Assessing the impact of fractional vegetation cover on urban thermal environment: A case study of Hangzhou, China

Zhang, Maomao; Tan, Shukui; Zhang, Cheng; Han, Seunghee; Zou, Shangjun; Chen, E.

doi:10.1016/j.scs.2023.104663

Cited by 42 publications

(10 citation statements)

References 89 publications

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“…It can be stated that cities have a pivotal role in both mitigation [5,6] and adaptation activities [7], aiming to reduce the adverse effects of climate change by taking well-targeted and effective actions [8,9]. In order to provide detailed information to local decision-makers, addressing micro and mesoscale urban climate patterns is essential by analyzing different aspects of local features, such as LULC patterns [10], urban heat island (UHI) characteristics [11], the role of green areas regarding cooling benefits [12] through the normalized difference vegetation index [13], urban hotspots [14], or the presence of surface urban heat island [15,16]. For these purposes, remote sensing data are commonly used [17,18] along with GISsupported analyses [19].…”

Section: Introductionmentioning

confidence: 99%

Spatial-temporal analysis of urban climate dynamics in major Hungarian cities

Buzási,

Beszedics-Jäger,

Hortay

2024

Environ. Res. Commun.

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Increasing heatwaves are making cities and their populations more vulnerable, parallel to urban sprawl and the aging population in Hungary. The increasing number of hot days is predicted to worsen urban climate anomalies at the local scale, which, in parallel with changing land use patterns, may contribute to a significant increase in vulnerability to heatwaves. Local stakeholders and decision-makers need to understand the critical role of spatiotemporal land use – land cover (LULC) patterns and urban climate aspects to address relevant challenges for urban development. The current literature does not contain a synthesis analysis of major Hungarian cities that includes urban climate and sustainability findings hand by hand; therefore, this study aims to analyze LULC patterns, urban hotspots and surface urban heat island effects. In addition, the Normalized Difference Vegetation Index (NDVI) was determined as an important indicator for assessing the health and density of green spaces in major Hungarian cities from 2006 to 2018 using remote sensing data. Our results show that each city experienced significant urban sprawl, while above-average NDVI areas decreased over time. The average increase in the share of built-up areas was 1.3% from 2006 to 2018, while the calculated average decline in agricultural areas was 2%, so the expansion of residential areas and artificial areas is not the only driving force of this shrinking trend in agricultural areas. Furthermore, we found that urban hotspots are generally concentrated in industrial areas and represent new spaces of heat islands on the outskirts of cities. Székesfehérvár has the most intense industrial heat islands, with the largest proportion of urban hotspots (approximately 3.5% of the total area) concentrated in industrial zones. Our study contributes to uncovering inter-urban processes of land use patterns and urban climate issues in major Hungarian cities, moreover revealing sustainability-related issues from a lock-in perspective.

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Section: Introductionmentioning

confidence: 99%

Spatial-temporal analysis of urban climate dynamics in major Hungarian cities

Buzási,

Beszedics-Jäger,

Hortay

2024

Environ. Res. Commun.

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“…This has resulted in a variety of health problems caused by mental stress or inactivity, especially in China [ 6 ]. As an accessible natural resource for urban residents, urban green spaces (UGSs) play a positive role not only in physical [ 7 ] and cultural [ 8 ] aspects, but also in promoting overall health [ 9 ]. Positive emotional changes, increased physical activities, the relief of psychological stress, and enhanced social support have all been associated with exposure to UGS [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Identify the significant landscape characteristics for the perceived restorativeness of 8 perceived sensory dimensions in urban green space

Wang,

2024

Heliyon

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“…Additionally, some studies have pointed out that urban green spaces and water bodies can mitigate the increase in the LST to some extent. Regarding modeling of the driving factors, methods such as multiple linear regression [16], geographical detectors, Geographically Weighted Regression (GWR), and machine learning are commonly used [27][28][29][30]. Among these methods, multiple linear regression and geographic detector models mainly focus on investigating the influence of driving factors on the LST [27].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of the Seasonal Driving Factors of the Urban Heat Environment Using Machine Learning: Evidence from the Wuhan Urban Agglomeration, China, 2020

Xu,

Huang,

Zhang

2024

Atmosphere

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With the ongoing advancement of globalization significantly impacting the ecological environment, the continuous rise in the Land Surface Temperature (LST) is increasingly jeopardizing human production and living conditions. This study aims to investigate the seasonal variations in the LST and its driving factors using mathematical models. Taking the Wuhan Urban Agglomeration (WHUA) as a case study, it explores the seasonal characteristics of the LST and employs Principal Component Analysis (PCA) to categorize the driving factors. Additionally, it compares traditional models with machine-learning models to select the optimal model for this investigation. The main conclusions are as follows. (1) The WHUA’s LST exhibits significant differences among seasons and demonstrates distinct spatial-clustering characteristics in different seasons. (2) Compared to traditional geographic spatial models, Extreme Gradient Boosting (XGBoost) shows better explanatory power in investigating the driving effects of the LST. (3) Human Activity (HA) dominates the influence throughout the year and shows a significant positive correlation with the LST; Physical Geography (PG) exhibits a negative correlation with the LST; Climate and Weather (CW) show a similar variation to the PG, peaking in the transition; and the Landscape Pattern (LP) shows a weak positive correlation with the LST, peaking in winter while being relatively inconspicuous in summer and the transition. Finally, through comparative analysis of multiple driving factors and models, this study constructs a framework for exploring the seasonal features and driving factors of the LST, aiming to provide references and guidance for the development of the WHUA and similar regions.

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Assessing the impact of fractional vegetation cover on urban thermal environment: A case study of Hangzhou, China

Cited by 42 publications

References 89 publications

Spatial-temporal analysis of urban climate dynamics in major Hungarian cities

Spatial-temporal analysis of urban climate dynamics in major Hungarian cities

Identify the significant landscape characteristics for the perceived restorativeness of 8 perceived sensory dimensions in urban green space

Comparative Analysis of the Seasonal Driving Factors of the Urban Heat Environment Using Machine Learning: Evidence from the Wuhan Urban Agglomeration, China, 2020

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