Urban mapping using DMSP/OLS stable night-time light: a review

Article · January 2017with303 Reads
DOI: 10.1080/01431161.2016.1274451
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
Urban mapping using DMSP/OLS stable night-time light:
a review
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
eect, 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 dierent 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 eect in NTL, and synthesis of dierent NTL
satellites, in global urban extent mapping.
Received 1 October 2016
Accepted 5 December 2016
1. Introduction
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 eets, industrial sites, gas ares, and res (Croft 1978;Elvidgeetal.
1997b;Imhoet al. 1997;Huangetal.2014). The DMSP/OLS sensor contains two
CONTACT Yuyu Zhou yuyuzhou@iastate.edu Department of Geological and Atmospheric Sciences, Iowa State
University, Ames, IA 50011, USA
© 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°inlongitudeand65° to 65° in
latitude), and it spans two decades (19922013). 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 specic years without continuous time series; and (2) the average DN
dataset may contain other emissions sources (e.g. res 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 dierent develop-
ment levels (Zhang and Seto 2011;Maetal.2012). Few eorts have been made to
summarize the dierence of current approaches or comparison of dierent mapping
results, although there are some general works on meta-analysis or summary of
specic applications of NTL data (Huang et al. 2014;Li,Zhao,andXi2016a).
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 denition of urban extentis dierent when referring to dierent 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 rst 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
Potere 2010;Lo2002).
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
deciencies of NTL dataset, that is, the saturated DN values in the urban core region
and blooming eects on the urbanrural 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
more details.
2.1. Spatial dimension
2.1.1. Saturation of NTL luminosity
There exists a notable saturation eect 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.
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 dierentiate the
saturated luminosity in the urban core region, it is still limited for dynamic urban
mapping because the implementation of radiance calibration is dicult, 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 eect
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 eect 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 eect
of NTL, which has been conrmed 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 dierence 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 ecient 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 dierence 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 ner 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
classication (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 specic census unit,
which can be used to dierentiate DN values that are saturated but have dierent
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 eect 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 dierentiating saturated
DN values. Zhuo et al. (2009) performed a polynomial regression to calibrate the
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 inuencing 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 eect in NTL
The blooming eect in the NTL data we discussed here specically refers to the fact that
outside of the actual urban extent, the DN values of NTL are still signicantly above
zeros. The blooming eect in NTL data increases diculties 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) classication based.
Because of the blooming eect in NTL data, threshold-based approaches have been
extensively used to extract urban extent from NTL data (see Figure 3) (Elvidge et al.
1997a; Imhoet 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 classied 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.
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 dierent 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; Imhoet 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 pickingapproach 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 nd the sudden jumppoint through searching continuous thresholds (Imhoet 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, classied LULC data at a ner 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 modied NTL
indices (e.g. VANUI or NUACI) (Li et al. 2016b; Liu et al. 2015a).
Classication-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 lObservation
de la Terre (SPOT) NDVI. Urban training samples were initially selected as seeds and
thereafter they were iteratively updated through using newly classied urban pixels
within a 3 3 window of these seeds to composite a new training set. This method
outperforms those results derived from global-xed 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 eect in NTL data. For instance, Townsend and
Bruce (2010) developed an Overglow Removal Model (ORM) to correct the diusion 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 dier-
ence of NTL data between urban and associated non-urban regions in the Pearl River
Delta (China). Pre-dened thresholds are not needed in this method, but the mapped
urban extent is sensitive to the neighbourhood morphology (e.g. conguration 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 buers based on the empirical relationship between sur-
veyed urban area and lit area of NTL data for mitigating the blooming eect. These
methods are similar with the approach of dynamic optimal thresholds in determining
the buers 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 (F12F16) and dierent years (19922013)
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 dierent 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 dierent 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 dierent 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 lter
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
calibration process are very sensitive to the calibrated results (Zhang, Pandey, and
Seto 2016).
