Artificially lit surface of Earth at night increasing in radiance and extent

Abstract

A central aim of the “lighting revolution” (the transition to solid-state lighting technology) is decreased energy consumption. This could be undermined by a rebound effect of increased use in response to lowered cost of light. We use the first-ever calibrated satellite radiometer designed for night lights to show that from 2012 to 2016, Earth’s artificially lit outdoor area grew by 2.2% per year, with a total radiance growth of 1.8% per year. Continuously lit areas brightened at a rate of 2.2% per year. Large differences in national growth rates were observed, with lighting remaining stable or decreasing in only a few countries. These data are not consistent with global scale energy reductions but rather indicate increased light pollution, with corresponding negative consequences for flora, fauna, and human well-being.

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ENVIRONMENTAL SCIENCES Copyright © 2017
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Artificially lit surface of Earth at night increasing in
radiance and extent
Christopher C. M. Kyba,
1,2
* Theres Kuester,
1
Alejandro Sánchez de Miguel,
3,4†
Kimberly Baugh,
5
Andreas Jechow,
1,2
Franz Hölker,
2
Jonathan Bennie,
6
Christopher D. Elvidge,
7
Kevin J. Gaston,
8
Luis Guanter
1
A central aim of the “lighting revolution”(the transition to solid-state lighting technology) is decreased energy
consumption. This could be undermined by a rebound effect of increased use in response to lowered cost of
light. We use the first-ever calibrated satellite radiometer designed for night lights to show that from 2012 to
2016, Earth’s artificially lit outdoor area grew by 2.2% per year, with a total radiance growth of 1.8% per year.
Continuously lit areas brightened at a rate of 2.2% per year. Large differences in national growth rates were
observed, with lighting remaining stable or decreasing in only a few countries. These data are not consistent
with global scale energy reductions but rather indicate increased light pollution, with corresponding negative
consequences for flora, fauna, and human well-being.
INTRODUCTION
Continued improvement in the luminous efficacy of light sources and in-
creases in gross domestic product (GDP) have resulted in tremendous
growth in artificial light use over several centuries (1). Historically, lighting
has been subject to a strong rebound effect, in which increases in luminous
efficacy result in correspondingly greater light use rather than energy sav-
ings (2). Regardless of historical or geographical context, humans tend to
use as much artificial light as they can buy for ~0.7% of GDP (3). Outdoor
lighting became commonplace with the introduction of electric light and
grew at an estimated rate of 3 to 6% per year during the second half of the
20th century (4). As a result, the world has experienced widespread “loss of
the night,”with half of Europe and a quarter of North America ex-
periencing substantially modified light-dark cycles (5).
A critical question for sustainable development is whether the use of
outdoor light will continue to grow exponentially or whether developed
countries are nearing saturation in demand (3). In addition to the pos-
sibility that the existing light levels are already sufficient for any desired
visual task, factors that reduce demand include greater public recogni-
tion of the unintended ecological (6) and astronomical (5,7)impactsof
outdoor light pollution, official warnings that overexposure to artificial
light may be affecting human sleep and health (8), efforts to transition
to a sustainable society with decreased electricity demand (9), the de-
sire of local governments to reduce the costs of lighting (10), and the
establishment of protected “dark sky”areas (11). If demand saturation
has not been reached, then the increasing luminous efficacy made pos-
sible by the solid-state lighting revolution(12) will increase light emis-
sions instead of saving energy.
Changes in outdoor lighting can be measured on the global scale
only via Earth-observing satellites, but no calibrated satellite sensor
has made global observations of night lights until recently. The well-
known older images of Earth at night (13) were based on an uncali-
brated sensor from a defense satellite [Defense Meteorological Satellite
Program (DMSP)], which had frequent and unrecorded changes in sen-
sorgain.Despitethisdrawback,therehavebeenattemptstousestatis-
tical methods to try to intercalibrate the time series. These methods
sometimes rely on questionable assumptions, such as the assumption
that Sicily experienced no changes in lighting over a 15-year period
(14). In addition to the lack of an on-board radiance calibration, DMSP
experienced saturation in cities and had low (8 bit) radiometric resolu-
tion and an intrinsic spatial resolution of 5 km (15). Nevertheless, the
inherent connection between artificial light and human activity means
that DMSP data display strong correlations with many socioeconomic
factors (16).
Although considerable research has been done using DMSP time
series, most analyses have been focused on other remotely sensed factors
[for example, human settlement, socioeconomic activity, and detection
of fishing vessels (17)] and have not reported on trends in lighting itself.
