Remote sensing of night lights: A review and an outlook for the future

Noam Levin, Christopher C.M. Kyba, Qingling Zhang, Alejandro Sánchez de Miguel, Miguel O. Román, Xi Li, Boris A. Portnov, Andrew L. Molthan, Andreas Jechow, Steven D. Miller, Zhuosen Wang, Ranjay M. Shrestha, Christopher D. Elvidge

Research output: Contribution to journalArticlepeer-review

Abstract

Remote sensing of night light emissions in the visible band offers a unique opportunity to directly observe human activity from space. This has allowed a host of applications including mapping urban areas, estimating population and GDP, monitoring disasters and conflicts. More recently, remotely sensed night lights data have found use in understanding the environmental impacts of light emissions (light pollution), including their impacts on human health. In this review, we outline the historical development of night-time optical sensors up to the current state of the art sensors, highlight various applications of night light data, discuss the special challenges associated with remote sensing of night lights with a focus on the limitations of current sensors, and provide an outlook for the future of remote sensing of night lights. While the paper mainly focuses on space borne remote sensing, ground based sensing of night-time brightness for studies on astronomical and ecological light pollution, as well as for calibration and validation of space borne data, are also discussed. Although the development of night light sensors lags behind day-time sensors, we demonstrate that the field is in a stage of rapid development. The worldwide transition to LED lights poses a particular challenge for remote sensing of night lights, and strongly highlights the need for a new generation of space borne night lights instruments. This work shows that future sensors are needed to monitor temporal changes during the night (for example from a geostationary platform or constellation of satellites), and to better understand the angular patterns of light emission (roughly analogous to the BRDF in daylight sensing). Perhaps most importantly, we make the case that higher spatial resolution and multispectral sensors covering the range from blue to NIR are needed to more effectively identify lighting technologies, map urban functions, and monitor energy use.

Original languageEnglish
Article number111443
JournalRemote Sensing of Environment
Volume237
DOIs
StatePublished - Feb 2020

Bibliographical note

Funding Information:
CCMK acknowledges funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 689443 via project GEOEssential, and funding from the Helmholtz Association Initiative and Networking Fund under grant ERC-RA-0031 . Some aspects of this manuscript were based upon work from COST Action ES1204 LoNNe (Loss of the Night Network), supported by COST ( European Cooperation in Science and Technology ). ASdM acknowledges funding from the EMISS@N project ( NERC grant NE/P01156X/1 ) and the Cities at Night project. QLZ acknowledges funding from the One Hundred Talents Program of the Chinese Academy of Science ([2015], No. 70). AJ is supported by the Leibniz Association , Germany within the ILES ( SAW-2015-IGB-1 ) and CONNECT ( SAW-K45/2017 ) projects and by the IGB Leibniz Institute through the Frontiers in Freshwater Science project (IGB Frontiers 2017). Black Marble product suite data created at NASA's Goddard Space Flight Center with support from the NASA's Earth Observing System Data and Information System , Terrestrial Ecology , and Group on Earth Observations programs under grants NNH16ZDA001N-GEO-0055 and NNH17ZDA001N-TASNPP-0007 . We thank the anonymous reviewers for their constructive comments on previous versions of this manuscript. Finally, we would like to pay our respects to Abraham Haim, who passed away this January. Abraham was a leader in the field of ecological and health impacts of artificial light, and was involved in a number of studies relating remotely sensed night lights data to human health impacts. Appendix A

Funding Information:
One of the first famous observations of cities? lights from space is attributed to US astronaut John Glenn, who in his orbit of the Earth in February 20th, 1962, saw Perth as the ?City of Lights?, thanks to local citizens and businesses who have turned on as many lights as they could as a sign of support for his mission (Biggs et al., 2012). In many ways, the subsequent development of remote sensing of night lights, can be compared to the general development of Earth observation using daytime images for environmental monitoring. However, as will be described below, remote sensing of night lights suffers from a lack of sensors, and consequently there is a temporal lag in the development of algorithms and customer-ready products.An example of the first point above is disaggregating National GDP data to spatial grids. This was first carried out to produce 5?km resolution GDP map for 11 European Union countries and the United States (Doll et al., 2006), and it was further used, supported by ancillary data including a population density map (Landscan), to produce a global GDP map at 1?km resolution, showing that Singapore had the highest GDP density (Ghosh et al., 2010). Similarly, night-time lights can be used as a proxy of GDP for estimating wealth, allowing regional economic phenomenon such as inequality (Elvidge et al., 2012; Xu et al., 2015) and poverty to be mapped (Elvidge et al., 2009b; Wang et al., 2012; Yu et al., 2015; Jean et al., 2016). Henderson et al. (2016) showed that physical geography (such as climate, biomes, topography, etc.) has a strong influence on the spatial distribution of economic activity, however, that there are differences between developed and developing countries in the relative importance of agriculture and trade variables, to explain spatial variability in night-time lights.CCMK acknowledges funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 689443 via project GEOEssential, and funding from the Helmholtz Association Initiative and Networking Fund under grant ERC-RA-0031. Some aspects of this manuscript were based upon work from COST Action ES1204 LoNNe (Loss of the Night Network), supported by COST (European Cooperation in Science and Technology). ASdM acknowledges funding from the EMISS@N project (NERC grant NE/P01156X/1) and the Cities at Night project. QLZ acknowledges funding from the One Hundred Talents Program of the Chinese Academy of Science ([2015], No. 70). AJ is supported by the Leibniz Association, Germany within the ILES (SAW-2015-IGB-1) and CONNECT (SAW-K45/2017) projects and by the IGB Leibniz Institute through the Frontiers in Freshwater Science project (IGB Frontiers 2017). Black Marble product suite data created at NASA's Goddard Space Flight Center with support from the NASA's Earth Observing System Data and Information System, Terrestrial Ecology, and Group on Earth Observations programs under grants NNH16ZDA001N-GEO-0055 and NNH17ZDA001N-TASNPP-0007. We thank the anonymous reviewers for their constructive comments on previous versions of this manuscript. Finally, we would like to pay our respects to Abraham Haim, who passed away this January. Abraham was a leader in the field of ecological and health impacts of artificial light, and was involved in a number of studies relating remotely sensed night lights data to human health impacts.

Publisher Copyright:
© 2019 Elsevier Inc.

Keywords

  • DMSP/OLS
  • Human activity
  • ISS
  • Light pollution
  • Night lights
  • Urban
  • VIIRS/DNB

ASJC Scopus subject areas

  • Soil Science
  • Geology
  • Computers in Earth Sciences

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