Estimating Canopy Height of Deciduous Forests at a Regional Scale with Leaf-Off, Low Point Density LiDAR
Bibliographic record
Abstract
Abstract. As we strive toward a more accurate understanding and quantification of carbon pools in forested ecosystems, the development of regional-scale maps of forest characteristics is essential in order to establish baselines and monitor change. Light Detection and Ranging (LiDAR) is increasingly being used to improve our understanding of forested ecosystems on a broad spatial scale, although obtaining data can be expensive and time consuming. We evaluated the effectiveness of using freely available low point density, leaf-off LiDAR collected for the entire state of Pennsylvania, in the United States, to create an accurate regional-scale dominant/codominant canopy height model for state forests in Pennsylvania. We evaluated several methodologies using an inventory dataset with over 1400 sample plots. The developed canopy height model was accurate to about 10% of the field-measured dominant/codominant tree heights for each plot, although it underestimated the field values. Root mean square error relativ...
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".