Detection of rural electrification in Africa using DMSP-OLS night lights imagery

Abstract

We report on the first systematic ground-based validation of DMSP-OLS night lights imagery to detect rural electrification in the developing world. Drawing upon a unique survey of villages in Senegal and Mali, this study compares nighttime light output from the U.S. Air Force Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) against ground-based survey data on electricity use in 232 electrified villages and additional administrative data on 899 unelectrified villages. The analysis reveals that electrified villages are consistently brighter than unelectrified villages across annual composites, monthly composites, and a time series of nightly imagery. Electrified villages appear brighter because of the presence of streetlights and brighter villages tend to have more streetlights. By contrast, the correlation with household electricity use and access is low. Villages with more streetlights are brighter than those with fewer streetlights. We further demonstrate that a detection algorithm using data on nighttime light output and the geographic location of settlements can accurately classify electrified villages. This research highlights the potential to use night lights imagery for the planning and monitoring of ongoing efforts to connect the 1.4 billion people who lack electricity around the world.

Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data

“…Due to the aforementioned reasons, such saturated pixels are all given the value of 63. This effect introduced inaccuracies to GDP and EPC modeling in some areas, especially in the centers of large cities with strong artificial lighting [10,14,23,30].…”

“…Propastin and Kappas [22] revealed that the DMSP-OLS data became an effective tool for the monitoring of both spatial and temporal variability of the examined socioeconomics. Min et al [23] detected rural electrification in Africa using DMSP-OLS data. Furthermore, Zhao et al [24] produced a GDP change map of China based on a regression between DMSP-OLS data and the population.…”

VIIRS night-time lights

“…Global satellite-observed night-time lights have emerged as one of the widely used geospatial data products (Amaral et al 2005;Small, Pozzi, and Elvidge 2005;Sutton et al 2007;Bharti et al 2009;Chand et al 2009;Ghosh et al 2010;Oda and Maksyutov 2011;Witmer and O'loughlin 2011;He et al 2012;Mazor et al 2013;Min et al 2013;Falchi et al 2016). These products show the locations where artificial lighting is present and a measure of the brightness as observed from space.…”

“…However, close inspection of moonless night-time DNB data reveals the dim outline of high albedo clouds and land surface features. This remarkable capability has been linked to nocturnal airglow (Min et al 2013). In addition, the DNB records several other types of phenomena unrelated to electric lighting, including stray light, lightning, biomass burning, gas flares, high energy particle (HEP) detections, atmospheric glow surrounding bright sources, and background noise.…”

Tracking Electrification in Vietnam Using Nighttime Lights

“…Studies show that nighttime light output strongly correlates with electricity generating capacity and economic activity at the regional and national levels [1][2][3][4][5][6][7][8]. Yet there remains little knowledge of how well nighttime satellite imagery detects the use of electricity in rural settings across the developing world, particularly at the level of individual villages and small towns [9].…”