Understanding Arctic Greening Trends: A Multispectral Approach to Shrubification and Ecological Shifts
Notice bibliographique
Résumé
The Arctic tundra vegetation is going through major changes as global warming alters atmospheric functions and weather patterns. These changes have been shown to affect for instance phenological patterns, plant community structures, herbivory patterns as well as carbon storage in biomass. Extensive remote sensing research with multispectral sensors has revealed significant greening trends and events as well as shrub expansion, also known as shrubification, across the Arctic. These trends have been hypothesized to counteract increases in carbon content in the atmosphere. However, the magnitude of this effect as well as the shrub expansion rates are still unanswered due to low data availability as well as topographic and phenological differences across the region. This research was conducted on the Yamal Peninsula in Russian Arctic, where, in addition to climate change, vegetation is strongly influenced by the reindeer grazing practiced by the indigenous Nenets reindeer herders, as well as the expanding gas and oil drilling activities, which are accompanied by extensive infrastructure development. In this study our aim is to assess the opportunities of multispectral remote sensing data with varying spatial and temporal resolutions to examine shrubification in ecologically complex Arctic landscapes. Our research questions are the following: 1) Do Landsat-derived vegetation indices from a 30-year timespan show significant amount of greening in Arctic Russia; 2) How does image availability and phenology affect the way greening trends are analyzed; 3) Has shrub height and area increased during the study period and what implications does reindeer grazing have for shrub expansion and plant community structures; 4) Are greening trends associated with increased shrub height and area.Methodologically, we first extracted several vegetation indices from Landsat-satellite collections to evaluate greening trends. After satellite sensor cross-calibration with Random Forests, we examined how phenology and imaging frequency affects these trends and the analysis. We then compared the results with high-resolution QuickBird-2 and WorldView-2/3 imagery from 2004, 2013, 2017 and 2023. Secondly, we utilized drone imagery and VHR images to upscale vegetation height field data collected in 2017, and to delineate shrub areas with GeoSAM AI algorithm. In the last part, we created a classification with machine learning to estimate shrub expansion and height as well as change in community structure. Our preliminary results suggest that Landsat maximum vegetation indices have increased slightly across the entire study area. However, we also found a connection between image availability and the amount of greening detected. In addition, we found that shrub area and height has increased during the study period which could potentially benefit herbivore grazing activity. We therefore suggest coupling plant community changes with herbivore dynamics in the future studies on shrubification in the Arctic tundra.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| 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 | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».