Abstract
Mapping Impervious Surface Area (ISA) at regional and global scales has attracted increasing interest. DMSP-OLS nighttime light (NTL) data have proven to be successful for mapping urban land in large areas. However, the well-documented issues of pixel blooming and saturation limit the ability of DMSP-OLS data to provide accurate urban information. In this paper, a multi-source composition index is proposed to overcome the limitations of extracting urban land using only the NTL data. We combined three data sources (i.e., DMSP-OLS, MODSI EVI and NDWI) to generate a new index called the Normalized Urban Areas Composite Index (NUACI). This index aims to quickly map impervious surface area at regional and global scales. Experimental results indicate that NUACI has the ability to reduce the pixel saturation of NTL and eliminate the blooming effect. With the reference data derived from Landsat TM/ETM+, regression models based on normalized DMSP-OLS, Human Settlement Index (HSI), vegetation adjusted NTL urban index (VANUI), and NUACI are then established to estimate ISA. Our assessments reveal that the NUACI-based regression model yields the highest performance. The NUACI-based regression models were then used to map ISA for China for the years 2000, 2005 and 2010 (Free download link for the ISA products can be found at the end of this paper).
Highlights
Over the past 30 years, global urban population has increased from 1.5 billion in 1975 to 3.4 billion in 2010
This paper proposes a novel index, called the Normalized Urban Areas Composite Index (NUACI), to obtain accurate and timely urban dynamics in large areas based on the combination of DMSP-OLS, the vegetation index, and the Normalized Difference Water Index (NDWI)
The NUACI for the whole of China was calculated using the combination of DMSP-OLS, NDWI and EVImax images with ArcMap model builder, which is used to implement GIS processes by linking input data automatically
Summary
Over the past 30 years, global urban population has increased from 1.5 billion in 1975 to 3.4 billion in 2010. Previous studies indicated that the threshold-based technique posed a problem to retrieve urban areas accurately These approaches omit a large number of small settlements with low light brightness and overestimate urban extents in larger-scale cities due to the blooming effect [9,10,24,25]. This method cannot distinguish water bodies near cities, such as coastal areas, from urban areas Another primary barrier to using NTL data for urban studies is pixel saturation, which refers to the fact that data values in urban core areas tend to be truncated because of the limited radiometric range of DMSP-OLS. Other types, while farmlands have a higher EVImax but lower NDWI This characteristic pattern of differences makes urban lands distinguishable from farmlands, water bodies and barren lands, even though a few sample plots of barren lands and sparse grass fall into the circle region. Atytyppicicaall ssppaatitaial lpapttaetrtneronf loanfdlaconvderc(oe.vge.,rgr(aes.sgc.,ovgerraesdswcitohvleorweddewnsiitthy olof vwegdeteantisointy of vegeatraotuiondnuarrboaunnladndu)rbinaancliatyn. d) in a city
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