The few lighting studies that have done so were on the national [for
example, 4% annual increase in Spain (18)] or continental scale [for ex-
ample, (19)] or else examined only a specific class of lighting [for exam-
ple, (14)]. The official radiance-calibrated DMSP time series of the
National Oceanic and Atmospheric Administration (NOAA) showed
little change in the sum of lights of several large cities, but the inter-
calibration was based on the assumption that the lights of Los Angeles
did not change over the period of 1996–2010 (20). In contrast, a recent
analysis using a different methodology found an increase in global lights
of a factor of 2 from 1992 to 2013 (~ 3.5% per year) (21). However,
because of the limitations of the DMSP, and particularly the saturation
in city centers, many analyses have been limited to change in lit area
rather than change in radiance.
The Visible Infrared Imaging Radiometer Suite Day-Night Band
(VIIRS DNB) came online just as outdoor use of light-emitting diode
(LED) lighting began in earnest (22). This sensor provides the first-ever
global calibrated nighttime radiance measurements in a spectral band of
500 to 900 nm, which is close to the visible band, with a much higher
radiometric sensitivity than the DMSP, and at a spatial resolution of
near 750 m (15). This improved spatial resolution allows neighborhood
(rather than city or national) scale changes in lighting to be investigated
for the first time (23).
1
GFZ German Research Centre for Geosciences, Potsdam 14473, Germany.
2
Leibniz
Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin 12587, Germany.
3
Instituto de Astrofísica de Andalucía, Glorieta de la Astronomía s/n, Granada C.P.
18008, Spain.
4
Dept. Astrofísica y CC. de la Atmósfera, Universidad Complutense de
Madrid, Madrid 28040, Spain.
5
Cooperative Institute for Research in the
Environmental Sciences, University of Colorado, Boulder, CO 80309, USA.
6
Centre
for Geography, Environment and Society, University of Exeter, Penryn TR10, UK.
7
Na-
tional Oceanic and Atmospheric Administration, Boulder, CO 80305, USA.
8
Environment and Sustainability Institute, University of Exeter, Penryn, Cornwall
TR10 9FE, UK.
*Corresponding author. Email: kyba@gfz-potsdam.de
†Present address: Environment and Sustainability Institute, University of Exeter,
Penryn, Cornwall TR10 9FE, UK.
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RESULTS
The cloud-free DNB data show that over the period of 2012–2016, both
lit area and the radiance of previously lit areas increased in most coun-
tries (Fig. 1) in the 500–900-nm range, with global increases of 2.2% per
year for lit area and 2.2% per year for the brightness of continuously lit
areas (see Materials and Methods). Overall, the radiance of areas lit
above 5 nWcm
−2
sr
−1
increased by 1.8% per year. These factors de-
creased in very few countries, including several experiencing warfare
[for example, Yemen (24) and Syria]. They were also stable in only a
few countries, interestingly including some of the world’s brightest
(for example, Italy, Netherlands, Spain, and the United States). With
few exceptions, growth in lighting occurred throughout South
America, Africa, and Asia. Because the analysis of lit area and total
radiance is not subject to a stability criterion, transient lights such as
wildfires can cause large fluctuations. Australia experienced a major de-
crease in lit area from 2012 to 2016 for this reason (Figs. 1A and 2).
However, fire-lit areas failed the stability test and were therefore not in-
cluded in the radiance change analysis (Fig. 1B). A small number of
countries have “no data”because of either their extreme latitude
(Iceland) or the lack of observed stable lights above 5 nWcm
−2
sr
−1
in the cloud-free composite (for example, Central African Republic).
Brightly lit areas are uncommon: For most countries, over half of
the national light emission above the analysis threshold came from
areas lit below 20 nWcm
−2
sr
−1
(Fig. 3, A and B, and figs. S1 to S3).
For context, small towns in the American West with populations of
several hundreds are typically slightly above the 5 nWcm
−2
sr
−1
thresh-
old, whereas the radiance observed at international airports is typically
~150 nWcm
−2
sr
−1
(23). The area-radiance curve is often approximate-
ly power-law, but the shape and the slope are not consistent across
countries (figs. S4 to S27; see the study of Small et al. (25) for a discussion
Fig. 1. Geographic patterns in changes in artificial lighting. Changes are shown as an annual rate for both lit area (A) and radiance of stably lit areas (B). Annual
rates are calculated based on changes over the four year period, that is, ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
A2016=A2012
4
p, where A
2016
is the lit area observed in 2016. See fig. S28 for total radiance change
instead of stable light radiance change.
