Urban mapping using DMSP/OLS stable night-time light: a review
ABSTRACTThe Defense Meteorological Satellite Program/Operational Linescane System (DMSP/OLS) stable night-time light (NTL) data showed great potential in urban extent mapping across a variety of scales with historical records dating back to 1990s. In order to advance this data, a systematic methodology review on NTL-based urban extent mapping was carried out, with emphases on four aspects including the saturation of luminosity, the blooming effect, the intercalibration of time series, and their temporal pattern adjustment. We think ancillary features (e.g. land surface conditions and socioeconomic activities) can help reveal more spatial details in urban core regions with high digital number (DN) values. In addition, dynamic optimal thresholds are needed to address issues of different exaggeration of NTL data in the large scale urban mapping. Then, we reviewed three key aspects (reference region, reference satellite/year, and calibration model) in the current intercalibration framework of NTL time seri
IJRS SPECIAL ISSUE PAPER
Urban mapping using DMSP/OLS stable night-time light:
Xuecao Li and Yuyu Zhou
Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA, USA
The Defense Meteorological Satellite Program/Operational
Linescane System (DMSP/OLS) stable night-time light (NTL) data
showed great potential in urban extent mapping across a variety
of scales with historical records dating back to 1990s. In order to
advance this data, a systematic methodology review on NTL-based
urban extent mapping was carried out, with emphases on four
aspects including the saturation of luminosity, the blooming
eﬀect, the intercalibration of time series, and their temporal pat-
tern adjustment. We think ancillary features (e.g. land surface
conditions and socioeconomic activities) can help reveal more
spatial details in urban core regions with high digital number
(DN) values. In addition, dynamic optimal thresholds are needed
to address issues of diﬀerent exaggeration of NTL data in the large
scale urban mapping. Then, we reviewed three key aspects (refer-
ence region, reference satellite/year, and calibration model) in the
current intercalibration framework of NTL time series, and sum-
marized major reference regions in literature that were used for
intercalibration, which is critical to achieve a globally consistent
series of NTL DN values over years. Moreover, adjustment of
temporal pattern on intercalibrated NTL series is needed to trace
the urban sprawl process, particularly in rapidly developing
regions. In addition, we analysed those applications for urban
extent mapping based on the new generation NTL data of
Visible/Infrared Imager/Radiometer Suite. Finally, we prospected
the challenges and opportunities including the improvement of
temporally inconsistent NTL series, mitigation of spatial heteroge-
neity of blooming eﬀect in NTL, and synthesis of diﬀerent NTL
satellites, in global urban extent mapping.
Received 1 October 2016
Accepted 5 December 2016
Although the Defense Meteorological Satellite Program/Operational Linescane
System (DMSP/OLS) was originally developed for the purpose of detecting the global
distribution of clouds and cloud top temperature, it has become a predominate
source for observing a series of faint emission sources since 1970s, such as city
lights, shipping ﬂeets, industrial sites, gas ﬂares, and ﬁres (Croft 1978;Elvidgeetal.
1997b;Imhoﬀet al. 1997;Huangetal.2014). The DMSP/OLS sensor contains two
CONTACT Yuyu Zhou firstname.lastname@example.org Department of Geological and Atmospheric Sciences, Iowa State
University, Ames, IA 50011, USA
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2017
© 2017 Informa UK Limited, trading as Taylor & Francis Group
spectral bands (visible/near-infrared –VNIR, 0.4 –1.1 μm and thermal infrared –TIR,
10.5 –12.6 μm) with a swath of ~3000 km (Doll 2008). In addition, this data set has a
near global coverage (spanning from −180°to180°inlongitudeand−65° to 65° in
latitude), and it spans two decades (1992–2013). Through geolocation processing, the
nominal resolution of DMSP/OLS data set is 30 arc second, which equals to 1 km in
According to the latest World Urbanization Prospects (United Nations 2015), the
percentage of global urban population has exceeded 54% in 2014, and this proportion
is estimated to reach 66% by 2050. More importantly, most of the newly increased
population in the near future is likely to occur in developing regions (Africa and Asia),
which will lead to a series of environmental or ecological issues related to the rapid
urbanization process (Li and Gong 2016b). Therefore, acquiring the historical record of
urban sprawl or urban population change, as well as predicting its future trajectories, is
of great importance to sustainable urban development. The DMSP/OLS night-time light
(NTL) data provide a particular perspective with a unique data set to study urban
expansion and relevant sociodemographic activities across a variety of spatial scales,
such as population density (Zhuo et al. 2009; Sutton, Elvidge, and Obremski 2003;
Sutton et al. 2001;Lo2002; Amaral et al. 2006), physical urban extent mapping
(Elvidge et al. 1997b,2007; Zhou et al. 2014; Small, Pozzi, and Elvidge 2005), energy
consumption (Doll and Pachauri 2010; Letu et al. 2010), socioeconomic activities (Chen
and Nordhaus 2011; Zhao and Samson 2012), and environmental changes (e.g. light
pollution) (Davies et al. 2013; Falchi et al. 2016). Presently, there are three categories of
DMSP/OLS data sets, including the stable lights, the calibrated radiance, and the
average digital number (DN) (Elvidge et al. 1999; Doll 2008; Elvidge et al. 2009 2009a).
