Estimating 3D individual tree crown growth using multi-temporal LiDAR
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 it