Next Article in Journal
Hierarchical Segmentation Framework for Identifying Natural Vegetation: A Case Study of the Tehachapi Mountains, California
Next Article in Special Issue
Industrial Wastewater Discharge Retrieval Based on Stable Nighttime Light Imagery in China from 1992 to 2010
Previous Article in Journal
Remote Geophysical Observatory in Antarctica with HF Data Transmission: A Review
Previous Article in Special Issue
Application of DMSP/OLS Nighttime Light Images: A Meta-Analysis and a Systematic Literature Review
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2014, 6(8), 7260-7275; doi:10.3390/rs6087260

Estimation of Gross Domestic Product Using Multi-Sensor Remote Sensing Data: A Case Study in Zhejiang Province, East China

Institute of Land Science and Property Management, Zhejiang University, Hangzhou 310058, China
Ocean College, Zhejiang University, Hangzhou 310058, China
Author to whom correspondence should be addressed.
Received: 30 June 2014 / Revised: 28 July 2014 / Accepted: 29 July 2014 / Published: 4 August 2014
(This article belongs to the Special Issue Remote Sensing with Nighttime Lights)
View Full-Text   |   Download PDF [7323 KB, uploaded 4 August 2014]   |  


There exists a spatial mismatch between socioeconomic data, such as Gross Domestic Product (GDP), and physical and environmental datasets. This study provides a dasymetric approach for GDP estimation at a fine scale by combining the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) nighttime imagery, enhanced vegetation index (EVI), and land cover data. Despite the advantages of DMSP/OLS nighttime imagery in estimating human activities, its drawbacks, including coarse resolution, overglow, and saturation effects, limit its application. Hence, high-resolution EVI data were integrated with DMSP/OLS in this study to create a Human Settlement Index (HSI) for estimating the GDP of secondary and tertiary industries. The GDP of the primary industry was then estimated on the basis of land cover data, and the area with the GDP of the primary industry was classified by a threshold technique (DN ≤ 8). The regression model for GDP distribution estimation was implemented in Zhejiang Province in southeast China, and a GDP density map was generated at a resolution of 250 m × 250 m. Compared with the outcome of taking DMSP/OLS as a unique parameter, estimation errors obviously decreased. This study offers a low-cost and accurate approach for rapidly estimating high-resolution GDP distribution to construct an important database for the government when formulating developmental strategies. View Full-Text
Keywords: dasymetric approach; gross domestic product; DMSP/OLS; EVI dasymetric approach; gross domestic product; DMSP/OLS; EVI

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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

Yue, W.; Gao, J.; Yang, X. Estimation of Gross Domestic Product Using Multi-Sensor Remote Sensing Data: A Case Study in Zhejiang Province, East China. Remote Sens. 2014, 6, 7260-7275.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top