Instituto de
Altos Estudios Espaciales
“Mario Gulich”
Comisión Nacional de
Actividades Espaciales
Universidad Nacional
de Córdoba
Light At Night: some Sensors and some
Applications
Marina V. Compagnucci
‘Master in Emergency Early Warning and Response Space
Applications’
Seminar, September 2nd, 2010
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Comisión Nacional de
Actividades Espaciales
Universidad Nacional
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Contents
Abstract--------------------------------------------------------------------------------
3
Introduction --------------------------------------------------------------------------
4
Night-Time Light Sensors ----------------------------------------------------------
6
SAC-C satellite and the onboard HSTC -----------------------------------------AVIRIS ------------------------------------------------------------------------------The DMPS-OLS sensor -----------------------------------------------------------The ideal Night-time light sensor: NightSat -------------------------------------
6
7
8
9
DMPS-OLS types of products-----------------------------------------------------
10




Frequency
detection (Stable lights)--------------------------------------------------- 10
Radiance calibrated --------------------------------------------------------------------- 11
Average Digital Number -------------------------------------------------------------11
Differences among the three data sets-----------------------------------------------12
Spatial and Temporal properties of the DMSP-OLS data set -------------- 13


Spatial Characteristics --------------------------------------------------------------Coarse spatial resolution ------------------------------------------------Large Overlap between pixels ------------------------------------------Errors in the geolocation ------------------------------------------------Temporal Characteristics -----------------------------------------------------------
13
13
13
13
14
Some applications for Night-time Light products ---------------------------- 16






Urban Extent ------------------------------------------------------------------------Population ---------------------------------------------------------------------------Mapping Economy and Energy Consumption----------------------------------Economy… --------------------------------------------------------------------Energy consumption… ------------------------------------------------------Fisheries -----------------------------------------------------------------------------Protected areas ----------------------------------------------------------------------Night-time Light and Breast Cancer ----------------------------------------------
Conclusions --------------------------------------------------------------------------
16
17
18
18
20
21
22
25
27
Bibliography ------------------------------------------------------------------------- 28
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Abstract
Light measured at night is an unmistakable sign of humankind presence on Earth. It
points out human activity, be it on land or in water. This data coming from various
sensors offers information about human settlements and their development, shipping
fleets, and epidemiology.
Likewise it shows how mankind can affect their own surroundings and the possible
consequences on them, for instances protected areas near urban areas.
Light is not only seen as a pollutant but also as a proxy for mapping economy, resource
use and urban extension.
This works aims to present the sensors that measure Night-time Light, their products
and some of the various and creative uses of this data.
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Introduction
The presence of light across the Earth’s surface provides some of the most visually
stunning and thought provoking scenes from space. In this way, the human occupation
footprint is uniquely visible from space in the form of artificial night lighting – ranging
from the burning of the rainforest to massive offshore fisheries to the omnipresent lights
of cities and towns and related connecting road networks (Aubrecht et al. 2008, Doll
2008).
The discovery that lights could be observed at night from a sensor that was initially
conceived to observe clouds at night is one of the most fortuitous unforeseen benefits to
have come from remote sensing technology.
Figure 1: The World Atlas of the Artificial Night Sky Brightness
Cinzano et al. (2001) once said that light pollution is “the alteration of the ambient light
levels in the night environment produced by man-made light.”
Light pollution is a broad term referring to excessive or obtrusive artificial light caused
by bad lighting design. It includes such things as glare, sky glow, and light trespass.
Excessive and misdirected light from streetlights, homes, and towns not only interferes
with wildlife, stargazing, sleep habits, and professional astronomy, but it also wastes a
vast amount of energy (Gallaway et al., 2010).
Light pollution, a problem that affects almost any urban areas, is produced by a large
number of lighting sources, which spill light into the sky. Due to the presence of dust
and aerosols in the atmosphere the light is scattered, brightening the sky (Cinzano et al.,
2001). One of the effects of the brightened sky is that stars and other astronomical
objects, that are relatively faint, are lost in the background glow.
While most people have a sense that artificial lighting can interfere with birds and
insects, the effects are far more common, widespread, and serious than commonly
realized. Light pollution does substantial damage to wildlife, aesthetics, and even to
human health. Mammals, birds, amphibians, insects, fish and even plants are all affected
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by light pollution. Light pollution disrupts feeding, reproduction, sleeping and
migration. Indeed, problems from light pollution are so pervasive that “unless we
consider protection of the night, our best-laid conservation plans will be inadequate”
(Rich and Longcore, 2006).
For example, light pollution disrupts the migration patterns of nocturnal birds and can
cause hatchling sea turtles to head inland, away from the sea, and be eaten by predators
or run over by cars (Salmon et al., 2000). Human physiology is not immune to the
problem of light pollution. Davis et al., (2001) have concluded that there is an increased
risk of breast cancer in women due to lower levels of melatonin production that results
from light pollution. Ostensibly, light pollution keeps people from falling into a deep
sleep, which causes their bodies to decrease the production of melatonin..
Light pollution also interferes with both professional and amateur astronomy by
reducing the visibility of galaxies, nebulae, and other celestial objects.
