Estimating potential tree height in <i>Pinus radiata</i> plantations using airborne laser scanning data
Bibliographic record
Abstract
Representing the spatial distribution of trees and competition interactions in growth models improves growth prediction and provides insights into spatially explicit forecasts for precise silvicultural interventions. However, this information is rarely taken into account over large areas because obtaining the spatial distribution of individual trees and estimating their competition is both expensive and time consuming. Airborne laser scanning enables rapid estimation of tree height and other attributes over large areas. In this study, we implemented an individual tree detection approach to first extract tree attributes of Pinus radiata D. Don plantations, and second to use this spatially explicit information on tree location and competition to forecast potential tree height, defined as a maximum projected tree height at rotation age. To do so, using a chronosequence of tree heights, we developed a tree height growth model using a Chapman–Richards function, utilizing the effect of inter-tree competition and stand-level top height (TH) on the tree height growth. The results showed that using chronosequence of heights, competition, and TH resulted in accurate predictions of potential tree height (root mean square error = 2.9 m; mean absolute percentage error = 0.154%). We concluded that individual tree height growth is significantly influenced by competition, with increased competition values associated with reductions in potential height growth by 22.2% at 30 years.
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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 | 0.000 |
| 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) | 0.000 | 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".