Fig. 2. Absolute change in lit area from 2012 to 2016. Pixels increasing in area are shown as red, pixels decreasing in area are shown as blue, and pixels with no
change in area are shown as yellow. Each pixel has a near-equal area of 6000 ± 35 km
2
. To ease interpretation, the color scale cuts off at 200 km
2
, but some pixels had
changes of up to ±2000 km
2
.
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of how these relationships may emerge naturally, and compare the
studies of Small and Elvidge (26) for DMSP and Kuechly et al. (27)
for higher-resolution aerial photos). For example, compared to China,
the United States has twice as much area illuminated with radiances
in the range of 5 to 6.1 nWcm
−2
sr
−1
but nearly 20 times as much area
illuminated in the range of 132 to 162 nWcm
−2
sr
−1
.Theshapedifferenceis
even more striking for Bolivia and Pakistan. In many countries, there is
little or no area lit above 100 nWcm
−2
sr
−1
.
The global 9.1% increase in stable light radiance from 2012 to 2016
(2.2% per year) applies nearly independently of radiance in 2014 (Fig. 3C).
However, in some individual countries, radiance change was not
uniform across the 2014 radiance classes. In the UK, for example, the
rate of lighting change was positively correlated with 2014 radiance
(fig. S26). Nevertheless, even large increases in bright areas have rela-
tively little effect on the country-level radiance change, because these
areas typically account for a small fraction of the national light emission
(Fig. 3B and figs. S1 to S27).
Summed national per capita and total light emissions above the
5nWcm
−2
sr
−1
threshold are correlated with per capita and national
GDP (Spearman rank-order correlation coefficient of 0.76 and 0.85,
respectively, P≪0.001; Fig. 4, A and B). This confirms the results of
earlier studies using DMSP data [for example, the studies of Doll et al.
(28) and Nordhaus and Chen (29)]. Nevertheless, there are large (up to
order of magnitude) differences between countries with similar wealth,
and the relationship between per capita light and GDP appears to be
nonlinear (Fig. 4A). Note that for a small number of northern coun-
tries (for example, Finland), the national sum of lights does not include
lights located above 60° North. The size of changes in lights and
changesinGDPwaslargerinpoorercountriesandsmallerinwealthier
countries. For the median country, the sum of total radiance grew by
15% from 2012 to 2016, which is quite close to the median country’s
GDPincrease(13%)overthesametimeframe.However,theSpearman
rank-order correlation between GDP and light change (Fig. 4D) was
only 0.17 (P= 0.05). The Spearman rank-order correlation between
GDP and lit area change (Fig. 4C) was slightly larger, at 0.24 (P= 0.006).
Many large cities had decreases in DNB radiance in the city center
but increases in outlying areas. These decreases can often be directly
attributed to replacement of older lamps with LEDs. This is vividly dem-
onstrated by photographs of Milan, Italy, taken by astronauts on the
International Space Station in 2012 and 2015 (Fig. 5, A and B). The
ABC
Fig. 3. Patterns in lit area, radiance, and lighting change for the world and five selected countries. (A) The 2014 lit area (in km
2
) for each (logarithmically spaced)
bin of radiance in nWcm
−2
sr
−1
.(B) Normalized cumulative distribution of light in 2016 (that is, what fraction of the total light is emitted below the given radiance).
(C) Mean change in radiance from 2012 to 2016 for each bin.
Fig. 4. Relationships between light and economic parameters. (A) National sum of lights (SOL) per capita compared to per capita GDP, (B) sum of lights versus
national GDP, (C) change in lit area from 2012 to 2016 versus change in GDP (one outlier not shown), and (D) change in sum of lights from 2012 to 2016 versus change
in GDP. Colors and symbols indicate per capita GDP in 2016: <$2000 (red triangles), $2000 to $6000 (green squares), $6000 to $17,000 (blue stars), and >$17,000 (black
circles). Solid lines show an extrapolation based on the value of the median country.
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street lights in the city changed from yellow/orange (sodium vapor) to
white (LED), whereas the surrounding areas remained yellow/orange.
As a result, the radiance observed by the DNB decreased (Fig. 5C)
because of the sensor’s lack of sensitivity to light in the range of 400 to
500 nm (23). Similar transitions can be seen (and verified with newspaper
accounts) in many cities worldwide.
Increases in radiance in areas around cities may result from several
processes. In many cases, cities expand into new areas, causing their
edges (which were previously only partly urbanized) to become brighter.
In other cases, radiance increase may be due to expansion of electrifica-
tion or to increasing wealth in adjacent areas. Finally, some of the light
observed by the satellite is not direct but rather scattered by the atmo-
sphere. For very bright cities, this causes a glow over adjacent areas that
have little or no lighting. Transitions to LED lighting greatly increase this
“skyglow,”because the clear sky predominantly scatters short-
wavelength light (30). This effect would be considerably more noticeable
if the DNB was sensitive to light below 500 nm. Similarly, whereas de-
creases in city center radiance in the DNB band likely indicate absolute
energy reductions (see Materials and Methods), white LED transitions
will often increase the skyglow experienced on the ground (30–32).
