Abstract
Abstract. Land cover classification was conducted for Landsat ETM image of Urmqi. Maximum likelihood classification algorism was used for this purpose. Classification classes were urban, water body, forest, soil, bare ground1, bare ground2, vegetation1, vegetation2 and vegetation3. Mask image of each land cover was created from the obtained classification image. Thermal band image of each land cover was extracted by using the mask image. In general, mean value and standard deviation are calculated for the thermal band image. However, these values were affected by the difference of ground resolution. In this study, we introduced quantiles to avoid this problem. Quantiles are points taken at regular intervals from the cumulative distribution function. Quantiles showed the effectiveness of decreasing the error caused from the difference of ground resolution.
Highlights
The air temperature at the city center is higher than that of the surrounding non-urban areas so that it looks like an island
In this study we aim at specifying the urban expansion characteristics of Urmqi City using Landsat ETM images to detect and evaluate the land use and land cover change and analyze the relationship between land use and heat environment of Urmqi city
Land cover classification was conducted for Landsat ETM image of Urmqi
Summary
The air temperature at the city center is higher than that of the surrounding non-urban areas so that it looks like an island. This phenomenon is so called “Urban Heat Island”. Land use/cover change has significant impacts on regional environment. Land surface temperature is an important indicator for assessment of regional environment especially in big cities such as Urumqi where urban heat island can usually be relatively obvious. In this study we aim at specifying the urban expansion characteristics of Urmqi City using Landsat ETM images to detect and evaluate the land use and land cover change and analyze the relationship between land use and heat environment of Urmqi city.
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