Detecting and excluding disturbed forest areas improves site index determination using bitemporal airborne laser scanner data
Bibliographic record
Abstract
Abstract Bitemporal airborne laser scanning (ALS) data are increasingly being used in forest management inventories for the determination of site index (SI). SI determination using bitemporal ALS data requires undisturbed height growth of dominant trees. Therefore, areas with disturbed top height development are unsuitable for SI determination, and should be identified and omitted before modelling, predicting and estimating SI using bitemporal ALS data. The aim of this study was to explore methods for classifying the suitability of forest areas for SI determination based on bitemporal ALS data. The modelling approaches k-nearest neighbour, logistic regression and random forest were compared for classifying disturbed (at least one dominant tree has disappeared) and undisturbed plots. A forest inventory with plot re-measurements and corresponding bitemporal ALS data from the Petawawa Research Forest in Ontario, Canada, was used as a case study. Based on the field data, two definitions of a disturbed plot were developed: (1) at least one dominant tree had died, was harvested or had fallen during the observation period, or (2) at least one dominant tree was harvested or had fallen during the observation period. The first definition included standing dead trees, which we hypothesized would be more difficult to accurately classify from bitemporal ALS data. Models of disturbance definition 1 and 2 yielded Matthews correlation coefficients of 0.46–0.59 and 0.62–0.80, respectively. Fit statistics of SI prediction models fitted to undisturbed plots were significantly better (P < 0.05) than fit statistics of SI prediction models fitted to all plots. Our results show that bitemporal ALS data can be used to separate disturbed from undisturbed forest areas with moderate to high accuracy in complex temperate mixedwood forests and that excluding disturbed forest areas significantly improves fit statistics of SI prediction models.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".