Applying Remote Sensing to Determine the Percent Imperviousness for Urban Drainage Modelling

Background and Objective: Urban areas have become more vulnerable to flooding due to decreased imperviousness in cities and more severe rain events. Sewer modelling is used to examine urban drainage systems and their performance in case of severe rain. Accurate data for impervious surfaces is a key input factor to obtain valuable models. In this study, the use of data acquired automatically by remote sensing techniques is investigated as an alternative to other electronic databases and is manually interpreted to obtain information on the surface conditions. Materials and Methods: The study is carried out in a neighbourhood of Copenhagen and the sewer system is modelled by the commercial software MIKE URBAN 2014. Airborne images with 20 cm resolution and 4-band orthophotos were analyzed by following an object-oriented approach and used as input for calculating the percent imperviousness. Results: The results show that different types of impervious areas are determined with different accuracy. Road area coverage is underestimated, building coverage is classified accurately and the area of other impervious surfaces is over estimated. When applying the achieved classification and using this to determine the imperviousness, the sewer system is accurately modelled despite the inaccuracies in the area coverage. Conclusion: This study validates the automatic classification of areas using airborne images. The methodology, however, should be optimized with respect to road surfaces and some specific pervious surfaces.