Detecting China’s Urban Expansion Over the Past Three Decades Using Nighttime Light Data

Xiao, Pengfeng; Wang, Xiaohui; Xia, Feng; Zhang, Xueliang; Yang, Yongke

doi:10.1109/jstars.2014.2302855

Cited by 91 publications

(54 citation statements)

References 41 publications

(71 reference statements)

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“…The threshold may be improved to adapt to a larger spatial scale with larger economic variability. For example, the cluster-based optimal threshold [39] and the local SVM-based region growing method [40] can be introduced to extract urban areas with higher accuracy for regions at different development levels. Another issue, which may hinder the accuracy of extraction, was the inherent limitation of spatial and radiometric resolution of DMSP/OLS nighttime images.…”

Section: Discussionmentioning

confidence: 99%

Monitoring Urban Dynamics in the Southeast U.S.A. Using Time-Series DMSP/OLS Nightlight Imagery

Weng

et al. 2016

Remote Sensing

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Abstract:The Defense Meteorological Satellite Program (DMSP)'s Operational Line-scan System (OLS) stable nighttime light (NTL) imagery offers a good opportunity for characterizing the extent and dynamics of urban development at the global and regional scales. However, their ability to characterize intra-urban variation is limited due to saturation and blooming of the data values. In this study, we adopted the methods of Mann-Kendall and linear regression to analyze urban dynamics from time series Vegetation Adjusted NTL Urban Index (VANUI) data from 1992 to 2013 in the Southeast United States of America (U.S.A.), which is one of the fastest growing regions in the nation. The newly built urban areas were effectively detected based on the trend analysis. In addition, the VANUI-derived urban areas with an optimal threshold method were found highly consistent with the Landsat-derived National Land Cover Database. The total urbanized areas in large metropolitan areas in southeastern U.S.A. increased from 8524 km 2 in 1992 to 14,684 km 2 in 2010, accounting for 5% and 9% of the total area, respectively. The results further showed that urban expansion in the region cannot be purely explained by population growth. Our results suggested that the VANUI time series provided an effective method for characterizing the spatiotemporal dynamics of urban extent at the regional scale.

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Section: Discussionmentioning

confidence: 99%

Monitoring Urban Dynamics in the Southeast U.S.A. Using Time-Series DMSP/OLS Nightlight Imagery

Weng

et al. 2016

Remote Sensing

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“…To standardize the data, we calculated the ratio of the annual increase in the CNLI in a certain region relative to that of the whole country [39] (Equation (5)). Likewise, we standardized the annual increase in PNP and PBA in the same way.…”

Section: Dynamics Of Urbanization Levels Measured By the Cnli Was Conmentioning

confidence: 99%

Dynamics of Urbanization Levels in China from 1992 to 2012: Perspective from DMSP/OLS Nighttime Light Data

et al. 2015

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Abstract:The authenticity and reliability of urbanization levels measured by different indicators in China have not reached a consensus, which may impede our understanding of the process of urbanization and its impacts on the environment. The objective of this study was to describe a reliable method of estimating urbanization level based on the Operational Line-scan System (OLS) on the Defense Meteorological Satellite Program (DMSP) nighttime light data and to analyze the dynamics of urbanization levels in China from 1992 to 2012. We calculated the comprehensive urbanization level at the national, provincial, and county scales using a compounded night light index (CNLI) and compared the change rate of CNLI with those of the other two conventional urbanization level indicators, proportion of the nonagricultural population and proportion of built-up area. Our results showed that CNLI derived from the DMSP/OLS data set provided a relatively reliable and accurate measure of the comprehensive urbanization level in China. During the last two decades, China has experienced continued and rapid urbanization with large regional variations. The CNLI increased 3.12 times, from 1.72 × 10 . The annual increases of CNLI in eastern provinces were much faster than those in western provinces. In addition, we found that the rates of change in these three indicators were consistent for OPEN ACCESS Remote Sens. 2015, 7 1722 most provinces with the exception of the four municipalities (Beijing, Tianjin, Shanghai, and Chongqing) and a few eastern coastal provinces (Jiangsu, Zhejiang, Fujian, and Guangdong). Because the imbalance among population growth, urban expansion and socioeconomic development may affect cities' sustainable development, we should pay more attention to these regions with large disparities between different indicators.

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“…Because satellites are subject to rapid upgrades, the data from different types of satellites present varying quality and light saturation phenomena [43,44]. Therefore, this article used the calibration method for long time series DMSP-OLS nighttime light image datasets, proposed by Cao et al in 2009, to preprocess the remote sensing data for nighttime lights in 2010 [45].…”

Section: Data Pre-processingmentioning

confidence: 99%

Optimized Sample Selection in SVM Classification by Combining with DMSP-OLS, Landsat NDVI and GlobeLand30 Products for Extracting Urban Built-Up Areas

Tong

Luo

et al. 2017

Remote Sensing

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Abstract:The accuracy of training samples used for data classification methods, such as support vector machines (SVMs), has had a considerable positive impact on the results of urban area extractions. To improve the accuracy of urban built-up area extractions, this paper presents a sample-optimized approach for classifying urban area data using a combination of the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) for nighttime light data, Landsat images, and GlobeLand30, which is a 30-m global land cover data product. The proposed approach consists of three main components: (1) initial sample generation and data classification into built-up and non-urban built-up areas based on the maximum and minimum intervals of digital numbers from the DMSP-OLS data, respectively; (2) refined sample selection and optimization by the probability threshold of each pixel based on vegetation-cover, using the Landsat-derived normalized differential vegetation index (NDVI) and artificial surfaces extracted from the GlobeLand30 product as the constraints; (3) iterative classification and urban built-up area data extraction using the relationship between these three aspects of data collection together with the training sets. Experiments were conducted for several cities in western China using this proposed approach for the extraction of built-up areas, which were classified using urban construction statistical yearbooks and Landsat images and were compared with data obtained from traditional data collection methods, such as the threshold dichotomy method and the improved neighborhood focal statistics method. An analysis of the empirical results indicated that (1) the sample training process was improved using the proposed method, and the overall accuracy (OA) increased from 89% to 96% for both the optimized and non-optimized sample selection; (2) the proposed method had a relative error of less than 10%, as calculated by an accuracy assessment; (3) the overall and individual class accuracy were higher for artificial surfaces in GlobeLand30; and (4) the average OA obviously improved and the Kappa coefficient in the case of Chengdu increased from 0.54 to 0.80. Therefore, the experimental results demonstrated that our proposed approach is a reliable solution for extracting urban built-up areas with a high degree of accuracy.

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Detecting China’s Urban Expansion Over the Past Three Decades Using Nighttime Light Data

Cited by 91 publications

References 41 publications

Monitoring Urban Dynamics in the Southeast U.S.A. Using Time-Series DMSP/OLS Nightlight Imagery

Monitoring Urban Dynamics in the Southeast U.S.A. Using Time-Series DMSP/OLS Nightlight Imagery

Dynamics of Urbanization Levels in China from 1992 to 2012: Perspective from DMSP/OLS Nighttime Light Data

Optimized Sample Selection in SVM Classification by Combining with DMSP-OLS, Landsat NDVI and GlobeLand30 Products for Extracting Urban Built-Up Areas

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