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remote sensing
Article
The Dimming of Lights in India during
the COVID-19 Pandemic
Tilottama Ghosh 1, * , Christopher D. Elvidge 1 , Feng-Chi Hsu 1 , Mikhail Zhizhin 1,2 and
Morgan Bazilian 3
1 Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines,
Golden, CO 80401, USA; celvidge@mines.edu (C.D.E.); fengchihsu@mines.edu (F.-C.H.);
mzhizhin@mines.edu (M.Z.)
2 Russian Space Research Institute, 117997 Moscow, Russia
3 Payne Institute for Public Policy, Colorado School of Mines, Golden, CO 80401, USA; mbazilian@mines.edu
* Correspondence: tghosh@mines.edu
Received: 4 September 2020; Accepted: 8 October 2020; Published: 10 October 2020
Abstract: The monthly Suomi National Polar-orbiting (NPP) Visible Infrared Imaging Radiometer
Suite (VIIRS) Day–Night Band (DNB) composite reveals the dimming of lights as an effect of
the lockdown enforced by the government of India in response to the COVID-19 pandemic.
The changes in lighting are examined by creating difference maps of a pre-pandemic pair and
comparing it with two pandemic pairs. The visual raster difference maps are substantiated with
quantitative analysis showing the proportion of population affected by the changes in the lighting
brightness levels. In the pre-pandemic images of February and March 2019, 60% of the population
lived in administrative units that became brighter in March 2019. However, in the first pandemic
pair, 87% of the population lived in administrative units that became dimmer in March 2020 after
the lockdown in comparison to February 2020. The nightly DNB profile at the airport in Delhi
illustrate how the dimming of lights coincide with the date of the onset of the lockdown (in March
2020). The study shows the usefulness of the DNB nightly and monthly composites in examining
economic impacts of the pandemic as countries throughout the world go through economic declines
and move towards recovery.
Keywords: remote sensing of nighttime lights; VIIRS day–night band; COVID-19 pandemic;
economic effects
1. Introduction
The year 2020 will be etched in our lifetimes as the most economically and emotionally stressful
year in a generation because of the COVID-19 global pandemic. It is estimated that the pandemic
could slow down the global economic growth from 3% to 6 % in 2020 [1]. The pandemic has caused
an economic slump beyond anything the world has experienced in nearly a century, with massive
levels of unemployment and the economic and social costs associated with the many lives lost. In this
paper, we present results from low-light imaging satellite data collected by the National Aeronautics
and Space Administration/National Oceanic and Atmospheric Administration (NASA/NOAA) Visible
Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) to study the economic impacts of
the COVID-19 pandemic in India. This paper uses concepts developed by Elvidge et al. for studying
the impacts of COVID-19 in China [2].
Since the first COVID-19 case was diagnosed, it has spread to over 200 countries. India reported
its first case of COVID on January 30 in Kerala’s Thrissur district [3], from a student who had returned
home for a vacation from Wuhan University in China. As the number of confirmed cases slowly spread
Remote Sens. 2020, 12, 3289; doi:10.3390/rs12203289 www.mdpi.com/journal/remotesensing
Remote Sens. 2020, 12, 3289 2 of 17
and the number of cases in India reached 500, Prime Minister Narendra Modi asked all citizens to
follow a “Janata” curfew for 14 h (7 a.m. to 9 p.m.) on 22 March. Following the “Janata” curfew
Prime Minister Modi ordered the First Phase of lockdown on 24 March, the largest lockdown ever,
asking 1.3 billion people to stay at home for the next 21 days [4]. This was followed by three more
phases of lockdown—Phase 2 (15 April–3 May), Phase 3 (4 May–17 May), and Phase 4 (18–31 May) [3].
These measures were taken in view of the ominous predictions for India, the second most populous
country in the world, with a large majority of the population living in poverty and unsanitary, crowded
conditions, and with a hospital bed capacity of just 0.7 persons per 1000 people [4].
The economic impacts of the lockdown were disastrous for the 100 million migrant workers in
India who make up 20% of the workforce [5]. These daily wage earners lost their income overnight
and rushed to their homes in packed buses and trains, potentially carrying the virus to rural areas.
When they were left with no transportation options to travel, they started walking to their distant
villages [4]. The economic fallout in the service sector, accounting for 60% of India’s Gross Domestic
Product (GDP), and in the industrial sector because of the closure of the industries and factories,
have been phenomenal [6]. The Indian economy had already slowed down in 2019 partly because of
the slowdown of the global economy [7]. According to an estimate by the economists at Goldman
Sachs, the economic slowdown because of the pandemic is expected to shrink the Gross Domestic
Product to 5% for the next fiscal year, which began in April and will be ending in March 2021 [8].
The usage of VIIRS DNB data to study economic landscapes of countries at the granular level
is well established, and there have been studies specific to India [9–11]. Low-light imaging satellite
sensors have also been used to detect dimming and recovery of lights after natural disasters [12–14],
wars [15,16], other humanitarian crisis [17], and economic collapse [18]. With this background
information, this paper explores the effect of the COVID-19 pandemic in India through the percentage
change in the brightness difference images of monthly VIIRS DNB data. The difference images were
created between the February 2020 image and the image of March covering the period of the lockdown,
which was 24–31 March; the second set is the difference image between February 2020 and the image
of April. As a reference, the same analysis was also conducted on a pair of months from the previous
year—February 2019 and March 2019. Further, a nightly temporal profile is examined, which clearly
depicts the dimming of lights from the exact starting date of the lockdown on 24 March.
