Estimating Stem Diameter Distributions with Airborne Laser Scanning Metrics and Derived Canopy Surface Texture Metrics
Bibliographic record
Abstract
This study aimed to determine the optimal approach for estimating stem diameter distributions (SDD) from airborne laser scanning (ALS) data using point cloud metrics (Mals), a canopy height model (CHM) texture metrics (Mtex), and a combination thereof (Mcomb). We developed area-based models (i) to classify SDD modality and (ii) predict SDD function parameters, which we tested for 5 modelling techniques. Our results demonstrated little variability in the performance of SDD modality classification models (mean overall accuracy: 72%; SD: 2%). Our best SDD function parameter models were generally fitted with Mcomb, with R2 improvements up to 0.25. We found the variable Correlation, originating from Mtex, to be the most important predictor within Mcomb. Trends in the performance of the predictor groups were mostly consistent across the modelling techniques within each parameter. Using an Error Index (EI), we determined that differentiating modality prior to estimating SDD improved the accuracy of estimates for bimodal plots (~12% decrease in EI), which was trivially not the case for unimodal plots (<1% increase in EI). We concluded that (i) CHM texture metrics can be used to improve the estimate of SDD parameters and that (ii) differentiating for modality prior to estimating SSD is especially beneficial in stands with bimodal SDD.
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How this classification was reachedexpand
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 | 0.002 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".