Mapping Eastern Spruce Budworm Cumulative Defoliation Severity from Landsat and SPOT
Bibliographic record
Abstract
The eastern spruce budworm (Choristoneura fumiferana Clem.) is among the most damaging of forest insects in Canada’s boreal forest. In Eastern Canada, aerial observations have recently reported increasing outbreaks that are raising concerns to assess and monitor the location, area and potential impacts from this pest. In this study, field and remote sensing estimates of cumulative defoliation were compared for an outbreak in Baie-Comeau, Quebec, to determine the extent that observed spectral response differences on remote sensing images were related to observations derived from the field. A method based on the relative difference in infrared simple ratio as a proxy for detecting differences in leaf area that was previously applied to aspen defoliation was adapted for this study. A postdefoliation Spot 4 multispectral image was registered and normalized to a pre-defoliation Landsat 5 image from which the relative difference in infrared simple ratio was empirically related to field-derived defoliation estimates. While both ocular and branch sample defoliation ratings were statistically correlated, branch sample estimates were more highly correlated to observed image values than ocular ratings. First iteration spruce budworm defoliation severity models were developed and applied to susceptible areas comprising dominant conifer and mixedwood land cover.
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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 | 0.000 |
| 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".