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 eect 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), simplied rst-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):
Vadjust ¼C0þC1VþC2V2
where Vadjust is the calibrated DN value, Vis the original value, C0;C1;and C2are the
coecients, 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 modication 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.
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 modied 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 modication of NTL series to combine sequences of 19922013 (i.e.
green arrow in Figure 5(a)) and 20131992 (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 oset (Zhao, Zhou, and Samson 2015; Liu and
Leung 2015).
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 reect 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 ecient 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) 19922013 and (b)
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 ner 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 benets 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 eect
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-specic 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 eect 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 inuenced 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
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 dierent satellites and years.
(2) Mitigation of blooming eect and its spatial heterogeneity in NTL. Presently,
mapping approaches using NTL dataset at the global scale is still under development.
The rst 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 ner 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 eorts are needed in the future.
(3) Synthesis of DMSP/OLS and VIIRS NTL datasets. The temporal coverage of DMSP/
OLS NTL dataset is 19922013, 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 eorts 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.
Disclosure statement
No potential conict of interest was reported by the authors.
This work was supported by the NASA ROSES LULC Program NNH11ZDA001N-LCLUC.
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    • One of the primary focus areas of night-light remote-sensing research has been the monitoring of urban expansion and the associated increase in urban light pollution.Li and Zhou (2017), in a review of the extensive literature on this subject, emphasize methodological challenges in this work, such as dealing with saturation, blooming, intercalibration between various sensors, and temporal pattern adjustment. Several articles in this special issue present pixel-based analyses of night-time light trajectories for mapping
    Article · Nov 2017
    • Thus, an appropriate scale factor method based on segmentation's quality criteria can be better than data from an empirical setting. Although nighttime light data have higher spatial stability and can guarantee reliable land parcel shaping, which work well for segmentation, the " blooming " effect (Li and Zhou, 2017) makes this type of data less effective for presenting an inner-city structure. In addition, spaces that emit bright light are not always areas with a high density of human activity, because areas such as manufacturing districts and cargo terminals can emit bright light without attracting much population flow (Zhang et al., 2013), whereas thecheck-in records collected by social media can clearly reveal the spatiotemporal distribution of human activities (Jiang et al., 2012).
    [Show abstract] [Hide abstract] ABSTRACT: Identifying the structure of a polycentric city is vital to various studies, such as urban sprawl and population movement dynamics. This paper presents an efficient and reliable method that uses multi-source geospatial big data, including nighttime light imagery and social media check-in maps, to locate the main center and subcenters of a polycentric city. Unlike traditional methods that rely on statistical data categorized by administrative units, the proposed method can effectively identify the boundaries of urban centers, and the data source guarantees a timely monitoring and update. Four main procedures are involved: 1) a new observation unit is developed using object-oriented segmentation; 2) main centers are located using cluster analysis (Local Moran's I); 3) subcenter candidates are selected using significant positive residuals from geographically weighted regression (GWR); and 4) final centers are filtered using global natural breaks classification (NBC). These steps can be reproduced in different regions. To evaluate the effectiveness, the method was applied to three rapidly developing Chinese cities: Beijing, Shanghai, and Chongqing with different natural and economic characteristics. The performance of the proposed method has been carefully evaluated with qualitative and quantitative analyses. Comparative experiments were also conducted across different datasets to prove the benefits of combining a social media check-in map with remotely sensed imagery in a human environment study.
    Article · Jul 2017
    • It measures lights from cities, towns, and other continuous lighting areas at night, in a form of DN ranging from 0 to 63. The spatial resolution of stable DMSP/OLS NTL is a 30 arc second (equals 1 km in equator), with a near global coverage of −180 @BULLET to 180 @BULLET in longitude and −65 @BULLET to 65 @BULLET in latitude[11]. In addition, for some years, there are two annual composites of NTL images derived from two satellites (see temporally overlaid images inTable 1).
