文章基本信息

标题：The urban heat island of Basel – seen from different perspectives
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作者：Eberhard Parlow ; Roland Vogt ; Christian Feigenwinter 等
期刊名称：Erde
印刷版ISSN：0013-9998
出版年度：2014
卷号：145
期号：1-2
页码：96-110
DOI：10.12854/erde-145-8
出版社：Gesellschaft fur Erdkunde zu Berlin
摘要：For decades thermal infrared satellite imagery has been used for climate studies of a variety of geosystems, including urban areas. Additionally, airborne thermal remotely sensed data can provide high resolution information about urban land surface temperatures (LST). Numerous studies make use of these data for the investigation of urban-rural LST differences, commonly known as the urban heat island (UHI) phenomenon. Most of these studies try to analyse the urban heat island by means of the LST distribution. It seems that the UHI is easy to measure, easy to explain, easy to find, and easy to illustrate. Due to this apparent simplicity some people seem to jump into UHI studies without fully understanding the nature of the phenomenon as far as time and spatial scales, physical processes and the numerous methodological pitfalls inherent to UHI studies are concerned. In this study the use of thermal infrared satellite data with respect to the assessment of the surface UHI is investigated. The need to clearly distinguish between different types of UHI is emphasised by recalling the (surface) temperature and the UHI terminology. The pretended simplicity of UHI effects is in reality a result of complex interactions between local radiation conditions, earth surface heat budget, the urban structure and the boundary layer atmosphere. Different methods may provide completely different results. This paper aims to bring more clearness into the subject by assessing the urban heat island of the city of Basel, Switzerland, by the use of thermal data provided by satellites (Landsat TM/ETM+), helicopter-borne infrared camera (InfraTec VarioCAM®) and ground-based measurements of air temperature profiles. It is shown that UHIs vary essentially with the choice of the respective temperature (LST, air temperature) and height (surface level, street/canopy level, roof level).
其他摘要：For decades thermal infrared satellite imagery has been used for climate studies of a variety of geosystems, including urban areas. Additionally, airborne thermal remotely sensed data can provide high resolution information about urban land surface temperatures (LST). Numerous studies make use of these data for the investigation of urban-rural LST differences, commonly known as the urban heat island (UHI) phenomenon. Most of these studies try to analyse the urban heat island by means of the LST distribution. It seems that the UHI is easy to measure, easy to explain, easy to find, and easy to illustrate. Due to this apparent simplicity some people seem to jump into UHI studies without fully understanding the nature of the phenomenon as far as time and spatial scales, physical processes and the numerous methodological pitfalls inherent to UHI studies are concerned. In this study the use of thermal infrared satellite data with respect to the assessment of the surface UHI is investigated. The need to clearly distinguish between different types of UHI is emphasised by recalling the (surface) temperature and the UHI terminology. The pretended simplicity of UHI effects is in reality a result of complex interactions between local radiation conditions, earth surface heat budget, the urban structure and the boundary layer atmosphere. Different methods may provide completely different results. This paperaims to bring more clearness into the subject by assessing the urban heat island of the city of Basel, Switzerland, by the use of thermal data provided by satellites (Landsat TM/ETM+), helicopter-borne infrared camera (InfraTec VarioCAM®) and ground-based measurements of air temperature profiles. It is shown that UHIs vary essentially with the choice of the respective temperature (LST, air temperature) and height (surface level, street/canopy level, roof level).
其他关键词：Urban heat island;urban surface heat island;thermal remote sensing