Transferability of Economy Estimation Based on DMSP/OLS Night-Time Light

Despite the fact that economic data are of great significance in the assessment of human socioeconomic development, the application of this data has been hindered partly due to the unreliable and inefficient economic censuses conducted in developing countries. The night-time light (NTL) imagery from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) provides one of the most important ways to evaluate an economy with low cost and high efficiency. However, little research has addressed the transferability of the estimation across years. Based on the entire DN series from 0 to 63 of NTL data and GDP data in 31 provinces of mainland China from 2000 to 2012, this paper aims to study the transferability of economy estimation across years, with four linear and non-linear data mining methods, including the Multiple Linear Regression (MLR), Local Weighted Regression (LWR), Partial Least Squares Regression (PLSR), and Support Vector Machine Regression (SVMR). We firstly built up the GDP estimation model based on the NTL data in each year with each method respectively, then applied each model to the other 12 years for the evaluation of the time series transferability. Results revealed that the performances of models differ greatly across years and methods: PLSR (mean of ) and SVMR (mean of ) are superior to MLR (mean of ) and LWR (mean of ) for model calibration; only PLSR (mean of , mean of ) holds a strong transferability among different years; the frequency of three DN sections of (0–1), (4–16), and (57–63) are especially important for economy estimation. Such results are expected to provide a more comprehensive understanding of the NTL, which can be used for economy estimation across years.

[1]  W. Cleveland,et al.  Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting , 1988 .

[2]  C. Elvidge,et al.  Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption , 1997 .

[3]  Hava T. Siegelmann,et al.  Support Vector Clustering , 2002, J. Mach. Learn. Res..

[4]  C. Elvidge,et al.  Night-time lights of the world: 1994–1995 , 2001 .

[5]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[6]  P. Sutton,et al.  SPECIAL ISSUE: The Dynamics and Value of Ecosystem Services: Integrating Economic and Ecological Perspectives Global estimates of market and non-market values derived from nighttime satellite imagery, land cover, and ecosystem service valuation , 2002 .

[7]  C. Murray,et al.  From wealth to health: modelling the distribution of income per capita at the sub-national level using night-time light imagery , 2005, International journal of health geographics.

[8]  C. Jun,et al.  Performance of some variable selection methods when multicollinearity is present , 2005 .

[9]  J. Muller,et al.  Mapping regional economic activity from night-time light satellite imagery , 2006 .

[10]  P. Shi,et al.  Restoring urbanization process in China in the 1990s by using non-radiance-calibrated DMSP/OLS nighttime light imagery and statistical data , 2006 .

[11]  R. V. Rossel,et al.  Determining the composition of mineral-organic mixes using UV–vis–NIR diffuse reflectance spectroscopy , 2006 .

[12]  P. Sutton,et al.  Estimation of Gross Domestic Product at Sub-National Scales using Nighttime Satellite Imagery , 2007 .

[13]  Christopher D. Elvidge,et al.  Estimation of Mexico's Informal Economy and Remittances Using Nighttime Imagery , 2009, Remote. Sens..

[14]  R. V. Rossel,et al.  Using data mining to model and interpret soil diffuse reflectance spectra. , 2010 .

[15]  J. Henderson,et al.  A Bright Idea for Measuring Economic Growth. , 2011, The American economic review.

[16]  J. Henderson,et al.  Measuring Economic Growth from Outer Space , 2009, The American economic review.

[17]  Pavel Propastin,et al.  Assessing Satellite-Observed Nighttime Lights for Monitoring Socioeconomic Parameters in the Republic of Kazakhstan , 2012 .

[18]  Zhifeng Liu,et al.  Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008 , 2012 .

[19]  Xi Li,et al.  Potential of NPP-VIIRS Nighttime Light Imagery for Modeling the Regional Economy of China , 2013, Remote. Sens..

[20]  Zhifeng Liu,et al.  Urban expansion dynamics and natural habitat loss in China: a multiscale landscape perspective , 2014, Global change biology.

[21]  Jing Li,et al.  Mapping and Evaluating the Urbanization Process in Northeast China Using DMSP/OLS Nighttime Light Data , 2014, Sensors.

[22]  Zhifeng Liu,et al.  Modeling the spatiotemporal dynamics of electric power consumption in Mainland China using saturation-corrected DMSP/OLS nighttime stable light data , 2014, Int. J. Digit. Earth.

[23]  Wei Song,et al.  Object-based spatial cluster analysis of urban landscape pattern using nighttime light satellite images: a case study of China , 2014, Int. J. Geogr. Inf. Sci..

[24]  A. Thomson,et al.  A cluster-based method to map urban area from DMSP/OLS nightlights , 2014 .

[25]  T. Pei,et al.  Responses of Suomi-NPP VIIRS-derived nighttime lights to socioeconomic activity in China’s cities , 2014 .

[26]  A. Thomson,et al.  A global map of urban extent from nightlights , 2015 .

[27]  Yi-Yun Chen,et al.  [Transferability of Hyperspectral Model for Estimating Soil Organic Matter Concerned with Soil Moisture]. , 2015, Guang pu xue yu guang pu fen xi = Guang pu.

[28]  Maria Francisca Archila Bustos,et al.  Nighttime lights and population changes in Europe 1992–2012 , 2015, Ambio.

[29]  Bin Gao,et al.  Dynamics of Urbanization Levels in China from 1992 to 2012: Perspective from DMSP/OLS Nighttime Light Data , 2015, Remote. Sens..

[30]  Jianping Wu,et al.  Poverty Evaluation Using NPP-VIIRS Nighttime Light Composite Data at the County Level in China , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[31]  W. Nordhaus,et al.  A sharper image? Estimates of the precision of nighttime lights as a proxy for economic statistics , 2015 .

[32]  Yiyun Chen,et al.  Estimating Soil Organic Carbon of Cropland Soil at Different Levels of Soil Moisture Using VIS-NIR Spectroscopy , 2016, Remote. Sens..

[33]  Frank Bickenbach,et al.  Night lights and regional GDP , 2016 .

[34]  Yiyun Chen,et al.  Construction of the Calibration Set through Multivariate Analysis in Visible and Near-Infrared Prediction Model for Estimating Soil Organic Matter , 2017, Remote. Sens..

[35]  Xiaoping Liu,et al.  The Impact of Energy Consumption on the Surface Urban Heat Island in China's 32 Major Cities , 2017, Remote. Sens..

[36]  Chen Wang,et al.  Assessing Light Pollution in China Based on Nighttime Light Imagery , 2017, Remote. Sens..

[37]  M. Bennett,et al.  Advances in using multitemporal night-time lights satellite imagery to detect, estimate, and monitor socioeconomic dynamics , 2017 .

[38]  Xiaoping Liu,et al.  Analyzing Parcel-Level Relationships between Urban Land Expansion and Activity Changes by Integrating Landsat and Nighttime Light Data , 2017, Remote. Sens..

[39]  Qinghu Jiang,et al.  Estimation of soil organic carbon and total nitrogen in different soil layers using VNIR spectroscopy: Effects of spiking on model applicability , 2017 .