    [Show abstract] [Hide abstract] ABSTRACT: The Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) stable nighttime light (NTL) data provide a wide range of potentials for studying global and regional dynamics, such as urban sprawl and electricity consumption. However, due to the lack of on-board calibration, it requires inter-annual calibration for these practical applications. In this study, we proposed a stepwise calibration approach to generate a temporally consistent NTL time series from 1992 to 2013. First, the temporal inconsistencies in the original NTL time series were identified. Then, a stepwise calibration scheme was developed to systematically improve the over- and under- estimation of NTL images derived from particular satellites and years, by making full use of the temporally neighbored image as a reference for calibration. After the stepwise calibration, the raw NTL series were improved with a temporally more consistent trend. Meanwhile, the magnitude of the global sum of NTL is maximally maintained in our results, as compared to the raw data, which outperforms previous conventional calibration approaches. The normalized difference index indicates that our approach can achieve a good agreement between two satellites in the same year. In addition, the analysis between the calibrated NTL time series and other socioeconomic indicators (e.g., gross domestic product and electricity consumption) confirms the good performance of the proposed stepwise calibration. The calibrated NTL time series can serve as useful inputs for NTL related dynamic studies, such as global urban extent change and energy consumption.
    Full-text · Article · Jun 2017
    • The temporal coverage of the DMSP-OLS data spans the period of 1992–2013, and the acquisition of VIIRS data is ongoing[42]. The different settings of the sensors used to acquire the DMSP-OLS and NPP-VIIRS data may cause difficulties in integrating these two datasets for the continuous modeling of the spatiotemporal dynamics of GDP in China since the 1990s.
    [Show abstract] [Hide abstract] ABSTRACT: Nighttime light data derived from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) in conjunction with the Soumi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) possess great potential for measuring the dynamics of Gross Domestic Product (GDP) at large scales. The temporal coverage of the DMSP-OLS data spans between 1992 and 2013, while the NPP-VIIRS data are available from 2012. Integrating the two datasets to produce a time series of continuous and consistently monitored data since the 1990s is of great significance for the understanding of the dynamics of long-term economic development. In addition, since economic developmental patterns vary with physical environment and geographical location, the quantitative relationship between nighttime lights and GDP should be designed for individual regions. Through a case study in China, this study made an attempt to integrate the DMSP-OLS and NPP-VIIRS datasets, as well as to identify an optimal model for long-term spatiotemporal GDP dynamics in different regions of China. Based on constructed regression relationships between total nighttime lights (TNL) data from the DMSP-OLS and NPP-VIIRS data in provincial units (R2 = 0.9648, P < 0.001), the temporal coverage of nighttime light data was extended from 1992 to the present day. Furthermore, three models (the linear model, quadratic polynomial model and power function model) were applied to model the spatiotemporal dynamics of GDP in China from 1992 to 2015 at both the country level and provincial level using the extended temporal coverage data. Our results show that the linear model is optimal at the country level with a mean absolute relative error (MARE) of 11.96%. The power function model is optimal in 22 of the 31 provinces and the quadratic polynomial model is optimal in 7 provinces, whereas the linear model is optimal only in two provinces. Thus, our approach demonstrates the potential to accurately and timely model long-term spatiotemporal GDP dynamics using an integration of DMSP-OLS and NPP-VIIRS data.
    Full-text · Article · Jun 2017
  • [Show abstract] [Hide abstract] ABSTRACT: Understanding urbanization dynamics, or how intensity of urbanization changes over time, is an important basis for urban planning and management, which has been investigated using various data-driven approaches. Considering the advantages and constraints of different data sources, we use pixel-based, time-series nighttime light (NTL) trajectories to characterize urbanization dynamics in mainland China where massive urban development has been occurring in recent decades. After pre-processing the data, we extracted time-series NTL trajectories for each 1 km by 1 km pixel between 1992 and 2013 and used the unsupervised k-means classification to identify the major typologies of these trajectories as urbanization dynamics based on their main statistical parameters. The classification identified five urbanization dynamics, namely, stable urban activity, high-level steady growth, acceleration, low-level steady growth, and fluctuation. Their distributions and spatial patterns were further summarized and compared among different Chinese administrative divisions. We specifically analysed the acceleration trajectories that showed rapid transitions from rural to urban, as we considered these trajectories as potential indicators for aggressive urbanization. We found several clusters at prefecture city and county levels with high proportion of the acceleration, and referred to the underlying socioeconomic characteristics and developmental history to understand how these clusters could had been formed. Through this study, we revealed the dominant tendencies of urbanization in China over space and time, and developed an analysis framework that could be extended to other regions.