DISCUSSION
MajorargumentsfortransitiontoLEDsforoutdoorlightingarecost
savings and reductions in energy consumption (9). These goals have
been realized in many cities that have switched to LED street lights,
and therefore, decreases in observed DNB radiance likely indicate local
energy savings. However, on the global (and often national) scale, these
local decreases are outweighed by increases in radiance in other areas,
most likely because of additional lighting being installed. This should
not be surprising, because decreases in cost allow increased use of light
in areas that were previously unlit, moderately lit, or lit only during the
early evening hours. The “energy saving”effects of outdoor LED lighting
for country-level energy budgets are therefore smaller than might be
expected from the increase in luminous efficacy compared to older
lamps (33).
The large differences in per capita light use compared to per capita
GDP (Fig. 4A) suggest that in brightly lit countries, major decreases in
energy consumption for outdoor lighting could potentially be achieved
through reduced light use. The extremely large differences in per capita
light use in Germany and the United States reported in the study of
Kyba et al. (23) and observed again here (Fig. 3, A and B) demonstrate
that prosperity, safety, and security can be achieved with conservative
light use. This has also been shown on local scales: Demonstration
Fig. 5. Change in lighting technology in Milan, Italy, observed from space. Color astronaut photographs from 2012 (A) and 2015 (B) courtesy of the Earth Science
and Remote Sensing Unit, NASA Johnson Space Center, with identification and georeferencing by the European Space Agency, the International Astronomical Union,
and Cities at Night (48). (C) Change from 2012 to 2016 in the DNB radiance band.
Fig. 6. Expansion of DNB lighting from September 2012 (cyan) to September
2016 (red) in Doha, Qatar. Newly lit areas are expressed as bright red, as they
were not lit (black) in 2012.
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projects have shown that LEDs can allow approximately order-of-
magnitude reductions in illuminance compared to current practice with-
out compromising user acceptance (34). In addition, lighting can be
reduced or turned off late at night without compromising safety in the
moderately lit places responsible for most of the artificial light emis-
sions (10).
Major (factor of 2 or more) reductions in the energy cost and
environmental impact of lighting should be accompanied by large
absolute decreases in light emissions observable from space. The fact
that the median country’s 15% increase in lighting from 2012 to 2016
nearly matched the median 13% increase in GDP suggests that out-
door light use remains subject to a large rebound effect on the global
scale. Therefore, the results presented here are inconsistent with the
hypothesis of large reductions in global energy consumption for out-
door lighting because of the introduction of solid-state lighting. The
correlation between GDP and light increase at the national scale is
likely modest because of the relatively short-term nature of the data
set, compared to the ~20-year time window for replacement of city
street lights. The size of the outdoor lighting rebound effect should
therefore be re-examined when a longer time series of lights and GDP
becomes available. Restricting the analysis to stable electric lighting, and
lowering the analysis threshold from 5 nWcm
−2
sr
−1
to the smallest
practical value, would also likely improve these correlations.
In the near term, it appears that artificial light emission into the
environment will continue to increase, further eroding Earth’sremain-
ing land area that experiences natural day-night light cycles. This is
concerning, because artificial light is an environmental pollutant. In
addition to threatening the 30% of vertebrates and more than 60% of
invertebrates that are nocturnal (35), outdoor artificial light also affects
plants and microorganisms (36,37) and is increasingly suspected of
affecting human health (8,38). In the longer term, perhaps the de-
mand for dark skies and unlit bedrooms will begin to outweigh the
demand for light in wealthy countries, leading to an “environmental
Kuznets curve”for outdoor light. The nonlinearity between per capita
light emission and GDP is reminiscent of such a relationship (Fig. 4A).
If this is the case, then it will be readily apparent in the continued time
series of satellites observing artificial light at night.
MATERIALS AND METHODS
Three analyses were conducted: an “area change”analysis, a “total
radiance change”analyses, and a “stable light radiance change”analysis.
All three analyses compare relative rather than absolute changes to
facilitate comparisons.
