Combining land surface temperature and shortwave infrared reflectance for early detection of mountain pine beetle infestations in western Canada
Bibliographic record
Abstract
The current mountain pine beetle (Dendroctonus ponderosae Hopkins) outbreak, which began in 1999, continues to be the leading cause of pine tree mortality in British Columbia. Information regarding the location and spatial extent of the current attack is required for mitigating practices and forest inventory updates. This information is available from spaceborne observations. Unfortunately, the monitoring of the mountain pine beetle outbreak using remote sensing is usually limited to the visible stage at which the expansion of the attack beyond its initial hosts is unpreventable. The disruption of the sap flow caused by a blue-staining fungi carried by the beetles leads to: 1. a decrease in the amount of liquid water stored in the canopy, 2. an increase in canopy temperature, and 3. an increase in shortwave infrared reflectance shortly after the infestation. As such, the potential for early beetle detection utilizing thermal remote sensing is possible. Here we present a first attempt to detect a mountain pine beetle attack at its earliest stage (green attack stage when the foliage remains visibly green after the attack) using the temperature condition index (TCI) derived from Landsat ETM+ imagery over an affected area in British Columbia. The lack of detailed ground survey data of actual green attack areas limits the accuracy of this research. Regardless, our results show that TCI has the ability to differentiate between affected and unaffected areas in the green attack stage, and thus it provides information on the possible epicenters of the attack and on the spatial extent of the outbreak at later stages (red attack and gray attack). Furthermore, we also developed a moisture condition index (MCI) using both shortwave infrared and thermal infrared measurements. The MCI index is shown to be more effective than TCI in detecting the green attack stage and provides a more accurate picture of beetle spread patterns.
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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 | 0.001 | 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".