Among them, the stable NTL dataset is the most widely used one for regional or global
urban studies (Huang et al. 2014) because (1) the radiance calibrated dataset is only
available for speciﬁc years without continuous time series; and (2) the average DN
dataset may contain other emissions sources (e.g. ﬁres and other background noise)
in addition to city light.
One of the most important applications of DMSP/OLS stable NTL dataset is
mapping urban extent (or boundary) and its temporal dynamics at the regional or
global scales (Elvidge et al. 2007; Huang et al. 2014;Zhouetal.2014;E
1997a). Although a wide range of relevant studies have been carried out, most of
them focus on particular local or regional areas using varying ancillary datasets or
mapping approaches (Huang et al. 2014;LiuandLeung2015;Maetal.2014;He
et al. 2006;Liuetal.2012;Yietal.2014;Milesietal.2003). Potential challenges are
still remaining for pursing a globally consistent mapping of urban area using the
DMSP/OLS stable NTL dataset. These challenges include the sensitivity of threshold
for obtaining urban clusters (Liu and Leung 2015;Zhouetal.2014), saturated DN
values in urban core regions (Zhang, Schaaf, and Seto 2013; Cao et al. 2009),
temporally inconsistency of NTL dataset over years (Zhao, Zhou, and Samson 2015;
Elvidge et al. 2009b), and complicated urban sprawl patterns with diﬀerent develop-
ment levels (Zhang and Seto 2011;Maetal.2012). Few eﬀorts have been made to
summarize the diﬀerence of current approaches or comparison of diﬀerent mapping
results, although there are some general works on meta-analysis or summary of
speciﬁc applications of NTL data (Huang et al. 2014;Li,Zhao,andXi2016a).
2X. LI AND Y. ZHOU
Hence, a systematic methodology review on these topics is urgently needed, for
achieving a globally consistent mapping of urban dynamics with NTL datasets over
past 20 years (Elvidge et al. 2007;Zhouetal.2015).
This article aims to provide a comprehensive review on methodologies of urban
mapping using DMSP/OLS NTL data. The reminder of this article is organized as follows.
In Section 2, we discussed the challenges and reviewed current studies in urban map-
ping using DMSP/OLS stable NTL data. Thereafter, a brief introduction of the upgraded
Visible/Infrared Imager/Radiometer Suite/Day Night Band (VIIRS/DNB) data was pre-
sented in Section 3. At the end, we prospected future opportunities of spatiotemporal
urban extent mapping using NTL data in Section 4.
2. NTL-based urban mapping and challenges
The deﬁnition of ‘urban extent’is diﬀerent when referring to diﬀerent cases (Liu et al.