One important issue with observing artificial night lighting from space that needs to be
addressed is a phenomenon known as skyglow. Even in its pristine state the night sky is
not completely dark. Some light comes from the stars, some from sunlight scattered by
space dust in the plane of the solar system, and some from atmospheric gases subject to
radiation and particle fluxes mostly from the sun (Clark, 2008). This is called natural
skyglow. Light emitted from human settlements in the atmosphere is refracted or
scattered by air and water molecules and suspended particles (atmospheric aerosol)
caused by dust, pollen, salt from sea spray, and waste products from industry.
Artificially illuminating the sky over great distances this is called artificial skyglow.
According to Clark (2008) the total artificial light flux emitted by a city tends to be
proportional to the product of two quantities, (1) the number of light sources and (2)
their mean output of light. Related to a growing economy and urban population growth
typically both of these quantities increase over time. Many people assume artificial light
provides safety and improves visibility. However, a large portion of lighting does
neither. Lighting that is overused, misdirected, or otherwise obtrusive is simply
pollution.
This works aims to present the sensors that measure Night-time Light, their products
and some of the various and creative uses of this data as well as their limitations.
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Night-time Light Sensors
 SAC-C
satellite and the onboard High Sensitivity Technology Camera
(HSTC)
The HSTC is a camera travelling onboard the SAC-C, the first Argentinean satellite
scientifically used, launched on 2000.
This camera has a spatial resolution of 300 m, the swath is 700 km and a spectral
coverage between 450 and 850 nm. It operates during the night overpass (22:30 local
time).
The purpose of this instrument is to measure light use and misuse in human settlements,
monitor thunder storms as well as forest fires (Figures 2 and 3). It is also used to study
dynamic and evolution of polar auroras (extracted from: www.conae.gov.ar ).
Figure. 2: Buenos Aires city Night-time light detection by SAC-C.
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Figure. 3: Santiago de Chile and Mendoza cities Night-time light detection by SAC-C.
 AVIRIS
In the absence of space borne sensors, researchers have used sensors mounted on
aircraft to fly high altitude missions. The Airborne Visible/Infrared Imaging
Spectrometer (AVIRIS) is one such sensor that may be used to acquire high-resolution
data over individual cities at night. The AVIRIS sensor is a hyperspectral imaging
system that senses in 224 very narrow bands (~10nm) from 0.41-2.45 μm. This
additional data source offers not only the advantage of an enhanced spatial resolution,
but also of enhanced spectral resolution too. AVIRIS data could address this issue. A
test flight over Las Vegas in 1998 suggested that there are distinctive spectral signatures
over the city (Elvidge and Jansen, 1999; Doll, 2003). Combining these two data sources
would be of use to help understand what the DMSP-OLS data is really showing at the
small scale, and therefore aid the assumptions one makes in macro-scale models using
nighttime imagery. There are various types of lighting used in cities. Each has distinct
spectral characteristics depending on the element used.
Commonly used types of high intensity discharge lights are high pressure sodium used
for street lights, mercury vapour and metal halide used in lighting car-parks and sports
stadiums. Mapping spectral patterns over cities could help to identify patterns of
residential, commercial and industrial land-use (Elvidge and Jansen, 1999). This could
be one way of filtering out the population component if concerned with assessing areas
of high economic activity.
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DMSP-OLS sensor
The Defense Meteorological Satellite Program, (DMSP) is the meteorological program
of the US Department of Defense, which originated in the mid-1960s with the objective
of collecting worldwide cloud cover on a daily basis (Kramer, 1994).
Orbiting the Earth 14 times a day means that global coverage can be obtained every 24
hours. It has incorporated the Operational Linescan System (OLS) instrument, having
two broadband sensors, one in the visible/near-infrared (VNIR, 0.4 – 1.1μm) and
thermal infrared (10.5 – 12.6μm) wavebands.
The OLS is an oscillating scan radiometer with a broad field of view (~3,000km swath)
and captures images at a nominal resolution of 0.56km (see Table I for details).
Low-level light amplification in the visible channel is facilitated through the use of a
photomultiplier tube (PMT) so clouds illuminated by moonlight at night can be
observed.
The boost in gain enables the unique capability of observing lights present at the earth’s
surface at night. Most of the lights are from human settlements (Elvidge et al. 1997) and
ephemeral fires (Elvidge et al. 2001). Furthermore gas flares and offshore platforms as
well as heavily lit fishing boats can be identified. NOAA-NGDC archives the long-term
DMSP data from 1992 to present.
Although this was done with the initial aim of producing night-time cloud imagery on
which to base short term cloud cover forecasts, a fortuitous unforeseen benefit was also
discovered: city lights, gas flaring, shipping fleets and biomass burning can also be
detected in the absence of cloud cover (Elvidge et al., 1997, Croft, 1978).
