Estimation of Vertical Fuel Layers in Tree Crowns Using High Density LiDAR Data
Bibliographic record
Abstract
The accurate prediction and mitigation of wildfire behaviour relies on accurate estimations of forest canopy fuels. New techniques to collect LiDAR point clouds from remotely piloted aerial systems (RPAS) allow for the prediction of forest fuels at extremely fine scales. This study uses a new method to examine the ability of such point clouds to characterize the vertical arrangement and volume of crown fuels from within individual trees. This method uses the density and vertical arrangement of LiDAR points to automatically extract and measure the dimensions of each cluster of vertical fuel. The amount and dimensions of these extracted clusters were compared against manually measured clusters that were collected through the manual measurement of over 100 trees. This validation dataset was composed of manual point cloud measurements for all portions of living crown fuel for each tree. The point clouds used for this were ground-based LiDAR point clouds that were ~80 times denser than the RPAS LiDAR point clouds. Over 96% of the extracted clusters were successfully matched to a manually measured cluster, representing ~97% of the extracted volume. A smaller percentage of the manually measured clusters (~79%) were matched to an extracted cluster, although these represented ~99% of the total measured volume. The vertical arrangement and dimensions of the matched clusters corresponded strongly to one another, although the automated method generally overpredicted each cluster’s lower boundary. Tree-level volumes and crown width were, respectively, predicted with R-squared values of 0.9111 and 0.7984 and RMSE values of 44.36 m2 and 0.53 m. Weaker relationships were observed for tree-level metrics that relied on the extraction of lower crown features (live crown length, live crown base height, lowest live branch height). These metrics were predicted with R-squared values of 0.5568, 0.3120, and 0.2011 and RMSE values of 3.53 m, 3.55 m, and 3.66 m. Overall, this study highlights strengths and weaknesses of the developed method and the utility of RPAS LiDAR point clouds relative to ground-based point clouds.
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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) | 0.000 | 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) | 0.000 | 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".