The Modified Normalized Urban Area Composite Index: A Satelliate-Derived High-Resolution Index for Extracting Urban Areas

The accurate and efficient extraction of urban areas is great significance for better understanding sprawl, built environment, economic activities, population distribution. Night-Time Light (NTL) data have been widely used to extract areas. However, most the existing NTL indexes are incapable identifying non-luminous built-up high-resolution imagery derived from Luojia 1-01 satellite, with low saturation blooming effect, can be map at a finer scale. A new spectral index, named Modified Normalized Urban Areas Composite Index (MNUACI), improved upon (NUACI), was proposed in this study, which integrated Human Settlement (HSI) generated data, Difference Vegetation (NDVI) Landsat 8 imagery, Water (MNDWI). Our results indicated that MNUACI spatial variability differentiation components by eliminating effect increasing variation nighttime luminosity. Compared area classification yielded accuracy than NTL, NUACI, HSI, EVI-Adjusted (EANTLI) alone. Furthermore, quadratic polynomial regression analysis showed model based on had best R2 Root-Mean Square Error (RMSE) compared EANTLI terms estimation impervious surface area. It concluded could improve identification accuracy.