2014). For NTL relevant studies, the commonly used terms include impervious surface,
human settlement, urban clusters, population density, human population, and urban
boundary (Elvidge et al. 1997a; Sutton et al. 1997; Elvidge et al. 1999; Sutton et al. 2001;
Henderson et al. 2003; Elvidge et al. 2007; Zhou et al. 2015). In this review, we covered
three groups of studies in NTL-based urban mapping, including population density,
urban extent, and impervious surface area (Figures 1(a)–(f)). The ﬁrst is population
density mapping in a perspective of land use (Figure 1(a)), by linking NTL data with
census (e.g. demographic) data (Figures 1(d) and (e)). The second one is urban extent
(Figure 1(b)), which indicates the boundary that separates urban areas from surrounding
rural areas based on NTL images (Figure 1(e)). The third one refers to impervious surface
mapping (Figure 1(c)), which excludes other land cover types (e.g. water, vegetation, and
bare land) within the urban domain (Figures 1 (e) and (f)). We included population
density mapping in this review because (1) NTL datasets are often conjunctively used
with demographic inventory (or census data), and the output of them can be used as an
intermediate to map the urban extent (Elvidge et al. 2007; Lu and Weng 2006;
Martinuzzi, Gould, and Ramos González 2007); and (2) population density essentially is
a crucial indicator to describe the urban extent (Angel et al. 2005; Schneider, Friedl, and
Presently, studies on NTL-based urban mapping mainly focus on two domains as
shown in Figure 2. Both spatial and temporal dimensions of NTL data have been
extensively explored for urban mapping. At the spatial dimension, the inherent
deﬁciencies of NTL dataset, that is, the saturated DN values in the urban core region
and blooming eﬀects on the urban–rural boundary, limit its application in urban
mapping at a large extent (Zhang, Schaaf, and Seto 2013;Elvidgeetal.2007). At the
temporal dimension, due to the lack of on-board calibration, additional processes on
the annual composites of stable NTL data, such as intercalibration or temporal
pattern adjustment, are needed to investigate the urban dynamics (Elvidge et al.
2009b;ZhangandSeto2011). Consequently, a wide range of studies have been
carried out to address these issues for consistent urban mapping at the regional or
global scales. In this review, we discussed these issues in the following sections with
INTERNATIONAL JOURNAL OF REMOTE SENSING 3
2.1. Spatial dimension
2.1.1. Saturation of NTL luminosity
There exists a notable saturation eﬀect of luminosity (i.e. the same or similar DN values
in urban core area) in the DMSP/OLS NTL data because (1) the nominal resolution of
Figure 2. Research domains on NTL-based urban mapping.
Figure 1. Night-time light (NTL)-based urban mapping: (a)–(c) are contents of NTL-based urban
mapping; (d)–(f) illustrate necessary inputs for generating these maps.
4X. LI AND Y. ZHOU
1 km is resampled from the 2.7 km native resolution (Doll 2008); and (2) the limit of
DMSP/OLS sensor sensitivity is 6 bit (i.e. DN value ranges from 0 to 63). Although DMSP/
OLS radiance data with on-board gain setting is an accurate way to diﬀerentiate the
saturated luminosity in the urban core region, it is still limited for dynamic urban
mapping because the implementation of radiance calibration is diﬃcult, and this data
set is only available for limited years (Elvidge et al. 1999; Doll 2008; Elvidge et al. 2001;
Letu et al. 2012). A variety of attempts have been made to mitigate this saturation eﬀect
by using ancillary data to retrieve the heterogeneity within the urban extent. In general,
there are two widely used ancillary datasets, land surface features and census popula-
tion data, for conjunctively use with NTL data to map urban extent.
The saturation eﬀect of luminosity in urban core region can be mitigated by incor-
porating land surface features as an intermediate output to map urban impervious
surface. For instance, vegetation cover is a useful variable to reduce the saturation eﬀect
of NTL, which has been conﬁrmed by a variety of studies (Li and Gong 2016a; He et al.
2014; Zhang, Schaaf, and Seto 2013; Liu et al. 2015a; Zhou et al. 2014). Lu et al. (2008)
proposed a human settlement index (HSI) that incorporated Moderate Resolution
Imaging Spectroradiometer (MODIS) normalized diﬀerence vegetation index (NDVI)
with NTL data for settlement mapping in the southeastern China. Zhang, Schaaf, and
Seto (2013) proposed a vegetation-adjusted NTL urban index (VANUI), which is simple
but eﬃcient in revealing the heterogeneity in regions with saturated DN values (Ma
et al. 2014; Shao and Liu 2014; Li et al. 2016b). Liu et al. (2015a) combined both NDVI
and normalized diﬀerence water index (NDWI) with NTL to reduce the pixel saturation in
HSI and VANUI with a new indicator of normalized urban areas composite index (NUACI).
Through incorporating remotely sensed land surface index, regions belong to non-urban
but with high DN values can be recognized and removed in further processing. In
addition, land-use/land-cover (LULC) datasets at a ﬁner spatial resolution (e.g. 30 m) is
able to provide more details in saturated regions in NTL data (Zhou et al. 2014; Liu et al.
