Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data

doi:10.1186/s13059-017-1378-9

	Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data
	Wang, Run 1; Wan, Bo 1; Guo, Qinghua2 ; Hu, Maosheng 1; Zhou, Shunping 1
	2017
发表期刊	REMOTE SENSING
卷号	9 期号:8
摘要	The accurate and timely monitoring of regional urban extent is helpful for analyzing urban sprawl and studying environmental issues related to urbanization. This paper proposes a classification scheme for large-scale urban extent mapping by combining the Day/Night Band of the Visible Infrared Imaging Radiometer Suite on the Suomi National Polar-orbiting Partnership Satellite (NPP-VIIRS DNB) and the Normalized Difference Vegetation Index from the Moderate Resolution Imaging Spectroradiometer products (MODIS NDVI). A Back Propagation (BP) neural network based one-class classification method, the Present-Unlabeled Learning (PUL) algorithm, is employed to classify images into urban and non-urban areas. Experiments are conducted in mainland China (excluding surrounding islands) to detect urban areas in 2012. Results show that the proposed model can successfully map urban area with a kappa of 0.842 on the pixel level. Most of the urban areas are identified with a producer's accuracy of 79.63%, and only 10.42% the generated urban areas are misclassified with a user's accuracy of 89.58%. At the city level, among 647 cities, only four county-level cities are omitted. To evaluate the effectiveness of the proposed scheme, three contrastive analyses are conducted: (1) comparing the urban map obtained in this paper with that generated by the Defense Meteorological Satellite Program/Operational Linescan System Nighttime Light Data (DMSP/OLS NLD) and MODIS NDVI and with that extracted from MCD12Q1 in MODIS products; (2) comparing the performance of the integration of NPP-VIIRS DNB and MODIS NDVI with single input data; and (3) comparing the classification method used in this paper (PUL) with a linear method (Large-scale Impervious Surface Index (LISI)). According to our analyses, the proposed classification scheme shows great potential to map regional urban extents in an effective and efficient manner.
关键词	urban mapping one-class NPP-VIIRS DNB MODIS NDVI large scale
学科领域	Biotechnology & Applied Microbiology ; Genetics & Heredity
DOI	10.1186/s13059-017-1378-9
收录类别	SCI
语种	英语
WOS关键词	ONE-CLASS CLASSIFICATION ; SPECTRAL MIXTURE ANALYSIS ; NIGHTTIME LIGHT ; CLASS IMBALANCE ; URBANIZATION DYNAMICS ; HUMAN-SETTLEMENTS ; LAND ; SUPPORT ; CHINA ; AREAS
WOS记录号	WOS:000418818400001
出版者	MDPI
文献子类	Article
出版地	BASEL
EISSN	2072-4292
资助机构	National Key Research & Development (R&D) Plan of China [2016YFB0502304]
作者邮箱	runwang@cug.edu.cn ; magicwan1105@163.com ; guo.qinghua@gmail.com ; humsh@cug.edu.cn ; zhoushunping@mapgis.com
作品OA属性	gold, Green Published
引用统计	被引频次：36[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ibcas.ac.cn/handle/2S10CLM1/22321
专题	植被与环境变化国家重点实验室
作者单位	1.China Univ Geosci, Fac Informat Engn, 388 Lumo Rd, Wuhan 430074, Peoples R China 2.Natl Engn Res Ctr Geog Informat Syst, Wuhan 430074, Peoples R China 3.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China
推荐引用方式 GB/T 7714	Wang, Run,Wan, Bo,Guo, Qinghua,et al. Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data[J]. REMOTE SENSING,2017,9(8).
APA	Wang, Run,Wan, Bo,Guo, Qinghua,Hu, Maosheng,&Zhou, Shunping.(2017).Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data.REMOTE SENSING,9(8).
MLA	Wang, Run,et al."Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data".REMOTE SENSING 9.8(2017).

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