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Remote Sens. 2017, 9(6), 626; doi:10.3390/rs9060626

Modeling the Spatiotemporal Dynamics of Gross Domestic Product in China Using Extended Temporal Coverage Nighttime Light Data

1
,
1,* , 1,2,3
and
1
1
Chongqing Key Laboratory of Karst Environment, School of Geographical Sciences, Southwest University, Beibei, Chongqing 400715, China
2
Norwegian Institute of Bioeconomy Research (NIBIO), 1431 Ås, Norway
3
CEES, Department of Biosciences, University of Oslo, Blindern, 0316 Oslo, Norway
*
Author to whom correspondence should be addressed.
Academic Editors: Bailang Yu, Yuyu Zhou, Chunyang He, Xiaofeng Li and Prasad S. Thenkabail
Received: 12 May 2017 / Revised: 12 June 2017 / Accepted: 15 June 2017 / Published: 18 June 2017
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
View Full-Text   |   Download PDF [4578 KB, uploaded 18 June 2017]   |  

Abstract

Nighttime light data derived from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) in conjunction with the Soumi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) possess great potential for measuring the dynamics of Gross Domestic Product (GDP) at large scales. The temporal coverage of the DMSP-OLS data spans between 1992 and 2013, while the NPP-VIIRS data are available from 2012. Integrating the two datasets to produce a time series of continuous and consistently monitored data since the 1990s is of great significance for the understanding of the dynamics of long-term economic development. In addition, since economic developmental patterns vary with physical environment and geographical location, the quantitative relationship between nighttime lights and GDP should be designed for individual regions. Through a case study in China, this study made an attempt to integrate the DMSP-OLS and NPP-VIIRS datasets, as well as to identify an optimal model for long-term spatiotemporal GDP dynamics in different regions of China. Based on constructed regression relationships between total nighttime lights (TNL) data from the DMSP-OLS and NPP-VIIRS data in provincial units (R2 = 0.9648, P < 0.001), the temporal coverage of nighttime light data was extended from 1992 to the present day. Furthermore, three models (the linear model, quadratic polynomial model and power function model) were applied to model the spatiotemporal dynamics of GDP in China from 1992 to 2015 at both the country level and provincial level using the extended temporal coverage data. Our results show that the linear model is optimal at the country level with a mean absolute relative error (MARE) of 11.96%. The power function model is optimal in 22 of the 31 provinces and the quadratic polynomial model is optimal in 7 provinces, whereas the linear model is optimal only in two provinces. Thus, our approach demonstrates the potential to accurately and timely model long-term spatiotemporal GDP dynamics using an integration of DMSP-OLS and NPP-VIIRS data. View Full-Text
Keywords: nighttime lights; DMSP-OLS; NPP-VIIRS; Gross Domestic Product (GDP); spatiotemporal dynamics; optimal regression model nighttime lights; DMSP-OLS; NPP-VIIRS; Gross Domestic Product (GDP); spatiotemporal dynamics; optimal regression model
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Zhu, X.; Ma, M.; Yang, H.; Ge, W. Modeling the Spatiotemporal Dynamics of Gross Domestic Product in China Using Extended Temporal Coverage Nighttime Light Data. Remote Sens. 2017, 9, 626.

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