Type
Sensor
Satellite
Altitude and orbital period
Spectral range
Spatial resolution
Swath
Dynamic range
OLS – Oscillating Scan Radiometer
Photo Multiplier Tube (PMT)
NOAA-DMSP, sun-synchronous polar
orbit
830 km, 101 min
0.41-0.99 nm-10.0-13.4 nm
2.8 km at nadir (on-board averaging of
5×5 blocks at 0.56 km)
3000 km
10–9 W cm–2 sr–1 nm–1
range of 400-1000
Table I. Main properties of the DSMP-OLS instrument
(Extracted from: Barducci et al, 2006 Hyperspectral remote sensing for light pollution
monitoring)
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ideal Night-time light sensor: NightSat
The spatial resolution is recommended to be 25-50m. Based on experiments resampling
the 1.5m Cirrus imagery, this was determined to be the maximum resolution permissible
for delineating primary night-time lighting patterns. At this resolution and a swath of
80-90km, there would be a revisit period of ~30 days, at the equator yielding 12 views
per year. The overpass pass time would be 9.30pm local time to provide the temporal
consistency for change detection. As with DMSP, cloud and fire screening would be
done with a separate thermal band.
A key feature would include on-board calibration or a repeatable procedure for
calibrating sensor data to radiance units and allow comparisons over time and between
future sensors.
There are essentially three types of lights which are detected:
Flames such as lanterns and gasflares; Incandescent, where light is produced from a
heated filament; and Vapour lamps where lighting is generated by electrically charged
gasses such as mercury, sodium and neon.
Incandescent and vapour lamps are most common for outdoor lighting. Each type of
light has a distinctive spectral signature, which would be detectable if the new satellite
had four band multispectral sensors to define the predominant type or mixture of
lighting present (Figure 4).
The ability to distinguish different types of lighting will have benefit for a number of
applications. Classifying urban land use the use of different types of light is one
promising area, especially as lighting practices tend to be homogeneously determined at
some municipal, regional or even national scale.
For ecological applications, the presence of certain wavelengths determines whether
species will respond to lights or not. Sea turtle nesting and seafinding behaviors are not
affected by lights with only yellow wavelengths (Salmon 2006), whilst salamanders and
some birds show difficulty to navigate under certain lighting conditions. Some
salamanders are unable to navigate properly under yellow light, while insects are
attracted to short, ultraviolet light (Wiltschko and Wiltschko, 2002).
Figure. 4: Field spectra of four different
types of nocturnal lighting(extracted from
Doll,2008)
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DMSP-OLS types of products
At the time of writing, there are currently three processed versions of night-time light
data sets products which are released by NOAA-NDGC.
There are three different types of imagery associated with the DMSP-OLS data set.
• Frequency detection (Stable lights)
• Radiance calibrated
• Average Digital Number
 Frequency
detection (Stable lights)
Whilst lunar illumination was crucial to imaging clouds at night, it is a hindrance to
observing light sources from the ground due to the reduced contrast of light sources
from the ground. Other hindrances include glare from scattered sunlight and bad scan
lines. Filtering out bad scan lines (defined as 10 consecutive lights with no lights above
of below) also removes lit pixels caused by lightning (Elvidge, 2001). Over the sixmonth period a temporal composite was built up of cloud free images of the earth at
night. Compositing not only allowed clouds to be excluded, but also facilitated the
analysis of ‘stable lights’. The presence of stable lights is important in distinguishing
different light sources (e.g. city lights, shipping fleets or forest fires). However, the
variation in brightness between orbits means that it is not possible to establish a single
digital number (DN – or at sensor radiance) threshold for identifying VNIR emission
sources (Elvidge et al. 1997).
To overcome this, an algorithm was developed to automatically detect light using a
nested configuration of 200x200 and 50x50 pixel blocks. The light-picking algorithm
applies a threshold to the central 50x50 pixel block based on the histogram of the
surrounding 200x200 pixel block.
Using this detection algorithm, the pixel value is assigned according to the percentage
of times light was detected during cloud-free overpasses. Analysing the temporal
frequency and stability of lights can help to distinguish their most likely source. City
lights can be identified because they are temporally stable. However, forest fires can
also be identified due to their location and lack of temporal stability over the
compositing period. Through this process, the global night-time light composite can be
filtered into a variety of different products:
lights from human settlements and industrial facilities (city lights)
fires
gas flaring
shipping fleets
One issue with this data set is that certain areas of the globe receive more cloud-free
views than others. This creates problems for the fire product, which often occurs in
cloud-covered tropical. It should be noted that NOAA-NDGC do not feel 6 months
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worth of data was sufficient to fully discriminate between stable lights and fire. This is
currently being investigated using dedicated fire products from other satellites such as
MODIS, part of NASA’s Earth Observation System.
One of the biggest problems encountered with this first version of night-time lights was
low-light level pixel saturation.
 Radiance
calibrated
The problems of relatively low-level pixel saturation from the 6-bit sensor over bright
urban areas led to the experimentation and ultimate production of a new low-gain data
set. by varying the gain of the sensor
The thresholding technique used to create the stable lights data set
was found to perform poorly at identifying diffuse lighting, which is often dim and
spatially scattered across the landscape
The range was made deliberately ample on either side to allow for any future variations
in gain. Since the DN variation is a physically meaningful quantity as opposed to a ‘litfrequency’ observation, this makes it a flexible data set for use in a variety of modelling
schemes subject to finding appropriate relationships between radiance and the
parameters of interest.
 Average
Digital Number
The latest and now most extensive release of night-time light data comes in the form of
average Digital Number (DN) values.
