Historical insect disturbance maps from 1985 onwards for Canadian forests derived using earth observation data
Bibliographic record
Abstract
Despite the major impact of insect outbreaks on the Canadian boreal forest and its significance in carbon monitoring, current monitoring efforts primarily rely on costly and subjective aerial survey interpretations. While satellite remote sensing has been widely used to map wildfire and harvesting disturbances, no consistent, long-term dataset exists for severe canopy loss events in coniferous forests. This paper presents the development and evaluation of annual maps of boreal forest insect pest severe disturbances in Canada from 1985 to 2024. We introduce a methodology that leverages Landsat imagery with a 30 m spatial resolution to provide a standardized, long-term record of severe pest-related defoliation. The overall prediction accuracy between the aggregated moderate and severe pest and non-pest classes was evaluated as 90%. This historical dataset offers valuable insights for forest ecology and disturbance monitoring and research, forest carbon modeling, and forest management.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".