An Update System for Urban Biotope Maps Based on Hyperspectral Remote Sensing Data

Urban biotopes are of high importance for ecological urban planning. In contrast to the common practice of producing urban biotope maps – by visual interpretation of colour-infrared aerial photographs in combination with field investigations – this investigation presents an efficient system for updating existing urban biotope maps by automatic analysis of remote sensing (RS) data. For such a system a method for the classification of biotopes is needed. However, the automatic classification of urban biotopes from RS data is not a task for a pixel-based classifier. All pixels of a biotope have to be taken into account during the classification. Therefore, fuzzy logic models for biotope types are built with regard to the composition of the biotopes of different surface materials and their arrangement in the biotopes. The models consist of lists of numerical features, that yield the best separation of two biotope types, and associated membership functions. The features are calculated on hyperspectral images and on a normalized digital surface model and are able to numerically capture the characteristic differences between the biotopes of different types. The application of the developed models to the biotopes of six selected types in the 14.5 km test area in the city of Dresden, Germany, yields an overall accuracy of 87%. The types of the classified biotopes, the corresponding similarity values as well as additional attributes can be assigned to the biotopes in the output vector layer and compared to the former biotope map.