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Open AccessArticle

Delineating Spatial Patterns in Human Settlements Using VIIRS Nighttime Light Data: A Watershed-Based Partition Approach

by Ting Ma 1,2,3,*, Zhan Yin 1,2 and Alicia Zhou 4
1
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
4
Department of Mathematics & Statistics, College of Art and Science, Boston University, Boston, MA 02215, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(3), 465; https://doi.org/10.3390/rs10030465
Received: 7 February 2018 / Revised: 14 March 2018 / Accepted: 14 March 2018 / Published: 15 March 2018
(This article belongs to the Special Issue Remote Sensing of Night Lights – Beyond DMSP)
As an informative proxy measure for a range of urbanization and socioeconomic variables, satellite-derived nighttime light data have been widely used to investigate diverse anthropogenic activities in human settlements over time and space from the regional to the national scale. With a higher spatial resolution and fewer over-glow and saturation effects, nighttime light data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument with day/night band (DNB), which is on the Suomi National Polar-Orbiting Partnership satellite (Suomi-NPP), may further improve our understanding of spatiotemporal dynamics and socioeconomic activities, particularly at the local scale. Capturing and identifying spatial patterns in human settlements from VIIRS images, however, is still challenging due to the lack of spatially explicit texture characteristics, which are usually crucial for general image classification methods. In this study, we propose a watershed-based partition approach by combining a second order exponential decay model for the spatial delineation of human settlements with VIIRS-derived nighttime light images. Our method spatially partitions the human settlement into five different types of sub-regions: high, medium-high, medium, medium-low and low lighting areas with different degrees of human activity. This is primarily based on the local coverage of locally maximum radiance signals (watershed-based) and the rank and magnitude of the nocturnal radiance signal across the whole region, as well as remotely sensed building density data and social media-derived human activity information. The comparison results for the relationship between sub-regions with various density nighttime brightness levels and human activities, as well as the densities of different types of interest points (POIs), show that our method can distinctly identify various degrees of human activity based on artificial nighttime radiance and ancillary data. Furthermore, the analysis results across 99 cities in 10 urban agglomerations in China reveal inter-regional variations in partition thresholds and human settlement patterns related to the urban size and form. Our partition method and relative results can provide insight into the further application of VIIRS DNB nighttime light data in spatially delineated urbanization processes and socioeconomic activities in human settlements. View Full-Text
Keywords: nighttime light; Suomi-NPP VIIRS; human settlement; watershed-based partition; second-order exponential decay nighttime light; Suomi-NPP VIIRS; human settlement; watershed-based partition; second-order exponential decay
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MDPI and ACS Style

Ma, T.; Yin, Z.; Zhou, A. Delineating Spatial Patterns in Human Settlements Using VIIRS Nighttime Light Data: A Watershed-Based Partition Approach. Remote Sens. 2018, 10, 465.

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