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

doi:10.1186/s13059-017-1378-9

CORC > 植物研究所 > 中国科学院植物研究所 > 植被与环境变化国家重点实验室

	Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data
	Wang, Run 3; Wan, Bo 3; Guo, Qinghua 1; Hu, Maosheng 3; Zhou, Shunping 3
刊名	REMOTE SENSING
	2017
卷号	9 期号:8
关键词	urban mapping one-class NPP-VIIRS DNB MODIS NDVI large scale
DOI	10.1186/s13059-017-1378-9
文献子类	Article
英文摘要	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.
学科主题	Biotechnology & Applied Microbiology ; Genetics & Heredity
电子版国际标准刊号	2072-4292
出版地	BASEL
WOS关键词	ONE-CLASS CLASSIFICATION ; SPECTRAL MIXTURE ANALYSIS ; NIGHTTIME LIGHT ; CLASS IMBALANCE ; URBANIZATION DYNAMICS ; HUMAN-SETTLEMENTS ; LAND ; SUPPORT ; CHINA ; AREAS
语种	英语
出版者	MDPI
WOS记录号	WOS:000418818400001
资助机构	National Key Research & Development (R&D) Plan of China [2016YFB0502304]
内容类型	期刊论文
源URL	[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.Natl Engn Res Ctr Geog Informat Syst, Wuhan 430074, Peoples R China 2.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 3.China Univ Geosci, Fac Informat Engn, 388 Lumo Rd, Wuhan 430074, 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).