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Coupling an Intercalibration of Radiance-Calibrated Nighttime Light Images and Land Use/Cover Data for Modeling and Analyzing the Distribution of GDP in Guangdong, China

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  • Ziyang Cao

    (Key Laboratory of Marginal Sea Geology, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
    University of Chinese Academy of Sciences, Beijing 100089, China)

  • Zhifeng Wu

    (School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China)

  • Yaoqiu Kuang

    (Key Laboratory of Marginal Sea Geology, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China)

  • Ningsheng Huang

    (Key Laboratory of Marginal Sea Geology, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China)

  • Meng Wang

    (Key Laboratory of Marginal Sea Geology, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
    University of Chinese Academy of Sciences, Beijing 100089, China)

Abstract

Spatialized GDP data is important for studying the relationships between human activities and environmental changes. Rapid and accurate acquisition of these datasets are therefore a significant area of study. Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) radiance-calibrated nighttime light (RC NTL) images exhibit the potential for providing superior estimates for GDP spatialization, as they are not restricted by the saturated pixels which exist in nighttime stable light (NSL) images. However, the drawback of light overflow is the limited accuracy of GDP estimation, and GDP data estimations based on RC NTL images cannot be directly used for temporal analysis due to a lack of on-board calibration. This study develops an intercalibration method to address the comparability problem. Additionally, NDVI images are used to reduce the light overflow effect. In this way, the secondary and tertiary industry outputs are estimated by using intercalibrated RC NTL images. Primary industry production is estimated by using land use/cover data. Ultimately, four 1 km gridded GDP maps of Guangdong for 2000, 2004, 2006 and 2010 are generated. The verification results of the proposed intercalibration method demonstrate that this method is reasonable and can be effectively implemented. These maps can be used to analyze the distribution and spatiotemporal changes of GDP density in Guangdong.

Suggested Citation

  • Ziyang Cao & Zhifeng Wu & Yaoqiu Kuang & Ningsheng Huang & Meng Wang, 2016. "Coupling an Intercalibration of Radiance-Calibrated Nighttime Light Images and Land Use/Cover Data for Modeling and Analyzing the Distribution of GDP in Guangdong, China," Sustainability, MDPI, vol. 8(2), pages 1-18, January.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:2:p:108-:d:62865
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    References listed on IDEAS

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    Cited by:

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    2. Shengnan Jiang & Guoen Wei & Zhenke Zhang & Yue Wang & Minghui Xu & Qing Wang & Priyanko Das & Binglin Liu, 2020. "Detecting the Dynamics of Urban Growth in Africa Using DMSP/OLS Nighttime Light Data," Land, MDPI, vol. 10(1), pages 1-19, December.
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    4. Senkai Xie & Wenjia Zhang & Yi Zhao & De Tong, 2022. "Extracting Land Use Change Patterns of Rural Town Settlements with Sequence Alignment Method," Land, MDPI, vol. 11(2), pages 1-17, February.
    5. Juan C Duque & Nancy Lozano-Gracia & Jorge E Patino & Paula Restrepo, 2022. "Urban form and productivity: What shapes are Latin-American cities?," Environment and Planning B, , vol. 49(1), pages 131-150, January.
    6. Hu, Ting & Wang, Ting & Yan, Qingyun & Chen, Tiexi & Jin, Shuanggen & Hu, Jun, 2022. "Modeling the spatiotemporal dynamics of global electric power consumption (1992–2019) by utilizing consistent nighttime light data from DMSP-OLS and NPP-VIIRS," Applied Energy, Elsevier, vol. 322(C).
    7. Juan C. Duque & Nancy Lozano‐Gracia & Jorge E. Patino & Paula Restrepo Cadavid, 2021. "Institutional fragmentation and metropolitan coordination in Latin American cities: Are there links with city productivity?," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(4), pages 1096-1128, August.
    8. Shi, Kaifang & Chen, Yun & Yu, Bailang & Xu, Tingbao & Yang, Chengshu & Li, Linyi & Huang, Chang & Chen, Zuoqi & Liu, Rui & Wu, Jianping, 2016. "Detecting spatiotemporal dynamics of global electric power consumption using DMSP-OLS nighttime stable light data," Applied Energy, Elsevier, vol. 184(C), pages 450-463.
    9. Thomas Ferreira, 2018. "Using satellite data to track socio-economic outcomes: a case study of Namibia," Working Papers 12/2018, Stellenbosch University, Department of Economics.
    10. Bin Guo & Wencai Zhang & Lin Pei & Xiaowei Zhu & Pingping Luo & Weili Duan, 2022. "Remote Sensing Evidence for Significant Variations in the Global Gross Domestic Product during the COVID-19 Epidemic," Sustainability, MDPI, vol. 14(22), pages 1-21, November.
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