The area change analysis measured the total area that was lit above a
certain radiance threshold in 2012 and 2016. In this analysis, the radi-
ance of individual pixels was reduced to a single bit (lit or unlit, based on
acutof5nWcm
−2
sr
−1
). Areas that increased in radiance to cross the
threshold in 2016 therefore increased the lit area compared to 2012,
whereas increases in radiance in city centers that were already lit in
2012 had no impact on the lit area. Transient and natural light sources
such as wildfires necessarily affected the area change analysis. This is
because if a light is only present in the 2012 or 2016 data set, then there
is no way to know whether it was a formerly permanent light that
turned off after 2012, a new permanent light that turned on in 2016,
or a transient light in one of the two years. [NOAA is working on annual
“stable lights”composites that remove firelight on the basis of infrared
observations (39), but these are not yet published for all years, and the
outlier removal method it is based on cannot be applied to monthly
data.] A selection of maps showing area changes at high resolutions
using the same data but a different analysis were recently published
by Nelson (40).
The total radiance change analysis measured the national sum of the
radiance of all pixels that were above 5 nWcm
−2
sr
−1
in 2012, 2016, or
both 2012 and 2016 (SOL in Fig. 4). This means that the area under
consideration is the same as in the area change analysis, and an identical
area is considered in the two years. As in the area change analysis, tran-
sient light sources such as fire were included in the total.
The stable light radiance change analysis measured how radiance
changed in areas continuously lit with relatively stable lights from
2012 to 2016. Transient and wildfire lights were removed by checking
that the area was lit above a 5 nWcm
−2
sr
−1
threshold in the entire
period of 2012–2016 and that the change in radiance from 2012 to
2016 did not exceed a set value (details below). Areas were binned
according to their radiance in 2014 in order to test whether, for example,
city centers have different trends compared to more modestly lit areas.
Because transient and natural light sources were removed, the study ar-
ea used in the radiance change analysis was smaller than the area ob-
served in the other two analyses. Wildfires outside artificially
illuminated areas in a single year should therefore have no effect on
the stable light radiance change analysis.
The DNB cloud-free monthly composites for the month of October
in 2012–2016 were downloaded from NOAA (41). These data in-
clude only overpasses for which clouds were not present (based on ob-
servations by infrared channelsonthesameinstrument),andthetotal
number of overpasses therefore differs between pixels. In a few areas,
some pixels are so persistently covered by clouds that no cloud-free ob-
servations are available in a given month. In this case, the area was
A Germany B Peru
Fig. 7. Comparison of radiance changes from 2012–2016 in dimly lit areas of Germany and Peru. Histograms of radiance changes of pixels in the 5 to 6.1 nWcm
−2
sr
−1
bin for
Germany (A) and rapidly brightening Peru (B). Pixels were assigned to this bin on the basis of their radiance in 2014. The vertical line shows the value of 1 (no change from 2012 to 2016).
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removed from all the analyses presented here. October is a particularly
good month for comparisons of nighttime lights data for several reasons.
Stray light does not affect the observation at high latitudes in Europe,
and these areas are less likely to experience snow than later in the year
(however, note that Austria was particularly affected by snow in October
2016). Seasonal changes in DNB observations were recently discussed by
Levin and Zhang (42). In addition to the effect of snow, they found a neg-
ative relationship between the number of cloud-free observations and the
radiance of cities. This should be further investigated, but we note that it
could potentially be due to a complete lack of cloud-free observations in
some of their study areas, or perhaps more likely, an interaction in their
model between location, cloud cover, and season. Long-term changes in
the radiometric calibration of the DNB itself are well understood and
corrected (43).
The DNB monthly composites report the surface radiance in equal-
anglepixelsof15arcsecinlatitudeandlongitude.Ratherthanrepro-
jecting these data onto an equal-area map, we assigned a weight to each
pixel on the basis of its surface area (assuming a spherical Earth). To
reject auroral light, we cropped images to cover only the region of
60°S to 60°N (from the original 65°S to 75°N). Some auroral light
remained over the ocean in the Southern Hemisphere (Fig. 2), so the
area below 48° was removed from the analyses with the exception of
50°W to 80°W. An array containing the surface area dA of the 15″pixels
was generated in Python according to
dA ¼R2
Earth cos qdqdfð1Þ
A¼R2
Earth
∫
q2
q1cos qdq
∫
f2
f1
dfð2Þ
A¼R2
Earth
½
sin q

q2
q1
½
f

f2
f1ð3Þ
where qis the latitude in radians (that is, q= 0 at the equator) and fis
the longitude.
The stable light radiance change analysis examines how radiance
changed from 2012 to 2016 in dimly, moderately, and brightly lit areas.
