Notice bibliographique
Résumé
Terrestrial vegetation contributes strongly to dynamic biosphere-atmosphere exchanges of mass and energy, through activities such as photosynthesis, that help shape the Earth’s climate. The boreal forest is located in high latitudes and subject to large seasonal temperature fluctuations and a changing climate. Understanding the response of the boreal forest to seasonal and climate changes requires a means of effectively monitoring vegetation phenology at large spatial and temporal scales. Optical remote sensing can be applied at such scales, providing a powerful means of observing how ecosystems respond to changing environmental conditions. However, continued work that integrates both optical remote sensing and plant physiology at multiple scales is necessary to correctly apply and interpret large scale remote sampling of vegetation. Key questions regarding the application of optical remote sensing across ecosystems remain unanswered: 1) which remote sensing metrics are most effective at monitoring phenology of different functional types? and 2) how do these remote sensing metrics relate to actual changes in plant physiology when sampling different vegetation? To address these questions, experimental forest stands for several boreal tree species, both evergreen and deciduous were established in pots in Edmonton, Alberta, Canada, allowing for continuous monitoring across seasons using a variety of metrics to track phenology of representative boreal vegetation at multiple scales. This involved the use of different optical indices: the normalized difference vegetation index (NDVI), the photochemical reflectance index (PRI), the chlorophyll/carotenoid index (CCI), and steady-state chlorophyll fluorescence (FS), as an analogue of solar-induced fluorescence (SIF). These optical metrics were then compared to actual rates of photosynthesis to determine their efficacy in tracking seasonal changes in photosynthetic activity, or photosynthetic phenology. Results indicated that NDVI and PRI exhibited a complementary ability to monitor photosynthetic phenology of both evergreen and deciduous functional types. NDVI effectively tracked photosynthetic phenology of deciduous species, but less so for evergreens, while PRI closely paralleled photosynthetic phenology of evergreens, but less so for deciduous species. CCI showed strong parallels with photosynthetic activity in both evergreen and deciduous species, with FS showing a similar ability. These results indicated subtle differences in seasonal patterns of optical metrics and photosynthetic activity across and within functional types. Overall, these results revealed the efficacy of different remote sensing metrics at tracking photosynthetic phenology of different boreal tree species. This project provides an important foundation for the assessment of plant physiology by means of optical remote sensing, expanding the value of large-scale ecosystem monitoring.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 |
| 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 | 0,001 |
| 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 ».