2. Materials and Methods
The NASA/NOAA VIIRS DNB was originally designed to detect moonlit clouds in the visible band.
However, with million times intensification of the signal, the DNB detects electrification on the Earth’s
surface [19]. The VIIRS DNB data are available in daily, monthly, and annual formats. The annual
data go through several steps of processing to remove cloud cover, solar and lunar contamination,
background noise, and features such as fires and flares, which are not related to electric lighting [20].
When the user is interested in studying any monthly event or daily event, the monthly and daily
DNB data can be used. However, the monthly data need some pre-processing before use. This is
because the monthly data are not filtered to remove clouds, ephemeral events like biomass burning, or
background noise. Thus, it is left to the discretion of the analyst to apply the filters and make the data
appropriate for analysis.
In this paper, we have used three pairs of filtered DNB monthly images, which were comprised
of five individual images—February 2019, March 2019, February 2020, 24–31 March 2020, and April
2020. The monthly composites comprised a pair of images, the average radiance, and the tally of
the cloud-free coverages. In order to create a standard set of images having a consistent set of lit
grid cells, the images were filtered on the basis of low cloud-free coverages, low radiance levels, and
snow cover.
Remote Sens. 2020, 12, 3289 3 of 17
2.1. Filtering
Remote Based
Sens. 2020, 12, x on
FORLow Cloud-Free
PEER REVIEW Coverages 3 of 17
Visual examination of the cloud-free coverage grids showed that there were significant data gaps
Visual examination of the cloud-free coverage grids showed that there were significant data gaps
in areas of zero, one, or two cloud-free coverages. Thus, for each of the five months, a binary on–off
in areas of zero, one, or two cloud-free coverages. Thus, for each of the five months, a binary on–off
mask was created for grid cells having less than or equal to two cloud-free coverages. The masks of all
mask was created for grid cells having less than or equal to two cloud-free coverages. The masks of
the five months were added up, and in the final step an “off” mask was produced to exclude all grid
all the five months were added up, and in the final step an “off” mask was produced to exclude all
cells with less than or equal to two cloud free coverages in any of the five months (Figure 1).
grid cells with less than or equal to two cloud free coverages in any of the five months (Figure 1).
Figure 1. Cloud-free coverage mask. The “black areas” are the “off mask” areas with less than or equal
Figure 1. Cloud-free coverage mask. The “black areas” are the “off mask” areas with less than or equal
to two cloud-free coverages in all five months.
to two cloud-free coverages in all five months.
2.2. Filtering Based on Low Average Radiance
2.2. Filtering Based on Low Average Radiance
The background areas with very low average radiances are usually contributed by biomass
The and
burning background areas with
fires. Although very low
the values average
are very low, radiances
when theyareareusually
summed contributed by biomass
up over a considerable
burning and fires. Although the values are very low, when they are summed up over a
spatial extent and over a period, it provides a “false” higher value of the sum of the average radiances.considerable
spatial
Again, extent
through and over examination
visual a period, it provides a “false” higher
it was determined that value of thegrid
excluding sumcells
of the average
with radiances.
radiance values
Again, through visual examination it was determined that excluding grid cells
less than or equal to 0.6 nanowatt/cm /sr would help to exclude all the background noise from
2 with radiance values
fires
less
and than or equal
biomass to 0.6Thus,
burning. nanowatt/cm 2/sr would help to exclude all the background noise from fires
binary on–off masks were created for each of the five months, and then
and biomass
the masks burning.
of all Thus, were
five months binary on–off
added up.masks
In thewere
finalcreated
step, anfor each
“off” of the
mask wasfive months,
created and then
to exclude all
the
grid cells with average radiances less than or equal to 0.6 nanowatt/cm /sr in any of the five exclude
masks of all five months were added up. In the final step, an “off” mask
2 was created to months
all grid cells
(Figure 2). with average radiances less than or equal to 0.6 nanowatt/cm /sr in any of the five months
2
(Figure 2).
Remote Sens. 2020, 12, x FOR PEER REVIEW 4 of 17
Remote Sens. 2020, 12, 3289 4 of 17
Figure 2. Low average radiance mask. The “black areas” are the “off mask” areas with average radiance
Figure
values2.less
Low average
than radiance
or equal to 0.6 inmask.
all fiveThe “black areas” are the “off mask” areas with average
months.
radiance values less than or equal to 0.6 in all five months.
2.3. Filtering of Snow Cover
2.3. Filtering
Snow of Snowincreases
cover Cover the brightness of the lighting because of the high visible wavelength
reflectivity.
Snow cover Snow cover masks
increases for each ofof
the brightness thethe
five monthsbecause
lighting were derived
of the from
high the NOAA
visible AMSU-A
wavelength
(Advanced Microwave Sounding Unit-A) daily snow cover product by tallying
reflectivity. Snow cover masks for each of the five months were derived from the NOAA AMSU-A the number of times
snow coverMicrowave
(Advanced was detected for eachUnit-A)
Sounding of the five
dailymonths
snow [21].
coverMonthly
product snow cover the
by tallying tallies of oneofand
number two
times
were removed to exclude false detections. The snow cover grids for each of the five
snow cover was detected for each of the five months [21]. Monthly snow cover tallies of one and two months having
tallies
were of greater
removed than or equal
to exclude to three were
false detections. Theadded
snowup, andgrids
cover thenfor
an each
“off” of
mask
the was
five created
months to “zero”
having
out the
tallies of lighting affected
greater than by snow
or equal cover
to three in any
were addedof the
up,five
andmonths
then an(Figure 3). was created to “zero”
“off” mask
out the lighting affected by snow cover in any of the five months (Figure 3).
