Greening in Rural Areas Increases the Surface Urban Heat Island Intensity

2019 ◽  
Vol 46 (4) ◽  
pp. 2204-2212 ◽  
Author(s):  
Rui Yao ◽  
Lunche Wang ◽  
Xin Huang ◽  
Wei Gong ◽  
Xiangao Xia
Keyword(s):  
Urban Heat Island ◽  
Rural Areas ◽  
Urban Heat Island Intensity ◽  
Heat Island ◽  
Urban Heat ◽  
Surface Urban Heat Island

Statistical analysis of surface urban heat island intensity variations: A case study of Babol city, Iran

2018 ◽  
Vol 56 (4) ◽  
pp. 576-604 ◽  
Author(s):  
Qihao Weng ◽  
Mohammad Karimi Firozjaei ◽  
Amir Sedighi ◽  
Majid Kiavarz ◽  
Seyed Kazem Alavipanah
Keyword(s):  
Statistical Analysis ◽  
Urban Heat Island ◽  
Urban Heat Island Intensity ◽  
Heat Island ◽  
Urban Heat ◽  
Surface Urban Heat Island

scholarly journals Comparasion of NDBI and NDVI as Indicators of Surface Urban Heat Island Effect in Landsat 8 Imagery: A Case Study of Iasi

2017 ◽  
Vol 11 (2) ◽  
pp. 141-150 ◽  
Author(s):  
Paul Macarof ◽  
Florian Statescu
Keyword(s):  
Urban Heat Island ◽  
Rural Areas ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Heat Island ◽  
Sensing Technology ◽  
Urban Heat ◽  
Surface Urban Heat Island

Abstract This study compares the normalized difference built-up index (NDBI) and normalized difference vegetation index (NDVI) as indicators of surface urban heat island effects in Landsat-8 OLI imagery by investigating the relationships between the land surface temperature (LST), NDBI and NDVI. The urban heat island (UHI) represents the phenomenon of higher atmospheric and surface temperatures occurring in urban area or metropolitan area than in the surrounding rural areas due to urbanization. With the development of remote sensing technology, it has become an important approach to urban heat island research. Landsat data were used to estimate the LST, NDBI and NDVI from four seasons for Iasi municipality area. This paper indicates than there is a strong linear relationship between LST and NDBI, whereas the relationship between LST and NDVI varies by season. This paper suggests, NDBI is an accurate indicator of surface UHI effects and can be used as a complementary metric to the traditionally applied NDVI.

scholarly journals Effect of Land Cover Fractions on Changes in Surface Urban Heat Islands Using Landsat Time-Series Images

2019 ◽  
Vol 16 (6) ◽  
pp. 971 ◽  
Author(s):  
Tao Chen ◽  
Anchang Sun ◽  
Ruiqing Niu
Keyword(s):  
Land Cover ◽  
Urban Heat Island ◽  
Land Surface ◽  
Urban Heat Islands ◽  
Urban Heat Island Intensity ◽  
Heat Island ◽  
Impervious Surfaces ◽  
Heat Islands ◽  
Urban Heat ◽  
Surface Urban Heat Island

Man-made materials now cover a dominant proportion of urban areas, and such conditions not only change the absorption of solar radiation, but also the allocation of the solar radiation and cause the surface urban heat island effect, which is considered a serious problem associated with the deterioration of urban environments. Although numerous studies have been performed on surface urban heat islands, only a few have focused on the effect of land cover changes on surface urban heat islands over a long time period. Using six Landsat image scenes of the Metropolitan Development Area of Wuhan, our experiment (1) applied a mapping method for normalized land surface temperatures with three land cover fractions, which were impervious surfaces, non-chlorophyllous vegetation and soil and vegetation fractions, and (2) performed a fitting analysis of fierce change areas in the surface urban heat island intensity based on a time trajectory. Thematic thermal maps were drawn to analyze the distribution of and variations in the surface urban heat island in the study area. A Multiple Endmember Spectral Mixture Analysis was used to extract the land cover fraction information. Then, six ternary triangle contour graphics were drawn based on the land surface temperature and land cover fraction information. A time trajectory was created to summarize the changing characteristics of the surface urban heat island intensity. A fitting analysis was conducted for areas showing fierce changes in the urban heat intensity. Our results revealed that impervious surfaces had the largest impacts on surface urban heat island intensity, followed by the non-chlorophyllous vegetation and soil fraction. Moreover, the results indicated that the vegetation fraction can alleviate the occurrence of surface urban heat islands. These results reveal the impact of the land cover fractions on surface urban heat islands. Urban expansion generates impervious artificial objects that replace pervious natural objects, which causes an increase in land surface temperature and results in a surface urban heat island.

