Automated airborne lidar-based assessment of timber measurements for forest management
Bibliographic record
Abstract
Received: 12 July 2012 Abstract Accepted: 30 August 2012 This paper presents processing and analysis techniques to apply LiDAR data to estimate tree diameter at breast height (DBH) – a critical variable applied in a large number of forest management tasks. Our analysis focuses on the estimation of DBH using only LiDAR-derived tree height and tree crown dimensions, i.e., variables accessible from aerial observations. The modeling process was performed using 161 white and red pine trees from four 3850 m plots in the Foret de l’Aigle located in southwestern Quebec. Segments of the LiDAR data extracted for DBH estimation were obtained using the Individual Tree Crown (ITC) delineation method. Regression models were investigated using height as well as crown dimensions, which increased the precision of the model. This study demonstrates that DBH can be modeled to acceptable accuracy using altimetry data and automated data processing procedures and then be used in high-precision timber volume assessment.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 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".