Remote Sens. 2020, 12, x FOR PEER REVIEW 5 of 17
Remote Sens. 2020, 12, 3289 5 of 17
Figure 3. Snow cover mask. The “black areas” are the “off mask” areas with snow cover of greater than
Figure 3. Snow
or equal cover
to 3 days mask.
in all five The “black areas” are the “off mask” areas with snow cover of greater
months.
than or equal to 3 days in all five months.
2.4. Calculating the Sum-of-Lights
2.4. Calculating the Sum-of-Lights
After applying all the three masks on each of the monthly composites, the 15 arcsecond grids were
After for
adjusted applying all the three
area. Because of themasks on each
spherical shapeofofthe
themonthly composites,
earth, the thelargest
cell areas are 15 arcsecond grids
at the equator
were adjusted at
and smallest forthe
area. Because
poles. of the spherical
Therefore, the monthly shape of the earth,
composite gridsthe cellmultiplied
were areas are largest
with theat area
the
equator and smallest
grid derived at the poles.
from LandScan [22].Therefore, the monthly
The district-level composite
shapefile gridshaving
of India, were multiplied with the
667 administrative
area
unitsgrid derived
[23], from LandScan
was overlaid on each of[22].
theThe
five district-level shapefileand
monthly composites, of India, having
the sum of the667 administrative
radiance values or
units [23], was (SOL)
sum-of-lights overlaid
wereonextracted.
each of the five
The monthly
sum composites,
differences and the between
were calculated sum of the radiance
2019/02 values
and 2019/03;
or2020/02
sum-of-lights (SOL) were extracted. The sum differences were calculated between
and 2020/03/24–31; and 2020/02 and 2020/04. The percentage differences were also calculated. 2019/02 and
2019/03; 2020/02 of
The population andthe2020/03/24–31;
administrativeand 2020/02
units and extracted
were also 2020/04. The
usingpercentage differences
the LandScan weregrid
population alsoof
calculated.
2018, whichThe waspopulation
also adjustedof for
thearea.
administrative units were also extracted using the LandScan
population grid of 2018, which was also adjusted for area.
3. Results
3. Results
3.1. Colorized Difference Images
3.1. Colorized Difference
The lockdown Images
effects due to Covid-19 is clearly visible in the difference images of February 2020
andThe(24–31) Marcheffects
lockdown 2020, dueand to
inCovid-19
Februaryis2020 andvisible
clearly April in2020
the for severalimages
difference urban areas in India.
of February 2020In
the (24–31)
and difference images,
March 2020,theandgains in brightness
in February 2020have been colored
and April 2020 forcyan and urban
several the declines
areas inin India.
brightness
In theas
red. The pre-pandemic difference pair of February 2019 and March 2019 is added
difference images, the gains in brightness have been colored cyan and the declines in brightness as to further understand
the effects
red. of the pandemic.
The pre-pandemic In addition,
difference pair the
of VIIRS DNB2019
February composite image 2019
and March of December
is added 2019to isfurther
shown
to provide athe
understand gray-scale
effects ofvisual display ofInthe
the pandemic. originalthe
addition, DNB composites.
VIIRS The eight
DNB composite citiesofincluded
image December are
Delhi, Mumbai, Pune, Chennai, Kolkata, Lucknow, Kanpur, and Hyderabad (Figure
2019 is shown to provide a gray-scale visual display of the original DNB composites. The eight cities 4). Except for
Kolkata and
included Mumbai,
are Delhi, it is seen
Mumbai, thatChennai,
Pune, the lightsKolkata,
had brightened
Lucknow, in the pre-pandemic
Kanpur, pair for all
and Hyderabad the other
(Figure 4).
Except for Kolkata and Mumbai, it is seen that the lights had brightened in the pre-pandemic pair forin
cities. The effects of the lockdown that began on 24 March is shown in the dimming of the lights
thethe
all first pandemic
other difference
cities. The pair
effects of theset, and the “red”
lockdown gets more
that began on 24widespread
March is shownin theinsecond pandemic
the dimming of
difference pair set, which extends to the end of April. The slowdown in the
the lights in the first pandemic difference pair set, and the “red” gets more widespread in the secondmovement of traffic
Remote Sens. 2020, 12, 3289 6 of 17
becomes Remote Sens. 2020,
evident from 12, xthe
FORdimming
PEER REVIEW of the lights along the highways extending out from the 6 of cities
17 and
connecting the satellite cities and towns. Moreover, interestingly in the case
pandemic difference pair set, which extends to the end of April. The slowdown in the movement of
of Delhi it is seen that, as
the lights dimmed
traffic becomes inevident
the city core,
from themany
dimming of the towns
of the lightsbetween
along the Gurgaon and Faridabad
highways extending out from inthe
the south,
and in the north-west
cities and connecting towards Haryana,
the satellite citiesbecame brighter
and towns. Moreover,in both the pandemic
interestingly difference
in the case of Delhipair
it is images.
