Extending ALS-Based Mapping of Forest Attributes with Medium Resolution Satellite and Environmental Data
Bibliographic record
Abstract
Airborne laser scanner (ALS) data are used to map a range of forest inventory attributes at operational scales. However, when wall-to-wall ALS coverage is cost prohibitive or logistically challenging, alternative approaches are needed for forest mapping. We evaluated an indirect approach for extending ALS-based maps of forest attributes using medium resolution satellite and environmental data. First, we developed ALS-based models and predicted a suite of forest attributes for a 950 km2 study area covered by wall-to-wall ALS data. Then, we used samples extracted from the ALS-based predictions to model and map these attributes with satellite and environmental data for an extended 5600 km2 area with similar forest and ecological conditions. All attributes were predicted well with the ALS data (R2 ≥ 0.83; RMSD% < 26). The satellite and environmental models developed using the ALS-based predictions resulted in increased correspondence between observed and predicted values by 13–49% and decreased prediction errors by 8–28% compared with models developed directly with the ground plots. Improvements were observed for both multiple regression and random forest models, and for the suite of forest attributes assessed. We concluded that the use of ALS-based predictions in this study improved the estimation of forest attributes beyond an approach linking ground plots directly to the satellite and environmental data.
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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 | 0.003 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".