Poster about mapping the risk of forest wind damage using airborne scanning LiDAR
Bibliographic record
Abstract
Wind damage is known for causing threats to sustainable forest management and yield value in boreal forests. Information about wind damage risk can aid forest managers in understanding and possibly mitigating damage impacts especially when wind damage events have increased in recent years.The objective of this research was to better understand and quantify drivers of wind damage, and to map the probability of wind damage and to provide information that could be used to support decision making in forest management planning, as well as in other sectors (e.g. electricity companies). To accomplish this, we used open-access airborne scanning light detection and ranging (LiDAR) data. LiDAR data can provide wall-to-wall coverage and are best suited for monitoring of the dominant trees. In addition multitemporal LiDAR is highly capable of monitoring abiotic tree or stand level changes. The LiDAR data used are openly accessible for public from NLS and are mainly used for generating digital terrain model (DTM). Potential drivers associated with the probability of wind-induced forest damage were examined using a multivariate logistic regression model which was well suited to the discrete nature of the dependent variable (i.e., damage, no damage) and it has been used widely in the modelling of forest disturbances. Risk model predictors related to topography and vegetation height were extracted from the LiDAR-derived surface models such as DTM and canopy height model (CHM). The strongest predictors in the risk model were mean canopy height and mean elevation. Damaged sample grid cells covered 45,6% of the entire sample and they were mainly dominated by Norway spruce. CHM mean and maximum were higher in damaged sample cells which can be expected to correlate with the result where mean volume was also larger in damaged sample cells than in undamaged. Regression model output was a continuous probability surface whereby the probability for wind damage is interpreted as risk (e.g. areas with high probability of wind damage can be described as high risk areas). With increasing frequency of wind damage events, there is a need to identify areas of high wind damage risk. The selected predictor variables, mean elevation describing local topography and mean canopy height, can provide valuable information on the damage probability (i.e. risk) in a robust way.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.064 | 0.018 |
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".