This mayseenbethat,
dueastothe lights
the dimmed
migrant in the city core,
population many
leaving theof city
the towns between
and going Gurgaon
back andtowns/villages
to their Faridabad in
in the south, and in the north-west towards Haryana, became brighter in
the outskirts. Similarly, for Kolkata it is observed that in the pandemic difference pairs, as the lights both the pandemic
difference pair images. This may be due to the migrant population leaving the city and going back to
dimmed in the city, the lights became brighter in the towns towards the east and the north (Figure 4).
their towns/villages in the outskirts. Similarly, for Kolkata it is observed that in the pandemic
For Pune, speckspairs,
difference of cyan
as theare seen
lights to beinscattered
dimmed within
the city, the the city
lights became core as
brighter well
in the as on
towns the outskirts
towards the in
the second
east pandemic
and the north difference
(Figure 4).pair, probably
For Pune, specksindicating
of cyan aremovement of people
seen to be scattered for the
within work cityor returning
core as to
their towns
well asoron
villages.
the outskirts in the second pandemic difference pair, probably indicating movement of
people for work or returning to their towns or villages.
December 2019 2019/02–2019/03 2020/02–2020/03/24–31 2020/02–2020/04
a
b
c
d
e
Figure 4. Cont.
Remote Sens. 2020, 12, 3289 7 of 17
Remote Sens. 2020, 12, x FOR PEER REVIEW 7 of 17
f
Figure 4. Examples of three pairs of difference images for eight Indian cities: (a) Delhi; (b) Mumbai
Figure 4. Examples of three pairs of difference images for eight Indian cities: (a) Delhi; (b) Mumbai
and Pune; (c) Chennai; (d) Kolkata; (e) Lucknow and Kanpur; (f) Hyderabad.
and Pune; (c) Chennai; (d) Kolkata; (e) Lucknow and Kanpur; (f) Hyderabad.
The qualitative analysis is corroborated through the quantitative analysis shown in Table 1. As
The qualitative analysis is corroborated through the quantitative analysis shown in Table 1. As
seen in the images of Mumbai (city and suburban), the lights had become dimmer in the pre-
seen in pandemic
the images timesof Mumbai
by 4% and(city 6%; itand suburban),
declined to aboutthe 18% lights hadlockdown
after the become in dimmer
March, inandthe pre-pandemic
to 19% in
times by 4% For
April. andKolkata,
6%; it declined
lights hadto about by
declined 18% 7%after the lockdown
between February and inMarch
March, of and
2019,to 19%
and thisinwas
April. For
Kolkata, more thanhad
lights the decline
declined afterbythe7%shutdown
betweenon 24 March, when
February anditMarch
declinedofby2019,
1.5% from
and what
this it wasmore
was in than
February 2020. However, there was almost a 15% decline in lighting
the decline after the shutdown on 24 March, when it declined by 1.5% from what it was in February in April in comparison to
February 2020. For the rest of the six cities, there was a gain in lighting in the pre-pandemic period
2020. However, there was almost a 15% decline in lighting in April in comparison to February 2020.
between February and March of 2019, ranging from 3% in Hyderabad to 13% in Lucknow.
For theNevertheless,
rest of the six thecities, there
lockdown wasisaseen
effect gainininalllighting
the cities,inwith
thediminished
pre-pandemic period
lighting rangingbetween
from 4%February
and March of 2019,
in Kanpur ranging
to 19% from 3%ininthe
in Hyderabad Hyderabad
first differenceto 13%pair,in
andLucknow. Nevertheless,
lights diminishing further the lockdown
in the
effect issecond
seen indifference
all the cities, with diminished
pair ranging between 7% lighting
in Pune and ranging
32% infrom 4% in Kanpur
Hyderabad. However, tofor
19% in Hyderabad
Pune, the
percentage
in the first difference
difference in lighting
pair, and lightsbecame less in the
diminishing second
further inpandemic
the second pair in comparison
difference pair to the firstbetween
ranging
pandemic pair. This is confirmed by what is seen in the difference image.
7% in Pune and 32% in Hyderabad. However, for Pune, the percentage difference in lighting became
less in the second
Table 1. pandemic pair in
Percent difference inbrightness
comparison to the firstand
for pre-pandemic pandemic
pandemicpair.
monthlyThis is for
pairs confirmed
eight of by what is
seen in the India’s
difference image.
cities. Please note Mumbai City and Mumbai Suburban have been taken together to represent
Mumbai in the map (Figure 4b).
Table 1. Percent difference in brightness for pre-pandemic and pandemic monthly pairs for eight of
Percentage Percentage difference Percentage
India’s cities. Please note Mumbai
difference City and Mumbai
between Suburban
between 24 and 31 have been taken
difference together to represent
between
Name Population
Mumbai in the map (Figure 4b).
March 2019 and March 2020 and April 2020 and
February 2019 February 2020 February 2020
Delhi Percentage
8.33 Percentage
−13.53 Percentage 18,063,518
−19.16
Mumbai City Difference between
−3.98 Difference
−18.23 between Difference
−19.71 between 2,914,088
Name
Mumbai Suburban March 2019
−5.88 and
Population
24 and −17.81
31 March 2020 −19.28 2020 and 9,803,906
April
Pune February8.042019 −12.84
and February 2020 −7.38
February 2020 10,049,999
Chennai 4.87 −2.67 −15.63 5,439,132
DelhiKolkata 8.33
−7.15 −13.53
−1.54 −19.16
−14.56 18,063,518
4,568,384
MumbaiLucknow
City −3.98
12.71 −18.23
−12.34 −19.71
−19.38 2,914,088
5,002,592
Mumbai Suburban
Kanpur −5.88
10.91 −17.81
−4.45 −19.28
−15.42 9,803,906
4,992,403
Pune
Hyderabad 8.04
2.82 −12.84
−18.70 −7.38
−31.89 10,049,999
3,947,029
Chennai 4.87 −2.67 −15.63 5,439,132
Kolkata
3.2. −7.15
Percent Change Maps with Population −1.54 −14.56 4,568,384
Lucknow 12.71 −12.34 −19.38 5,002,592
Three maps of the entire country were created to spatially visualize the effects of the pandemic
Kanpur 10.91 −4.45 −15.42 4,992,403
in reducing the brightness of lights in reference to population numbers. All three maps show the
Hyderabad 2.82 −18.70 −31.89 3,947,029
outline of the second-level administrative units (that is, districts) in black. The circles are
proportionate to the population numbers, and the diameter size of the circles are around the centroid
3.2. Percent
of theChange Maps with
administrative units.Population
Five classes of population numbers are shown. The circles are color-coded
and divided into five classes on the basis of changes in brightness levels. The three classes of increased
Three maps of the entire country were created to spatially visualize the effects of the pandemic in
brightness are shown in yellow (0–10% increase), light green (10–20% increase), and dark green
reducing the brightness
(greater of lightsThe
than 20% increase). in reference
circles withtodecreased
population numbers.
