Sentinel-1A SAR Data for Updating Global Urban Land Cover Maps
ISBN 978-85-88783-11-9
Authors
1Ban, Y.; 2Jacob, A.
1Division of Geoinformatics KTH Royal Institute of Technology Email: yifang@kth.se
2Division of Geoinformatics KTH Royal Institute of Technology
Abstract
Since 2008, more than half of the world population has been living in cities. It is estimated that the world is expected to add an additional 1.4 billion urban dwellers by 2030. Although only a small percentage of global land cover, urban areas significantly alter climate, biogeochemistry, and hydrology at local, regional, and global scales. Cities are hot spots of production, consumption, and waste generation. According to the United Nations, cities are responsible for 75% of global energy consumption and 80% of greenhouse gas emissions. Therefore, reliable and timely information on the spatial distribution and the temporal changes of urban areas is therefore critical to a wide array of research questions related to the effect of humans on the local, regional and global environment as well as to support sustainable urban development. Urban extent and land cover have been mapped using a range of datasets and algorithms. Very High Resolution optical and/or SAR imagery and object-based approaches dominate urban remote sensing at the local level while Landsat, ENVISAT ASAR, MERIS as well as MODIS or night-time light data and pixel-based techniques are mostly used for regional and global analysis. With its all-weather/illumination capability and its unique information content, SAR data have been increasingly investigated for global urban extent extraction at various spatial resolutions with promising results. With the recent launch of Sentinel-1A, SAR data with global coverage, operational reliability and quick data delivery became freely available, thus provide excellent opportunity for developing SAR-based methods for global urban mapping. The objective of this research is to evaluate Sentinel -1A SAR data for updating global urban land cover maps. The methodology is based on the original approach developed by Gamba et al. (2011) using both spatial indices and texture measures. The improvements mainly involve preprocessing, contrast enhancement, post-processing as well as decision level fusion using multitemporal and multipolarization data. The original method shown in blue is based on “Local Indicators of Spatial Association” (L.I.S.A.), including the Moran index, the Geary index and the Getis-Ord index and GLCM variance and correlation textures. Single-date Sentinel-1 SAR data over Beijing, Milan, Jakarta, Mexico City, Sao Paolo were acquired for the research. Multitemporal SAR data over these cities and over Rio de Janeiro will be collected when more Sentinel-1 SAR data become available. The preliminary urban extraction results showed that urban areas and small towns could be well extracted using a single-date Sentinel-1 SAR data with the KTH-Pavia urban extractor. It is expected that the results will be further improved using multitemporal Sentinel-1 SAR data. These results indicate that Sentinel-1A data is a promising data source for updating urban cover in global land cover maps.
Keywords
Sentinel-1A SAR; Urban Land Cover; Updating