Detection of Rural Electrification in Africa using DMSP-OLS Night Lights Imagery

We report on the first systematic ground-based validation of the US Air Force Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) night lights imagery to detect rural electrification in the developing world. Drawing upon a unique survey of villages in Senegal and Mali,...

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Main Authors:	Min, Brian, Gaba, Kwawu Mensan, Sarr, Ousmane Fall, Agalassou, Alassane
Format:	Journal Article
Language:	en_US
Published:	Taylor and Francis 2013
Subjects:	nighttime lights night lights imagery electrification DMSP validation rural electrification
Online Access:	http://hdl.handle.net/10986/16192

id	okr-10986-16192
recordtype	oai_dc
spelling	okr-10986-161922021-04-23T14:03:27Z Detection of Rural Electrification in Africa using DMSP-OLS Night Lights Imagery Min, Brian Gaba, Kwawu Mensan Sarr, Ousmane Fall Agalassou, Alassane nighttime lights night lights imagery electrification DMSP validation rural electrification We report on the first systematic ground-based validation of the US Air Force Defense Meteorological Satellite Program’s Operational Linescan System (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 night-time light output from the 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 of light output with household electricity use and access is low. We further demonstrate that a detection algorithm using data on night-time 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. 2013-10-17T22:19:54Z 2013-10-17T22:19:54Z 2013-09-23 Journal Article International Journal of Remote Sensing 0143-1161 http://hdl.handle.net/10986/16192 en_US CC BY-NC-ND 3.0 IGO http://creativecommons.org/licenses/by-nc-nd/3.0/igo/ World Bank Taylor and Francis Publications & Research :: Journal Article Publications & Research Africa Mali Senegal
repository_type	Digital Repository
institution_category	Foreign Institution
institution	Digital Repositories
building	World Bank Open Knowledge Repository
collection	World Bank
language	en_US
topic	nighttime lights night lights imagery electrification DMSP validation rural electrification
spellingShingle	nighttime lights night lights imagery electrification DMSP validation rural electrification Min, Brian Gaba, Kwawu Mensan Sarr, Ousmane Fall Agalassou, Alassane Detection of Rural Electrification in Africa using DMSP-OLS Night Lights Imagery
geographic_facet	Africa Mali Senegal
description	We report on the first systematic ground-based validation of the US Air Force Defense Meteorological Satellite Program’s Operational Linescan System (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 night-time light output from the 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 of light output with household electricity use and access is low. We further demonstrate that a detection algorithm using data on night-time 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.
format	Journal Article
author	Min, Brian Gaba, Kwawu Mensan Sarr, Ousmane Fall Agalassou, Alassane
author_facet	Min, Brian Gaba, Kwawu Mensan Sarr, Ousmane Fall Agalassou, Alassane
author_sort	Min, Brian
title	Detection of Rural Electrification in Africa using DMSP-OLS Night Lights Imagery
title_short	Detection of Rural Electrification in Africa using DMSP-OLS Night Lights Imagery
title_full	Detection of Rural Electrification in Africa using DMSP-OLS Night Lights Imagery
title_fullStr	Detection of Rural Electrification in Africa using DMSP-OLS Night Lights Imagery
title_full_unstemmed	Detection of Rural Electrification in Africa using DMSP-OLS Night Lights Imagery
title_sort	detection of rural electrification in africa using dmsp-ols night lights imagery
publisher	Taylor and Francis
publishDate	2013
url	http://hdl.handle.net/10986/16192
_version_	1764432451838410752

Detection of Rural Electrification in Africa using DMSP-OLS Night Lights Imagery

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