brightness All three
are shown maps(0show
in orange the outline
to −10%
of the second-level
decrease) and administrative
red (greater than units (that is,Administrative
10% decline). districts) in black. The
units for circles
which are proportionate
lighting has been to
completely masked out are shown in gray.
the population numbers, and the diameter size of the circles are around the centroid of the administrative
units. Five classes of population numbers are shown. The circles are color-coded and divided into five
classes on the basis of changes in brightness levels. The three classes of increased brightness are shown
in yellow (0–10% increase), light green (10–20% increase), and dark green (greater than 20% increase).
The circles with decreased brightness are shown in orange (0 to −10% decrease) and red (greater than
10% decline). Administrative units for which lighting has been completely masked out are shown
in gray.
Remote Sens. 2020, 12, 3289 8 of 17
Remote Sens. 2020, 12, x FOR PEER REVIEW 8 of 17
The pre-pandemic
The pre-pandemicreference
referencemapmap (Figure
(Figure 5) shows
5) shows that inthat in the heavily
the heavily populated populated areas in
areas in northern,
northern, west-central and southern India green and yellow circles dominate,
west-central and southern India green and yellow circles dominate, indicating that the brightness indicating that the
levels had increased between February and March 2019. However, the eastern and north-eastern north-
brightness levels had increased between February and March 2019. However, the eastern and states,
eastern
and states, and
the extreme the extreme
western westerna states
states showed declineshowed a decline
in brightness in brightness
levels, even in thelevels, even in the
pre-pandemic pre-
period.
pandemic period. In the first pandemic percentage difference map (Figure 6), red
In the first pandemic percentage difference map (Figure 6), red and orange circles are widespread and orange circles
areover
all widespread
India in all overall
almost India
the in almostpopulated
heavily all the heavily populatedunits
administrative administrative
and also inunits and also
the areas withinlow
the
areas with low population. Very few specks of yellow and green are seen in this
population. Very few specks of yellow and green are seen in this map. Interestingly, the densely map. Interestingly,
the densely
populated populated administrative
administrative units in
units in the eastern theof
state eastern state ofwhich
West Bengal, West Bengal,
were redwhich
in thewere red in the
pre-pandemic
pre-pandemic
map, either showmap, a 0either
to –10%show a 0 toof
decline –10% declineor
brightness of even
brightness or even
an increase in an increase up
brightness in brightness
to 10% in
up to 10% in the first pandemic difference map. The north-eastern administrative
the first pandemic difference map. The north-eastern administrative units, which were mostly red in
units, which were
mostly red in the pre-pandemic map, were also yellow and green in Figure 6, showing an increase in
the pre-pandemic map, were also yellow and green in Figure 6, showing an increase in brightness
brightness levels. The second pandemic percentage difference map (Figure 7) has predominantly red
levels. The second pandemic percentage difference map (Figure 7) has predominantly red and orange
and orange circles with a few administrative units showing improvements in brightness levels.
circles with a few administrative units showing improvements in brightness levels.
Figure 5. Map of percentage change in brightness for the pre-pandemic reference period (March 2019
Figure 5. Map of percentage change in brightness for the pre-pandemic reference period (March 2019
minus February 2019). Please refer to the Supplementary Materials for the tabular data used to make
minus February 2019). Please refer to the supplementary material for the tabular data used to make
these maps.
these maps.
Remote
RemoteSens. 2020,12,
Sens.2020, 12,3289
x FOR PEER REVIEW 99 of
of 17
17
Figure 6. Map of percentage change in brightness for the first pandemic pair (24–31 March 2020
Figure 6. Map of percentage change in brightness for the first pandemic pair (24–31 March 2020 minus
minus February 2020). Please refer to the Supplementary Materials for the tabular data used to make
February 2020). Please refer to the supplementary material for the tabular data used to make these
these maps.
maps.
Remote Sens. 2020, 12, 3289 10 of 17
Remote Sens. 2020, 12, x FOR PEER REVIEW 10 of 17
Figure 7. Map of percentage change in brightness for the second pandemic pair (April 2020 minus
Figure 7. Map of percentage change in brightness for the second pandemic pair (April 2020 minus
February 2020). Please refer to the Supplementary Materials for the tabular data used to make
February 2020). Please refer to the supplementary material for the tabular data used to make these
these maps.
maps.
