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Remote Sens. 2017, 9(8), 862;

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

Faculty of Information Engineering, China University of Geosciences, No. 388 Lumo Road, Wuhan 430074, China
National Engineering Research Center of Geographic Information System, Wuhan 430074, China
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
Author to whom correspondence should be addressed.
Academic Editors: Bailang Yu, Yuyu Zhou, Chunyang He, Xiaofeng Li, James B. Campbell and Prasad S. Thenkabail
Received: 21 June 2017 / Revised: 5 August 2017 / Accepted: 17 August 2017 / Published: 21 August 2017
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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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. View Full-Text
Keywords: urban mapping; one-class; NPP-VIIRS DNB; MODIS NDVI; large scale urban mapping; one-class; NPP-VIIRS DNB; MODIS NDVI; large scale

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Wang, R.; Wan, B.; Guo, Q.; Hu, M.; Zhou, S. Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data. Remote Sens. 2017, 9, 862.

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