Identification of roof materials in high-resolution multispectral images for urban planning and monitoring

While the application of very high-resolution imagery is widely acknowledged, the use of sensors of spatial resolutions of five meters and above is constrained in urban settings because of limitations due to the mixed pixel information. This study investigates the potential of Sentinel-2 products for the detection of roof materials in the city of Kigali as input for planning decisions and monitoring of urban expansion. A linear spectral unmixing is applied to spectral information of both artificial and natural surfaces collected in the field. Results indicate that both spatial and radiometric resolution are sufficient to detect even small fractions of roof materials, although a proper calibration and endmember optimization is required. Challenges remain regarding surfaces of similar spectral signatures, such as clay roofs and argillaceous soil, and surfaces which are strongly under-represented within pixels. The spatial distribution of clay roofs as an indicator for upper-class houses matches the reference data collected in the fields, but for a reliable prediction of all materials more attention must be placed on metal roofs of different colors.