IGSNRR OpenIR
A New Urban Index for Expressing Inner-City Patterns Based on MODIS LST and EVI Regulated DMSP/OLS NTL
Liu, Yangxiaoyue1,2; Yang, Yaping1,3; Jing, Wenlong4,5,6; Yao, Ling1,3; Yue, Xiafang1,3; Zhao, Xiaodan1,3
2017-08-01
Source PublicationREMOTE SENSING
ISSN2072-4292
Volume9Issue:8Pages:17
Corresponding AuthorYang, Yaping(yangyp@igsnrr.ac.cn)
AbstractWith 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.
KeywordLERNCI vegetation coverage land surface temperature (LST) urban pattern DMSP/OLS NTL NPP/VIIRS DNB WorldPop
DOI10.3390/rs9080777
WOS KeywordLAND-SURFACE TEMPERATURE ; NIGHTTIME SATELLITE IMAGERY ; HEAT-ISLAND ; POPULATION-DENSITY ; LIGHT DATA ; CHINA ; URBANIZATION ; CONSUMPTION ; SATURATION ; SCALES
Indexed BySCI
Language英语
Funding ProjectChina Knowledge Center for Engineering Sciences and Technology[CKCEST-2015-1-4] ; National Special Program on Basic Science and Technology Research of China[2013FY110900] ; Data Sharing Infrastructure of Earth System Science
Funding OrganizationChina Knowledge Center for Engineering Sciences and Technology ; National Special Program on Basic Science and Technology Research of China ; Data Sharing Infrastructure of Earth System Science
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000408605600015
PublisherMDPI AG
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/61881
Collection中国科学院地理科学与资源研究所
Corresponding AuthorYang, Yaping
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China
4.Guangzhou Inst Geog, Guangzhou 510070, Guangdong, Peoples R China
5.Key Lab Guangdong Utilizat Remote Sensing & Geog, Guangzhou 510070, Guangdong, Peoples R China
6.Guangdong Open Lab Geospatial Informat Technol &, Guangzhou 510070, Guangdong, Peoples R China
Recommended Citation
GB/T 7714
Liu, Yangxiaoyue,Yang, Yaping,Jing, Wenlong,et al. A New Urban Index for Expressing Inner-City Patterns Based on MODIS LST and EVI Regulated DMSP/OLS NTL[J]. REMOTE SENSING,2017,9(8):17.
APA Liu, Yangxiaoyue,Yang, Yaping,Jing, Wenlong,Yao, Ling,Yue, Xiafang,&Zhao, Xiaodan.(2017).A New Urban Index for Expressing Inner-City Patterns Based on MODIS LST and EVI Regulated DMSP/OLS NTL.REMOTE SENSING,9(8),17.
MLA Liu, Yangxiaoyue,et al."A New Urban Index for Expressing Inner-City Patterns Based on MODIS LST and EVI Regulated DMSP/OLS NTL".REMOTE SENSING 9.8(2017):17.
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