Abstract
With the rapid pace of urban expansion, comprehensively understanding urban spatial patterns, built environments, green-spaces distributions, demographic distributions, and economic activities becomes more meaningful. Night Time Lights (NTL) images acquired through the Operational Linescan System of the US Defense Meteorological Satellite Program (DMSP/OLS NTL) have long been utilized to monitor urban areas and their expansion characteristics since this system detects variation in NTL emissions. However, the pixel saturation phenomenon leads to a serious limitation in mapping luminance variations in urban zones with nighttime illumination levels that approach or exceed the pixel saturation limits of OLS sensors. Consequently, we propose an NTL-based city index that utilizes the Moderate-resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and Enhanced Vegetation Index (EVI) images to regulate and compensate for desaturation on NTL images acquired from corresponding urban areas. The regulated results achieve good performance in differentiating central business districts (CBDs), airports, and urban green spaces. Consequently, these derived imageries could effectively convey the structural details of urban cores. In addition, compared with the Vegetation Adjusted NTL Urban Index (VANUI), LST-and-EVI-regulated-NTL-city index (LERNCI) reveals superior capability in delineating the spatial structures of selected metropolis areas across the world, especially in the large cities of developing countries. The currently available results indicate that LERNCI corresponds better to city spatial patterns. Moreover, LERNCI displays a remarkably better “goodness-of-fit” correspondence with both the Version 1 Nighttime Visible Infrared Imaging Radiometer Suite Day/Night Band Composite (NPP/VIIRS DNB) data and the WorldPop population-density data compared with the VANUI imageries. Thus, LERNCI can act as a helpful indicator for differentiating and classifying regional economic activities, population aggregations, and energy-consumption and city-expansion patterns. LERNCI can also serve as a valuable auxiliary reference for decision-making processes that concern subjects such as urban planning and easing the central functions of metropolis.
Highlights
As locations where modern industries and populations aggregate, cities are one of the key products of human civilization engaged in the process of changing the natural environment, and the focus of numerous environmental regulation and protection mechanisms
LST-and-EVI-regulated-NTL-city index (LERNCI) is proposed to be applicable for distinguishing light-intensity differences in urban-core areas and, for improving image-element resolution in Night Time Lights (NTL)-saturated regions to allow the identification of urban-core structures
Beijing and New York City were selected to validate the effectiveness of LERNCI
Summary
As locations where modern industries and populations aggregate, cities are one of the key products of human civilization engaged in the process of changing the natural environment, and the focus of numerous environmental regulation and protection mechanisms. When the natural landscape becomes a largely water-impervious surface because of the impact of human activities, it gradually but significantly influences local vegetation distribution patterns, which leads to local, and eventually regional, climate changes [1]. There is not enough discussion devoted to the spatial patterns of cities around the world [4]. There remains a pressing need to have the ability to consistently compare city patterns of both developed and developing countries and within various climate zones
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