A simple area-based model for predicting airborne LiDAR first returns from stem diameter distributions: an example study in an uneven-aged, mixed temperate forest
Bibliographic record
Abstract
Tree size distributions are of fundamental importance in forestry. Airborne laser scanning (i.e., light detection and ranging, LiDAR) provides high-resolution information on canopy structure and may have potential as a tool for mapping and monitoring tree stem diameter distributions across forest landscapes. We present an area-based allometric model (with three levels of species specificity) that links ground-based plot data to the height distribution of LiDAR first returns, demonstrating the approach with survey data from a mixed, uneven-aged forest in central Ontario, Canada. Our model translates stem diameters into estimates of exposed crown area within 1 m height intervals; we then compared those estimates with the height distribution of LiDAR first returns. This basic approach gave reasonable goodness of fits (root mean squared error = 32%), but accuracy was improved by adding mechanistic features (root mean squared error = 17%) to adjust crown shapes and crown permeability and allow for crown overlap and gaps. The model showed no bias in predicting LiDAR returns in the mid to upper canopy (18–30 m) but tended to underestimate the returns from the understory level (2–8 m) and overestimate returns from the ground level and lower canopy (8–18 m). Our model represents an important contribution towards the remote mapping of tree size distributions by showing that LiDAR first returns can be accurately predicted from standard plot data via the inclusion of a few fundamental canopy properties.
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".