Estimates of bark beetle infestation expansion factors with adaptive cluster sampling
Bibliographic record
Abstract
Abstract Insects have infested over 37 million hectares of forested land, the most aggressive forest insect pest in North America is the mountain pine beetle that has attacked 14 million hectares. To determine infestation extent and spread rates, we examined mountain pine beetle damage at two sites over two consecutive years (2007–2008). High spatial resolution (20 cm) airborne digital imagery was acquired over a range of infestation intensities (High: site A; Low: site B). An adaptive cluster sampling approach assessed the extent and severity of damage from the imagery. In 2007, site A contained 5.22 infested trees per hectare (variance: 10.65) increasing in 2008 to 11.02 trees per hectare (variance: 24.83). In contrast, site B had 0.25 infested trees per hectare in 2007 (variance: 0.02), which increased in 2008 to 0.47 trees per hectare, with a variance of 0.08 trees per hectare. At both sites, infestations approximately doubled over a 1-year period. Adaptive cluster sampling applied to high spatial resolution airborne imagery can provide estimates of the severity of attack on the landscape. Keywords: object-based classificationhigh spatial resolutionsatellitedigital aerial imagerybark beetlesforest inventory, adaptive cluster sampling Acknowledgements We acknowledge funding for this research from the following agencies: (1) the Government of Canada, through the Mountain Pine Beetle Program, a 6-year, $40 million program administered by Natural Resources Canada – Canadian Forest Service; (2) the Pacific Forestry Centre Graduate Student Award to Sam Coggins, administered by Natural Resources Canada – Canadian Forest Service; (3) a University Graduate Fellowship (UGF) award to Sam Coggins; and (4) a Natural Sciences and Engineering Research Council (NSERC) grant to Nicholas Coops, supported by the Government of Canada. Lastly, we thank Peter Marshall for his assistance with the adaptive cluster sampling and helpful comments from the editor and three anonymous reviewers who strengthened the manuscript.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".