Quantifying Cumulative Effects of Harvesting on Aspen Regeneration through Fuzzy Logic Suitability Mapping
Bibliographic record
Abstract
Core Ideas Multivariate statistics and fuzzy logic analysis were used to assess aspen regeneration. Vegetation indices were the dominant factors in determining regeneration suitability. Vegetation indices were significantly higher in high suitability areas. Skidder traffic was significantly lower on high regeneration suitability land. The percentage of slash coverage was significantly lower on high suitability land. Vigorous aspen (Populus tremuloides Michx.) regeneration immediately following a harvesting event is important to ensuring the continued health and productivity of the future forest. This study aimed to examine the potential of using unoccupied aerial vehicle, multispectral remote sensing, and GIS mapping techniques to develop a comprehensive approach for predicting aspen regeneration success at the harvest block scale. Three winter harvested blocks were studied at Duck Mountain Provincial Park in east‐central Saskatchewan, Canada. Ten regeneration predictor variables (number of skidder passes, percentage slash coverage, topographic wetness index, slope, aspect, slope position, and four vegetation indices: green normalized vegetation index [GNDVI], normalized red‐edge index [NDRE], simple RED to NIR ratio [SR], and chlorophyll index green [CIG]) were determined for 168 measurement plots 1 yr after harvest. Principal component analysis, principal component regression, fuzzy logic analysis, and GIS mapping techniques, were combined for the first time in this study to determine cumulative effects on aspen regeneration. On average, low suitability areas had significantly more skidder traffic (34 passes) compared to below average (17), above average (10), and high (7) suitability areas. Low suitability areas also had significantly more slash coverage (13.1%) compared to below average (8.49%) or high suitability land (7.18%). High suitability areas had significantly higher GNDVI, NDRE, SR, and CIG indices, compared to low and below average suitability land. Not only does this method of analysis help to assess how a combination of factors may influence aspen regeneration, it can also act as a decision support system tool for industry, or government, to improve aspen regeneration assessments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".