2012), which can be served as a fraction of urban area when aggregated them to the
same resolution as NTL data, or a statistic of total urban area over a particularly region.
These land surface features have been used in NTL-based urban mapping together using
classiﬁcation (e.g. Support Vector Machine (SVM), random forest or spatially adaptive
regression) or threshold methods (Cao et al. 2009; Xiao et al. 2014; Liu and Leung 2015;
Shao and Liu 2014; Li et al. 2016b; Huang, Schneider, and Friedl 2016).
Demographic features can also help mitigate the saturation issue in NTL data in urban
core areas by incorporating additional socioeconomic information to get the density of
population (Sutton et al. 1997,2001;L
o2002; Sutton, Elvidge, and Obremski 2003;
Amaral et al. 2006). In general, these datasets are associated with speciﬁc census unit,
which can be used to diﬀerentiate DN values that are saturated but have diﬀerent
demographic levels (e.g. population or density) (Zhuo et al. 2009). Spatially explicit
demographic information (e.g. demographic level or zone) can be introduced to
group saturated pixels in the raw NTL data for further applications (e.g. population
density mapping). It is worth noting that this mitigation of saturation eﬀect in NTL
luminosity depends on scales (or resolutions) of census data (Sutton, Elvidge, and
Obremski 2003). More sophisticated approach with incorporation of demographic fea-
tures and land surface factors can improve the performance in diﬀerentiating saturated
DN values. Zhuo et al. (2009) performed a polynomial regression to calibrate the
INTERNATIONAL JOURNAL OF REMOTE SENSING 5
relationship between NTL and population at the county level in China, and then
allocated the estimated population density to each pixel with the consideration of
natural habitable condition (e.g. vegetation). The relationships between population
and NTL vary among cases, and the spatial unit and level (e.g. state, province, county,
and city) are a crucial factor inﬂuencing the relationship. In addition, NTL-derived
population (or density) estimation can be used to delineate urban extent (Elvidge
et al. 2007; Lu and Weng 2006; Martinuzzi, Gould, and Ramos González 2007).
2.1.2. Blooming eﬀect in NTL
The blooming eﬀect in the NTL data we discussed here speciﬁcally refers to the fact that
outside of the actual urban extent, the DN values of NTL are still signiﬁcantly above
zeros. The blooming eﬀect in NTL data increases diﬃculties to separate urban from its
surrounding non-urban regions (Liu et al. 2015a; Zhang, Schaaf, and Seto 2013). A
number of studies have been performed to address this issue for urban extent mapping
(Henderson et al. 2003; Gallo et al. 2004; He et al. 2006; Cao et al. 2009; Liu et al. 2012).
Among these studies, the approaches can be grouped roughly into two categories: (1)
threshold based and (2) classiﬁcation based.
Because of the blooming eﬀect in NTL data, threshold-based approaches have been
extensively used to extract urban extent from NTL data (see Figure 3) (Elvidge et al.
1997a; Imhoﬀet al. 1997; Henderson et al. 2003; He et al. 2006; Zhou et al. 2014; Liu et al.
2015a). Commonly, the status of urban and non-urban is determined by the threshold,
that is, if the DN greater than the threshold, then it will be assigned as urban; otherwise
it is classiﬁed as non-urban (see Figure 3, yellow rectangles). Essentially, the extracted
urban extent is very sensitive to the threshold, and an optimal one is needed to
Figure 3. Schematic diagram of threshold approach using NTL.
6X. LI AND Y. ZHOU
maximally separate urban and non-urban regions using the NTL data (see Figure 3, red
rectangles) (Zhou et al. 2014; Liu et al. 2012). Given that the spatial heterogeneity of
urbanization features (e.g. urbanization level and urban size) over diﬀerent regions, the
optimal thresholds (see Figure 3, blue texts) vary across space and a scheme of dynamic
(spatial and temporal) thresholds is required for large-scale and temporal dynamic urban
extent mapping (Zhou et al. 2014; Elvidge et al. 1997b; Imhoﬀet al. 1997; Small, Pozzi,
and Elvidge 2005; Elvidge et al. 2009b; Cao et al. 2009). Previous attempts on threshold
approaches focused on NTL data only. For example, the ‘light picking’approach was
proposed to estimate the threshold for a local window based on the background
information (Elvidge et al. 1997b), and urban shape (e.g. area or perimeter) was used
to ﬁnd the ‘sudden jump’point through searching continuous thresholds (Imhoﬀet al.
