Research on the cooling island effects of water body: A case study of Shanghai, China

Du, Han; Song, Xuejun; Jiang, Hong; Kan, Zenghui; Wang, Zhibao; Cai, Yongli

doi:10.1016/j.ecolind.2016.02.040

Cited by 229 publications

(98 citation statements)

References 24 publications

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“…In contrast to the trend of human activity on the LST, the relative importance of water cover decreased with increasing grain size, and the spatial extent of 20 km is a threshold because the relative importance of water cover decreased significantly when the spatial extent exceeded 20 km (Figure 3). These results challenge our past understanding that the contribution of water cover to the LST generally increases with spatial scale, while the contribution of human activities to the LST is usually greater on a smaller spatial scale [6,26,35,53].…”

Section: Scale Dependency and System Specificity Of Urban Heat Islandcontrasting

confidence: 78%

“…While we cannot be certain whether this contradiction results from the different data sources and analysis methods used, it is very likely that it is attributed to system-specific scaling relations, because humped scaling relations of the NDVI were also found in some Chinese big cities in our study (Figure 5b). It has been documented that water bodies have a cooling effect [18,35,53], however, the scaling relations of water cover are not really clear. This multi-scale analysis revealed that 20 km is the threshold spatial extent for understanding the contribution of water cover to the LST pattern (Figure 3g), which further deepens the understanding of the contribution of water cover to LST and its cooling effect.…”

Section: Scale Dependency and System Specificity Of Urban Heat Islandmentioning

confidence: 99%

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Remote Sensing and Social Sensing Data Reveal Scale-Dependent and System-Specific Strengths of Urban Heat Island Determinants

Luan

Zhang

et al. 2020

Remote Sensing

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Urban natural surfaces and non-surface human activities are key factors determining the urban heat island (UHI), but their relative importance remains highly controversial and may vary at different spatial scales and focal urban systems. However, systematic studies on the scale-dependency system-specificity remain largely lacking. Here, we selected 32 major Chinese cities as cases and used Landsat 8 images to retrieve land surface temperature (LST) and quantify natural surface variables using point of interest (POI) data as a measure of the human activity variable and using multiple regression and relative weight analysis to study the contribution and relative importance of these factors to LST at a range of grain sizes (0.25–5 km) and spatial extents (20–60 km). We revealed that the contributions and relative importance of natural surfaces and human activities are largely scale-dependent and system-specific. Natural surfaces, especially vegetation cover, are often the most important UHI determinants for a majority of scales, but the importance of non-surface human activities is increasingly pronounced at a coarser spatial scale with respect to both grain and spatial extent. The scaling relations of the UHI determinants and their relative importance were mostly linear-like at the city-collective level, but highly diverse across individual cities, so reducing non-surface heat emissions could be the most effective measure in particular cases, especially at relatively large spatial scales. This study advances the understanding of UHI formation mechanisms and highlights the complexity of the scale issue underpinning the UHI effect.

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Section: Scale Dependency and System Specificity Of Urban Heat Islandcontrasting

confidence: 78%

Section: Scale Dependency and System Specificity Of Urban Heat Islandmentioning

confidence: 99%

Remote Sensing and Social Sensing Data Reveal Scale-Dependent and System-Specific Strengths of Urban Heat Island Determinants

Luan

Zhang

et al. 2020

Remote Sensing

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“…At present, many scholars have explored the driving mechanism of the UHI effect from the perspectives of land cover [10,26,27], landscape [28,29] and social economy [30][31][32][33][34] by using the 3S technology (remote sensing, Global Position Systems, Geographic Information Systems), combined with correlation analyses, fractal mathematics and gray system theory. The main types of surface cover that affect the LST are vegetation [10,30,[35][36][37][38][39][40][41], construction [42][43][44][45] and surface water [46][47][48][49]. Human activities are also one of the driving forces of the UHI [50,51].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Evolution Analysis of the Urban Heat Island: A Case Study of Zhengzhou City, China

2018

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During the progress of urbanization in China, a large number of natural landscapes have been replaced by impervious surfaces. The strong interference from human activities has led to the intensification of urban heat island (UHI) effects and has had a negative influence on the health of residents. Zhengzhou, as a new representative city of rapid urbanization, can be used as a case study for UHI. This study built an inversion model of the land surface temperature (LST) of Zhengzhou

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“…Shanghai has experienced a remarkable increase in urban area over the past 50 years [66]. Currently, Shanghai is one of the most urbanized cities in China and the degree of urbanization was defined as the percentage of the total population living in urban areas [67]. Our study focused on the urbanized area within the Shanghai center city, an area of about 430 km 2 ( Figure 1).…”

Section: Study Areamentioning

confidence: 99%

Effects of Spatial Pattern of Forest Vegetation on Urban Cooling in a Compact Megacity

Zhou

Cao

Wang

2019

Forests

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Urban forests can be an effective contributor to mitigate the urban heat island (UHI) effect. Understanding the factors that influence the cooling intensity of forest vegetation is essential for creating a more effective urban greenspace network to better counteract the urban warming. The aim of this study was to quantify the effects of spatial patterns of forest vegetation on urban cooling, in the Shanghai metropolitan area of China, using correlation analyses and regression models. Cooling intensity values were calculated based on the land surface temperature (LST) derived from remote sensing imagery and spatial patterns of forest vegetation were quantified by eight landscape metrics, using standard and moving-window approaches. The results suggested that 90 m × 90 m was the optimal spatial scale for studying the cooling effect of forest vegetation in Shanghai’s urban area. It also indicated that woodland performed better than grassland in urban cooling and the size, shape, and spatial distribution of woodland patches had significant impacts on the urban thermal environment. Specifically, the increase of size and the degree of compactness of the patch shape can effectively reduce the LST within the woodland. Areas with a higher percentage of vegetation coverage experienced a greater cooling effect. Moreover, when given a fixed amount of vegetation covers, aggregated distribution provided a stronger cooling effect than fragmented distribution and increasing overall shape complexity of woodlands can enhance the cooling effect on surrounding urban areas. This study provides insights for urban planners and landscape designers to create forest adaptive planning strategies to effectively alleviate the UHI effect.

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Research on the cooling island effects of water body: A case study of Shanghai, China

Cited by 229 publications

References 24 publications

Remote Sensing and Social Sensing Data Reveal Scale-Dependent and System-Specific Strengths of Urban Heat Island Determinants

Remote Sensing and Social Sensing Data Reveal Scale-Dependent and System-Specific Strengths of Urban Heat Island Determinants

Spatio-Temporal Evolution Analysis of the Urban Heat Island: A Case Study of Zhengzhou City, China

Effects of Spatial Pattern of Forest Vegetation on Urban Cooling in a Compact Megacity

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