Automated individual tree crown delineation from LIDAR data using morphological techniques
Bibliographic record
Abstract
In current tree crown delineation from LiDAR data, treetops and 3D geometric shapes of tree crowns are frequently extracted from LiDAR-derived Crown Height Model (CHM) and used as references to localize and delineate crowns. However, it is difficult to detect deciduous treetops and delineate deciduous tree crowns. The 3D shape of a crown, which can be derived from CHM, may be taken as a half ellipsoid, and any horizontal slice of the ellipsoid contains the treetop and indicates not only the location but also the spatial extent of the crown. Based on such slices, a novel multi-scale method for individual tree crown delineation from CHM was proposed in this study. This method consists mainly of two steps: (1) morphologically open the CHM over the scale range of target tree crowns; and (2) take local maxima within each resulting opened CHM as the horizontal slices of target crowns at the corresponding scale level and integrate all the slices within the scale range together to represent the spatial distribution of target crowns. In an experiment on CHMs over two natural closed canopy forests in Ontario, Canada, the proposed method accurately delineated the majority of the tree crowns from closed canopy 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".