3.3. Histograms of Percent Differences Versus Population
3.3. Histograms of Percent Differences Versus Population
Figure 8 shows the population numbers in million on the Y axis and the percent difference in
Figurefor
brightness 8 shows the population
the reference numbersminus
period (2019/03 in million on the
2019/02) in Y axisand
blue andthethefirst
percent difference
pandemic in
period
brightness for the
(2019/03/24–31 reference
minus period
2020/02) (2019/03
in orange. minus
A very 2019/02)
distinct inin
shift blue
the and the firstnumbers
population pandemic period
between
(2019/03/24–31 minus 2020/02) in orange. A very distinct shift in the population
the pre-pandemic period and the first phase of the pandemic period is seen. In the reference period, numbers between
the majority
the pre-pandemic
of the period and the
population lived first
in phase of the pandemic
the administrative unitsperiod
whereisthe seen. In the reference
brightness period,
levels increased
the majority of the population lived in the administrative units where the brightness
in March 2019 in comparison to February 2019. However, in the first phase of the lockdown, which levels increased
in March
started on2019 in comparison
24 March, to February
a clear “shift” 2019. However,
in the population in theare
numbers first
seenphase of the
in the lockdown, which
administrative units,
started on 24 March, a clear “shift” in the population numbers are seen in the
which became dimmer after the lockdown in comparison to the previous month of February 2020. In administrative units,
which
the became dimmer
pre-pandemic after
period the lockdown
about 60% of thein comparison
population to the
lived previous monthunits,
in administrative of February 2020. In
which became
the pre-pandemic period about 60% of the population lived in administrative
brighter in March 2019 in comparison to the previous month. However, after the lockdown units, which became
on 24
brighter in March 2019 in comparison to the previous month. However, after
March 2020, it was observed that about 87% of the population lived in administrative units where the lockdown on 24
March 2020, it was observed that about 87% of the population lived in administrative
lighting dimmed after the lockdown in comparison to the previous month of February 2020. In April units where
lighting dimmed after the lockdown in comparison to the previous month of February 2020. In AprilRemote Sens. 2020, 12, 3289 11 of 17
Remote Sens. 2020, 12, x FOR PEER REVIEW 11 of 17
2020 there was a very slight shift with about 84% of the population living in the areas where lighting
2020 there
dimmed inwas a very slight
comparison shift with
to February about 84% of the population living in the areas where lighting
2020.
dimmed in comparison to February 2020.
Figure 8. Population
Figure 8. Population histograms for aa gradation
histograms for gradation of
of brightening
brightening and
and dimming
dimming levels
levels for
for the
the reference
reference
(blue)
(blue) and
and the
the first
first pandemic
pandemicpair
pair(orange).
(orange).Please
Pleaserefer
referto
tothe
theSupplementary
supplementaryMaterials for the
material for the tabular
tabular
data used to make these
data used to make these maps.maps.
3.4. Correlation with Total Number of Cases
3.4. Correlation with Total Number of Cases
The percent change in the sum-of-lights (SOL) were correlated with the total number of
The percent change in the sum-of-lights (SOL) were correlated with the total number of COVID-
COVID-19 cases per million people at the district level. The total number of cases were derived from
19 cases per million people at the district level. The total number of cases were derived from
covidindia.org [24]. The total cases were defined as the “cumulative number of all reported cases for
covidindia.org [24]. The total cases were defined as the “cumulative number of all reported cases for
the district or state from 30 January to that date”. The cumulative cases from 30 January to the end of
the district or state from 30 January to that date”. The cumulative cases from 30 January to the end of
March per million people were correlated with the percent difference in the SOL of the first pandemic
March per million people were correlated with the percent difference in the SOL of the first pandemic
pair, 2020/03/24 minus 2020/02 (Figure 9), and the cumulative cases per million people from 30 January
pair, 2020/03/24 minus 2020/02 (Figure 9), and the cumulative cases per million people from 30
to the end of April were correlated with the percent difference in the SOL of the second pandemic
January to the end of April were correlated with the percent difference in the SOL of the second
pair, 2020/04 minus 2020/02 (Figure 10). In both Figures 9 and 10, it is seen that there is no specific
pandemic pair, 2020/04 minus 2020/02 (Figure 10). In both Figures 9 and 10, it is seen that there is no
correlation between dimming of lighting and the number of cases per million population. In other
specific correlation between dimming of lighting and the number of cases per million population. In
words, lights were dimmed in areas where there were a lower number of total cases and in areas with
other words, lights were dimmed in areas where there were a lower number of total cases and in
a high number of cases per million people.
areas with a high number of cases per million people.Remote Sens.
Remote Sens. 2020,
2020, 12,
12, 3289
x FOR PEER REVIEW 12
12 of 17
of 17
Remote Sens. 2020, 12, x FOR PEER REVIEW 12 of 17
Figure 9. Percentage change in sum-of-lights (SOL) from the first pandemic pair versus total number
Figure 9.
Figure Percentage change
9. Percentage change inin sum-of-lights
sum-of-lights (SOL)
(SOL) from
from the
the first
first pandemic
pandemic pair
pair versus
versus total
total number
number
of COVID-19 cases per million people.
of COVID-19 cases per million people.
of COVID-19 cases per million people.
Figure
Figure 10. Percentage change
10. Percentage changein
inSOL
SOLfrom
fromthe
thesecond
secondpandemic
pandemicpair
pairversus
versustotal number
total of of
number COVID-19
COVID-
Figure
cases 10.
per Percentage
million change
people.
19 cases per million people. in SOL from the second pandemic pair versus total number of COVID-
19 cases per million people.
