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

Yue, Wenze; Gao, Jiabin; Yang, Xuchao

doi:10.3390/rs6087260

Cited by 54 publications

(45 citation statements)

References 37 publications

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“…However, there is no consistent trend being found in all of these cities regardless of the improvement on R 2 that thus reveals the inappropriate use of a non-linear model in this specific case study. Such an argument is somehow supported in the existing literature from [11] to [14], where all of these studies examined the use of the linear regression approach to analyse the data, which either focused on the national scale or on a specific city, and the majority of the results represent moderate to strong linear regression between the remote sensing-derived information with respect to the socio-economic data. Although the parameters being analysed may not be identical, the use of linear regression somehow has its grounds in accordance with these existing literatures.…”

Section: Discussionsupporting

confidence: 54%

“…Yue et al [14] proposed another approach in Zhejiang Province located in the southeast China for real GDP estimation. The main objectives of the study is: (1) to propose a low-cost and accurate approach for real GDP estimation by using a diversity source of remote sensing data; and (2) to provide an important database for the government for future developmental strategies.…”

Section: Introductionmentioning

confidence: 99%

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Modeling the Relationship between the Gross Domestic Product and Built-Up Area Using Remote Sensing and GIS Data: A Case Study of Seven Major Cities in Canada

Faisal

Shaker

Habbani³

2016

IJGI

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City/regional authorities are responsible for designing and structuring the urban morphology based on the desired land use activities. One of the key concerns regarding urban planning is to establish certain development goals, such as the real gross domestic product (GDP). In Canada, the gross national income (GNI) mainly relies on the mining and manufacturing industries. In order to estimate the impact of city development, this study aims to utilize remote sensing and Geographic Information System (GIS) techniques to assess the relationship between the built-up area and the reported real GDP of seven major cities in Canada. The objectives of the study are: (1) to investigate the use of regression analysis between the built-up area derived from Landsat images and the industrial area extracted from Geographic Information System (GIS) data; and (2) to study the relationship between the built-up area and the socio-economic data (i.e., real GDP, total population and total employment). The experimental data include 42 multi-temporal Landsat TM images and 42 land use GIS vector datasets obtained from year 2005 to 2010 during the summer season (June, July and August) for seven major cities in Canada. The socio-economic data, including the real GDP, the total population and the total employment, are obtained from the Metropolitan Housing Outlook during the same period. Both the Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI) were used to determine the built-up areas. Those high built-up values within the industrial areas were acquired for further analysis. Finally, regression analysis was conducted between the real GDP, the total population, and the total employment with respect to the built-up area. Preliminary findings showed a strong linear relationship (R 2 = 0.82) between the percentage of built-up area and industrial area within the corresponding city. In addition, a strong linear relationship (R 2 = 0.8) was found between the built-up area and socio-economic data. Therefore, the study justifies the use of remote sensing and GIS data to model the socio-economic data (i.e., real GDP, total population and total employment). The research findings can contribute to the federal/municipal authorities and act as a generic indicator for targeting a specific real GDP with respect to industrial areas.

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Section: Discussionsupporting

confidence: 54%

Section: Introductionmentioning

confidence: 99%

Modeling the Relationship between the Gross Domestic Product and Built-Up Area Using Remote Sensing and GIS Data: A Case Study of Seven Major Cities in Canada

Faisal

Shaker

Habbani³

2016

IJGI

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“…Also, the land index derived from different data sources is incomparable. For example, according to the National Bureau of Statistics of China [16], the built-up area in China increased from 20 [17]. In addition, revealing the whole picture of the regional urbanization level is difficult if we just use a single index because urbanization is a complicated process involving economic, demographic, and societal changes [9].…”

Section: Introductionmentioning

confidence: 99%

“…An Operational Line-scan System (OLS) on the Defense Meteorological Satellite Program (DMSP) provided a valuable data source for elucidating the dynamics of China's urbanization [18][19][20][21]. This sensor can detect city lights, gas flares, and fires at night with a low-light detecting capability [22].…”

Section: Introductionmentioning

confidence: 99%

Dynamics of Urbanization Levels in China from 1992 to 2012: Perspective from DMSP/OLS Nighttime Light Data

et al. 2015

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Abstract:The authenticity and reliability of urbanization levels measured by different indicators in China have not reached a consensus, which may impede our understanding of the process of urbanization and its impacts on the environment. The objective of this study was to describe a reliable method of estimating urbanization level based on the Operational Line-scan System (OLS) on the Defense Meteorological Satellite Program (DMSP) nighttime light data and to analyze the dynamics of urbanization levels in China from 1992 to 2012. We calculated the comprehensive urbanization level at the national, provincial, and county scales using a compounded night light index (CNLI) and compared the change rate of CNLI with those of the other two conventional urbanization level indicators, proportion of the nonagricultural population and proportion of built-up area. Our results showed that CNLI derived from the DMSP/OLS data set provided a relatively reliable and accurate measure of the comprehensive urbanization level in China. During the last two decades, China has experienced continued and rapid urbanization with large regional variations. The CNLI increased 3.12 times, from 1.72 × 10 . The annual increases of CNLI in eastern provinces were much faster than those in western provinces. In addition, we found that the rates of change in these three indicators were consistent for OPEN ACCESS Remote Sens. 2015, 7 1722 most provinces with the exception of the four municipalities (Beijing, Tianjin, Shanghai, and Chongqing) and a few eastern coastal provinces (Jiangsu, Zhejiang, Fujian, and Guangdong). Because the imbalance among population growth, urban expansion and socioeconomic development may affect cities' sustainable development, we should pay more attention to these regions with large disparities between different indicators.

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“…Obtaining accurate and up-to-date information on the spatial distribution of GDP is important to better understand a country's social and economic condition [2]. Modern satellite remote sensing technology can provide a foundation for large-scale and high-frequency studies.…”

Section: Introductionmentioning

confidence: 99%

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

Dai

Zhao

2017

Sustainability

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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.

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

Cited by 54 publications

References 37 publications

Modeling the Relationship between the Gross Domestic Product and Built-Up Area Using Remote Sensing and GIS Data: A Case Study of Seven Major Cities in Canada

Modeling the Relationship between the Gross Domestic Product and Built-Up Area Using Remote Sensing and GIS Data: A Case Study of Seven Major Cities in Canada

Dynamics of Urbanization Levels in China from 1992 to 2012: Perspective from DMSP/OLS Nighttime Light Data

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

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