Revealing the driving factors of urban wetland park cooling effects using Random Forest regression and SHAP algorithm DOI
Yue Deng, Weiguo Jiang, Ziyan Ling

и другие.

Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106151 - 106151

Опубликована: Янв. 1, 2025

Язык: Английский

How can urban parks be planned to maximize cooling effect in hot extremes? Linking maximum and accumulative perspectives DOI

Chunlei Du,

Wenxiao Jia, Mo Chen

и другие.

Journal of Environmental Management, Год журнала: 2022, Номер 317, С. 115346 - 115346

Опубликована: Май 26, 2022

Язык: Английский

Процитировано

71

The roles of surrounding 2D/3D landscapes in park cooling effect: Analysis from extreme hot and normal weather perspectives DOI
Dongrui Han, Xinliang Xu,

Zhi Qiao

и другие.

Building and Environment, Год журнала: 2023, Номер 231, С. 110053 - 110053

Опубликована: Янв. 30, 2023

Язык: Английский

Процитировано

71

Using buffer analysis to determine urban park cooling intensity: Five estimation methods for Nanjing, China DOI
Yi Xiao, Yong Piao, Chao Pan

и другие.

The Science of The Total Environment, Год журнала: 2023, Номер 868, С. 161463 - 161463

Опубликована: Янв. 13, 2023

Язык: Английский

Процитировано

51

Quantifying the nonlinear relationship between block morphology and the surrounding thermal environment using random forest method DOI
Yuejing Gao, Jingyuan Zhao, Li Han

и другие.

Sustainable Cities and Society, Год журнала: 2023, Номер 91, С. 104443 - 104443

Опубликована: Фев. 1, 2023

Язык: Английский

Процитировано

48

Cooling island effect in urban parks from the perspective of internal park landscape DOI Creative Commons

Xiaoyu Cai,

Jun Yang, Yuqing Zhang

и другие.

Humanities and Social Sciences Communications, Год журнала: 2023, Номер 10(1)

Опубликована: Окт. 10, 2023

Abstract Urban parks can effectively reduce surface temperatures, which is an important strategic approach to reducing the urban heat island effect. Quantifying cooling effect of and identifying their main internal influencing factors for improving thermal environment, achieving maximum benefits, sustainability. In this study, we extracted data frobut often unrealisticm 28 in Zhengzhou, China. We combined multivariate data, such as Landsat 8 retrieve land temperature (LST), extract park interior landscape, quantify using three indices: distance ( L ∆max ), difference magnitude (∆ T max gradient G temp ). Furthermore, relationship between landscape characteristics average LST indices was analyzed. The results showed that different buffer ranges affect LST-distance fitting parks, a 300-m zone optimal interval. However, specific should be analyzed select range index calculation errors. Additionally, mean values LST, ∆ , Zhengzhou were 34.11, 3.22 °C, 194.02 m, 1.78 °C/hm, respectively. Park perimeter (PP), area, green area (GA), shape (LSI) both significantly correlated with associated maintaining low parks. mainly affected by GA, LSI, perimeter-area ratio, whereas positively PP. Finally, threshold value efficiency 0.83 ha, comprehensive every aspect.

Язык: Английский

Процитировано

45

Optimizing the spatial pattern of the cold island to mitigate the urban heat island effect DOI Creative Commons

Jiang Qiu,

Xiaoyu Li, Wenqi Qian

и другие.

Ecological Indicators, Год журнала: 2023, Номер 154, С. 110550 - 110550

Опубликована: Июнь 27, 2023

Numerous studies on reducing the urban heat island effect have concentrated isolated cold islands, analyzing their cooling impact in terms of size and shape. From an international perspective, shown that enhancing connectivity islands can enhance but they do not suggest specific processes ideas for connectivity. This study aims to investigate how connect optimize spatial pattern island. Therefore, a framework is constructed this study: source area - network. Firstly, core was identified by morphological analysis. Then, analysis applied identify sources. Afterward, minimum cumulative resistance model used construct In Nanjing, case point, results reveal total 27 areas 52 corridors been identified. 6 first-level CSAs situated northern suburbs Nanjing prevent spread effect. 2 second-level 18 third-level are scattered throughout improve climate. The 29 primary help mitigate transfer from city center. 23 secondary mainly located centers contributing preventing aggregating. be as strategic measure fragmentation isolation island, which provides implications further expansion

Язык: Английский

Процитировано

44

Vertical canopy structure dominates cooling and thermal comfort of urban pocket parks during hot summer days DOI
Siqi Zhou, Zhaowu Yu, Weiqiang Ma

и другие.

Landscape and Urban Planning, Год журнала: 2024, Номер 254, С. 105242 - 105242

Опубликована: Окт. 31, 2024

Язык: Английский

Процитировано

20

Coupled cooling effects between urban parks and surrounding building morphologies based on the microclimate evaluation framework integrating remote sensing data DOI
Qingyan Meng, Jianfeng Gao, Linlin Zhang

и другие.

Sustainable Cities and Society, Год журнала: 2024, Номер 102, С. 105235 - 105235

Опубликована: Янв. 24, 2024

Язык: Английский

Процитировано

19

Examining water bodies' cooling effect in urban parks with buffer analysis and random forest regression DOI
Yu Qiao, Hao Sun,

Jialing Qi

и другие.

Urban Climate, Год журнала: 2025, Номер 59, С. 102301 - 102301

Опубликована: Янв. 30, 2025

Язык: Английский

Процитировано

3

Dominant Factors and Spatial Heterogeneity of Land Surface Temperatures in Urban Areas: A Case Study in Fuzhou, China DOI Creative Commons
Yang Liu-qing,

Kunyong Yu,

Jingwen Ai

и другие.

Remote Sensing, Год журнала: 2022, Номер 14(5), С. 1266 - 1266

Опубликована: Март 4, 2022

The urban heat island (UHI) phenomenon caused by rapid urbanization has become an important global ecological and environmental problem that cannot be ignored. In this study, the UHI effect was quantified using Landsat 8 image inversion land surface temperatures (LSTs). With spatial scale of street units in Fuzhou City, China, ordinary least squares (OLS) regression, geographically weighted regression (GWR) models, multi-scale (MGWR), we explored heterogeneities influencing factors LST. results indicated that, compared with traditional OLS GWR improved model fit considering heterogeneity, whereas MGWR outperformed terms goodness effects different bandwidths on Building density (BD), normalized difference impervious index (NDISI), sky view factor (SVF) were influences elevated LST, while building height (BH), forest percentage (Forest_per), waterbody (Water_per) negatively correlated addition, built-up (Built_per) population (Pop_Den) showed significant non-stationary characteristics. These findings suggest need to consider heterogeneity analyses impact factors. This study can used provide guidance mitigation strategies for UHIs regions.

Язык: Английский

Процитировано

55