Estimation of Forest Structure and Fuel Change Across Mountain Pine Beetle – Attacked Forests Using Mobile and RPAS – Based LiDAR
Bibliographic record
Abstract
The recent mountain pine beetle (Dendroctonus ponderosae) outbreak has resulted in widespread mortality of pine trees across western Canada over the past two decades. The changes to forest structure caused by the beetle are well known through ground-based observations. However, the potential changes to fuels for wildfires associated with altered forest structure are not well known nor incorporated into fire fuel models. In this study, we used light detection and ranging (LiDAR) to quantify variations in forest structure and wildfire fuels caused by mountain pine beetle (MPB) infestation. From this data, we created models that characterize fuels following MPB attack. LiDAR metrics were extracted from three-dimensional point clouds acquired using remotely piloted aircraft systems (RPAS) and mobile laser scanning (MLS), both individually and combined. Fuel components in the stand were then modeled across a range of MPB attack severities. Results indicated the fused model was most accurate at predicting canopy fuel load (R2 = 0.80), while MLS had the best model performance for shrub fuel load (R2 = 0.68) and coarse woody debris fuel load (R2 = 0.68). This study demonstrates the ability of LiDAR to accurately characterize forest fuel loads in MPB-infested 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".