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

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

1
, 1
 and 2,*
Received: 30 June 2014; in revised form: 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]   |   Browse Figures
Abstract: 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.
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 which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


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.

AMA 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 Sensing. 2014; 6(8):7260-7275.

Chicago/Turabian Style

Yue, Wenze; Gao, Jiabin; Yang, Xuchao. 2014. "Estimation of Gross Domestic Product Using Multi-Sensor Remote Sensing Data: A Case Study in Zhejiang Province, East China." Remote Sens. 6, no. 8: 7260-7275.


Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert