Quantifying land surface temperature changes associated with land cover changes
Notice bibliographique
Résumé
Significant transformations are being observed globally across ecosystems driven by natural pro- cesses and human activities. Using moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST) product: MOD21A1 and European Space Agency Climate Change Ini- tiative (ESA CCI) land cover (LC) product, this study provides a comprehensive analysis of global land cover change (LCC) since 2000 and their impacts on LST. Using a systematic approach, the overall mean and its annual LST variations were quantified for 484 LCC transitions derived from the 22 x 22 LC classifications, revealing distinct cooling and warming patterns. Transitions that increased vegetation cover such as bare areas to grasslands, and bare areas to sparse vegetation consistently resulted in cooling effects, with temperature decreases up to −0.45◦C at a rate of −0.030◦C/year and −0.20◦C at a rate of −0.017◦C/year respectively. Conversely, warming ef- fects were linked to deforestation, urbanization, and water loss. Transitions such as flooded veg- etation to needleleaf forests (+0.84◦C) at a rate of +0.076◦C/year and cropland to urban areas (+0.39◦C) at a rate of +0.019◦C/year highlighted the critical role of land use in amplifying sur- face temperatures. Regional analysis revealed cooling trends in northern areas, such as Canada and Greenland, driven by vegetation recovery, while warming was prominent in tundra regions, where forest loss and snow cover reduction amplified surface heating. The largest global transi- tions included the conversion of bare areas to sparse vegetation, indicating ecological recovery in degraded regions, and the shift from sparse vegetation to grasslands, highlighting changes within the "Grass & Shrubs" classification, which experienced the highest levels of disturbance. Although many findings aligned with established patterns, anomalies such as unexpected cooling in ever- green needleleaf forest transitions and warming in tundra regions involving deciduous needleleaf forests underscored the complexities of LCC and their localized impacts on LST. The anomalies emphasize the need for further investigation into factors such as neighboring pixel effects, data ac- curacy, and climatic influences. By improving data accuracy and alignment, addressing resolution mismatches, and adopting regionalized analysis, future research can improve the understanding of the LCC-LST dynamics. Despite the challenges, these findings highlight the importance of sustainable land management practices, including reforestation and urban greening programs to mitigate the adverse effects of LCC on global and regional LST.
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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 | 0,001 | 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 ».