    Full-text · Article · Mar 2017
  • [Show abstract] [Hide abstract] ABSTRACT: Urban cellular automata (CA) models propagate and accumulate errors during the modeling process due to the model structure or stochastic processes involved. It is feasible to assimilate real-time observations into an urban CA model to reduce model uncertainties. However, the assimilation performance is sensitive to the spatio-temporal units in the assimilation algorithm, that is, spatial block size and window length (temporal interval). In this study, we coupled an assimilation model, an ensemble Kalman filter (EnKF) and a Logistic-CA model to simulate the urban dynamic in Beijing over a period of two decades. Our results indicate that the coupled EnKF-CA model outperforms the CA-alone counterpart by about 10% in terms of the figure of merit, which reflects the agreement of modeled pixels. We also find that the assimilation performance using a finer block (1 km) is better than that using a coarser block (5 km and 10 km) because of the better depiction of spatial heterogeneity using a finer block. Moreover, the improvement of intermediate outputs using the coupled EnKF-CA model is effective for a certain period (e.g. 5 years). This implies that a high-frequency assimilation may not significantly improve the model performance. The sensitivity analyses of spatio-temporal assimilation in the EnKF-CA model provide a better understanding of the assimilation mechanism that couples with land-use change models.
    Article · Jul 2017
This project is to contribute to the NASA ROSES LCLUC program by generating a consistent global urban map series and developing an integrated modeling framework to project future urban expansion. O…" [more]
see Project of https://www.researchgate.net/project/Understanding-and-Simulating-Global-Urban-Expansion-in-the-Context-of-Climate-Change
This Special Issue aims to publish original manuscripts of latest innovative research in recent advances in nighttime lights remote sensing. Comprehensive reviews of this research field are also we…" [more]
June 2017 · Remote Sensing · Impact Factor: 3.18
    The Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) stable nighttime light (NTL) data provide a wide range of potentials for studying global and regional dynamics, such as urban sprawl and electricity consumption. However, due to the lack of on-board calibration, it requires inter-annual calibration for these practical applications. In this study, we proposed... [Show full abstract]
    June 2017 · Remote Sensing of Environment · Impact Factor: 6.39
      With over two decades of historical observations, DMSP/OLS nighttime lights (NTL) are an invaluable asset for monitoring, characterizing, and understanding human activity. Due to the lack of on-board calibration, there are systematic biases in NTL data. Consequently, a key deterrent to the use of the entire NTL archive is the difficulty in generating a consistent NTL time series. Currently,... [Show full abstract]
      June 2015 · Acta Geodaetica et Cartographica Sinica
        When observing the Earth from above at night, it is clear that the human settlement and major economic regions emit glorious light. At cloud-free nights, some remote sensing satellites can record visible radiance source, including city light, fishing boat light and fire, and these nighttime cloud-free images are remotely sensed nighttime light images. Different from daytime remote sensing,... [Show full abstract]
        December 2016 · Remote Sensing of Environment · Impact Factor: 6.39
          To address the problem of assessing large-scale urban dynamics from incompatible time series DMSP/OLS night-time light (NTL) data, this study proposed an Object-based Urban Thresholding method for NTL image data (i.e., NTL-OUT method) to estimate the optimal thresholds of urban objects in different NTL images. The optimal threshold for an urban object was determined by comparing the reference... [Show full abstract]
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