Automated GIS-based derivation of urban ecological indicators using hyperspectral remote sensing and height information

Urban ecological indicators allow the objective and quantitative characterisation of ecological conditions in a spatially continuous way by evaluating the influence of urban surface types with respect to ecological functions and ecosystem services. Although the concept had already been developed in the 1980s, the variety of existing indicators had not been widely applied yet in urban planning practice, because of the high manual mapping effort that is required for spatially differentiated urban surface mapping. This paper presents a new automated remote sensing and GIS-based system for the flexible and user-defined derivation of urban ecological indicators. The system is based on automated surface material mapping using airborne hyperspectral image data and height information. Because the material classes obtained from remote sensing analysis differ in part from the surface types needed for the calculation of urban ecological indicators, they have been transformed into so-called linking categories representing the basis for the automated GIS-based derivation of urban ecological indicators. For this purpose, a computer-based system for flexible indicator derivation has been developed, allowing the user-defined integration of indicators based on the variable determination of mapping units, linking categories and respective weighting factors. Based on a comprehensive review of existing ecological indicators, 14 indicators have been selected and implemented in the system. To demonstrate the potential of the new system, a variety of indicators has been derived for two test sites situated in the German cities of Dresden and Potsdam, using city blocks defined by the municipal authorities as spatial mapping units. The initial mapping of surface materials was automatically performed on the basis of airborne hyperspectral image data acquired by the HyMAP system. The results of subsequent GIS-based indicator calculation were validated using results from field-based reference mapping that had been carried out for selected city blocks situated in both cities. An accuracy assessment for these reference city blocks has revealed mean errors of approximately 4%, confirming the suitability of the developed automated GIS-based system for flexible and efficient indicator calculation.