1997; Liu and Leung 2015). More attentions have been given to determine those
dynamic thresholds using ancillary information (He et al. 2006; Cao et al. 2009; Zhou
et al. 2014; Liu et al. 2015a). For instance, He et al. (2006) iteratively searched optimal
thresholds to match the statistical urban area at the province level. In a similar manner,
statistical information of urban area of the region or city has been used to derive the
optimal thresholds at these levels (Liu et al. 2012; Milesi et al. 2003; Yu et al. 2014). In
addition, classiﬁed LULC data at a ﬁner spatial resolution have been used to derive
optimal thresholds over the conterminous space (Liu et al. 2015a; Li, Gong, and Liang
2015). Using the aggregated LULC information, Zhou et al. (2014) developed a method
to derive dynamic optimal thresholds to map urban extent for each urban cluster, which
was generated using a segmentation algorithm. This method was then extended to map
urban extent at the global level (Zhou et al. 2015). Similarly, there are other site-based
studies to estimate the empirical threshold based on the collected referred dataset (e.g.
existing land-use cover data or impervious surface information) and the modiﬁed NTL
indices (e.g. VANUI or NUACI) (Li et al. 2016b; Liu et al. 2015a).
Classiﬁcation-based methods have always been used to extract the urban extent from
NTL data with additional features such as NDVI and NDWI (Huang, Schneider, and Friedl
2016; Cao et al. 2009; Xiao et al. 2014). Cao et al. (2009) proposed a SVM-based region-
growing algorithm to extract urban area using NTL data and Satellite Pour l’Observation
de la Terre (SPOT) NDVI. Urban training samples were initially selected as seeds and
thereafter they were iteratively updated through using newly classiﬁed urban pixels
within a 3 3 window of these seeds to composite a new training set. This method
outperforms those results derived from global-ﬁxed or local-optimized approaches (Cao
et al. 2009; Xiao et al. 2014). Huang, Schneider, and Friedl (2016) used a Random Forest
regression model to estimate the urban percentage from stacked time series of NTL and
MODIS NDVI data. In this method, urban percentage aggregated from Landsat-based
land-cover data were used in the model training.
In addition to these two prevailing branches of methodology, there are other
approaches to mitigate the blooming eﬀect in NTL data. For instance, Townsend and
Bruce (2010) developed an Overglow Removal Model (ORM) to correct the diﬀusion of
NTL based on the empirical relationship between the light strength (sum of the total DN
value) and the dispersion distance. But this relationship needed to be calibrated in
advance with additional information (e.g. electricity use and population of each city). Su
et al. (2015) adopted a neighbourhood statistics approach to detect the spatial diﬀer-
ence of NTL data between urban and associated non-urban regions in the Pearl River
INTERNATIONAL JOURNAL OF REMOTE SENSING 7
Delta (China). Pre-deﬁned thresholds are not needed in this method, but the mapped
urban extent is sensitive to the neighbourhood morphology (e.g. conﬁguration and size)
and NTL magnitude within the neighbourhood (e.g. maximum and minimum), which
needs be examined when being applied in other regions. Tan (2016) developed a
method to generate inside buﬀers based on the empirical relationship between sur-
veyed urban area and lit area of NTL data for mitigating the blooming eﬀect. These
methods are similar with the approach of dynamic optimal thresholds in determining
the buﬀers to separate urban and non-urban regions. However, it should be cautious
when applying them in a large area with high spatial heterogeneity.
2.2. Temporal dimension
2.2.1. Intercalibration of annual NTL data
Due to the absence of on-board calibration, the DMSP/OLS stable NTL annual compo-
sites product derived from multiple sensors (F12–F16) and diﬀerent years (1992–2013)
are not comparable directly (Doll 2008). Therefore, intercalibration of annual NTL com-
posites product is highly needed to investigate urban dynamics using the NTL data.
Elvidge et al. (2009b) built the framework of intercalibration for annual NTL composites
product, which is the most widely used framework currently (Elvidge et al. 2014;Ma
et al. 2014; Liu and Leung 2015; Zhao, Zhou, and Samson 2015; Huang, Schneider, and
Friedl 2016; Li et al. 2016b; Zhang, Pandey, and Seto 2016; Tan 2016; Yi et al. 2014). This
proposed framework includes three procedures: (1) selection of the reference region; (2)
determination of the reference satellite and year for calibration; and (3) model develop-
ment for intercalibration. Currently, most works requiring intercalibration of NTL series
followed these procedures.
