【作者】 谢启姣；

【导师】 周志翔；

【作者基本信息】 华中农业大学 ， 园林植物与观赏园艺， 2011， 博士

【摘要】 城市化导致城市人口和建筑的迅速增加,城市自然地表逐渐被城市不透水地表如水泥、柏油路面及混凝土等所代替,地表覆盖性质的改变使得地表与大气间的水分、物质和能量交换发生变化,形成城市地区气温或者地表温度明显高出城市周围的特殊小气候特征——城市热岛效应。城市热岛引起的城市地表温度的升高将产生一系列的生态环境效应,调节和控制着城市生态系统的结构和功能,研究城市化过程中的城市热岛时空演变特征及其主要影响因素是城市热岛的核心,将为改善城市人居环境、城市规划、环境管理及城市的可持续发展提供理论依据和技术支撑。本文以武汉市为例,以1987-2007年约20年间的遥感卫星影像Landsat TM数据为主要数据来源,结合气象站点的统计资料和城市土地利用信息,以遥感定量反演和GIS空间分析为技术支撑,以景观生态学理论为指导,全面系统的分析了城市化地表覆盖类型变化、不透水面分布、城市热岛时空演变特征及其主要影响因素分析,并得出以下结论：(1)从入口城市化、经济城市化、空间城市化和生活方式的城市化四个层次选取15项代表城市化特征指标,并分析武汉市1987-2007年的城市化发展与武汉市气温变化趋势的影响,结果表明城市化对武汉市20年的气温具有明显的贡献。(2)对武汉市地表温度的时空分布特征的分析结果表明武汉市地表温度的分布与城市发展有着较好的一致性,高温集中分布于建设密度大、人口集中的城市中心区和城镇的建成区,而低温则分布于大型水体及武汉市的近郊和郊区的植被覆盖区：随着城市化的发展对城市热环境特征的影响,武汉市高温区面积有着明显的增加趋势,热岛范围也明显扩张。(3)1987-2007年各土地利用/覆盖类型变化明显,其中变化最大的是建设用地的面积,20年间增加了303.14%,不同的土地利用／覆盖类型的热环境特征表现出明显的差异：建设用地和裸地表现出类似的高温,成为热岛最容易发生的区域,而有植被覆盖的耕地、林地和草地则表现出较低的温度,最低温度则主要集中在水体类型；超过90%的建设用地区域和超过80%的裸地区域被次高温和高温占据,说明这两种类型的地表覆盖更容易成为热岛中心,只有0.01%到1.6%的水体面积被确定为热岛。(4)20年间中密度发展区域(45%<ISA<80%)和高密度发展区域(ISA>80%)面积分别增加了424.34%和221.00%,说明城市化的发展导致城市新增道路和建设面积的增加,从而使城市不透水面率不断增加；城市热岛范围的扩张与城市不透水面的增加趋势表现出良好的一致性,这就说明不透水面率能很好的诠释城市热岛的变化；利用1%和0.01的增量分别统计ISA和LST平均地表温度并进行分析,结果表明平均NDVI与地表温度之间具有更强的线性相关性,并且对地表温度的影响也更大。(5)对武汉市不同尺度范围内城市热岛的主要影响因素进行分析,并对各影响因子进行主成分回归分析,结果表明,各影响因子对地表温度的影响具有明显的尺度性。本文所选与人类活动有关的影响因子对市域5000m×5000m尺度的地表温度影响并不明显,对中心城区2000m×2000m和建成区1000m×1000m尺度地表温度有较大的影响,且各影响因子的影响程度各不相同。总的来说,与硬化地表有关的指数如归一化建筑指数和不透水面率对地表温度的升高贡献最大,水体面积比例对地表温度有降低的作用,植被覆盖相关的因子对地表温度的影响随尺度变化最大。更多还原

【Abstract】 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.更多还原