Estimating 3D individual tree crown growth using multi-temporal LiDAR
Bibliographic record
Abstract
Predictions of individual tree crown growth provide key insights into future crown size and condition, and are important for sustainable forest management. This study presents a novel approach for forecasting three-dimensional (3D) crown growth using crown structural metrics derived from multi-temporal airborne lidar (ALS) at two-time intervals. Model development consisted of segmenting tree crowns from ALS point clouds, and producing convex hulls with matching vertex datums created for each crown pair (n = 110). A machine learning approach was then used to model the vertex shift (∆-xyz) between time-points, estimating growth for 33 independent crowns. Predictions of crown height (H), volume (V), and area (A2D) showed good to strong correlations (H R2 = 0.97, V R2 = 0.62, A2D R2 = 0.6). All metrics showed negative bias, with V and A2D to a greater extent than H, aligning with ∆-z being 5.6 times greater than ∆-xy. Future iterations of this model should be investigated at plot scale, incorporating model variables such as surrounding crown structure and competition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".