Satellite-derived nighttime light data have been increasingly used for studying urbanization and socioeconomic dynamics, because there are notable quantitative relationships between anthropogenic nocturnal radiance and the degree of human activity over time and space at different scales. When considering the visible impacts of saturation and over-glow effects from original nighttime light images, several composite indexes, which mainly include the introduction of vegetation index, have been studied to improve the application of nighttime light data for investigating the spatial patterns in human settlements. To overcome the shortcomings of previous composite indexes, especially in areas of highly intensified human activity, such as urban, non-man-made surfaces, and low density human activity, such as in rural residential sites, we propose a new human settlement composite index (HSCI). The establishment of this proposed HSCI is based on a combination of three different remote sensing datasets: nighttime light brightness (derived from the Defense Meteorological Satellite Program, DMSP), the normalized difference vegetation index (NDVI, derived from the Moderate Resolution Imaging Spectroradiometer, MODIS), and the percent impervious surface area (PISA, derived from the GlobeLand30 land cover and land use dataset produced from Landsat data). We defined the calculation of HSCI as the arithmetic mean of the normalized difference urban index and normalized difference imperviousness index with respect to both the magnitude of socioeconomic activity and the distribution of artificial surface across human settlement, respectively. Analysis results clearly demonstrate the utility of HSCI in delineating spatial patterns for different kinds of human settlement, particularly for identifying non-man-made surfaces in urbanized areas, various densities of human activities in peripheral areas and small human settlements in rural and remote areas. Our method and findings provide an effective way to investigate human settlements with a nighttime brightness-based composite index, as well as valuable insights into further studies of the composite index related to nocturnal luminosity data.
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