Monitoring patterns of urban heat islands of the fast-growing Shanghai metropolis, China: Using time-series of Landsat TM/ETM+ data

Li, Yingying; Zhang, Hao; Kainz, Wolfgang

doi:10.1016/j.jag.2012.05.001

Cited by 196 publications

(105 citation statements)

References 57 publications

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“…A second group of studies used a qualitative method to examine seasonal effects on UHI. Examples of this type of study included Li et al [28] and Meng and Liu [29], from China. In both studies, the authors examined whether urban expansion between two time periods increases the spatial extent of the UHI effect.…”

Section: Links Between Land Cover Type and The Uhi Effectmentioning

confidence: 99%

Correlation or Causality between Land Cover Patterns and the Urban Heat Island Effect? Evidence from Brisbane, Australia

2016

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Numerous studies have identified associations between the surface urban heat island (SUHI) effect (i.e., SUHI, hereinafter is referred to as UHI) and urban growth, particularly changes in land cover patterns. This research questions their causal links to answer a key policy question: If cities restrict urban expansion and encourage people to live within existing urban areas, will that help in controlling UHI? The question has been answered by estimating four models using data from Brisbane, Australia: Model 1-cross-sectional ordinary least square (OLS) regression-to examine the association between the UHI effect and land cover patterns in 2013; Model 2-cross-sectional geographically weighted regression (GWR)-to examine whether the outputs generated from Model 1 possess significant spatial variations; Model 3-longitudinal OLS-to examine whether changes in land cover patterns led to changes in UHI effects between 2004 and 2013; and Model 4-longitudinal GWR-to examine whether the outputs generated from Model 3 vary significantly over space. All estimations were controlled for potential confounding effects (e.g., population, employment and dwelling densities). Results from the cross-sectional OLS and GWR models were consistent with previous findings and showed that porosity is negatively associated with the UHI effect in 2013. In contrast, population density has a positive association. Results from the longitudinal OLS and GWR models confirm their causal linkages and showed that an increase in porosity level reduced the UHI effect, whereas an increase in population density increased the UHI effect. The findings suggest that even a containment of population growth within existing urban areas will lead to the UHI effect. However, this can be significantly minimized through proper land use planning, by creating a balance between urban and non-urban uses of existing urban areas.

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Section: Links Between Land Cover Type and The Uhi Effectmentioning

confidence: 99%

Correlation or Causality between Land Cover Patterns and the Urban Heat Island Effect? Evidence from Brisbane, Australia

2016

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“…Initially the satellite data was geometrically corrected to the Universe Transverse Mercator (UTM) grid system (WGS, 1984). It has also radiometrically corrected based on the darkest pixel theory [34,35,36,37]. Due to the Landsat sensor has had malfunction since 2003, therefore the data between 2003 and 2012 was having SLC-OFF problem that required GAP-FILL.…”

Section: Meteorological Datamentioning

confidence: 99%

Spatial Variability of Urban Heat Islands in Cairo City, Egypt using Time Series of Landsat Satellite Images

El-Magd

Ismail

Zanaty

2016

IJARSG

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Global warming has obtained more attention because of increasing global mean surface temperature since the late 19th century. Urbanization is considered an important contributor to global warming in big cities. Cairo is one of the heavily populated cities in the world, which has rapid urbanization that resulted in remarkable temperature variation compared to the rural surrounding areas. This phenomenon is known as "Urban Heat Islands" (UHIs) that influence both local and regional climate, environment, and socio-economic development. In this research time series of satellite images were used to map the spatial variability of Land Surface Temperature (LST) and Heat Islands in Cairo city. Historical and contemporary record of Landsat images nearly 16 dates from 1990 to 2014 were used to retrieve LST and land cover categories. Results showed that LST was highly influenced by Land use and land cover changes; heat island effect was dominant in urbanized areas, bare/desert land and industrial zones (e.g. Shobra El-khema). The industrial zones, in particular, were possibly high due to the aluminum roof material plus the thermal energy from production activities and high emissions of air pollutants. The pattern of spatial distribution of heat islands has significantly changed from bare/desert land and built up areas (as warmer) to cultivated land and water bodies (as colder). To analyze the relationship between Heat Islands and land-cover changes a quantitative correlation between LST and Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Build-up Index (NDBI) was employed. It was found that the correlation between LST and NDVI and MNDWI was negative with r 2 of 0.8 and 0.57 respectively; however, the correlation with NDBI was strongly positive with r 2 of 0.81. It is anticipated that the outcomes of this research will contribute to GEOSS for global integration and correlation.

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“…Urbanization is a widespread anthropogenic cause of loss of arable land [4], habitat destruction [5], and decline in natural vegetation cover. It has severe effects on local ecosystems [6], causes the urban heat island phenomenon [7], consumes valuable productive arable land [4], results in loss of habitat and biodiversity [8], and also causes global effects such as climate change [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Driving Forces of Informal Urbanization in the Western Part of the Greater Cairo Metropolitan Region

Osman

Divigalpitiya²,

Takafumi

2016

Environments

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This paper discusses the driving forces (DFs) of informal urbanization (IU) in the greater Cairo metropolitan region (GCMR) using the Analytic Hierarchy Process (AHP). The IU patterns in the GCMR have been extremely influenced by seven DFs: geographical characteristics, availability of life facilities, economic incentives, land demand and supply, population increase, administrative function, and development plans. This research found that these forces vary significantly in how they influence urban growth in the three study sectors, namely, the middle, north, and south areas in the western part of the GCMR. The forces with the highest influence were economic incentives in the middle sector, population increase in the north sector, and the administrative function in the south sector. Due to the lower availability of buildable land in the middle sector, the land demand and supply force had a lesser influence in this sector compared to in the north and south sectors. The development plans force had medium influence in all sectors. The geographical characteristics force had little influence in both the middle and the north sectors, but higher influence than economic incentives, availability of life facilities, and development plans in the south sector. Because of the spatial variances in life facilities organizations in the GCMR, the life facilities availability force had little effect on IU in the south sector.

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Monitoring patterns of urban heat islands of the fast-growing Shanghai metropolis, China: Using time-series of Landsat TM/ETM+ data

Cited by 196 publications

References 57 publications

Correlation or Causality between Land Cover Patterns and the Urban Heat Island Effect? Evidence from Brisbane, Australia

Correlation or Causality between Land Cover Patterns and the Urban Heat Island Effect? Evidence from Brisbane, Australia

Spatial Variability of Urban Heat Islands in Cairo City, Egypt using Time Series of Landsat Satellite Images

Quantifying the Driving Forces of Informal Urbanization in the Western Part of the Greater Cairo Metropolitan Region

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