Next Article in Journal
Long-Term Hydrocarbon Trade Options for the Maghreb Region and Europe—Renewable Energy Based Synthetic Fuels for a Net Zero Emissions World
Next Article in Special Issue
Compromise between Short- and Long-Term Financial Sustainability: A Hybrid Model for Supporting R&D Decisions
Previous Article in Journal
A Feasibility Assessment of Photovoltaic Power Systems in Ireland; a Case Study for the Dublin Region
Previous Article in Special Issue
Income Driven Patterns of the Urban Environment
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Sustainability 2017, 9(2), 305; doi:10.3390/su9020305

The Suitability of Different Nighttime Light Data for GDP Estimation at Different Spatial Scales and Regional Levels

1,2,3
,
1,2,* and 1,2,3
1
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
State Key Laboratory of Resources and Environmental Information System, Beijing 100101, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Academic Editors: Xiang Li, Jian Zhou, Hua Ke and Xiangfeng Yang
Received: 9 December 2016 / Revised: 10 February 2017 / Accepted: 14 February 2017 / Published: 19 February 2017
View Full-Text   |   Download PDF [4606 KB, uploaded 19 February 2017]   |  

Abstract

Nighttime light data offer a unique view of the Earth’s surface and can be used to estimate the spatial distribution of gross domestic product (GDP). Historically, using a simple regression function, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) has been used to correlate regional and global GDP values. In early 2013, the first global Suomi National Polar-orbiting Partnership (NPP) visible infrared imaging radiometer suite (VIIRS) nighttime light data were released. Compared with DMSP/OLS, they have a higher spatial resolution and a wider radiometric detection range. This paper aims to study the suitability of the two nighttime light data sources for estimating the GDP relationship between the provincial and city levels in Mainland China, as well as of different regression functions. First, NPP/VIIRS nighttime light data for 2014 are corrected with DMSP/OLS data for 2013 to reduce the background noise in the original data. Subsequently, three regression functions are used to estimate the relationship between nighttime light data and GDP statistical data at the provincial and city levels in Mainland China. Then, through the comparison of the relative residual error (RE) and the relative root mean square error (RRMSE) parameters, a systematical assessment of the suitability of the GDP estimation is provided. The results show that the NPP/VIIRS nighttime light data are better than the DMSP/OLS data for GDP estimation, whether at the provincial or city level, and that the power function and polynomial models are better for GDP estimation than the linear regression model. This study reveals that the accuracy of GDP estimation based on nighttime light data is affected by the resolution of the data and the spatial scale of the study area, as well as by the land cover types and industrial structures of the study area. View Full-Text
Keywords: NPP/VIIRS; DMSP/OLS; GDP; spatial scale suitability; regression model suitability; regional suitability NPP/VIIRS; DMSP/OLS; GDP; spatial scale suitability; regression model suitability; regional suitability
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Dai, Z.; Hu, Y.; Zhao, G. The Suitability of Different Nighttime Light Data for GDP Estimation at Different Spatial Scales and Regional Levels. Sustainability 2017, 9, 305.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sustainability EISSN 2071-1050 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top