The data was processed to use the high quality visible band data. Pixels were screened
to remove those with lunar illumination, glare, bad scanlines and lightning and other
marginal data (Elvidge et al., 2001).
This has recently been extended to a full archive of data from every sensor for every
year. This facilitates the analysis of changing lighting patterns in the following ways:
The appearance of new light sources
The disappearance of light sources
The expansion and contraction of light sources
Positive and negative changes in the brightness of lights.
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 Differences
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among the three data sets
When choosing which data sets to use, it is of great importance to keep in mind the
main scope of the research or even the field of study in which the results will be
involved.
A visual comparison is presented below in Figure 5 to gain an appreciation of the
differences between the three data sets described above. It is apparent the initial stable
lights product has far less variation than the other two and the imagery saturates very
rapidly at the maximum 100 percent frequency detection value. The stable lights
product has a large number of pixels taking the highest range of values. The distribution
of values is spread more evenly in the radiance-calibrated version with the majority of
pixels in the low range and only very few at the brightest radiance values indicating
sensor saturation (even with the gain turned down) (Doll, 2008).
Figure. 5: Comparison between the three different data sets over a portion of New England
(from left to right: Stable Lights, Radiance Calibrated, and Average DN). (extracted from Doll,
2008)
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Spatial and Temporal properties of the DMSP-OLS data set
There are both spatial and temporal properties of the DMSP-OLS data set which affect
the efficacy of the data set for its range of applications. Essentially these relate to
changes in the spatial extent of lit areas and variations in the brightness of a pixel over
time. Depending on the nature of the application at hand, the relative importance of
spatial extent versus information content (DN) will vary. Understanding how lights
behave in space and time will lead to the sound scientific use of the data set and
minimize misinterpretation of the results.
 Spatial
Characteristics
The principal spatial consideration to bear in mind when working with night-time light
imagery is the extent to which the spatial area depicted on images matches the true
extent of lit area on the ground. Imagery from the DMSP-OLS satellite has a tendency
to overestimate this parameter, an effect generally referred to as “blooming” (and more
recently “overglow”) in the literature. Small et al., (2005) cites three main reasons for
this phenomenon, which are discussed briefly.
 Coarse spatial resolution
Although the DMSP-OLS sensor has a nominal resolution of 1km, this has been
resampled from the 2.7km native resolution of the sensor. An inherent feature of
satellite imagery is that it will generalize ground based features to a single DN or
radiance value. In the case of night time light imagery, this manifests itself as pixels
appearing lit, when the light source is not being emitted over the entire pixel area.
 Large Overlap between pixels
A feature of the data acquisition process is that there is a large overlap (some 60%)
between pixels. This means that light observed in one location has the chance to be
recorded in more than one pixel. This can contribute to a larger lit-area being detected
than is actually the case.
 Errors in the geolocation
Errors are inherent in the projection process. Data is recorded in arrays, the spatial
position of these data values are calculated from the navigation data onboard the
satellite. These values are then projected onto a 1km grid. The grid itself is an
approximation of the Earth’s surface corrected for the topographic variation. Each
transformation introduces errors into the process. To this a fourth factor, the
atmospheric water vapour content can be added. Lights can appear dimmer and more
spatially diffuse where thin clouds are present, which is consistent with similar effects
of image quality reduction for other optical (or “passive”) sensors.
The combined effect of these factors ultimately results in a general overestimation of
area, which can be deceiving due to the visually stunning nature of the data set.
Figure 6 illustrates the blooming effect, and also shows different sources of light that
can be observed from the DMSP- OLS sensor. It is apparent that cities appear lit, but so
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too do traffic on unlit sections of highway and areas that are unlit but which are affected
by overglow.
To correct this effect, thresholding (excluding values below a certain value) has been
used to reduce the area of the lights.
However, it soon becomes apparent that there is no single threshold that can be applied
which would match the urban delimitation for all cities. In particular, thresholding large
urban areas tend to result in the attenuation of lights associated with smaller settlements.
The implication of this finding is that a range of thresholds needs to be applied
depending on the size of the settlement involved (Small et al., 2005)
Figure. 6: combined effect of the three previous factors ultimately results in a general
overestimation of area (extracted from Doll, 2008).

Temporal Characteristics
Little work has been done regarding this feature, but that published suggests that low
level saturation pixel, prevent city centre analysis, but it does allow investigation of the
spatial expansion of lights in peri-urban areas.
Temporal changes involve constructing tri-band red, green, blue false color composites
with a different year for each channel. The convention has been to put the 1992 year
through the red channel, 1998 in the blue, and 2003 in green.
Superimposition of these channels can reveal whether lighting has been lost (red hues),
gained (green hues) or emerged then disappeared (blue hues). This is most striking in
places which have undergone massive economic/political change such as the countries
of Eastern Europe following the fall of communism and the transition to free market
economies. In Figure 7, we see a temporal color composite, showing that the former
Soviet republics of Ukraine and Moldova are dominated by red hues indicating lights
were most prevalent in 1992 and then declined in 1998 and 2003. This is sharply
contrasted by Poland and Romania to the west whose greener and bluer hues indicate
spatial expansion and brightening of lights.
