Abstract
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.
Highlights
Urban land occupies a relatively small fraction of the Earth’s surface, but is the main area of human activities [1]
By 2030, urban land cover will grow by 1.2 million km2, nearly tripling the global urban land area of circa 2000 [4]
The main purpose of this study is to evaluate the effectiveness of combining NPP-VIIRS DNB
Summary
Urban land occupies a relatively small fraction of the Earth’s surface, but is the main area of human activities [1]. Rapid socio-economic development and population growth have greatly encouraged the expansion of urban land areas [2,3]. By 2030, urban land cover will grow by 1.2 million km , nearly tripling the global urban land area of circa 2000 [4]. This increase has a high-probability of occurring in developing countries with a rapid pace of urbanization, such as China. Urbanization is an effective promotion of social progress, urban expansion has inevitably caused numerous environmental problems, resulting in Remote Sens. 2017, 9, 862; doi:10.3390/rs9080862 www.mdpi.com/journal/remotesensing
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