Global spatial patterns between nighttime light intensity and urban building morphology

Wu, Bin; Huang, Hailan; Wang, Yu; Shi, Shuxian; Wu, Jianping; Yu, Bailang

doi:10.1016/j.jag.2023.103495

Cited by 6 publications

(2 citation statements)

References 53 publications

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“…This study focuses on quantifying urban spatial morphology indicators on the green areas' cooling effect, due to the correlation between many spatial morphology indicators to comprehensively and thoroughly reflect the characteristics of urban spatial morphology and to reduce redundant information. Referring to previous studies and relevant concepts based on urban form indicators [58][59][60], six urban spatial morphology indicators were selected to express the urban spatial morphology around the green city areas in Changsha (Table 1). They include building density (BD), building floor area ratio (BFR), building volume density (BVD), building evenness index (BEI), building average height (BH), and building height standard deviation (BSD).…”

Section: Urban Spatial Morphology Indicatorsmentioning

confidence: 99%

Quantifying Urban Spatial Morphology Indicators on the Green Areas Cooling Effect: The Case of Changsha, China, a Subtropical City

Li,

Wang,

Cai

et al. 2024

Land

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Green city areas are crucial in mitigating the Urban Heat Island Effect (UHI). However, the cooling effect of green city areas can be influenced by the surrounding complex urban spatial environment. This study focuses on Changsha, a subtropical city in China, where 40 green city areas were screened and analyzed. The study aims to quantify the specific impact of urban spatial morphology on the cooling effect of green city areas. Through statistical correlation and regression analysis, this study focused on six urban spatial morphology indicators: building density (BD), building floor area ratio (BFR), building volume density (BVD), building evenness index (BEI), building average height (BH), and building height standard deviation (BSD). The results indicate that the cooling effect of green city areas could be influenced by urban spatial morphology. Factors such as BD, BFR, BH, and BSD were found to be significantly correlated with the cooling effect of green city areas, with BH showing the strongest influence. BD and BFR were negatively correlated, while BH and BSD were positively correlated. The range values of BD, BFR, BH, and BSD were determined to achieve the optimal conditions for the cooling effect of green city areas. Additionally, the relative position of the green city areas in the neighboring urban areas affects the cooling effect of the green city areas. The cooling effect is most pronounced in the urban area situated to the south of the green city areas. These findings provide a solid foundation for urban planning around green city spaces and offer scientifically sound evidence for mitigating the UHI.

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Section: Urban Spatial Morphology Indicatorsmentioning

confidence: 99%

Quantifying Urban Spatial Morphology Indicators on the Green Areas Cooling Effect: The Case of Changsha, China, a Subtropical City

Li,

Wang,

Cai

et al. 2024

Land

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“…Zhu et al [39] applied nighttime light data, including NPP-VIIRS, Luojia 1-01 data, and NASA's Black Marble data, for urban boundary detection, showing that urban expansion could be captured by nighttime lights. Wu et al [40] compared NPP-VIIRS data with building height, area, and volume data, indicating a strong correlation between the two. Additionally, Kii and Matsumoto [41] applied NPP-VIIRS to detect residential development within agricultural land.…”

Section: Urban Structures and Ghg Emission Estimates Using Nighttime ...mentioning

confidence: 99%

Future Scenarios of Urban Nighttime Lights: A Method for Global Cities and Its Application to Urban Expansion and Carbon Emission Estimation

Kii,

Matsumoto,

Sugita

2024

Remote Sensing

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As of 2018, approximately 55% of the world’s population resides in cities, and it is projected that this proportion will reach 68% by 2050. Population growth in urban areas leads to various impacts on society and the environment. In this study, we have developed a method for generating future scenarios of nighttime lights. What makes this method unique is its ability to (1) generate future gridded nighttime light intensity scenarios for cities, (2) generate future scenarios that preserve the distribution pattern of nighttime light intensity, and (3) generate scenarios that reflect urban policies. By applying this developed method, we have estimated nighttime light data for 555 cities worldwide and predicted future urban expansion and changes in carbon emissions for each SSP scenario. Consequently, both urban areas and carbon emissions are estimated to increase for the entire set of target cities, with patterns varying among cities and scenarios. This study contributes to the advancement of urban scenario research, including the estimation of future urban area expansion and carbon emissions.

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Response of urban green space cooling effect to urbanization in the Three Ring Road area of Changsha City

Peng,

Dan,

et al. 2024

Sustainable Cities and Society

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Global spatial patterns between nighttime light intensity and urban building morphology

Cited by 6 publications

References 53 publications

Quantifying Urban Spatial Morphology Indicators on the Green Areas Cooling Effect: The Case of Changsha, China, a Subtropical City

Quantifying Urban Spatial Morphology Indicators on the Green Areas Cooling Effect: The Case of Changsha, China, a Subtropical City

Future Scenarios of Urban Nighttime Lights: A Method for Global Cities and Its Application to Urban Expansion and Carbon Emission Estimation

Response of urban green space cooling effect to urbanization in the Three Ring Road area of Changsha City

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