福州市城市不透水面景观指数与城市热环境关系分析

城市化致使城市环境问题的产生,城市热环境问题就是其中之一。本文从不透水面方面研究对城市热环境的影响。根据福州市1989年和2001年LandsatTM/ETM+遥感影像数据,利用线性光谱分解法提取两时相不透水面信息,并离散化分级为中低、中、中高、高密度区4个区域,分别计算这4个区域的地表温度(LST)、归一化植被指数(NDVI),并进行相关性分析;根据阈值法和范围法分别计算不透水面的PD、AI、LPI等景观指数,结果表明:两时段内不透水面的面积有所增加,在高密度区增加明显;不透水面与地表温度的呈正相关,相关系数分别为0.66和0.71;不透水面景观指数对FISA敏感,景观指数整体的变化趋势与地表温度的变化趋势相一致,FISA值越大,温度越高,且各斑块的形状越来越复杂,空间的连续性越强;聚集度越高,人类活动也越强。

Impacts of Impervious Surface Area and Landscape Metrics on Urban Heat Environment in Fuzhou City,China

With the economic development, urbanization has been accelerating in recent years in Fuzhou City, Fujian Province of China. Rapid change on land surface property and its patterns may lead to change of thermal properties in urban areas of Fuzhou City. One of the main impacts of rapid urbanization is the effect of urban thermal environment. Landscape patches in a region are different in size, shape and spatial arrangements, which contribute to the spatial heterogeneity of landscape and are linked to the distinct behaviour of urban thermal envi- ronments. Studies on landscape metrics extracted from discretized percent impervious surface area data are com- paratively rare. This research, which investigated the relationship of landscape metrics and urban thermal envi- ronments in Fuzhou City, Fujian Province, China, is based on both the analysis of land surface temperature （LST） in relation to normalized difference vegetation index （NDVI）, and the percent impervious surface area （FI- SA）. Two Landsat TM/ETM＋ images acquired on June 15 1989 and March 4, 2001 were used to estimate LST, NDVI, and impervious surface area （ISA）. This was extracted by applying linear spectral mixture analysis. We analyzed the relationship between the above-mentioned components of urban ecosystem. Using threshold value method and range method to discretize percent ISA into different categories. Landscape metrics such as cohe- sion, AI, LPI, etc. are calculated based on different FISA categories. The result showed that there is a positive lin- ear relationship between LST and impervious surface over the region. The correlation coefficient is .66（1989） and .71 （2001）. To find the relationship between landscape metrics and LST, we analyzed landscape metrics from three aspects： shape, area and structure. The study indicated that landscape metrics are sensitive to the variation of FISA and LST. Therefore, the integration of FISA and landscape metrics provided a feasible way to describe the spatial distribution and temporal variation in urban thermal patterns in a quantitative manner.