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The problem with the three color composites over long time periods is that they come
from sensors on board different satellites and there is no internal or cross calibration
between them. For practical purposes this means that we cannot say with any certainty
whether changes in the brightness of lights are due to changing ground conditions or to
changes in the sensor over time (Doll, 2008).
Poland
Ukraine
Moldova
Romania
Figure. 7: temporal color composite, showing that the former Soviet republics of Ukraine and
Moldova are dominated by red hues( lights were most prevalent in 1992). This is contrasted by
Poland and Romania to the west whose greener and bluer hues indicate spatial expansion and
brightening of lights. (Extracted from Doll, 2008)
Given the range of variations that can occur with the night-time lights data set, any
application will need to take into account the limitations of using this data source. For
applications where light will be used solely as a delimiter of urban extent, then
considerations of blooming (overglow) are most pertinent. Of the two phenomena,
overglow is currently the best understood and strategies exist in the literature (Small et
al., 2005) to account for its effects.
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Some applications for Night-time Light products
To shed new light on prevalent problems. Brief comments on: cancer, fisheries, poverty,
urban extent, energy consumption and protected areas.

Urban Extent
Global land cover maps are spatial classifications of the Earth’s surface. They are
traditionally focused on the major vegetated biomes and cropland areas, with urban
areas being the residual.
This tends to underestimate urban area. By contrast night-time light imagery explicitly
maps lit areas, however the overglow characteristic, means that the resulting maps tend
to overestimate urban extent. Doll and Muller (1999) found that unfiltered night-lights
covered 20 times as much area at the continental level compared to urban delineation of
the Digital Chart of the World data set.
Considering artificial skyglow entails that the DMSP satellite sensors record much
larger areas than just the immediate location of the lighting sources. Using satellite
observed nighttime lights for delineating urban areas (Small et al., 2005) and
approximating impervious surfaces (Elvidge et al., 2007) requires eliminating skyglow
from the data, i.e. by applying thresholds to the digital number values.
Previous studies of DMSP-OLS stable night lights have shown encouraging agreement
between temporally stable lighted areas and various definitions of urban extent.
However, these studies have also highlighted an inconsistent relationship between the
actual lighted area and the boundaries of the urban areas considered. Applying detection
frequency thresholds can reduce the spatial overextent of lighted area (‘‘blooming’’) but
thresholding also attenuates large numbers of smaller lights and significantly reduces
the information content of the night lights datasets.
This suggests that night lights could provide a repeatable, globally consistent
way to map size and spatial distributions of human settlements larger than some
minimum detectable size or brightness (Small et al, 2005).
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Population
The night-time satellite sensor data provided by the DMSP/OLS have been used for
global/continental urban mapping, showing linear relations with other socio-economic
variables such as population, Gross Domestic Product, and electrical power
consumption
Researchers used DMSP-OLS data to create the first quantitative and accurate depiction
of the artificial brightness of the night sky and make it available to the scientific
community and governments. This data is particularly valuable because of the singular
lack of data on light pollution. Direct measures of light pollution are ad hoc, groundbased measures are sporadic and limited (Cinzano et al., 2001).
This lack of direct data has forced researchers to rely almost exclusively on populationbased models of light pollution. Indeed, there is a very strong connection between
population and light pollution. Nevertheless, the apparent proportionality between
population and sky glow breaks down going from large scales to smaller scales and
looking in more detail (Cinzano et al., 2001).
Amaral et al, (2005) explored the potential of the DMSP sensor data for regional studies
analyzing the correlation between DMSP night-time light foci and population (Figure
8), and the correlation between DMSP night-time light foci and electrical power
consumption in the Amazonia.
It was found that the night-time light foci were related to human presence in the region,
including urban settlements, mining, industries, and civil construction.
Thus the DMSP/OLS imagery can be used as an indicator of human presence in the
analysis of spatial–temporal patterns in the Amazonia region. These results are very
useful considering the continental dimension of Amazonia, the absence of demographic
information between the official population census, taking place every 10 years, and the
dynamics and complexity of human activities in the region. Therefore DMSP night-time
light foci are a valuable data source for global studies, modeling, and planning activities
when the human dimension must be considered throughout Amazonia.
Figure. 8: Linear relation between DMSP night-time light area and the urban population for
municıpios of the state of Para, Brazil.(extracted from Amaral et al., 2005)
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Mapping Economy and Energy Consumption
The light pollution data used in this paper are remote sensing data from satellite
observations. The raw data are from the Defense Meteorological Satellite Program
(DMSP) Operational Linescan System (OLS).

Economy…
Night-time light remote sensing data has been shown to correlate with national-level
figures of Gross Domestic Product (GDP). Night-time light imagery was found to
correlate with Gross Regional Product (GRP) across a range of spatial scales.
The radiance-calibrated dataset is usually used in place of the time frequency composite
data (Elvidge et al., 1997) employed on previous researchs. The radiance calibrated data
set facilitates the investigation of the relationship between brightness of the lights and
GDP rather than lit area.
The first ever global map of GDP produced using a country level lit area–GDP
relationship, was done by Doll (2003).
