Sentinel-2 and machine learning regression for built-up and green fraction mapping across European settlements
Creators: name: Schug, Franz
name: Okujeni, Akpona
name: Frantz, David
name: van der Linden, Sebastian
name: Hostert, Patrick
Publisher: name: Zenodo
Year: 2019

description: Urban agglomerations play a key role in giving shelter to the growing world population. Mapping the global extent of urban settlements, their land cover composition and growth patterns, as well as interconnectedness contributes to an understanding of city efficiency, resilience and transformation, e.g. in the context of the UN sustainable development goals. Satellite remote sensing data is highly suited for human settlement mapping because of its large area coverage and high potential for standardization. Multiple satellite-based products provide maps of human settlement extent, growth or land use (with varying assumptions and definitions) on different scales and spatial resolutions, e.g. the Global Urban Footprint (GUF), the Global Human Settlement Layer (GHSL), the World Settlement Footprint (WSF) or the EEA Urban Atlas. Still, urban areas are a general challenge to remote sensing because of the heterogeneity of urban features and surfaces. Many products map built-up areas as binary masks without considering the gradual spatial patterns of land cover (LC) composition in human settlements and its important vegetation component. Sentinel-2 offers free and globally available data with an unprecedented spatial, temporal and spectral resolution, thus representing a highly interesting source for settlement mapping and monitoring at relevant spatio-temporal scales. We use Sentinel-2 A/B Analysis Ready Data for large area fraction mapping of urban LC with machine learning regression. Our approach employs library-based, synthetically mixed training spectra and support vector regression models to map surface fractions of four classes, i.e. impervious surfaces, woody vegetation, non-woody vegetation and soil. Our results show that regression modelling is able to provide accurate fractions of urban LC with a spatial resolution of up to 10m. We find that using spectral temporal metrics helps to improve the quantitative separation between spectrally similar but temporally variable cover types. Accounting for the temporal dimension improves the robustness and quality of regression models applied over large areas or distant from the reference spectra locations. We systematically study the effect of several spectral temporal metrics as well as temporal statistics of commonly used spectral indices. We find that an aggregation period for summarizing temporal information of one year or more reduces confusion between impervious surfaces and soils as well as uncultivated agricultural land in central Europe. This study shows that surface fraction mapping with regression models can add valuable information to existing binary or discrete products and that thematic information is extended by including maps of the urban green component. We systematically map different urban settlement types across Europe, and show that the applied method is also able to capture the character of rural settlements.