3.5. Examination of Dimming with a Nightly Temporal Profile
3.5. Examination of Dimming with a Nightly Temporal ProfileRemote Sens. 2020, 12, 3289 13 of 17
Remote Sens. 2020, 12, x FOR PEER REVIEW 13 of 17
3.5. Examination of Dimming with a Nightly Temporal Profile
The nightly temporal profiles (Figure 11) captured the effects of the lockdown precisely. Some
The nightly temporal profiles (Figure 11) captured the effects of the lockdown precisely. Some of
of the pixels with the sharpest decline in brightness were in and around the Indira Gandhi
the pixels with the sharpest decline in brightness were in and around the Indira Gandhi International
International airport in Delhi. It is seen in Figure 11 that the sharp decline in the radiance values
airport in Delhi. It is seen in Figure 11 that the sharp decline in the radiance values coincide exactly
coincide exactly with the first day of the lockdown on 24 March, and has been under 80 nW/cm2/sr
with the first day of the lockdown on 24 March, and has been under 80 nW/cm2 /sr since then.
since then.
Figure 11. Nightly temporal profile of DNB radiances for a grid cell at Indira Gandhi International
Figure 11. Nightly temporal profile of DNB radiances for a grid cell at Indira Gandhi International
airport, New Delhi, India.
airport, New Delhi, India.
4. Discussion
4. Discussion
The decline in the brightness of the lights in India during the month of March after lockdown
The
varied fromdecline in the
0.07 and brightness
30.9. of the
In the month oflights
April,in India
the during
decline wentthe month
down of March
further to 42.8.after
Thelockdown
dimming
varied from 0.07
of the lights was and 30.9. Ininthe
examined month
three of April,
different the decline went down further to 42.8. The dimming
ways.
of theThe
lights
firstwas
wayexamined
of examining in three
thedifferent
decline in ways.
the brightness levels was by differencing the monthly
The first way of examining the
radiance composite images. The monthly compositedecline in the brightness
imageslevels
were was by differencing
pre-processed to have thethe
monthly
same
radiance
number ofcomposite images.
lit pixels for all theThe monthly
months. composite images
Pre-processing were pre-processed
here implies excluding the gridsto have the same
impacted by
number of lit pixels for all the months. Pre-processing here implies excluding
snow cover, low numbers of cloud-free coverages, and background areas with no real lighting. the grids impacted by
snowForcover, low numbersreference
a pre-pandemic of cloud-free
pair,coverages,
the February and2019
background
and March areas with
2019 no real
images were lighting.
subtracted.
This For a pre-pandemic
difference pair shows reference
a greaterpair, the of
spread February
cyan for2019
most andof March 2019
the cities, images were
indicating areassubtracted.
becoming
This
brighter in March 2019 in comparison to February 2019, except for Mumbai and Kolkata. The becoming
difference pair shows a greater spread of cyan for most of the cities, indicating areas difference
brighter
images ofinMumbai
March and2019Kolkata
in comparison to February
shows a greater spread 2019,
of red,except for Mumbai
demonstrating and Kolkata.
that more areas becameThe
difference images of Mumbai and Kolkata
dimmer in March 2019, even in the pre-pandemic times. shows a greater spread of red, demonstrating that more
areasTwo
became
pairsdimmer in March
of pandemic 2019,
pairs even
were in the pre-pandemic
compared. The 24–31 March times. composite and the April 2020
Two pairs
composites were ofsubtracted
pandemic from pairs the
were compared.
February 2020The 24–31 March
composite. The composite and the
raster difference April were
images 2020
composites were subtracted from the February 2020 composite. The raster
density sliced to augment the visual understanding of the dimming and brightness. Of all the eight difference images were
density sliced to the
cities examined, augment the visualdifference
first pandemic understanding of theadimming
pair shows and brightness.
massive visual decline inOf theallbrightness
the eight
cities
levelsexamined, the first pandemic
with an extensive spread ofdifference
red in thepair shows acomposite,
difference massive visual and decline
the red in the brightness
becoming more
levels with an extensive spread of red in the difference composite, and
widespread in the second difference pair, indicating that more areas became dimmer as the lockdown the red becoming more
widespread in the second difference pair, indicating that more areas became dimmer as the lockdown
was extended. In the case of Delhi, it is seen that as the red became more widespread after the
lockdown, there was a simultaneous increase in brightness in the surrounding town and villages,Remote Sens. 2020, 12, 3289 14 of 17
was extended. In the case of Delhi, it is seen that as the red became more widespread after the lockdown,
there was a simultaneous increase in brightness in the surrounding town and villages, indicative of
migrant workers moving out of the city core. As for Mumbai, the pandemic difference pairs depict an
expanding spread of “redness” in the third difference pair in comparison to the second. For Kolkata,
in the first difference pandemic pair the dimming of lights seems to have got more centralized, with
areas becoming brighter in the northern extent. However, in the second difference pandemic pair,
red is seen to be more widespread with specks of cyan in the satellite towns, probably suggestive of
migrant workers going back to their homes in town and villages in the outskirts of the cities. For Pune,
the dense cluster of “redness” in the city core has become loosened in the second pandemic pair with
specks of cyan in the city core and in the outskirts.