The importance of scale as a concept is central to developing an understanding of
human-environment interactions. While scale can have spatial, temporal, quantitative or
analytical dimensions, the diversity of disciplines incorporated into the Human
Dimensions of Global Change may only use a sub-set of these domains to understand
their subject.
The previous section made reference to the mismatch in population and radiance at fine
scale resolutions. Given the coincidence of brightest lights and downtown areas which
are nodes of economic activity, an obvious extension of the application of night-time
lights is to map economic activity.
Generally, the maps obtained during these studies, use only light to distribute economic
activity (Figure 9). This is a reasonable assumption to make in developed countries
where industry and service sectors can comprise over 90% of the economy. Although
agricultural productivity is spatially more widespread it is represented in these maps as
nodes – i.e. the map records the agricultural activity in the towns which emit light, not
in the fields where crops are being grown. This is an important caveat to the maps
described here and one which would be the first item to address when improving these
maps and extending them into the developing world where agricultural comprises a
larger section of the national economy (Doll et al, 2006).
An inverse or complementary application of night-time light data is to identify the
location of the poor through the absence of light. However, some cultural differences in
light use, could have led to erroneous interpretation results.
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Figure 9: Map of estimated economic activity based on DMSP-OLS radiance-calibrated nighttime lights for 11 countries in the European Union (extracted from Doll et al, 2006).
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Energy consumption…
Economic studies quantifying night-time light damage are only now beginning, mainly
due to understanding that light pollution wastes energy. Accordingly, poor lighting
design contributes to increased carbon dioxide emissions and global warming.
Electricity needlessly being generated at a cost of $6.9 billion a year in the United
States. Furthermore, this unnecessary electricity usage generated an additional 66
million metric tons of CO2 (Doll et al., 2006).
With all of these, it is very often the case that the good in question becomes problematic
when it is found in the wrong location or in the wrong amount, or when it affects the
wrong population.
We might argue that the good becomes a pollutant when its effects are something other
than its intended purpose. Similarly, for humans, light that improves visibility is a good.
However when lighting causes glare, or deepens shadows, or washes out the stars, this
reduces visibility. Then, such light is light pollution.
Neon lights might improve the visibility of a sign or a storefront. However, a thousand
such displays merely add to the clutter and reduce the visibility of any individual sign.
(Gallaway et al., 2010).
A research group combined unique remote sensing data on light pollution with
economic data from the World Bank to estimate fractional logit regression light
pollution models (Gallaway et al., 2010), showing that population, as measured by the
percent of the population living in urban areas, remains an important explanation for the
existence of light pollution.
However, real per capita GDP also tends to be a highly significant variable in
explaining the percent of a country's population affected by different levels of light
pollution.
The relationship between income and light pollution is non-linear, since other economic
factors such as foreign investment and land use patterns also tend to be significant.
Quantifying the link between real GDP and various levels of light pollution across the
globe is a significant first step in correcting economists' neglect of this important
environmental issue.
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Fisheries
Illex argentinus, the Argentine short-finned squid, is an important species within the
Patagonian shelf ecosystem, where it supports a major multi-national fishery. The
fishing fleet operating in this region is comprised of jigging vessels which attract squid
using powerful incandescent lights. These fishing lights are detectable in remotely
sensed satellite imagery which makes the fishery unusually amenable to a large-scale
analysis of its spatial dynamics.
Using fishing light imagery allows a synoptic view of effort in both regulated and
unregulated fisheries, and has been used as a method of detecting
and quantifying fishery activity in a number of locations around the world.
Long-term inter-annual variability in fleet distribution and extent was examined using
imagery from the Defense Meteorological Satellite Program-Operational Linescan
System (DMSP-OLS) for the period 1993–2005, by Waluda et al (2010).
The fishery was found to occupy a wide area across the shelf and slope, with regions of
consistent fishing activity observed on the high seas (45–47◦ S) and to the north of the
Falkland Islands (Malvinas). Distribution of the fishery over the 13-year study period
was variable, with 28% of the fished area occupied in 1–2 years, and 7% of the area
occupied in 12–13 years.
Annual catch levels were positively associated with the extent of the area occupied by
the fleet. Higher catches corresponded to the fishery occupying a wide latitudinal range,
whereas lower catches were observed during 2004 and 2005 corresponding to a
contraction of the fishery away from the south of its range. In years of very high
catches, fishing took place along almost the entire latitudinal range of the species. Due
to the intensity of fishing, changes in the distribution of the fleet can reflect shifts in the
distribution of I. argentinus; this has potential for the long-term monitoring of this
highly variable squid fishery (Waluda et al , 2010).
The variable distribution of the fleet over the 13 years of the study is most likely to be
related to shifts in ocean dynamics. I. argentinus has been shown to be associated with
thermal gradient regions occurring between different water masses (for example at the
interface between the Falkland current and Patagonian shelf waters, or between the
Brazil and Falkland currents), which can vary widely in location and extent from year to
year. The distribution of the fleet is therefore likely to be indicative of feeding
aggregations of squid occurring at these fronts. An added complication is that squid
schools may occur at different depths dependent on water temperature variability
(Bazzino et al., 2005), which will further contribute to variability in fleet extent but
cannot be directly assessed using remotely sensed satellite imagery.
