Abstract
Urban climate modeling is a means to increase the understanding of the urban climate. In order to model real world environments, area-wide spatial information is required. This paper demonstrates how hyperspectral remote sensing data and additional height data can provide a large part of this information efficiently, reducing the need for extensive field surveys. From the hyperspectral data the surface materials, the LAI and the surface albedo are estimated. The height data supplies the heights of buildings and trees. Using these maps as input for the urban micro climate model ENVI-met, simulations of temperature, wind and humidity among others can be carried out.
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