To do this, and to allow the generation of histograms, we used the pixel
radiance (R) in the 2014 composite to assign each pixel a radiance class
(bin), with logarithmically growing width. The low edges of these bins
were assigned as
Rleft ¼log10 ðRhiÞ log10 ðRloÞðb1Þ=Nþlog10ðRloÞð4Þ
where R
hi
is 300 nWcm
−2
sr
−1
,R
lo
is 5 nWcm
−2
sr
−1
,Nis the number of
bins (20), and bis the bin number (from 1 to 20). The range of 5 to
300 nWcm
−2
sr
−1
was chosen on the basis of previous experience
examining the night light composites. The 5 nWcm
−2
sr
−1
cut is
far above the instrument sensitivity limit and noise level but still low
enough to include the lights from many faintly lit communities. In
the October 2012 composite, not a single pixel of Paris was brighter than
300 nWcm
−2
sr
−1
(the brightest was 230 nWcm
−2
sr
−1
at Charles De
Gaulle Airport), and both Los Angeles, California and London, UK had
only a single pixel brighter than 300 nWcm
−2
sr
−1
. The rare exceptions
of brighter urban areas (for example, the Las Vegas strip) are better
studied individually than as part of a global analysis.
The radiance bins for each pixel were stored in a Python array of
equivalent size to the DNB data (from 60°S to 60°N). To reject wildfires
and other temporary lights from the stable light radiance change anal-
ysis, we also checked the radiance of each pixel in the October compo-
sites for 2013–2015. Pixels were flagged to be removed from the analysis
if their radiance was outside the range of 1.67 to 900 nWcm
−2
sr
−1
in
any of these years. In practice, this was accomplished by setting the bin
number of these pixels to 0 in the Python array. At this stage, a set of
binary (lit/unlit) maps was also produced for each year from 2012 to
2016 by testing whether each pixel was above the 5 nWcm
−2
sr
−1
cut
(Fig. 6). The area above the threshold was summed on both the global
and national scales.
TheradianceratioR
2016
/R
2012
was then calculated for each of the
individual pixels with bin numbers in the range of 1 to 20. Pixels that
changed by greater than a factor of 4 were removed from the analysis.
The value of 4 was chosen as large enough to accommodate most changes
in rapidly brightening countries while still rejecting extreme changes to
prevent them from skewing the mean (Fig. 7). The area-corrected mean
radiance difference D
b
for each bin bwas then
Db¼∑wR2016
∑wR2012
ð5Þ
with a pixel area correction w¼A=

Ab,whereAis the area of each pixel
and

Abis the area of the average pixel in each bin. Only pixels that passed
all cuts were included in the sums.
The analysis was repeated for each country by recalculating

Aband
D
b
for only the pixels within the given country’s area. Country extents
were converted from the shapefiles of Esri Data & Maps 2003 to a
raster format, with a few corrections to reflect new boundaries (for ex-
ample, South Sudan). Overseas territories were included as part of the
larger associated country (for example, Christmas Island was included
as part of Australia). Some coastal pixels are misidentified as oceans be-
cause of the limited precision of the shapefile and, therefore, do not con-
tribute to country totals. For countries with lit areas below several
square kilometers, caution should be taken when interpreting the
change in radiance in figs. S4 to S27 because these changes are driven
by a small number of pixels.
Changes in national sum of lights for areas included in the total
radiance change analysis were compared to GDP and population data
from the World Bank (44). GDP is reported in “constant 2010 US$.”
A total of 134 countries had complete GDP and population data, as
well as at least 100 km
2
lit above the 5 nWcm
−2
sr
−1
threshold in
both 2012 and 2016. Countries were divided in roughly equal groups
based on per capita GDP in 2016 for display (Fig. 4A). The per capita
GDPs of the groups are <$2000 (n= 36, red triangles), from $2000 to
$6000 (n= 35, green squares), from $6000 to $17,000 (n= 30, blue
stars), and >$17,000 (n= 33, black circles). Spearman’s rank-order
correlation coefficients between sets of parameters were calculated
in Python using “scipy.stats.spearmanr.”
The 5 nWcm
−2
sr
−1
threshold means that not all artificially
produced light was included in these analyses. Away from cities, natural
light sources, such as airglow and reflected moonlight outshine artificial
light, and systematic errors on the DNB zero point could generate large
errors in the national sum of lights for countries with large unlit areas.
Further work would therefore be needed to estimate the total global
change in artificial radiance (including areas lit below the current anal-
ysis threshold).
