Remote sensing of the urban heat island effect in a highly populated urban agglomeration area in East China

Zhou, Decheng; Bonafoni, Stefania; Zhang, Liangxia; Wang, Ranghui

doi:10.1016/j.scitotenv.2018.02.074

Cited by 170 publications

(107 citation statements)

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“…At local and regional scales, Landsat-derived LST has been widely applied to examine SUHI variation [15], which in turn reflect the impacts of changing LST on the environments. SUHIs can be defined based on an LST classification that can be performed using an urban-suburban boundary method [15,56,57]. The terrain's effect on the SUHI intensity was not considered in this research, LST is a land surface parameter that is spatially heterogeneous, which implies that it may be strongly correlated with spatial coordinates [55].…”

Section: Analysis Methodsmentioning

confidence: 99%

Spatial Patterns of Land Surface Temperature and Their Influencing Factors: A Case Study in Suzhou, China

et al. 2019

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Land surface temperature (LST) is a fundamental Earth parameter, on both regional and global scales. We used seven Landsat images to derive LST at Suzhou City, in spring and summer 1996, 2004, and 2016, and examined the spatial factors that influence the LST patterns. Candidate spatial factors include (1) land coverage indices, such as the normalized difference built-up index (NDBI), the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI), (2) proximity factors such as the distances to the city center, town centers, and major roads, and (3) the LST location. Our results showed that the intensity of the surface urban heat island (SUHI) has continuously increased, over time, and the spatial distribution of SUHI was different between the two seasons. The SUHIs in Suzhou were mainly distributed in the city center, in 1996, but expanded to near suburban, in 2004 and 2016, with a substantial expansion at the highest level of SUHIs. Our buffer-zone-based gradient analysis showed that the LST decays logarithmically, or decreases linearly, with the distance to the Suzhou city center. As inferred by the generalized additive models (GAMs), strong relationships exist between the LST and the candidate factors, where the dominant factor was NDBI, followed by NDWI and NDVI. While the land coverage indices were the LST dominant factors, the spatial proximity and location also substantially influenced the LST and the SUHIs. This work improved our understanding of the SUHIs and their impacts in Suzhou, and should be helpful for policymakers to formulate counter-measures for mitigating SUHI effects.

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Section: Analysis Methodsmentioning

confidence: 99%

Spatial Patterns of Land Surface Temperature and Their Influencing Factors: A Case Study in Suzhou, China

et al. 2019

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“…(2) How does UHII and its spatiotemporal variation relate to the social and biophysical properties of urban and rural areas, such as urban size and urban-rural enhanced vegetation index (EVI) difference? Previous studies have found strong and significant seasonal and diurnal variations in UHII [22][23][24][25][26][27]. For example, the daytime surface urban heat island intensity (SUHII) was significantly higher in summer than in winter for all the cities in the Yangtze River Delta Urban Agglomeration [25] and other major cities in China [26], and it was more intense compared to in nighttime [10].…”

mentioning

confidence: 94%

“…Previous studies have found strong and significant seasonal and diurnal variations in UHII [22][23][24][25][26][27]. For example, the daytime surface urban heat island intensity (SUHII) was significantly higher in summer than in winter for all the cities in the Yangtze River Delta Urban Agglomeration [25] and other major cities in China [26], and it was more intense compared to in nighttime [10]. The UHII and its spatiotemporal variations are related to urban and rural characteristics, including biophysical and social attributes [10,[24][25][26][27][28][29].…”

mentioning

confidence: 98%

“…For example, the daytime surface urban heat island intensity (SUHII) was significantly higher in summer than in winter for all the cities in the Yangtze River Delta Urban Agglomeration [25] and other major cities in China [26], and it was more intense compared to in nighttime [10]. The UHII and its spatiotemporal variations are related to urban and rural characteristics, including biophysical and social attributes [10,[24][25][26][27][28][29]. For example, UHII had a strong negative relationship with the difference between the urban and rural vegetation indexes [10,25,27,[29][30][31], but a significantly positive relationship with urban-rural nighttime lights [26].Previous studies have shown that UHII increased along with urban expansion [32][33][34][35][36] in cities such as Beijing [37-40], Shanghai [41], Wuhan [42], and Lanzhou [22] and also in urban agglomerations [32,43].…”

mentioning

confidence: 99%

“…The UHII and its spatiotemporal variations are related to urban and rural characteristics, including biophysical and social attributes [10,[24][25][26][27][28][29]. For example, UHII had a strong negative relationship with the difference between the urban and rural vegetation indexes [10,25,27,[29][30][31], but a significantly positive relationship with urban-rural nighttime lights [26].…”

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confidence: 99%

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Time-Series Analysis Reveals Intensified Urban Heat Island Effects but without Significant Urban Warming

2019

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Numerous studies have shown an increased surface urban heat island intensity (SUHII) in many cities with urban expansion. Few studies, however, have investigated whether such intensification is mainly caused by urban warming, the cooling of surrounding nonurban regions, or the different rates of warming/cooling between urban and nonurban areas. This study aims to fill that gap using Beijing, China, as a case study. We first examined the temporal trends of SUHII in Beijing and then compared the magnitude of the land surface temperature (LST) trend in urban and nonurban areas. We further detected the temporal trend of LST (TrendLST) at the pixel level and explored its linkage to the temporal trends of EVI (TrendEVI) and NDBI (TrendNDBI). We used MODIS data from 2000 to 2015. We found that (1) SUHII significantly increased from 4.35 °C to 6.02 °C, showing an intensified surface urban heat island (SUHI) effect, with an annual increase rate of 0.13 °C in summer during the daytime and 0.04 °C in summer at night. In addition, the intensification of SUHII was more prominent in new urban areas (NUA). (2) The intensified SUHII, however, was largely caused by substantial cooling effects in nonurban areas (NoUA), not substantial warming in urban areas. (3) Spatially, there were large spatial variations in significant warming and cooling spots over the entire study area, which were related to TrendNDBI and TrendEVI. TrendNDBI significantly affected TrendLST in a positive way, while the TrendEVI had a significant positive effect (p = 0.023) on TrendLST only when EVI had an increasing trend. Our study underscores the importance of quantifying and comparing the changes in LST in both urban and nonurban areas when investigating changes in SUHII using time-series trend analysis. Such analysis can provide insights into promoting city-based urban heat mitigation strategies which focused on both urban and nonurban areas.

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Nexus Between Anthropogenic Heat Flux and Urban Heat Island

Sarda

Pal²

2023

Advancements in Urban Environmental Studies

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Remote sensing of the urban heat island effect in a highly populated urban agglomeration area in East China

Cited by 170 publications

References 50 publications

Spatial Patterns of Land Surface Temperature and Their Influencing Factors: A Case Study in Suzhou, China

Spatial Patterns of Land Surface Temperature and Their Influencing Factors: A Case Study in Suzhou, China

Time-Series Analysis Reveals Intensified Urban Heat Island Effects but without Significant Urban Warming

Nexus Between Anthropogenic Heat Flux and Urban Heat Island

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