Night-time Imagery as a Tool for Global Mapping of Socioeconomic Parameters and Greenhouse Gas Emissions

By Christopher Doll, J. Muller, Christopher D. Elvidge. 2000

523 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/cb74dca5384b169044f4f121ea5ec3cb7c8fc373

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In Global and Planetary Change

Citations in the corpus

Applications of Satellite Remote Sensing of Nighttime Light Observations: Advances, Challenges, and Perspectives (2019, background): “[111] firstly found a strong relationship between DMSP-OLS data and EPC and total CO2 emissions, respectively.”
A global poverty map derived from satellite data (2009, methodology): “Again, the strong relationship between economic activity and CO2 emissions with the total lit area were used to create global maps of these parameters by Doll et al. (2000).”
The Open-source Data Inventory for Anthropogenic Carbon dioxide (CO2), version 2016 (ODIAC2016): A global, monthly fossil-fuel CO2 gridded emission data product for tracer transport simulations and surface flux inversions. (2017, unknown): “…as an excellent spatial indicator for human settlements and intensities of some specific human activities (e.g., Elvidge et al., 1999, 2009) and have been used to infer the associated CO2 emissions or their spatial distributions (e.g., Doll et al., 2000; Ghosh et al., 2010; Rayner et al., 2010).”
A review of regional science applications of satellite remote sensing in urban settings (2013, unknown): “…scale from the Defense Meteorological Satellite Program (DMSP) has been used to estimate economic activity and poverty and to map urbanization dynamics at a global scale (Doll, 2008; Doll, Muller, & Elvidge, 2000; Elvidge et al., 2001; Elvidge, Sutton, Ghosh, & Tuttle, 2009; Zhang & Seto, 2011).”
The Nightsat mission concept (2007, unknown): “In some applications, night-time lights are used as a proxy for variables that are difficult to measure in a globally consistent manner, including many socio-economic variables, such as population density and gross domestic product (e.g. Doll et al. 2000, Balk et al. 2005).”
Downscaling and geo-spatial gridding of socio-economic projections from the IPCC Special Report on Emissions Scenarios (SRES) (2004, background): “The intensity of nocturnal lighting has been shown to correlate well with a number of anthropogenic indicators such as population density, energy consumption, electricity consumption, CO2 emissions, and GDP (Doll et al., 2000).”
Using Nighttime Satellite Imagery as a Proxy Measure of Human Well-Being (2013, methodology): “The close relation between the spatial distribution of nighttime lights and economic activity has been observed and utilized in several studies before [2–7].”
Estimation of Gross Domestic Product at Sub-National Scales using Nighttime Satellite Imagery (2007, background): “…on Net Primary Productivity and soil resources (Imhoff et al., 1997; Small, 2001; Milesi et al., 2003) , 2) Measuring Anthropogenic CO2 emissions (Doll et al., 2000), 3) Measuring Anthropogenic Impervious Surface Area (Elvidge et al., 2004), 4) Estimating Urban Populations (Sutton et al., 2001),…”
A global map of urban extent from nightlights (2015, background): “…potential for regional and even global urban maps and their application in studies of human activities, such as population density, economic activity, energy use, and CO2 emissions (Imhoff et al 1997, Doll et al 2000, Elvidge et al 2007a, Zhang and Seto 2011, He et al 2014, Naizhuo et al 2015).”
Forecasting China’s GDP at the pixel level using nighttime lights time series and population images (2017, methodology): “The Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime light imagery (originally designed to collect information about moonlit clouds) has been successfully used to estimate socioeconomic factors (e.g., GDP, electric power consumption, and fossil fuel carbon ”
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, background): “Studies of this imagery have shown it to correspond with the extent of urban land cover, population density, energy consumption, greenhouse gas emissions and other socio-economic parameters (Elvidge et al., 1997; Imhoff et al., 1997; Sutton et al., 1997; Doll et al., 2000).”