The reference regions vary among diﬀerent studies for particular applications at
the regional or global scales. There are two criteria in selecting reference regions: (1)
small changes in lighting over years and (2) covering a wide range of DN values
(Elvidge et al. 2009b;Wuetal.2013). Therefore, in addition to Sicily Island selected
by Elvidge et al. (2009b) for an early calibration work, many other reference regions
have been used to intercalibrate the annual NTL composites product for urban
dynamics analyses. We surveyed literature on NTL-based intercalibration and sum-
marized the hotspot map of reference regions (Figure 4). Presently, the collected
reference regions in Figure 4 include diﬀerent countries (Italy, USA, China, Japan, and
India), covering both mainland and islands (Puerto Rico, Mauritius, and Okinawa) (Wu
et al. 2013). Although these regions were selected for diﬀerent purposes, they
showed potential for intercalibration of NTL datasetatthegloballevel.Inaddition,
apart from those reference regions that contain a wide range of DN values, there are
also some attempts using automatically or manually collected sites (or points) as
references for intercalibration (Yi et al. 2014;Zhang,Pandey,andSeto2016;Lietal.
2013). For instance, Li et al. (2013) used a linear regression model to iteratively ﬁlter
out pixels that may be experienced a change of DN value to collect the referenced
sites for intercalibration. This method is more appropriate for local applications
because the iteration process is time consuming. Liu et al. (2015b)setasimple
rule (i.e. DN >30) for sample collection in New York for multi-temporal NTL data
intercalibration. However, it should be noted that those pixels involved in the
8X. LI AND Y. ZHOU
calibration process are very sensitive to the calibrated results (Zhang, Pandey, and
The reference satellite and year are always determined based on the criterion that the
sum (or averaged) of DN values in the reference region or the whole study area is the
highest (Elvidge et al. 2009b; Pandey, Joshi, and Seto 2013; Ma et al. 2012). Other
criterion in reference year/satellite selection is based on time-series of the NTL data,
which aims to choose the year/satellite that lies in the middle of series for minimizing
the eﬀect of NTL change in the long time period (Zhang, Pandey, and Seto 2016). Once
the reference year/satellite is selected, other NTL data were calibrated for achieving a
comparable series over time. There are a variety of calibration models developed, such
as six-order polynomial model (Bennie et al. 2014), second-order regression model
(Elvidge et al. 2009b), simpliﬁed ﬁrst-order regression model (Liu et al. 2015b) and
power function (Wu et al. 2013). Among them, the second-order regression model has
been extensively used to intercalibrate annual NTL composites product (Elvidge et al.
2009b; Zhao, Zhou, and Samson 2015; Ma et al. 2012; Liu et al. 2012; Liu and Leung
2015; Pandey, Joshi, and Seto 2013; Zhang, Pandey, and Seto 2016), and its formula can
be expressed as Equation (1):
where Vadjust is the calibrated DN value, Vis the original value, C0;C1;and C2are the
coeﬃcients, which were derived from the second-order regression model between DN
values of reference image and others to be calibrated.
2.2.2. Temporal pattern adjustment
It is critical to evaluate the temporal pattern of the annual NTL data in terms of its
consistency for tracing the urban sprawl process, particularly in rapidly developing
regions (e.g. China and India) (Liu et al. 2012; Ma et al. 2014). Although it is a somewhat
subjective modiﬁcation of the intercalibrated NTL series, it is still needed because (1) the
Figure 4. Reference regions used for intercalibration of annual NTL composites product. (a) Sicily
island (Italy) (Elvidge et al. 2009 2009a); (b) Jixi county (China) (Liu et al. 2012); (c) Swain county
(USA) (Li et al., 2016b); (d) Lucknow and Nawabganj (India) (Pandey, Joshi, and Seto 2013); (e), (f),
and (g) are Puerto Rico (USA), Mauritius (an Indian Ocean island), and Okinawa (Japan) (Wu et al.
2013). The background NTL image is derived from F121999.
