Coarse resolution satellite mapping of insect-induced tree defoliation and mortality
Bibliographic record
Abstract
Insect-induced tree defoliation and the resulting growth and mortality losses represent a significant disturbance in many forested regions. Most defoliation surveys are produced using conventional aerial sketch mapping techniques supplemented by field inspection. Digital airborne and fine resolution (/spl les/ 100m) satellite imagery has been frequently used, with mixed results, to assess both the extent and severity of defoliation described by R. F. Nelson (1983), V. C. Radeloff (1999) and J. Heikkila et al. (2002). Coarse resolution (/spl sim/1-km) satellite imagery, with its greater frequency of observation and spatial coverage, could also prove useful for monitoring and mapping large-scale defoliation events. The purpose of this study was to assess the potential for using multi-temporal SPOT VEGETATION (VGT) imagery for monitoring insect defoliation and mapping subsequent tree mortality. VGT imagery was examined for a severe outbreak of hemlock looper (Lambdina fiscellaria) in Quebec, Canada, which defoliated and killed more than 400,000 ha of balsam fir in 1999. Multi-temporal change metrics described by J. S. Borak (2000) based on reflectance and vegetation indices were derived using 10-day VGT composites from 1998-2000. A multiple logistic regression model developed using the 1998-2000 metrics could classify forest mortality within a 500-by-700 km study area with a commission error of 33-60 percent and omission error of 0-33 percent, depending on if the reference surveys were buffered by 2 km. The logistic model was also applied to simulate a near real-time application for detecting defoliation and monitoring its evolution. A time-series of 1999 change metrics could detect defoliation after July larval feeding with an omission error rate comparable to that from mapping mortality. However, the number of false detections was 2-3 times greater due to short-term variation in the satellite signal not related to real vegetation changes (e.g., cloud and atmospheric contamination, surface moisture). We conclude that coarse resolution imagery demonstrates considerable promise for monitoring insect defoliation and should be investigated for a range of defoliators and forest types.
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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) | 0.000 | 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 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".