Urbanization can be broadly defined as the transition of settlements from rural to urban environments and the growth of existing metropolitan areas. The proportion of the world’s population living in urban areas is projected to be 66% by 2050 [1
] and sub-Saharan Africa currently has the world’s highest annual urban growth rate of any continent at 4.2% [2
]. Urbanization is known to impact a range of socio-economic issues including public health, education, environmental quality, and economic development [3
]. The public health effects of urbanization are complex with both negative and positive outcomes. For example, numerous studies have indicated that urban environments may have a mitigating effect on malaria transmission [4
], while at the same time increasing the incidence of dengue fever [5
]. Thus, innovative and reliable methods and datasets for monitoring spatiotemporal changes in urban areas are paramount.
Nighttime lights imagery (NTL) [6
], maintained by the National Oceanic and Atmospheric Administration (NOAA), offer a unique viewpoint for studying urban trends. These data, available as annual composite images from 1992 to 2013 provide a means for spatiotemporal analysis on a global scale. Although noise removal and other corrective processing are applied to the NTL imagery by NOAA to make them usable for analyses, inherent shortcomings remain. Foremost among these is the lack of inter- and intra-annual calibration between satellites. The satellite sensors that collected these data lacked on-board calibration capabilities and a system for recording in-flight gain changes [7
]. When observing raw imagery, this causes annual fluctuations in recorded brightness rather than the gradual increase expected with typical growth in urban populations. Elvidge et al. [7
] developed an empirical procedure to allow ‘intercalibration’ of the NTL data. Often referred to as the invariant region and quadratic regression method (IRQR), this method has been applied across a number of settings [8
]. It is a regression based method that relies on a high gain reference image and a reference area where illumination has changed little over time.
An alternative intercalibration method was developed by Liu et al. [11
]. These authors built on the IRQR method by applying intra- and inter-annual corrective algorithms and make use of a thresholding technique [12
] that relies on land use/land cover data to extract urban information. Wu et al. [13
] presented an alternative strategy for applying the IRQR method that included pixel saturation correction and the use of the power-law function for regression analysis. Li et al. [14
] used an automatic algorithm to extract reference area pixels and entered them into a linear regression model where outliers were iteratively discarded to refine the intercalibration equation. Finally, Zhang et al. [15
] employed a novel sampling strategy along the ‘ridgeline’, i.e., the densest part of plots generated between the reference and target images, to derive calibration models. Regardless of the approach, the intercalibration methods reviewed here all showed improved performance as indicated by their respective evaluation procedures. In general, method evaluation was based on the use of GDP, which has demonstrated a positive linear relationship to NTL in various studies [14
]. However, the shortcomings of these alternative intercalibration methods is that they were conducted within a limited geographic scope, required multiple reference datasets such as land use/land cover or population, and were not sufficiently validated.
Precisely intercalibrated NTL data is ideal for mapping urban extents as Li and Zhou [18
] describe in their recent systematic methodology review of this subject. Others, such as Ju et al. [19
] have focused on the characterization of urban dynamics in China using pixel-based time series trajectories to identify five distinct patterns of urban growth. Similarly, Zhang and Seto [20
] identified urbanization typologies on a worldwide basis and validated the accuracy of their results with multispectral imagery. Ma et al. [21
] both analyzed and predicted urban development at the municipal level using three different regression models to measure the relationships between NTL and population, GDP, built-up area, and electric power. Finally, Cauwels et al. [22
] applied NTL to the concept of urban agglomerations by using a threshold method combined with a segmentation function that identifies clusters of contiguously lighted pixels.
The primary objective of this study was to generate an intercalibrated time series of annual NTL images at the continental scale for Africa from 2000 to 2013 for use in measuring changes in urbanization over time. This particular period was chosen to align with significant reductions in the incidence of malaria in Africa, which occurred primarily as a result of scale-up of large-scale interventions, but also due to environmental changes and economic development, including urbanization [23
]. Since substantial inter-annual noise remained even after IRQR intercalibration, we evaluated the use of Gaussian process methods (GP) to generate a smoothed series of NTL images free of temporal noise signals. We then demonstrated the utility of the smoothed dataset for describing patterns of urbanization in Africa and studying relationships between NTL and economic and population indices.
For the first time, we report here on the production of an intercalibrated open access NTL dataset spanning continental Africa 2000–2013 using a GP statistical approach. This improved dataset can be applied to a broad range of disciplines including public health, economic development, and environmental monitoring. While NTL data offer an opportunity to measure and map the human footprint, in its raw format, these data are difficult to interpret and can lead to spurious conclusions.
Gaussian process smoothing, the key enhancement of our intercalibration method, yielded a more intuitively smooth increase in SOL over Africa and was less noisy than that produced by using IRQR and annual averaging alone. While there is no gold standard short of calibration with known light sources as ground truth [38
] against which to validate this approach, the SOL plots indicate a relatively effective intercalibration. However, what is achieved through IRQR intercalibration is not the elimination of errors, but their re-scaling such that they have the same magnitude across satellite-years. Through the use Gaussian process methods, independent temporal noise signals have been separated from latent functions in the annual NTL datasets. We have attempted to validate the GP method results against the premises of the conventional IRQR method. That is, the assumption that the reference dataset for the invariant region represents the NTL brightness across the time series. While invariably information is lost in the process, the overall result is an NTL time series for Africa that is standardized and comparable across both time and space.
