Influence of Sampling Design Parameters on Biomass Predictions Derived from Airborne LiDAR Data
Bibliographic record
Abstract
This study investigated the influence of sampling design parameters on biomass prediction accuracy obtained from airborne lidar data. A one-factor-at-a-time and a global sensitivity analyses were applied to identify the parameters most impacting model accuracy. We focused on several lidar and field survey parameters that can be easily controlled by users. In this pine plantations study site, a decrease in pulse density (4 to 0.5 pulse/m2) led to a small decrease in prediction accuracy (−3%). However, variability in the number of field plots, positioning accuracy, and plot size, significantly impacted model performance. To obtain a robust model, a minimum of 40 field plots, along with field plot position accuracy of 5 m or lower, and field plot radius exceeding 13 m are recommended. The minimum diameter at breast height (DBH) threshold and the choice of the allometric biomass equation were found to have lesser impacts on model accuracy. In addition, accuracies of DBH and tree height measurements were respectively shown to have a minor and negligible contribution to the prediction error. Significant field measurement costs will still be needed to ensure good-quality models for biomass mapping. However, by reducing pulse density, cost savings can be made on lidar acquisition.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".