Forecasting Spatiotemporal Dynamics of Daytime Surface Urban Cool Islands in Response to Urbanization in Drylands: Case Study of Kerman and Zahedan Cities, Iran
Notice bibliographique
Résumé
Urban micro-climate plays an important role in human activities and in ensuring public health. For instance, the urban heat island effect is crucial to the thermal comfort of citizens and tourists, similar to the urban cool island effect’s importance on human and infrastructure resilience. Approximately 35% of global big cities are located in drylands. While existing research has focused on the spatial and temporal changes of surface urban cooling island intensity (SUCII) in drylands in the past, there is a gap in predicting the future spatiotemporal changes in SUCII for cities within these dryland regions. This study aims to forecast the spatiotemporal dynamics of daytime SUCII of representative growing cities with a dry and cold climate. Kerman and Zahedan cities, which are undergoing large urbanization and have harsh hot summer climates, were selected as the study area. Landsat 5 and 8 images and products were utilized for six timestamps within the timeframe of 1986–2023. Various methods, including a random forest algorithm, spectral indices, Cellular Automata-Markov (CA-Markov) model, the cross-tabulation model, and spatial overlay and zonal statistics, were employed to assess and model the spatiotemporal changes in SUCII. Initially, historical land cover maps, land surface temperature (LST), surface biophysical characteristics, and SUCII data were prepared, and their spatiotemporal changes were evaluated. Then, projected maps for these variables for the year 2045 were produced. The results indicated that the built-up areas, bare lands, and green spaces of Kerman (Zahedan) city in 1986 were 26.6 km2 (17.6 km2), 103 km2 (92.5 km2), and 44.4 km2 (5.6 km2), respectively, and these values reached 99.3 km2 (41.9 km2), 61.2 km2 (70.7 km2), and 13.5 km2 (3.2 km2) in 2023. The built-up lands area of Kerman (Zahedan) city is expected to increase by approximately 26% (36%) by 2045, while bare land and green space are expected to decrease by about 32% (20%) and 39% (31%), respectively. The greatest rise in average LST of Kerman (Zahedan) city is associated with the conversion of green spaces to barren land, resulting in a notable increase of 5.5 °C (4.3 °C) in 1986–2023. The conversion of barren land to built-up land in Kerman (Zahedan) city has led to a decrease of 4.6 °C (3.8 °C) in LST. The SUCII of Kerman (Zahedan) city for 1986, 1994, 2001, 2008, 2015, and 2023 were −0.3 °C (0.9 °C), −0.8 °C (0.4 °C), −1.4 °C (−0.5 °C), −1.9 °C (−1.5 °C), −2.6 °C (−2.5 °C), and −3.2 °C (−3.4 °C), respectively. The projected SUCII in Kerman (Zahedan) city for 2045 is about −4.3 °C (−4.5 °C), indicating an increasing trend in SUCII in the future. The area of zones without SUCII in Kerman (Zahedan) city decreased by 44.8 Km2 (54.8 Km2) from 1986 to 2023, while the areas of low, medium, and high SUCII classes increased by 9.1 Km2 (9.9 Km2), 10.9 Km2 (11.9 Km2), and 24.8 Km2 (33.1 Km2), respectively. The area of non-SUCII and high SUCII classes of Kerman (Zahedan) city in 2045 is expected to decrease by 31.5 Km2 (12.0 Km2) and increase by 51.2 Km2 (9.5 Km2) compared with 2023. The findings of this research indicate that the physical growth of cities in drylands can lead to the moderation of LST, contrary to mechanisms in humid and wet regions.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 ».