Investigating cottonwood leaf beetle, Chrysomela scripta F., defoliation in cottonwood plantations utilizing remote sensing and geostatistical techniques
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».