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Protected areas
The following paragraphs try to contribute with insights about a novel point of view
regarding global assessment of light pollution impact on protected areas.
Most applications related to ecological impacts of artificial night lighting focus on
adverse effects on light-sensitive ecosystems or species, such as coral reefs (Aubrecht et
al. 2008), sea turtles (Salmon, 2006), and migrating birds (Montevecchi, 2006).
As far as coral reefs concern, they are of great importance for a number of reasons: they
are areas with remarkable biodiversity; are important for coastal protection; they
provide people with seafood and new medicines; and they have a great recreational
value. Corals and coral reefs are extremely sensitive; slight changes in the reef
environment may have detrimental effects on the health of entire coral colonies
(Aubrech et al.; 2008).
Corals are highly photosensitive – many species synchronize their spawning through
detection of low light intensity from moonlight and coral reef structure is strongly
influenced by illumination. Other marine invertebrates in coral communities
synchronize reproduction by monthly patterns of lunar illumination (Bentley et al.,
2001).
Such extensive structuring of this community by light is undoubtedly disrupted by
artificial lighting, which has no ecological analogue – moonlight, starlight and
bioluminescence are the only sources of light to which marine organisms are adapted
(Hobson et al., 1981).
Aubrecht et al. (2008) calculated a lights proximity index (LPI) assuming that the
nearer a coral reef is located to an artificial night lighting source the greater is its
potential endangerment from direct and indirect impacts (Figure 10).
Figure 10: The area of Puerto Rico was chosen to show coral reefs being at high risk by
artificial night lighting caused by development (left part). There are many reefs within a 25 km
radius of cities and towns having high LPI values due to their close proximity to the lighting
sources (which are shown as reference on the right). Reefs in regions around big cities such as
the capital, San Juan, especially show particularly high LPI values and the corresponding red
colour in the image (extracted from Aubrecht et al., 2008).
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As the previously cited researches with coral reefs have demonstrated, that system is
highly susceptible to light interference, hence on a different approach as when dealing
with urban areas and their extent, when ecological issues are the ones being analyzed,
skyglow is a significant factor of light pollution as already very low light intensities
alter the natural environment. That is the reason why, when modeling ecological impact
of artificial night lighting skyglow is considered to be an important contribution
(Aubrecht et al., 2008).
Following a global assessment of the degree to which each country’s total land area is
legally protected, light pollution impact and approximated human influence were
calculated.
To delineate the protected areas worldwide, data from the 2007 Annual Release of the
World Database was used provided by UNEP-WCMC (United Nations Environment
Programme-World Conservation Monitoring Centre)
In the study carried out by Aubrecht et al., 2010, neither marine protected areas nor
historical, archaeological, or cultural sites, nor those areas that were listed as proposed
but not yet designated, were not considered for the analysis.
To carry on with the research, resources such as nighttime lights data acquired by the
U.S. Air Force Defense Meteorological Satellite Program (DMSP) Operational
Linescan System (OLS) and the WDPA data (provided online for download as ESRI
shapefiles and consist of both polygon and point features) were acquired (Figures 11
and 12).
Following what was described in section DMSP OLS products; to identify the best
nighttime lights data for creating an annual composite Aubrecht et al., 2010 followed
the next standards:
• Only the center half of the orbital swath was used (best geolocation and sharpest
features)
• Sunlight and moonlight were not present
• No solar glare contamination was allowed
• Only cloud-free images were used (based on thermal detection of clouds)
Two different approaches were used to relate the areal distribution of artificial night
lighting to the areas under protected status, by means of two new indices resulted from
combining the global protected area distribution data and nighttime lights data:
PALI, a Protected Area Light Pollution Index (report the proportion of protected
areas per country being directly affected by light pollution)
PAHI, a Protected Area Human Impact Index . The last one refers to a binary lights
data set in which the focal neighborhood operator assigned the value 1 to each
pixel within a 5px radius circle around a lighting source, whereas all pixels
further away we classified as 0 (not affected). The result was linked with a
country-protected area
With the first one, the direct impact of lighting is evaluated, hence referred to as light
pollution, i.e.the direct spatial overlap between satellite observed nighttime lights as
derived from DMSP-OLS and protected areas as delineated in the WDPA.
The second approach, considers that DMSP nighttime lights data, are an excellent proxy
measure for human activities that impact neighboring areas. This results in having
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pixels within a 5 pixel radius (about 5 km at the equator) of the actual lit area identified
as being potentially at risk.
The results indicate that regions in Europe and Asia Minor, the Caribbean, South and
East Asia as well as in the Eastern part of the United States are most affected.
Introducing aggregated data on biomes reveals that temperate broadleaf and mixed
forests suffer the biggest impact both in terms of general light pollution as well as
lighting in protected areas.
Figure 11: Data from DMSP-OLS, nighttime lights of the world, sample figure (extracted from
Aubrecht et al., 2010).
Figure 12: WDPA data (extracted from Aubrecht et al., 2010 ).
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Night-time Light and Breast Cancer
Recent studies of shift-working women have reported that excessive exposure to
light at night (LAN) may be a risk factor for breast cancer. However, no studies have
yet attempted to examine the co-distribution of LAN and breast cancer incidence on a
population level with the goal to assess the coherence of these earlier findings with
population trends. Coherence is one of Hill’s “criteria for an inference of causality.
