Estimating Tree Diameter at Breast Height (DBH) Using iPad Pro LiDAR Sensor in Boreal Forests
Bibliographic record
Abstract
Traditional Diameter at Breast Height (DBH) mensuration is labor-intensive and costly. This scoping study explored the possibility of using the Apple iPad Pro Light Detection And Ranging (LiDAR) sensor to estimate DBH. Three plots were scanned in a research plantation near Thunder Bay, Canada. Sites consisted of either Black Spruce (Picea mariana) or Red Pine (Pinus resinosa) planted with different initial densities. DBH was manually measured for validation. Point clouds were acquired for each plot using three scanning patterns; circular, figure-8, and transect. Single and five cross sections of 4 or 10 cm in thickness were extracted from each point cloud, centered at 1.3 m above the ground. Two circle fitting algorithms (Pratt, Taubin) and two ellipse fitting algorithms (Taubin, Szpak) were applied to the extracted cross-sections to estimate DBH. Scanning pattern and curve-fitting formula significantly impacted DBH estimate accuracy (p-value ≤ 0.001), while cross-section count and thickness did not. The circular scanning pattern with a single 4 cm cross-section and a combination of circle- and ellipse-fitting formulas was the most accurate DBH estimation method (RMSE = 1.1 cm; 6.17%).
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".