Relationship between NDVI and Precipitation and Temperature in Middle Asia during 1982-2002
Notice bibliographique
Résumé
The five countries in Middle Asia lie in the center of Eurasia. Most part of this region is arid and semi-arid zone with sparse vegetation cover. The study of the vegetation dynamics and environmental change in this region is important to the research of environment and climate in China. This paper explored the vegetation dynamics and its relationship with major climatic factors in middle Asia by using AVHRR-NDVI dataset at 8km spatial resolution and CRU climate data set at 0.5° spatial resolution between 1982 and 2002. These two datasets were unified to the same spatial resolution of 8km and Alberta geographic projection. The trend analysis showed that 53 percent of the land cover was relatively stable, with a very small NDVI change of ±0.005 NDVI per year. These regions, especially the two large deserts, were mainly in the center of Middle Asia. Forty percent of the land had a NDVI up-trend of more than 0.0005 NDVI per year, which was mainly in the north and south of Middle Asia, while only 6 percent of the land had a NDVI down-trend of less than 0.0005 NDVI per year. The analysis on land cover types indicated that evergreen forest and alpine grass (steppe) were among the best up-trend group with NDVI gains more than 0.0014 and 0.0009 per year, while the p values are 0.001 and 0.001 respectively. There were no obvious changes in deciduous forest, grass, crop and steppified desert. To investigate the possible driving forces, correlation analysis was conducted between AVHRR-NDVI and major climatic factors, which are precipitation and temperature. In 49 percent of the area, especially in the forest steppe in north Middle Asia, annual average AVHRR-NDVI was closely related to the annual precipitation, especially that in spring and summer. Only 17.78 percent of the area is related to the annual average temperature with a validation coefficient of more than 0.05. Annually speaking, the positive correlation coefficient of evergreen forest, alpine grass with the annual average temperature is relatively low, with the correlation coefficients of 0.432 and 0.557 as well as p value of 0.052 and 0.009 respectively. The positive correlation coefficient of crop and grass with annual precipitation are comparatively low with R values of 0.511and 0.476 as well as p values of 0.018 and 0.029 respectively. The R value between NDVI and precipitation for deciduous forest was 0.415 in summer and 0.461 in winter, while the p value was 0.01 in summer and 0.461 in winter. The positive correlation coefficient of re-vegetated desert cover with precipitation in spring is relatively lower with the R value of 0.415 and the p value of 0.0061.
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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 | 0,000 |
| 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 | 0,000 |
| 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 ».