Effects of Urban Tree Species and Morphological Characteristics on the Thermal Environment: A Case Study in Fuzhou, China DOI Open Access
Tao Luo,

Jia Jia,

Yao-Wen Qiu

и другие.

Forests, Год журнала: 2024, Номер 15(12), С. 2075 - 2075

Опубликована: Ноя. 25, 2024

Trees and their morphology can mitigate the urban heat island (UHI) effect, but impacts of tree species two-dimensional (2D) three-dimensional (3D) morphological characteristics on thermal environment residential spaces at building scale have not been effectively evaluated. This research extracted data trees in spatial range a 50 m radius sampling sites located subtropical humid city’s area based unmanned aerial vehicle (UAV) imagery field measurements. It included Ficus microcarpa L. f., Cinnamomum camphora (L.) J. Presl, Alstonia scholaris R. Br. as three typical evergreen six quantitative indicators trees, with number (N) serving fundamental indicator mean canopy width (MCW), height (MCH), (MTH), biomass (CV), (MCV) characteristic indicators. We analyzed impact above two parameters: Air temperature (AT) relative humidity (RH), by correlation analysis multiple linear regression analysis. Results showed that: (1) F. microcarpa, dominant local species, provided more than 65% volume within study (50 buffer zones), its contribution to cooling humidification effects was superior those C. A. scholaris. (2) The MTH CV are key factors influencing daytime AT RH, respectively, temporal fluctuation intensity during spring (May) daytime. (3) N show best effect (adjusted R2 = 0.731, p < 0.05) midday (13:00–14:00 p.m.), while 0.748, morning (9:00–10:00 a.m.) among species. 2D 3D describe variation microclimate small-scale spaces. work provides new insights into benefits brought growth features offers reference for areas planning management related selection, maintenance, improvement comfort inhabitants.

Язык: Английский

Investigating the attribution of urban thermal environment changes under background climate and anthropogenic exploitation scenarios DOI
Jiayi Ren, Jun Yang, Wenbo Yu

и другие.

Sustainable Cities and Society, Год журнала: 2024, Номер 107, С. 105466 - 105466

Опубликована: Апрель 22, 2024

Язык: Английский

Процитировано

14

Progress on green infrastructure for urban cooling: Evaluating techniques, design strategies, and benefits DOI Creative Commons
Amjad Azmeer, Furqan Tahir, Sami G. Al‐Ghamdi

и другие.

Urban Climate, Год журнала: 2024, Номер 56, С. 102077 - 102077

Опубликована: Июль 1, 2024

Green infrastructure (GI) can act as an effective cooling strategy to mitigate the urban heat island effect. The complex interdependencies in built environment make it challenging quantify GI accurately. Present literature on often lacks focus techniques and overlooks co-benefits. This review addresses this gap by consolidating recent research standard design approaches maximize cooling. temperature results from are segregated type, technique local climate zones, scale. ENVI-met Weather Research Forecasting model (WRF) most common numerical modeling methods utilized for microscale mesoscale. Results indicate that highest air reduction is achieved arid climates, followed temperate, tropical, continental respectively. study suggests integrate into successfully, researchers should consider influencing factors like spatial distribution, microclimate, plant selection. Climate change intensifies severity of overheating; therefore, integrating cities must be done holistically co-benefits related trade-offs.

Язык: Английский

Процитировано

13

Seasonal variation in vegetation cooling effect and its driving factors in a subtropical megacity DOI

Jianbiao Luo,

Tao Xu,

Chunhua Yan

и другие.

Building and Environment, Год журнала: 2024, Номер unknown, С. 112065 - 112065

Опубликована: Сен. 1, 2024

Язык: Английский

Процитировано

4

Urban greenery services for noise attenuation, pollutant filtration, and temperature lowering: Supply potential, demand, and budgets in Poznań, Poland DOI Creative Commons
Damian Łowicki, Beata Fornal-Pieniak, Axel Schwerk

и другие.

Ecosystem Services, Год журнала: 2025, Номер 73, С. 101713 - 101713

Опубликована: Март 12, 2025

Язык: Английский

Процитировано

0

Unravelling the 3D thermal environment differences between forest center and edge: A case study on 22 urban forests in Hefei city, China DOI
Qingqing Ma, Yongxian Su, Xiuzhi Chen

и другие.

Agricultural and Forest Meteorology, Год журнала: 2025, Номер 366, С. 110481 - 110481

Опубликована: Март 14, 2025

Язык: Английский

Процитировано

0

Traditional agroecosystems for urban temperature regulation: a remote sensing analysis of an historical palm grove DOI Creative Commons
Ignacio Meléndez Pastor

Remote Sensing Applications Society and Environment, Год журнала: 2025, Номер unknown, С. 101569 - 101569

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0

Revealing the contribution of urban green spaces to improving the thermal environment under realistic stressors and their interactions DOI
Jiayi Song, Arkadiusz Przybysz,

C.Y. Zhu

и другие.

Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106426 - 106426

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0

Assessing heat inequalities through the integration of building morphologies and socioeconomic conditions DOI
Yi Zhou, Yuchao Luo

Journal of Environmental Management, Год журнала: 2025, Номер 387, С. 125967 - 125967

Опубликована: Май 26, 2025

Язык: Английский

Процитировано

0

Community-scale microclimate simulation using Airborne Laser Scanning and object-based urban tree classification DOI Creative Commons
Xihan Yao, Minho Kim, Iryna Dronova

и другие.

Landscape and Urban Planning, Год журнала: 2025, Номер 263, С. 105420 - 105420

Опубликована: Июнь 2, 2025

Язык: Английский

Процитировано

0

Accurately mapping social functional zones of urban green spaces by integrating remote sensing images and crowd-sourced geospatial data DOI Creative Commons
Junjun Zhi,

Liangwei Ge,

Tao Geng

и другие.

International Journal of Digital Earth, Год журнала: 2024, Номер 17(1), С. 1 - 26

Опубликована: Июль 11, 2024

Both the physical features and social functions of urban green spaces (UGSs) are crucially important to ecological benefits residents. Increasing attention has been focused on exploring how UGS affect residents, but functional classification UGSs rarely studied, related efficient methods urgently needed. Thus, a novel methodological framework for accurately mapping zones was proposed by integrating remote sensing images, crowd-sourced geospatial data (i.e. point interest data, OpenStreetMap road network, Baidu Map boundary), deep learning algorithm. A sequence combination experiments ablation were designed performance validation quantifying contributions individual classification. The results showed that can precisely effectively map all kinds contributed improving accuracy This study assist planners government departments in rapid monitoring, effective management, scientific planning providing accurate sources an tool.

Язык: Английский

Процитировано

2