Support of urban planning by deriving planning indicators with multi-sensor remote sensing: a case study in the Munich region

In order to take well-considered decisions on urban development, planning authorities require a solid and up to date knowledge of the current situation. Important issues are the actual status of the urban environment regarding the sustainability of land use, the environmental quality and the livability and attractiveness of the built-up areas. Therefore an indicator-based decision support system is currently being developed at the University of Wuerzburg. This tool will provide planning organizations with an instrument to assess the sustainability of the urban development in terms of land use and urban sprawl.

Since certain information on the urban environment is difficult or time-consuming to obtain by established survey or statistics, such as the degree of impervious surface, this study focuses on the (semi-)automatic derivation of indicators characterizing the urban environment using hyperspectral and thermal remote sensing data and a (normalized) digital surface model (DSM) derived from optical stereo imagery.

In the study presented the information needs of planning organizations in the region of Munich, Germany are analyzed and it is explored how these needs can be met with remote sensing. It was shown in other studies that for analysis of urban areas high spatial and spectral resolution are required. Therefore, data acquired by DLR using the hyperspectral Hymap sensor and the multispectral Daedalus sensor with both a resolution of four meters in combination with a DSM derived from high resolution aerial photographs are used to explore the possibilities.

After discussion with local planning organizations a set of key indicators was derived. Examples of these indicators are imperviousness, building density, building volume, thermal exposure or urban structural types. In a next step, for each of the indicators it is investigated how they are represented in the remote sensing data. These findings are included in decision trees, with which the indicators are retrieved from the hyperspectral and thermal remote sensing data and the DSM.

First results are promising, indicating that the derived indicators can provide planning organizations with up to date information on the current status of the urban environment.