Besides the visual evidence of dimming, a quantitative analysis was conducted by summing up
the radiance values of the 667 district-level administrative units in India. Differences and percentage
differences were calculated for a pre-pandemic pair (February 2019 and March 2019) and two sets of
pandemic pairs (February 2020 and 24–31 March, and February 2020 and April 2020). The difference and
percentage difference values between these pairs provide a quantitative corroboration of what is seen
in the images. Two t-tests of unequal variances were carried out to determine whether the differences
of the means between the pre-pandemic and the pandemic pairs is significant or not. The unequal
variances t-test between the pre-pandemic and the first pandemic pair at the 0.05 significance level
(Table 2) is associated with a significant effect t (1288) = −13.24, p = 1.35 × 10−37 ; the unequal variances
t-test between the pre-pandemic and the second pandemic pair at the 0.05 significance level (Table 3)
is associated with a significant effect t (1148) = −13.19, p = 4.18 × 10−37 . Thus, both the pandemic
difference pairs have a statistically significant larger mean than the pre-pandemic pair.
Table 2. T-tests between the first set of difference pairs—pre-pandemic and the first difference pair.
Difference (2019/03–2019/02) Difference (2020/03/24–2020/02)
Mean −77.54 342.07
Variance 273,310.22 396,854.60
Observations 667.00 667.00
Hypothesized Mean Difference 0.00
df 1288.00
t Stat −13.24
P (TRemote Sens. 2020, 12, 3289 15 of 17
a drop in the radiance values from the exact start date of the lockdown; that is, 24 March. It did not go
above 80 nW/cm2 /sr through the different phases of the lockdown.
Correlation analyses were conducted between the percentage change in SOL of the two pandemic
difference pairs and the total number of COVID-19 cases per million people. No relation was found
between these variables as the lights were dimmed in most areas irrespective of the total number of
cases per million people.
The proportion of population affected by the dimming of lights between the pre-pandemic and
pandemic times were examined visually by drawing circles proportionate to the population numbers
and were color-coded based on the changes in brightness levels. The pandemic difference maps
show how the decrease in lighting is extensive in many of the heavily populated administrative units.
This visual analysis was further validated with a histogram analysis of population numbers against
the percent difference in brightness. A clear “shift” in the population numbers are seen from when
the lockdown started. In other words, more people are seen in administrative units where lights
became dimmer after the lockdown. This condition is reverse from the pre-pandemic times when more
people are seen to be in administrative units that became brighter.
5. Conclusions
Responses to the coronavirus pandemic forced people to stay at home, either by governmental
enforcement or by choice because of their own health and safety concerns. The shuttering of
industries, factories, small businesses, and schools has had a significant impact on economies
globally. This study considers the effects of government-enforced lockdown since 24 March in India.
The visual satellite-derived images were substantiated with quantitative analyses. The proportion of
the population affected by the shutdown and the subsequent changes in lighting were also shown
through mapping and histograms. Additionally, the temporal nightly profile shows a distinct decline
in brightness from the exact date of the initial shutdown in India.
The global nightly and monthly Day–Night Band data are valuable in studying the impact of
the pandemic on the global economy. Based on the concepts developed for Chinese cities, it is shown
that similar analyses can be extended to other countries and cities. However, when dealing with
monthly DNB composites, the filtering of pixels with a low cloud-free coverage, those affected by
snow cover, and background noise is necessary. As for the nightly profiles, the VIIRS cloud mask was
used for clear observations, and to avoid the effect of reflected moonlight, and only those nights with
a lunar illuminance < 0.0005 was selected.
The Indian economy was already descending the “historic stepwells” from 2016 [25]. Economists
argue that the sudden declaration of a lockdown without any policies in place for the migrant
population defeated the purpose. These migrant populations were forced to leave cities after losing
their jobs overnight, and “scattered” in the town and villages, thus also spreading the virus. In fact,
23% of the population affected by coronavirus were in rural areas in April, but now it has risen to
54% [25]. The “scatter” of the migrant population after the shutdown is also seen in the monthly
difference images of Delhi, Kolkata, and Pune, where the town and villages in the outskirts are brighter
in the difference images.
Since no correlation was found between the total number of COVID-19 cases per million people
and the dimming of lights, it can be said that the dimming was connected entirely to the lockdown
and people staying at home. Thus, hypothetically, the lights can serve as an economic indicator of
the slowing down of the economy due to the lockdown, and of economic recovery as the lockdown is
slowly lifted. Again, if dimming persists in any area, it can be indicative of a prolonged economic
crisis in the said area.
The economic predictions for 2020–21 for India are grim. The Economist Intelligent Unit has
lowered the forecast for India’s growth from −5.8% to–8.5% [25]. The VIIRS DNB images can provide
easy, timely, objective, and continuous analysis of the effect of the pandemic on the economic landscapeRemote Sens. 2020, 12, 3289 16 of 17
at a granular level. Further research could be extended to the months beyond April for India, and to
other cities and countries.
Supplementary Materials: The following are available online at http://www.mdpi.com/2072-4292/12/20/3289/s1,
File 1: India data supplementary file.
Author Contributions: Conceptualization, C.D.E. and M.B.; Methodology, C.D.E., T.G.; Software, M.Z., F.-C.H.,
T.G.; Validation, T.G.; Formal Analysis, T.G., F.-C.H.; Investigation, T.G.; Data Curation, T.G.; Writing-Original
Draft Preparation, T.G.; Writing-Review & Editing, T.G., C.D.E., M.B.; Visualization, T.G. All authors have read
and agreed to the published version of the manuscript.
Funding: Algorithm development for production of the cloud-free DNB composites was funded under
a NASA research grant. Algorithm development for the production of nightly DNB profiles is funded by
the Rockefeller Foundation.
Acknowledgments: The authors sincerely appreciate the NASA/NOAA Joint Polar Satellite System (JPSS) for
providing the VIIRS data used in this study.
Conflicts of Interest: The authors declare no conflict of interest.
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