Relation Between Neighborhood With Dense High-Rises And Thermal Landscape Using Remote Sensing

Remote sensing technology has been implemented in urban thermal environment study for a long time, and the outcome of these researches is considerable. Researches about impact of urban underlying surface on thermal environment mainly focused on urban boundary layer (UBL) researches in the field of meteorology and urban heat island (UHI) using remote sensing. However, spatial scales of such studies are a bit low. Meanwhile, researches in high spatial scale paid more attentions to influences to micro-thermal environment caused by a neighbourhood or a specific building. What's more, meteorologists have contributed a lot in relationships between buildings and microclimate. Nevertheless, study on relation between urban constructions and thermal environment is far more thorough while using remote sensing. This study is to bridge thermal environment between local level and city level, and then find out high-rise buildings' impact on thermal environment. Multi-source remotely sensed data with various specral and radiant characteristics, different spatial resolutions is utilized in building information extraction and thermal environment analysis. The word, neighborhood with dense high-rises, means the region where high-rise buildings distribute densely. They have no distinct confines. Such kind of neighborhood is chosen because with higher heights and densities, they play a more standing out role in influencing urban thermal environment and micromclimate, comparing to other construction clusters.Combining multi-spectral bands, the high spatial resolution SPOT-5 panchromatic band is used extract height distribution of high-rises over a large area sucessfully. As shadow cast by building owns low grayscales, shadow regions with 2.5m resolution are distinguished. The following method for calculating building height is undertaken automatically for shadow region. Height calculation result is in good accuracy.Thermal infrared bands in Landsat TM and ASTER can effectively reflect distributions of urban thermal landscape and thermal intensity. With thermal data from such two sensors, land surface temperature (LST), blackbody temperature (brightness temperature), and specral thermal radiance are worked out. By comparison, LST is ultimately elected to be the indicator of urban thermal landscape and thermal intensity. LSTs are retrieved by mono-window algorithm and temperature/emmisivity seperation algorithm, respectively.By introducing a heat transfer method, spatial-temporal surface temperature simulationis processed. Result of simulation shows that in the region of high-rise building and its nearby area, increase of surface temperature in a time order is chiefly influenced by short wave radiation and materials of objects. The intensity of short wave radiation is influenced by shadow cast from building. Furthermore, it turns out to be a linear temperature increasing in materials with different albedos from sunrise to the time satellite passing by. This provides a reference for ther latter normalization of thermal data.With the thought of pseudo-invariant feature calibration, thermal data in different date and with high correlation are normalized using a method basing on statistics, so that temporal influences caused by various time, seasons, and phenology are lessen.By overlaying thermal data and building height data of year 2007, within neighborhoods with dense high-rises, both correlation of building heights and LST, and correlation of building densities and LST, are negative. Change detection analysis of 4-year ASTER data and 2-year TM thermal data shows that there is a trend of temperature decreasing with the increasing of height and density in such areas in these years. By further analysis, temperature decrease within neighborhood with dense high-rises is not caused by the overall thermal background pattern change in the city level, but caused by their internal pattern changes. It is because more high-rise buildings are built in these neighborhoods. On integrating surface temperature simulation and thermal data, it can be found that clustering of high-rise buildings does increase the average area sizes in a local region, which makes accumulative sunlight reduce there. Hence, less region can get solar direct radiation. As a result, intensity and speed of surface temperature increase reduces in a given day. It can be concluded that heights and densities of constructions in urban area can affect thermal environment in local level by the modification of regional sunlight toward land surface.