SCIENCE ADVANCES |RESEARCH ARTICLE
Kyba et al., Sci. Adv. 2017; 3: e1701528 22 November 2017 6of8
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One of the consequences of the global transition to LED street
lighting is a shift in the spectra of artificial night lights (22,45). So-called
“white”LEDs emit a portion of their light at wavelengths below 500 nm
(blue), where the DNB is effectively blind (23). This means that a street
light transition from (orange) high-pressure sodium lamps to (white)
LEDs in which surface luminance is held constant results in a decrease
in the radiance observed by DNB (Fig. 5C). The measurements of
change in lighting reported here are therefore actually lower bounds
on the increase of lighting in the human visual range [see the study
of Sánchez de Miguel et al. (45)foramoredetaileddiscussion].
For this reason, decreases in radiance of ~30% could be due to a
complete lighting transition from high-pressure sodium to LED lamps
rather than a true decrease in visible light. The relationship between the
emission spectra of different lighting technologies (even among classes
such as “warm white LEDs”or “high-pressure sodium”) and the detec-
tion efficiencies of broadband sensors (for example, human vision and
VIIRS DNB) is complex and will be addressed in detail in a forthcoming
paper. From a remote sensing perspective, the situation could be greatly
improved if nighttime satellites had color sensitivity (46). Nevertheless,
the increased luminous efficacy of LEDs means that decreases in city
lighting likely indicate decreases in energy consumption. On the other
hand, despite the fact that nearly all new outdoor lighting installations
make use of LEDs (47), new lighting necessarily implies new energy
consumption. For this reason, increases in observed radiance are nearly
certain to be due to increases in installed visible light and, therefore,
raised energy consumption.
SUPPLEMENTARY MATERIALS
Supplementary material for this article is available at http://advances.sciencemag.org/cgi/
content/full/3/11/e1701528/DC1
fig. S1. Normalized cumulative radiance distribution for Afghanistan through Ghana.
fig. S2. Normalized cumulative radiance distribution for Greece through Paraguay.
fig. S3. Normalized cumulative radiance distribution for Peru through Zimbabwe.
fig. S4. Lit area (2014) and radiance change (2012–2016) in Afghanistan, Albania, Algeria,
Andorra, Antigua and Barbuda, Argentina, and Armenia.
fig. S5. Lit area (2014) and radiance change (2012–2016) in Australia, Austria, Azerbaijan,
Bahrain, Bangladesh, Barbados, Belarus, and Belgium.
fig. S6. Lit area (2014) and radiance change (2012–2016) in Belize, Benin, Bhutan, Bolivia,
Bosnia and Herzegovina, Botswana, Brazil, and Brunei.
fig. S7. Lit area (2014) and radiance change (2012–2016) in Bulgaria, Burkina Faso, Burundi,
Cambodia, Cameroon, Canada, Cape Verde, and Chad.
fig. S8. Lit area (2014) and radiance change (2012–2016) in Chile, China, Colombia, Comoros,
Republic of the Congo, Democratic Republic of the Congo, Costa Rica, and Cote d’Ivoire.
fig. S9. Lit area (2014) and radiance change (2012–2016) in Croatia, Cuba, Cyprus, Czech
Republic, Denmark, Djibouti, Dominica, and Dominican Republic.
fig. S10. Lit area (2014) and radiance change (2012–2016) in East Timor, Ecuador, Egypt, El
Salvador, Equatorial Guinea, Eritrea, Estonia, and Ethiopia.
fig. S11. Lit area (2014) and radiance change (2012–2016) in Fiji, Finland, France, Gabon, Gaza
Strip, Georgia, Germany, and Ghana.
fig. S12. Lit area (2014) and radiance change (2012–2016) in Greece, Grenada, Guatemala,
Guinea, Guinea-Bissau, Guyana, Haiti, and Honduras.
fig. S13. Lit area (2014) and radiance change (2012–2016) in Hungary, India, Indonesia, Iran,
Iraq, Ireland, Israel, and Italy.
fig. S14. Lit area (2014) and radiance change (2012–2016) in Jamaica, Japan, Jordan,
Kazakhstan, Kenya, Kosovo, Kuwait, and Kyrgyzstan.
fig. S15. Lit area (2014) and radiance change (2012–2016) in Laos, Latvia, Lebanon, Lesotho,
Liberia, Libya, Liechtenstein, and Lithuania.
fig. S16. Lit area (2014) and radiance change (2012–2016) in Luxembourg, Macedonia,
Madagascar, Malawi, Malaysia, Maldives, Mali, and Malta.
fig. S17. Lit area (2014) and radiance change (2012–2016) in Marshall Islands, Mauritania,
Mauritius, Mexico, Moldova, Monaco, Mongolia, and Montenegro.
fig. S18. Lit area (2014) and radiance change (2012–2016) in Morocco, Mozambique, Myanmar,
Namibia, Nauru, Nepal, Netherlands, and New Zealand.