The success of intercalibration with GP smoothing was also evaluated by comparing the resultant time series to known indicators of urbanization: GDP and UP. Improvements in the correlations found between the SOL and these indices when using the GP method provide further support for its use. When the same relationships are explored on a national basis, it provides sub-regional insights into urban and economic growth patterns, as well as the consequences of political and humanitarian events. While we have not compared GDP/UP figures to NTL values in countries outside Africa, we would expect similar relationships in these countries. The country based regression analysis for sub-Saharan Africa yielded generally positive results with only a few exceptions. The GP method performed notably better than the IC method in nearly all cases with correlation coefficients exceeding 0.9 for 80% and 90% of the countries for GDP and urban population, respectively. The exceptions were The Gambia and Eritrea, where positive correlation coefficients for the IC method exceeded that of our GP method for the GDP regression. For Eritrea, a partial explanation for this anomaly is the fact that GDP data for 2012–2013 are unavailable. However, also at the root of this data omission are a series of military disputes and border disputes with Ethiopia and Djibouti during our study period. In the case of The Gambia their economy, and therefore GDP, struggled between 2000 and 2004 and the unsmoothed IC dataset reflected this short term trend more accurately than the globally smoothed GP case.
Among the national statistics there were two countries with notably poor negative correlations to both GDP and UP: Zimbabwe and the Central African Republic. Notable is Zimbabwe’s unusual patterns with the SOL decreasing from 2000 through 2011 when it begins to recover. This pattern coincides with a disputed presidential and parliamentary election in 2008 preceded by periods of political unrest. The GDP trended slightly downward during the lead up to the election and then rose sharply thereafter. However, UP rose steadily from 2000 to 2013, even while SOL was decreasing. The Central African Republic also displays an unusual pattern, with relatively low SOL values, an urban population that rises steadily, and GDP that rises but drops abruptly in 2013 in response to a coup d’état. In general, anomalies such as these can be traced to the effects of insurgent warfare, political unrest, and humanitarian crises on nighttime lighting. Bennett and Smith [39
] studied socioeconomic changes that caused reductions in NTL in post-Soviet Russia and war-torn Syria, while Li et al. [40
] observed similar reductions as a result of the Islamic State of Iraq and Syria (ISIS) insurgency in Northern Iraq. Humanitarian crises often come in the form of drought, famine, earthquakes, and tropical storms. Gillespie et al. [41
] examined the effects of tsunami damage in Sumatra (2004–2008) using NTL imagery. Several authors have suggested NTL based metrics for monitoring at risk populations. For instance, Coscieme et al. [42
] propose an NTL based index of regional disparity and Li et al. [43
] similarly suggest a nighttime light variation index (NLVI) to predict the risk of armed conflicts. All these insights underscore the potential for well calibrated NTL data as a tool for monitoring the regional effects and outcomes of such events.
Urban growth analysis using the improved NTL time series data indicate that the extent of urban growth appears to vary widely across African countries, with an overall trend of increasing areas of the continent being lit, particularly around urban agglomerations. We have described basic patterns of urban growth in Africa by computing changes in the area of lit NTL pixels and agglomerated pixels (>250 km2) on a national basis. The lit area serves as an indicator of overall growth of human settlements, whereas the agglomerations are meant to be a measure of large metropolitan areas, a proxy for urbanization.
Most sub-Saharan countries exhibited substantially greater growth in lit area than agglomerated lit area. Exceptions included Cote d’Ivoire, Swaziland, Lesotho, and Djibouti which all showed greater growth in agglomerated area. In Cote d’Ivoire, this may be due the effects of civil wars occurring in 2002 and 2011, with increased agglomerated area resulting from migration away from centers of conflict to urban areas. In the case of Swaziland and Lesotho, this is likely related to their proximity to South Africa which has the highest growth rates. In both cases, lit area growth was concentrated along borders with South Africa and internal major roadways. Thus, merging of smaller, patchy urban areas caused a relatively high increase in agglomerated area. In the case of Djibouti, most growth in lit area occurred in the city of Djibouti causing it to surpass the 250 km2 threshold.
A variety of other anomalous patterns are expressed by countries with minimal growth in lit area many of which likely reflect human events, as previously discussed. Zimbabwe uniquely showed decreases in both lit and agglomerated area which were likely the effects of persistent political unrest during the study period. Malawi increased in lit area while decreasing its agglomerated area, apparently due to disaggregation of the city of Zomba metropolitan area. Finally, other notable anomalies are the Central African Republic and Guinea Bissau. The Central African Republic had no change in agglomerated area, while Guinea Bissau had no agglomerated area as of 2013 despite a modest 19% increase in lit area. In both of these situations, annual growth patterns may be attributed to their relatively sparse populations and the margin of error associated with the processed NTL data.
While we have demonstrated the utility of our enhanced approach to intercalibration of NTL imagery, for the purpose of this study we focused on imagery up to 2013. Continued growth in NTL applications hinge on new NTL data sources such as the Visible Infrared Imager Radiometer Suite (VIIRS) of the National Polar-Orbiting Operational Environmental Satellite System launched in 2011 [44
]. Although nighttime lights imagery from VIIRS are superior to DMSP-OLS data in both spatial resolution (0.5 km) and dynamic range (14-bit), it is currently only available as monthly composites and only since 2014. Furthermore, VIIRS data processing methods are relatively unexplored, with relatively few publications to date that make use of the imagery. Thus, DMSP-OLS will likely remain the de facto standard for nighttime lights based investigations for a number of years. As part of this study, integration of VIIRS data with DMSP-OLS data was briefly explored as way to extend the time series to 2015. However, the relative complexity of data processing necessary to interface the two datasets was prohibitive and deemed beyond the scope of this study. Future research on data processing of VIIRS imagery and their integration with DMSP-OLS data is vital if NTL based research and application development is to continue. Toward this end, release of a VIIRS NTL time series of processed annual composites similar to DMSP Version 4 would be of great benefit to the scientific community.