INTERNATIONAL JOURNAL OF REMOTE SENSING 9
intercalibration is likely to introduce errors for some sites with abnormal NTL sequences
that are not consistent over time; and (2) the pathway of urban expansion in rapidly
developing regions is more certain with continuously expansion and increasing lit areas,
whereas the obtained NTL series may not follow this trajectory (Liu et al. 2012; Li, Gong,
and Liang 2015; Mertes et al. 2015; Zhao, Zhou, and Samson 2015). Liu et al. (2012)
proposed an inter-annual series correction to modify the abnormal pixels (see Figure 5
(a)). In their study, based on the NTL series, temporally neighboured DN values are
compared. Inconsistent pixels in the series were modiﬁed to achieve a continuously
increasing pattern (see red and green circles in Figure 5(a)). Similar approaches can be
found in Huang, Schneider, and Friedl (2016). Furthermore, to reduce the possible
system errors caused by the initial year (e.g. 1992 in Figure 5(a)), Liu and Leung (2015)
proposed a two-way modiﬁcation of NTL series to combine sequences of 1992–2013 (i.e.
green arrow in Figure 5(a)) and 2013–1992 (i.e. red arrow in Figure 5(b)). The mean of
these two adjusted sequences was used in their studies based on the assumption that
the positive and negative errors were oﬀset (Zhao, Zhou, and Samson 2015; Liu and
The adjustment of temporal pattern on the intercalibrated NTL series is needed for
urban dynamics analyses in regions with rapid development while it may be not
necessary for all areas. The natural pattern of NTL series may reﬂect multiple pathways
(or archetypes) of urbanization, e.g. constant urban activity, earlier urban growth, de-
urbanization, constant urban growth, and recent urban growth (Zhang and Seto 2011;
Ma et al. 2012). Although most of these archetypes show temporally increasing total DN
values, an opposite trajectory is also seen due to crisis such as war (e.g. Syria war) (Li and
Deren 2014) or population migration due to poverty (Zhao, Zhou, and Samson 2015).
The adjustment of intercalibrated NTL series is helpful in analysing dynamics of urban
expansion, whereas the knowledge of the study area is needed for designing reasonable
adjustment rules (i.e. linearly changed or not). Given that the land cover change from
urban to non-urban rarely occurred (Li, Gong, and Liang 2015; Mertes et al. 2015), the
temporal pattern adjustment is eﬃcient for most urban lit areas on the planet.
Nevertheless, it is still challenging to distinguish those pseudo changes from the actual
expansion based on the calibrated NTL time series.
Figure 5. Temporal pattern adjustment of intercalibrated NTL time series: (a) 1992–2013 and (b)
10 X. LI AND Y. ZHOU
3. Successor of DMSP/OLS: VIIRS/DNB
The new generation of NTL, VIIRS, carried on the Suomi National Polar-orbiting
Partnership (NPP) satellite (http://npp.gsfc.nasa.gov) was launched in 2011. Compared
to the DMSP/OLS, the sensor DNB in the VIIRS is more advanced in (1) on-board
calibration; (2) spatial resolution (about four times ﬁner than DMSP); and (3) radiometric
resolution (14 bit) (Miller et al. 2012; Elvidge et al. 2013). As a consequence, VIIRS is able
to provide more details in terms of the detected night-time light (Small, Elvidge, and
Baugh 2013). However, due to the short period since the VIIRS data were available,
studies on urban mapping using VIIRS data are relatively limited currently. In addition,
most of them centred around the comparison with DMSP/OLS data using similar
approaches as we documented earlier, to enhance the beneﬁts of VIIRS with improved
spatial details. For example, Shi et al. (2014) evaluated the performance of VIIRS NTL data
for extracting urban areas using the thresholds calibrated from statistical data based on
12 cities in China, and they found that the obtained accuracies were higher than that
using DMSP/OLS data. Guo et al. (2015) integrated the VIIRS data with MODIS NDVI data
to map the impervious surface area in China using the regression model. This procedure
was similar to the approaches discussed in Section 2.1.1 to mitigate the saturation eﬀect
in NTL data with DMSP/OLS replaced by VIIRS. Sharma et al. (2016) made a similar
attempt to estimate the thresholds at the global scale using data such as MODIS-derived
‘Urban Built-up Index (UBI)’to estimate the thresholds. The thresholds for urban extent
delineation in their work were determined based on region-speciﬁc values in each 10°
10° tile for the whole globe. In addition, NTL-based observations with high spatial
resolution (1 m) are emerging now, such as the Israeli EROS-B satellite (Levin et al. 2014),
which is of great value in urban studies at the local scale.
