Species-specific modeling of tree diameter at breast height using tree height and relative density with implications for remote sensing-based forest inventory
Bibliographic record
Abstract
Accurate estimation of tree diameter at breast height (DBH) is essential for forest monitoring, biomass modeling, and carbon accounting. While DBH is traditionally measured in the field, this approach is labor-intensive and costly, especially at large scales. In contrast, tree height can now be efficiently obtained from remote sensing platforms such as airborne LiDAR and photogrammetry, creating opportunities to estimate DBH indirectly. To address this, we developed a species-specific nonlinear framework to predict DBH from tree height and stand-level relative density (RD) in the mixed temperate forests of New Brunswick, Canada. Our analysis used 1807 trees from 653 permanent sample plots (1985–2014), representing six dominant species: Abies balsamea, Acer rubrum, Acer saccharum, Picea mariana, Picea rubens, and Picea glauca. Allometric (height-only) models explained part of DBH variation, with R² ranging from 0.15 to 0.35 (broadleaves) and 0.41–0.74 (conifers), but predictive accuracy was notably low for Acer rubrum and Acer saccharum. Incorporating RD as a competition index substantially improved model performance, with R² increasing to 0.85–0.89 (broadleaves) and 0.72–0.88 (conifers). Prediction errors (RMSE and MAE) consistently decreased, with broadleaves showing the greatest improvement compared to conifers, reflecting their stronger sensitivity to stand density. These findings demonstrate that combining tree height with RD provides reliable estimates of DBH across diverse species. The framework bridges ground-based inventory with remote sensing applications, offering a scalable approach for biomass estimation, stand density analysis, and sustainable forest management in temperate mixed-species forests. • Developed species-specific nonlinear models to predict DBH from tree height and RD. • Used 1807 trees from 653 permanent sample plots across New Brunswick (1985–2014). • Including RD as a competition index improved model accuracy (R² up to 0.89). • Broadleaves exhibited greater DBH response to stand density compared to conifers. • Framework supports scalable DBH estimation in mixed temperate forests.
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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 | 0.002 |
| 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 | 0.002 | 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".