Mapping Impervious Surface Distribution with Integration of SNNP VIIRS-DNB and MODIS NDVI Data
AbstractData from the U.S. Defense Meteorological Satellite Program’s Operational Line-scan System are often used to map impervious surface area (ISA) distribution at regional and global scales, but its coarse spatial resolution and data saturation produce high inaccuracy in ISA estimation. Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite’s Day/Night Band (VIIRS-DNB) with its high spatial resolution and dynamic data range may provide new insights but has not been fully examined in mapping ISA distribution. In this paper, a new variable—Large-scale Impervious Surface Index (LISI)—is proposed to integrate VIIRS-DNB and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data for mapping ISA distribution. A regression model was established, in which LISI was used as an independent variable and the reference ISA from Landsat images was a dependent variable. The results indicated a better estimation performance using LISI than using a single VIIRS-DNB or MODIS NDVI variable. The LISI-based approach provides accurate spatial patterns from high values in core urban areas to low values in rural areas, with an overall root mean squared error of 0.11. The LISI-based approach is recommended for fractional ISA estimation in a large area. View Full-Text
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Guo, W.; Lu, D.; Wu, Y.; Zhang, J. Mapping Impervious Surface Distribution with Integration of SNNP VIIRS-DNB and MODIS NDVI Data. Remote Sens. 2015, 7, 12459-12477.
Guo W, Lu D, Wu Y, Zhang J. Mapping Impervious Surface Distribution with Integration of SNNP VIIRS-DNB and MODIS NDVI Data. Remote Sensing. 2015; 7(9):12459-12477.Chicago/Turabian Style
Guo, Wei; Lu, Dengsheng; Wu, Yanlan; Zhang, Jixian. 2015. "Mapping Impervious Surface Distribution with Integration of SNNP VIIRS-DNB and MODIS NDVI Data." Remote Sens. 7, no. 9: 12459-12477.