scholarly journals A new method to quantify surface urban heat island intensity

2018 ◽  
Vol 624 ◽  
pp. 262-272 ◽  
Author(s):  
Huidong Li ◽  
Yuyu Zhou ◽  
Xiaoma Li ◽  
Lin Meng ◽  
Xun Wang ◽  
...  
Keyword(s):  
Urban Heat Island ◽  
New Method ◽  
Urban Heat Island Intensity ◽  
Heat Island ◽  
Urban Heat ◽  
Surface Urban Heat Island

scholarly journals Evaluating the roles of green and built-up areas in reducing a surface urban heat island using remote sensing data

Urbani izziv ◽  
2019 ◽  
Vol 2 (30) ◽  
pp. 105-112
Author(s):  
Gordana Kaplan
Keyword(s):  
Urban Heat Island ◽  
Rural Areas ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Heat Island ◽  
Negative Effects ◽  
Rapid Urbanization ◽  
Green Areas ◽  
Urban Heat ◽  
Surface Urban Heat Island

Rapid urbanization has several negative effects on both the environment and human health. Urbanization has also become an important contributor to global warming. One of these effects is the urban heat island (UHI), which is caused by human activities and defined as the temperature difference between urban and surrounding rural areas. With rapid urbanization in the past few decades, Skopje has experienced remarkable UHI effects. To investigate the roles of built-up and green areas in a surface UHI, this article uses satellite data from Landsat ETM+ to analyse the land surface temperature and high-resolution Planet Scope DOVE data to analyse built-up and green areas. For geostatistical analyses, seventeen randomly selected subareas in Skopje were used. The results show a significant correlation between the UHI and built-up areas, and strong correlation between green areas and areas not affected by the UHI, indicating that the UHI effect can be significantly weakened with additional green areas. One of the significant findings in the study is the ideal proportion of built-up (40%) and green areas (60%), where the UHI effect is weak, or in some cases prevented. For future studies, investigating other factors that may contribute to the UHI phenomenon is suggested.

scholarly journals Spatiotemporal Patterns and Drivers of the Surface Urban Heat Island in 36 Major Cities in China: A Comparison of Two Different Methods for Delineating Rural Areas

2020 ◽  
Vol 12 (2) ◽  
pp. 478 ◽  
Author(s):  
Lu Niu ◽  
Ronglin Tang ◽  
Yazhen Jiang ◽  
Xiaoming Zhou
Keyword(s):  
Urban Heat Island ◽  
Rural Areas ◽  
Statistical Data ◽  
Mainland China ◽  
Urban Heat Islands ◽  
Anthropogenic Factors ◽  
Heat Island ◽  
Urban Heat ◽  
Surface Urban Heat Island ◽  
Moderate Resolution Imaging Spectroradiometer

Urban heat islands (UHIs) are an important issue in urban sustainability, and the standardized calculation of surface urban heat island (SUHI) intensity has been a common concern of researchers in the past. In this study, we used the administrative borders (AB) method and an optimized simplified urban-extent (OSUE) algorithm to calculate the surface urban heat island intensity from 2001 to 2017 for 36 major cities in mainland China by using Moderate Resolution Imaging Spectroradiometer (MODIS) images. The spatiotemporal differences between these two methods were analyzed from the perspectives of the regional and national patterns and the daily, monthly, and annual trends. Regardless of the spatial or temporal scale, the calculation results of these two methods showed extremely similar patterns, especially for the daytime. However, when the calculated SUHI intensities were investigated through a regression analysis with multiple driving factors, we found that, although natural conditions were the main drivers for both methods, the anthropogenic factors obtained from statistical data (population and gross domestic product) were more correlated with the SUHI intensity from the AB method. This trend was probably caused by the spatial extent of the statistical data, which aligned more closely with the rural extent in the AB method. This study not only explores the standardization of the calculation of urban heat intensity but also provides insights into the relationship between urban development and the SUHI.

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