Urban Heat Island Effect Change And The Major Affecting Variables Analysis

Rapid urbanization results in a tremendous growth of population and buildings in cities, as well as an increasing replacement of natural landscapes by impervious surface areas. With the reduction in vegetated area and an increase in impervious area, land surface properties (such as soil water content and vegetation cover) are modified. These modifications consequently alter the exchanges of water, matter and energy between land surface and boundary layers. Urban areas typically experience higher surface and air temperatures when compared to the surrounding rural areas, known as urban heat island (UHI) effect. Increased temperatures may lead to a series of environmental issues, which changes the ecological structures and functions in city. How urban heat island forms and what are the major factors are the key issues of UHI-related problems. The solutions can provide useful information for urban planning, environmental management and air quality improvement.In this study, remote sensing techniques were applied to derive information on land use/land cover (LULC) and land surface temperature (LST). The multi-temporal Landsat-5Thematic Mapper (TM) images of1987,1996and2007were used to detect the LULC changes, impervious surface area (ISA) distribution, UHI spatio-temporal variation and the associated major factors and analyze their relationships among these elements. The main findings are followed:(1) Fifteen variables indicating urbanization level were selected and their impacts on air temperature were analyzed. The linear regression correlations between the variables and air temperature were then modeled. Results show that air temperatures tended to positively correlate with all variables associated with urbanization. This implied that urbanization had a significant influence on urban air temperatures and urban heat island.(2) The LST spatial distribution corresponded to the urbanized area, with an ongoing expansion from1987to2007. Higher temperatures centralized in the center of the city where urban or built-up areas prevailed. This forms the vivid'heat island', which compares to lower temperatures found in suburbs with high vegetation coverage. The Yangtze River together with other water bodies had relatively lower temperatures, which produced a'cool corridor'flowing through the city.(3) Wuhan has experienced an ongoing and accelerated urbanization from1987to2007with the urban built-up area increasing by nearly303.14%. LST values varied with land use/land cover types. Generally, water type exhibited the lowest mean temperatures as compared to the other land cover types. The built-up and bare lands were found to have similarly higher temperatures. Only0.01%to1.60%of the water areas were identified as heat islands, with most areas classed into the classes of low and sub-low temperature. However, more than90%urban areas and nearly80%of the bare land areas were occupied by sub-high and high temperatures, indicating UHI effect frequently occurred in these two land cover types.(4) There has been a drastic change in urban built-up areas from1987to2007. The areas of medium-density (45-80%ISA) and high-density (>80%ISA) were112.80km2and174.67km2in1987, significantly increasing to591.45km2and560.69km2respectively in2007. The wide variation between1987and2007revealed that the city has experienced rapid urban expansion during the last two decades. In both1987and2007, the spatial patterns of LST show increasing values from lower ISA percentages to higher ones. The thermal response of different ISA percentages resulted in the surface temperature differences between urban and non-urban areas, which clearly illustrated the UHI effect. A zonal analysis was carried out to evaluate the mean LST at each1%and0.01increment of percent ISA and normalized difference vegetation index (NDVI) from0%to100%and-1to1, respectively. Compared to the percent ISA, the NDVI was found to have a stronger linear correlation with the mean LST.(5) To seek the major factors that influence land surface temperature at different scales, we modeled the principal component regression equation between land surface and the associated factors. Results showed that the effect of the selected factors associated with human activities on LST significantly varied on different scales. They significantly influenced LST variation within2000m X2000m grid cell in the urban area and1000m×1000m grid cell in the built-up area, while insignificantly within5000m X5000m grid cell in the administrative area of Wuhan. Generally, the impervious surface (IS)-related variables such as normalized difference built-up index (NDBI) and ISA did contribute to the increase of LST. The percentage of water area (PerWater) in a studied grid cell could efficiently decrease LST values. The vegetation-related indexes such as NDVI and the percentage of vegetation cover (PerVeget) had varing efficiency on LST with different scales.