Investigating cottonwood leaf beetle, Chrysomela scripta F., defoliation in cottonwood plantations utilizing remote sensing and geostatistical techniques
Bibliographic record
Abstract
1. The cottonwood leaf beetle (CLB), Chrysomela scripta F., is a serious defoliator of poplar (Populus spp.) in the United States and Canada and can reduce photosynthetic area, tree height, and cause tree mortality. A high-resolution airborne GeoVantage remote sensing system was used to detect simulated CLB defoliation in a cottonwood plantation near Sidon, Mississippi in the 450 ± 10 nm (blue), 550 ± 10 nm (green), 650 ± 10 nm (red), and 850 ± 10 nm (near infrared, NIR) wavelengths. 2. A split-plot experimental design with two background treatments (cut-grass and uncutgrass) as main plots and four simulated defoliation rate treatments (0%, 25%, 50% and 75%) as subplots was used to observe spectral properties and evaluate detection capabilities. 3. Reflectance values were represented by digital numbers extracted from images associated with defoliation and background treatments, along with various ground covers (bare soil, grass and intact tree canopies). We analyzed and evaluated these 1 Prepared in style and format for Agricultural and Forest Entomology 9 values and their derived vegetation indices (normalized difference vegetation index -NDVI and simple vegetation index -SVI). 4. There were significant reflectance differences (P = 0.05) between uncut-grass and cutgrass backgrounds in all four wavelengths. Vegetation indices differed significantly (P = 0.05) between uncut-grass and cut-grass background. In NIR and for NDVI and SVI there were no significant interactions between backgroundand defoliation-treatments. Significant interactions between backgroundand defoliationtreatments existed in the three visible wavelengths. The magnitude of reflectance difference in the NIR and the magnitude of difference in vegetation indices, between simulated defoliation rates, did not depend on background. 5. NIR, NDVI and SVI were best indictors for detecting defoliation rates. The 0% and 25% defoliation could be differentiated from the 75% defoliation treatments in the NIR. Utilizing NDVI and SVI vegetation indices, the 0% and 25% defoliation could be separated from the 50% and 75% defoliation rates. Only the 0% defoliation could be separated from other defoliation rates within uncut-grass background using reflectance in the three visible wavelengths. Four defoliation rates could not differentiated within cut-grass background using the three visible wavelengths. 6. Reflectance values significantly differed for all four bands and vegetation indices when comparing cut-grass and uncut-grass treatments for background. The reflectance in the three visible wavelengths was significantly higher in the cut-grass background than in the uncut-grass background. Compared to the visible wavelengths, NIR and vegetation indices (NDVI and SVI) are better for separating ground cover types: trees, grass and bare soil. 10
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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 | 0.001 |
| 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) | 0.000 | 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".