4. Discussion and future opportunities
The DMSP/OLS NTL data showed great potential in urban extent mapping across a
variety of scales with historical records dating back to 1990s. This article provides a
systematic review on NTL-based urban mapping, including the saturation of luminosity,
the blooming eﬀect of NTL data, the intercalibration of NTL series, and adjustment of
intercalibrated temporal patterns. Although NTL data are useful in urban extent map-
ping over large areas, it is worth to note that it is limited to its spatial resolution (1 km)
and could be inﬂuenced by other light disturbance (e.g. gas) (Zhang and Seto 2013). The
urban extent from NTL data may omit small city and include pseudo lit areas. However,
the DMSP/OLS NTL data are highly recommended for global urban mapping studies.
Compared to urban mapping using other datasets (e.g. MODIS, Landsat, and
Orthophoto) (Schneider, Friedl, and Potere 2010; Gong et al. 2013; Small, Pozzi, and
Elvidge 2005; Henderson et al. 2003; Zhou and Wang 2008), although they can provide
more details of urban structure or extent, NTL data show advantages in generating a
global consistent urban map series because of (1) more direct observations of night-time
city light; and (2) less data volume requirement with globally consistent measurements
(Zhou et al. 2015; Elvidge et al. 2007). However, due to the challenges discussed, there
still lacks multi-temporal urban products based on a consistent mapping scheme from
regional to global levels. These challenges also provide opportunities and open future
INTERNATIONAL JOURNAL OF REMOTE SENSING 11
research avenues in temporal dynamic urban mapping from regional to global levels
using NTL data.
(1) Improvement of temporally inconsistent NTL series. After Elvidge et al. (2009b)
proposed the general framework for intercalibration of global inconsistent NTL series,
few attempts have been made for multi-year global urban extent mapping. Recently,
Zhang, Pandey, and Seto (2016) improved the intercalibration with carefully selected
reference pixels to improve the initial NTL DN values. This study will undoubtedly promote
the global mapping studies over multiple years. However, there are still two concerns to be
addressed in this calibration framework in the future work. One is the notably disturbance
of DN values through implementing the calibration model for almost all the pixels. Another
is the shift of the initial pattern of NTL series over time after calibration (Wu et al. 2013).
Novel methods are needed to reduce the uncertainty introduced from the calibration by
detecting systematic errors of images of diﬀerent satellites and years.
(2) Mitigation of blooming eﬀect and its spatial heterogeneity in NTL. Presently,
mapping approaches using NTL dataset at the global scale is still under development.
The ﬁrst global impervious surface map was built using a linear relationship and
population data (Elvidge et al. 2007). However, the spatial heterogeneity of local socio-
economic development was not well considered in this method. Zhou et al. (2015) used
a logistic-model to estimate the optimal threshold for each urban clusters derived from
NTL dataset for global urban extent mapping. Although spatial heterogeneities have
been considered for each urban cluster, the ﬁner resolution land cover data used in
threshold estimation were merely based on two representative regions: China and USA
These challenging issues still exist in the global urban extent mapping using the NTL
data, and more eﬀorts are needed in the future.
(3) Synthesis of DMSP/OLS and VIIRS NTL datasets. The temporal coverage of DMSP/
OLS NTL dataset is 1992–2013, and the continuing project of VIIRS is ongoing. The new
satellite and sensor make it possible to detect more details of night-time city lights,
whereas the inconsistent setting of sensors and resolutions between DMSP/OLS and
VIIRS raise challenges to combine these two data sources for continuously monitoring of
global urban expansion since 1990s. Although there are several studies have been
carried out for comparison between VIIRS and DMSP/OLS datasets (Small, Elvidge, and
Baugh 2013; Shi et al. 2014; Guo et al. 2015), few attempts have been made to integrate
the DMSP/OLS and VIIRS/DNB for a consistent observation, which is of great importance
to understand the dynamics of long-term urban expansion. More eﬀorts are required to
take advantage of them for a continuing mapping of urban dynamics at the global scale.
This work was supported by the NASA ROSES LULC Program ‘NNH11ZDA001N-LCLUC’. We thank
three anonymous reviewers and editor for their valuable comments to improve this manuscript.
No potential conﬂict of interest was reported by the authors.
12 X. LI AND Y. ZHOU
This work was supported by the NASA ROSES LULC Program ‘NNH11ZDA001N-LCLUC’.
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