Abstract
Accurate and timely urban land mapping is fundamental to supporting large area environmental and socio-economic research. Most of the available large-area urban land products are limited to a spatial resolution of 30 m. The fusion of optical and synthetic aperture radar (SAR) data for large-area high-resolution urban land mapping has not yet been widely explored. In this study, we propose a fast and effective urban land extraction method using ascending/descending orbits of Sentinel-1A SAR data and Sentinel-2 MSI (MultiSpectral Instrument, Level 1C) optical data acquired from 1 January 2015 to 30 June 2016. Potential urban land (PUL) was identified first through logical operations on yearly mean and standard deviation composites from a time series of ascending/descending orbits of SAR data. A Yearly Normalized Difference Vegetation Index (NDVI) maximum and modified Normalized Difference Water Index (MNDWI) mean composite were generated from Sentinel-2 imagery. The slope image derived from SRTM DEM data was used to mask mountain pixels and reduce the false positives in SAR data over these regions. We applied a region-specific threshold on PUL to extract the target urban land (TUL) and a global threshold on the MNDWI mean, and slope image to extract water bodies and high-slope regions. A majority filter with a three by three window was applied on previously extracted results and the main processing was carried out on the Google Earth Engine (GEE) platform. China was chosen as the testing region to validate the accuracy and robustness of our proposed method through 224,000 validation points randomly selected from high-resolution Google Earth imagery. Additionally, a total of 735 blocks with a size of 900 × 900 m were randomly selected and used to compare our product’s accuracy with the global human settlement layer (GHSL, 2014), GlobeLand30 (2010), and Liu (2015) products. Our method demonstrated the effectiveness of using a fusion of optical and SAR data for large area urban land extraction especially in areas where optical data fail to distinguish urban land from spectrally similar objects. Results show that the average overall, producer’s and user’s accuracies are 88.03%, 94.50% and 82.22%, respectively.
Highlights
Information regarding urban land distribution has significant implications in the surrounding environments, e.g., biogeochemical cycles, the earth’s energy budget, local microclimate, and urban planning in terms of supporting management services [1]
The most generally accepted description of impervious surface was proposed by Arnold and Gibbons [3] as any material that prevents the infiltration of water into the soil
Selected region-specific thresholds used for the obtaining preliminary urban land and vegetation mask are shown in Figure 9 and Figure 10 for 12 regions
Summary
Information regarding urban land distribution has significant implications in the surrounding environments, e.g., biogeochemical cycles, the earth’s energy budget, local microclimate, and urban planning in terms of supporting management services [1]. Even though urban land accounts for a very small percent of the earth’s surface (less than 1%), it carries 90% of the world’s economic activities [2]. We refer to ‘urban land’ as an impervious surface (IS). The most generally accepted description of impervious surface was proposed by Arnold and Gibbons [3] as any material that prevents the infiltration of water into the soil. The impervious surface was divided into two categories (natural and artificial materials) according to their composition and our target including built-up, roads, parking lots, etc. We did not consider natural impervious surfaces, like bedrock outcrops or compacted soil as urban land because they do not perform the service functions that urban land should
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