fig. S19. Lit area (2014) and radiance change (2012–2016) in Nicaragua, Niger, Nigeria, North
Korea, Norway, Oman, Pakistan, and Panama.
fig. S20. Lit area (2014) and radiance change (2012–2016) in Papua New Guinea, Paraguay,
Peru, Philippines, Poland, Portugal, Qatar, and Romania.
fig. S21. Lit area (2014) and radiance change (2012–2016) in Russia, Rwanda, Samoa, San
Marino, Sao Tome and Principe, Saudi Arabia, Senegal, and Serbia and Montenegro.
fig. S22. Lit area (2014) and radiance change (2012–2016) in Seychelles, Sierra Leone,
Singapore, Slovakia, Slovenia, Solomon Islands, Somalia, and South Africa.
fig. S23. Lit area (2014) and radiance change (2012–2016) in South Korea, South Sudan, Spain,
Sri Lanka, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, and Sudan.
fig. S24. Lit area (2014) and radiance change (2012–2016) in Suriname, Swaziland, Sweden,
Switzerland, Syria, Taiwan, Tajikistan, and Tanzania.
fig. S25. Lit area (2014) and radiance change (2012–2016) in Thailand, The Bahamas, The
Gambia, Togo, Trinidad and Tobago, Tunisia, Turkey, and Turkmenistan.
fig. S26. Lit area (2014) and radiance change (2012–2016) in Uganda, Ukraine, United Arab
Emirates, UK, the United States, Uruguay, Uzbekistan, and Vanuatu.
fig. S27. Lit area (2014) and radiance change (2012–2016) in Vatican City, Venezuela, Vietnam,
West Bank, Western Sahara, Yemen, Zambia, and Zimbabwe.
fig. S28. Annual changes in lit area and total radiance.
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Acknowledgments: We thank the anonymous reviewers for a number of helpful
suggestions. This article is based on work from COST (European Cooperation in Science
and Technology) Action ES1204 LoNNe (Loss of the Night Network), supported by
COST. Funding: TheauthorsacknowledgethefundingreceivedbyERA-PLANET
(www.era-planet.eu) funded by the European Commission as part of H2020 (Horizon 2020)
(H2020) (contract no. 689443). NOAA’s participation was funded by NASA’s VIIRS science
program (contract number NNH15AZ01I). A.S.d.M.’s contribution was funded by ORISON
project (H2020-INFRASUPP-2015-2) Cities at Night. Image and data processing was
performed by NOAA’s National Geophysical Data Center. Figs. 1, 2, and 5 were created
using ArcGIS software by Esri. Author contributions: C.C.M.K., T.K., A.S.d.M., A.J., F.H.,
J.B., K.J.G., and L.G. conceived the study; A.S.d.M. independently verified some results;
C.C.M.K., T.K., and C.D.E. provided the figures; A.J. prepared the Supplementary Materials;
K.B. and C.D.E. created the monthly DNB composites and provided assistance in
interpreting them; C.C.M.K. wrote the first draft; and all authors edited and approved
the draft manuscripts. Competing interests: All authors declare that they have no
competing interests; however, in the interest of full disclosure, C.C.M.K. (2012–2015) and
A.S.d.M. (2016 to present) note that they have served as uncompensated members of
the board of directors of the International Dark-Sky Association. Data and materials
availability: All data needed to evaluate the conclusions in the paper are present in the
paper and/or the Supplementary Materials. Additional data related to this paper may be
requested from the authors.
Submitted 9 May 2017
Accepted 24 October 2017
Published 22 November 2017
10.1126/sciadv.1701528
Citation: C. C. M. Kyba, T. Kuester, A. Sánchez de Miguel, K. Baugh, A. Jechow, F. Hölker,
J. Bennie, C. D. Elvidge, K. J. Gaston, L. Guanter, Artificially lit surface of Earth at night
increasing in radiance and extent. Sci. Adv. 3, e1701528 (2017).
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Artificially lit surface of Earth at night increasing in radiance and extent
Jonathan Bennie, Christopher D. Elvidge, Kevin J. Gaston and Luis Guanter
Christopher C. M. Kyba, Theres Kuester, Alejandro Sánchez de Miguel, Kimberly Baugh, Andreas Jechow, Franz Hölker,
DOI: 10.1126/sciadv.1701528
(11), e1701528.3Sci Adv
ARTICLE TOOLS http://advances.sciencemag.org/content/3/11/e1701528
MATERIALS
SUPPLEMENTARY http://advances.sciencemag.org/content/suppl/2017/11/17/3.11.e1701528.DC1
REFERENCES http://advances.sciencemag.org/content/3/11/e1701528#BIBL
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