IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 7, NO. 10, OCTOBER 2014 4095 Detecting China’s Urban Expansion Over the Past Three Decades Using Nighttime Light Data Pengfeng Xiao, Member, IEEE, Xiaohui Wang, Xuezhi Feng, Xueliang Zhang, and Yongke Yang Abstract—China has experienced a rapid urban expansion over and land-use change phenomenon [1], which means that it is very the past three decades because of its accelerated economic growth. important to map the urban expansion of China over the past In this study, we detected and analyzed the urban expansion of three decades. Historically, optical remote sensing data have China during this period using multi-temporal Defense Meteoro- ’ logical Satellite Program Operational Linescan System been used to map China s urban areas and the expansion of urban (DMSP-OLS) nighttime light data and multi-source Normalized land-cover for individual cities, e.g., Shanghai [2], Guangzhou Difference Vegetation Index (NDVI) data. First, an intercalibration [3], [4], Nanjing [5], and Shijiazhuang [6], or local regions, e.g., was performed to improve the continuity and comparability of the Beijing–Tianjin–Hebei [7], Bohai Rim [8], Yangtze River Delta nighttime light data from 1992 to 2010. The nighttime light and [9], and Pearl River Delta [10]–[12], whereas little research has NDVI data were then subjected to a local support vector machine (SVM) based region-growing method to extract the urban areas focused on the national scale patterns of urban expansion from 1992 to 2010. The urban areas from 1981 to 1991 were [13]–[18]. identified using the areas in 1992 and NDVI data, based on the China’s urban extent is difficult to detect at a national scale hypothesis that China’s urban expansion continued during this using coarse-resolution remote sensing because of the tiny size of period. Finally, the extracted time-series urban maps were validated the urban area relative to the total land area. Fine-resolution with Landsat images. The proposed local SVM-based region- growing method performed better than a local thresholding method remote sensing of urban area is also challenging because of the and a global SVM-based region-growing method according to visual spectral and spatial complexity of the land cover within cities. and quantitative comparisons of the urban boundaries and areas. Fortunately, in contrast to most traditional sensors, nighttime We also analyzed the expansion rates to understand the dynamics light sensors have the unique capacity to map human activities of the urban areas in China and in its seven economic regions. In from space [19], [20]. The most commonly used nighttime light particular, the urban expansion patterns were investigated in three typical urban agglomerations, i.e., Beijing–Tianjin–Hebei, sensor is the Operational Linescan System (OLS) onboard the Yangtze River Delta, and Pearl River Delta. The proposed urban U.S. Defense Meteorological Satellite Program (DMSP), which expansion direction, urban expansion intensity, and relative ratio has been in operation since the early 1970s. The sensor has a of urban expansion demonstrated the regional variation among unique low-light imaging capability, which was developed for the three urban agglomerations. detecting clouds based on moonlight. In addition to moonlit Index Terms—Change detection algorithms, land surface, remote clouds, the OLS also detects lights from human settlements, fires, sensing, support vector machines (SVMs), urban areas. gas flares, heavily lit fishing boats, lightning, and the aurora [21], [22]. The potential use of OLS data for the observation of city lights and other visible and near-infrared emission sources was I. INTRODUCTION first noted in the 1970s. Many previous studies have employed RBANIZATION has been a significant feature of the OLS images to demonstrate their application in different areas, – development of China’s society since the country adopted e.g., as indicators of urban dynamics [1], [23] [26], population U – an economic reform and openness policy in 1978. Urban areas levels [27] [31], economic activities [32], gas emissions [33], fl concentrate people, infrastructure, and economic activities. and armed con icts [34]. Thus, urbanization is simultaneously a demographic, economic, Nighttime light images have some advantages over daytime images because they measure emitted rather than reflected radiation, which avoids certain classification problems when Manuscript received October 12, 2013; revised January 19, 2014; accepted separating developed versus nondeveloped land cover [29]. January 24, 2014. Date of publication February 27, 2014; date of current version December 22, 2014. This work was supported in part by the National Key Basic However, DMSP-OLS data still have several limitations related Research Program on Global Change (Grant 2011CB952001), and in part by a to their coarse spatial resolution, saturation in bright urban cores, project funded by the Priority Academic Program Development (PAPD) of and single spectral band, as well as inconsistencies in their Jiangsu Higher Education Institutions. The authors are with the Jiangsu Provincial Key Laboratory of Geographic radiometric properties within and between scenes and sensors. Information Science and Technology, Nanjing University, Nanjing 210023, Moreover, the biggest problem when using the nighttime light China, the Key Laboratory for Satellite Mapping Technology and Applications data as a proxy measure of the urban extent is that the lighted of State Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, Nanjing 210023, China, and the Department of Geographic areas detected by OLS are consistently larger than the geographic Information Science, Nanjing University, Nanjing 210023, China (e-mail: extents of the settlements with which they are associated. The [email protected]). larger spatial extent of the lighted area relative to the urban area is Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. sometimes referred to as blooming [35]. To offset the blooming Digital Object Identifier 10.1109/JSTARS.2014.2302855 effect, Imhoff et al. proposed the use of a threshold with an 89% 1939-1404 © 2014 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. 4096 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 7, NO. 10, OCTOBER 2014 detection frequency to eliminate less frequently detected lighted images acquired by six different DMSP satellites, i.e., F10, pixels at the peripheries of large urban areas [36]. A subsequent F12, F14, F15, F16, and F18. In cases where two satellites were analysis obtained a correlation of 0.68 between Ln(lighted area) collecting data, two separate composites were produced [33]. and Ln(population) when using a threshold of 80% [29]. The files are cloud-free composites, which were made using all However, the authors also highlighted the limitations of using the available archived OLS smooth resolution data from 1992 to a single threshold during a global analysis. 2010. The products are 30 arc-second grids, which span to Several methods are available for extracting urban information longitude and to latitude. The radiometric from nighttime light data, including an empirical thresholding resolution of the dataset is 6 bits, i.e., the digital number (DN) technique [37], [38], a thresholding technique based on mutation ranges from 0 to 63. detection [39], a statistical data comparison method [8], [40], a The average DN values and the number of lit pixels differed high-resolution data comparison method [41], [25], and an between two satellites in the same year [25], [26]. Thus, based on image-classification method [24], [26], [42]. Henderson et al. the data selection method proposed by Liu et al. [25], we selected developed a thresholding technique that used ancillary data, the one with better continuity and comparability from the two where they examined three urban areas as they appeared in both sets of data, i.e., the selected image dataset included the data OLS images and Landsat images on approximately the same products from F10 (1992, 1993, and 1994), F12 (1995 and 1996), dates [41]. Then light thresholds were calculated for each city by F14 (1997, 1998, 1999, 2000, 2001, and 2002), F15 (2003, 2004, minimizing the discrepancies between the OLS- and Landsat- 2005, and 2006), F16 (2007, 2008, and 2009), and F18 (2010). derived urban boundaries. To address the problems of thresh- After obtaining the time-series global nighttime light data from olding methods using empirical strategies or manual trial- and- 1992 to 2010, China’s nighttime light data were extracted error procedures, Cao et al. proposed a support vector machine according to the national boundaries. Next, they were reprojected (SVM) based region-growing algorithm for the semi-automatic onto the Lambert Conformal Conic Projection and resampled to a extraction of urban areas from OLS data [24]. Several simple pixel size of 1 km to facilitate the calculations. criteria were used to select the SVM training sets of urban and nonurban pixels, where an iterative classification and training B. NDVI Data procedure were used to identify the urban pixels. However, it was Two types of NDVI data were used, i.e., Système Probatoire difficult to accurately extract urban