Nighttime satellite images were used to estimate LAN levels in 147 communities in
Israel. Multiple regression analysis was performed to investigate the association
between LAN and breast cancer incidence rates and, as a test of the specificity of our
method, lung cancer incidence rates in women across localities under the prediction of a
link with breast cancer but not lung cancer.
After adjusting for several variables available on a population level, such as ethnic
makeup, birth rate, population density, and local income level, a strong positive
association between LAN intensity and breast cancer rate was revealed ( p , 0.05), and
this association strengthened ( p , 0.01) when only statistically significant factors were
filtered out by stepwise regression analysis. Concurrently, no association was found
between LAN intensity and lung cancer rate. These results provide coherence of the
previously reported case-control and cohort studies with the co-distribution of LAN and
breast cancer on a population basis. The analysis yielded an estimated 73% higher
breast cancer incidence in the highest LAN exposed communities compared to the
lowest LAN exposed communities (Figure 13) (Kloog et al, 2008).
Figure 13: Hotspot analysis of breast cancer
rates. Note: Red circles mark clusters of
adjacent localities with significantly high
rates of cancers (relative to the global
mean), while green circles mark geographic
clusters of localities with significantly low
cancer rates.
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Breast cancer incidence increases rapidly as societies industrialize. Many changes occur
during the industrialization process, one of which is a dramatic alteration in the lighted
environment from a sun-based system to an electricity-based system.
Increasingly, the natural dark period at night is being seriously eroded for the bulk of
humanity. Based on the fact that light during the night can suppress melatonin, and also
disrupt the circadian rhythm, it was proposed in 1987 that increasing use of electricity to
light the night accounts in part for the rising risk of breast cancer globally. Predictions
from the theory include: non-day shift work increases risk, blindness lowers risk, long
sleep duration lowers risk, and population level community nighttime light level codistributes with breast cancer incidence.
Thus far, studies of these predictions are consistent in support of the theory. A new
avenue of research has been on function of circadian genes and whether these are
related to breast cancer risk. In particular, a length variant of Per3 (5-VNTR) has been
associated with increased risk in young women, and this same 5-VNTR variant has also
been found to predict morning diurnal type and shorter sleep duration compared to the
4-VNTR variant.
An important question is how an effect of light-at-night (LAN) exposure on breast
cancer risk might be modified by polymorphisms and/or epigenetic alterations in the
circadian genes, and conversely whether light-at-night exposure (e.g., shift work) can
induce deleterious epigenetic changes in these genes (Stevens, 2009).
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Conclusions
Light at night is a visible evidence of our activities, our change in habits and the way we
change the surroundings.
As appreciated most Light at Night-Time research, uses data obtained from the Defense
Meteorological Satellite Program Operational Linescan System (DMPS-OLS) but there
are some that are incurring in the use of hyperspectral instruments to investigate light
pollution.
Night-time light is an excellent proxy for human activities as well as for human
settlements. Hence, it is a reliable tool when dealing with the lack of data on extended
territories or with little accessibility, such as Amazonia.
It can also provide information on resources use or misuse, for instance electrical power
utilization.
When light passes from benefit to a pollutant, it becomes a serious problem with
implications for wildlife, human health, scientific research, energy consumption, global
warming, and the unchanging pastime of observing the night sky.
Pristinely dark skies are very scarce in the developed world and most of the world's
population lives under skies with at least some light pollution.
Recognizing the importance of a dark period during the day should help us understand
the weight of the decisions made for example when planning human settlements near
protected areas, and also allows identify those ones that may require additional
resources for management owing to their proximity to urban areas.
Scotobiology, a science recently initiated as a branch of chronobiology, is bringing
about evidence supporting the need for that period of darkness, mainly in more
photosensitive ecological systems, such as corals and coral reefs.
An interesting and novel point of view is introduced when considering Light at Night
Time as a human risk factor, much more when referring to breast cancer. There is huge
amount of previous evidence mentioning some possible reasons for this relationship,
such as gene mutation, melatonin circadian rhythm disruption and others. Currently
there are some groups studying the likely connection between Light at Night time and
prostate cancer.
Everything previously cited is true, if the advantages in addition to the disadvantages of
this data are acknowledged. In that way, the results offered by it use can be properly
interpreted. Facors to take into account are among others, scale of the research’s
question, which product to use to asses in the most fitting way that question, as well as
the field involved in the decision making.
Light at night has many other uses than those cited in this seminar, for example ice and
snow detection. It can also detect auroras, green house emissions and could be involved
in disasters managements such as hurricane and forest fires. It could be also involved in
the control of fishing fleets and their activities areas.
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Instituto de Altos Estudios Espaciales “Mario Gulich” - Centro Espacial Teófilo Tabanera
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Comisión Nacional de
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Instituto de Altos Estudios Espaciales “Mario Gulich” - Centro Espacial Teófilo Tabanera
Ruta Prov. C45 – Km 8 (5187) Falda del Carmen – Pcia. De Córdoba, Argentina
Tel.: 54-3547-431000 int 1165/1034 - Fax: 54-3547-424566