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### 基于TM影像的城市地表湿度对城市热岛效应的调控机理研究

1. 1. 中国矿业大学环境与测绘学院, 江苏徐州221116;
2. 江苏省资源环境信息工程重点实验室, 江苏徐州221116;
3. 江苏师范大学土地资源研究所, 江苏徐州221116
• 收稿日期:2014-02-18 修回日期:2014-11-05 出版日期:2015-04-20 发布日期:2015-04-16
• 通讯作者: 陈龙乾(1964- ),男,江苏省阜宁县人,教授,博士生导师,主要从事土地利用与地理信息研究.E-mail:chenlq@cumt.edu.cn E-mail:chenlq@cumt.edu.cn
• 作者简介:张宇(1988- ),男,安徽省庐江县人,博士研究生,主要从事土地利用遥感监测研究.E-mail:ryanvsp@126.com
• 基金资助:

国家自然科学基金项目(41271121);江苏高校优势学科建设工程资助项目(SZBF2011-6-B35).

### Mechanism Research of Urban Land SurfaceWetness Regulating Urban Heat Island Effect Based on TM Images

ZHANG Yu1,2, CHEN Long-qian1,2, WANG Yu-chen1,2, CHEN Long-gao3, ZHOU Tian-jian1,2, ZHANG Ting1,2

1. 1. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China;
2. Jiangsu Key Laboratory of Resources and Environmental Information Engineering, Xuzhou 221116, China;
3. Land Resources Research Institute of Jiangsu Normal University, Xuzhou 221116, China
• Received:2014-02-18 Revised:2014-11-05 Online:2015-04-20 Published:2015-04-16

Abstract:

In recent years, the study of urban heat island effect regulation mechanism mainly focused on the influence of urban green space, urban landscape park and urban water on urban land surface temperature, few studies have considered the impact of land surface humidity on environment temperature. The purpose of this paper is exploring the regulation mechanism of urban land surface humidity on urban heat island effect. Xuzhou city was chosen to be the study case. The administrative boundary vector data of Xuzhou and four summer TM images of Xuzhou area from 1985 to 2010 were used. Research methods are as follows: First, Monowindow Algorithm was used to calculate the land surface temperatures of four periods. From 1985 to 2010, the acreage proportion of urban heat island area obviously increased from 31.87% to 38.57%, but from 1991 to 2010, the acreage proportion of high intensity urban heat island decreased from 23.32% to 20.52%. Second, a new index—Urban Land Surface Wetness (ULSW) which is on behalf of the vegetation and water coverage inside the city built-up area was extracted by using K-T transformation. The mean values of ULSW index of the four TM images from 1985 to 2010 are 5.91, 5.89, 6.33 and 6.94 respectively, which showed a trend of growth. Then, using spatial overlay analysis to calculate the correlation between the land surface temperature and the ULSW, the result showed that the slope of the four linear fitting equations are all negative which means the correlation of these two indicators are negative. At last, buffer analysis was used to compute the influence of ULSW index on the surrounding environment temperature. Four typical areas were chosen in each of the four TM images, and five buffers at the interval of 60 m were set up for each selected area. The result showed that the surrounding environment temperature of the center area of higher ULSW is cooler. The cooling effect of the first buffer layer is most obvious, the average temperature drop being 1.47 ℃. The cooling effect of the outermost buffer layer is minimum, the average temperature drop being 0.12 ℃ . The further the buffer is from the center area, the less the average surrounding environment temperature drops. The final research conclusions are: 1) ULSW index simplifies the extraction process of the vegetation and water in the construction land. 2) The area where the ULSW index value is high can effectively reduce the effect of urban heat island. 3) The inside and outside temperature of the area will decrease with the increase of ULSW. 4) ULSW index can be used to indicate the urban heat island regulation ability of an area. ULSW index can be also applied to other researches such as urban cooling channel planning, urban landscape and ecology planning, urban ecology and environment evaluation.

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