Detecting and mapping mountain pine beetle red-attack damage with SPOT-5 10-m multispectral imagery
Bibliographic record
Abstract
The objective of this study was to gauge the effectiveness of using SPOT-5 10-m multispectral imagery to detect and map red-attack damage for an area near Cranbrook, British Columbia, Canada. A logistic regression model was used to incorporate SPOT imagery with elevation and associated derivatives for redattack detection and mapping. Separate independent sets of calibration and validation data, collected via a detailed aerial survey, were used to train the classification algorithm and vet the output maps of red-attack damage. The output from the logistic regression model was a continuous surface indicating the probability of red-attack damage. Using a greater than 50% probability threshold, red-attack was mapped with 71% accuracy (with a 95% confidence interval of ?9%). This level of accuracy is comparable to that achieved with Landsat single-date imagery in an area with similar levels of infestation. If a synoptic view of mountain pine beetle red-attack damage at the landscape level is required, and if Landsat data are unavailable, SPOT-5 10-m multispectral imagery may be considered an alternative data source, albeit an expensive one, for detecting and mapping mountain pine beetle red-attack damage.
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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 | 0.002 |
| 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 | 0.001 | 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".