefore, introducing FCD into remote sensing-based AGB models has great potential to improve AGB estimation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".