Brightness Of Nighttime Lights Research Articles

Improve ground-level PM2.5 concentration mapping using a random forests-based geostatistical approach.

Accurate measurements of ground-level PM2.5 (particulate matter with aerodynamic diameters equal to or less than 2.5 μm) concentrations are critically important to human and environmental health studies. In this regard, satellite-derived gridded PM2.5 datasets, particularly those datasets derived from chemical transport models (CTM), have demonstrated unique attractiveness in terms of their geographic and temporal coverage. The CTM-based approaches, however, often yield results with a coarse spatial resolution (typically at 0.1° of spatial resolution) and tend to ignore or simplify the impact of geographic and socioeconomic factors on PM2.5 concentrations. In this study, with a focus on the long-term PM2.5 distribution in the contiguous United States, we adopt a random forests-based geostatistical (regression kriging) approach to improve one of the most commonly used satellite-derived, gridded PM2.5 datasets with a refined spatial resolution (0.01°) and enhanced accuracy. By combining the random forests machine learning method and the kriging family of methods, the geostatistical approach effectively integrates ground-based PM2.5 measurements and related geographic variables while accounting for the non-linear interactions and the complex spatial dependence. The accuracy and advantages of the proposed approach are demonstrated by comparing the results with existing PM2.5 datasets. This manuscript also highlights the effectiveness of the geographical variables in long-term PM2.5 mapping, including brightness of nighttime lights, normalized difference vegetation index and elevation, and discusses the contribution of each of these variables to the spatial distribution of PM2.5 concentrations.

Inventorying and Monitoring Nighttime Light Distribution and Dynamics in the Mediterranean Coast Network of Southern California

Light pollution has been of increasing concern as it relates to protected areas. As such, natural resource managers need information on the distribution, intensity, and dynamics of nighttime lights in protected areas. We examine the extent of nighttime light brightness from 1992 to 2012 in the Mediterranean Coast Network (Santa Monica Mountains National Recreation Area, Channel Islands National Park, and Cabrillo National Monument) using the Defense Meteorological Satellite Program (DMSP) Operational Linescan System, which has provided global annual nighttime light imagery at 0.9 km pixel resolution. Nighttime lights appeared stable in Santa Monica Mountains National Recreation Area, decreased in Cabrillo National Monument, and are extremely low in Channel Islands National Park. However, the mean brightness values in Santa Monica Mountains National Recreation Area and Cabrillo National Monument were very high compared to all other National Parks. Indeed, both were comparable to the two National Parks in the USA with the highest mean brightness values (Cuyahoga Valley National Park, Hot Springs National Park). Monitoring night light extent, intensity, time series, and change detection using remote sensing should be a standard practice for all protected areas managed by the National Park Service due to the no-cost nature of the data and ease at which analyses can be undertaken. The DMSP data and calibrated products can be used to monitor long-term changes in light distribution and intensity while the higher-resolution Visible Infrared Imaging Radiometer Suite data can be used to show changes in light distribution and density throughout the year and can be used to test how policy or ordinance changes impact light pollution.

Analysis of Spatial Pattern of Urban System along the Overland Silk Road Economic Belt Using DMSP-OLS Nighttime Light Data

As China promotes the Belt and Road (BAR) initiative, the overland SREB development is widely concerned. The cities (including towns), population centers, of urban system are the cores of the economy along the SREB. Therefore, it is necessary to monitoring the urbanization of the belt so that the new growing points of urban development and the valid coupling mechanism between human and nature will be explored to promote the regional socio-economic sustainable development and effectively implement the BAR initiative. Using the DMSP-OLS stable nighttime lights (NTL) data in 1992, 2003, and 2014, in this paper we studied the urbanized spatial patterns of and the urbanized characteristics and trends of the main city system along the SREB in the view of the whole regionalized economic zone and typical cities and settlements (towns). The results showed that in general the NTL intensities in the SREB’s city system had the obvious geographical differentiation characteristics where there was maximum brightness of NTLs over the western European countries as well as being gradually decreasing from west to east. There were obvious increases of the NTL digital number (DN) values and NTL covering areas in 2003 and 2013 comparatively with that of 1992, which indicates the great urbanization development during this period. As for the four types of urban development process, there was an apparent consistency in a certain local area but a large heterogeneity among different areas. By analyzing the 273 pivot cities and the most pivot 26 cities, we found the number of the relatively small cities being decreasing but that of the large and medium-sized cities increasing. This study would provide the scientific support for the related researches and decision making of urbanization and urban economic development to promote the socio-economic comprehensive development of the overland SREB.

Forecasting China’s GDP at the pixel level using nighttime lights time series and population images

China’s rapid economic development greatly affected not only the global economy but also the entire environment of the Earth. Forecasting China’s economic growth has become a popular and essential issue but at present, such forecasts are nearly all conducted at the national scale. In this study, we use nighttime light images and the gridded Landscan population dataset to disaggregate gross domestic product (GDP) reported at the province scale on a per pixel level for 2000–2013. Using the disaggregated GDP time series data and the statistical tool of Holt–Winters smoothing, we predict changes of GDP at each 1 km × 1 km grid area from 2014 to 2020 and then aggregate the pixel-level GDP to forecast economic growth in 23 major urban agglomerations of China. We elaborate and demonstrate that lit population (brightness of nighttime lights × population) is a better indicator than brightness of nighttime lights to estimate and disaggregate GDP. We also show that our forecast GDP has high agreement with the National Bureau of Statistics of China’s demographic data and the International Monetary Fund’s predictions. Finally, we display uncertainties and analyze potential errors of this disaggregation and forecast method.

These lit areas are undeveloped: Delimiting China’s urban extents from thresholded nighttime light imagery

Nighttime light imagery is a powerful tool to study urbanization because it can provide a uniform metric, lit area, to delimit urban extents. However, lit area is much larger than actual urban area, so thresholds of digital number (DN) values are usually needed to reduce the lit area. The threshold varies greatly among different regions, but at present it is still not very clear what factors impact the changes of the threshold. In this study, urban extent by province for China is mapped using official statistical data and four intercalibrated and geometrically corrected nighttime light images between 2004 and 2010. Lit area in the imagery for most provinces is at least 94% greater than the official amount of urban area. Regression analyses show a significant correlation between optimal thresholds and GDP per capita, and larger thresholds more commonly indicate higher economic level. Size and environmental condition may explain a province’s threshold that is disproportionate to GDP. Findings indicate one threshold DN is not appropriate for multiple (adjacent) province urban extent mapping, and optimal thresholds for one year may be notably different than the next. Province-level derived thresholds are not appropriate for other geographic levels. Brightness of nighttime lights is an advantage over imagery that relies on daylight reflection, and decreases in brightness indicate faster growth in the horizontal direction than the vertical. A province’s optimal threshold does not always maintain an increase with population and economic growth. In the economically developed eastern provinces, urban population densities decreased (and this is seen in the brightness data), while urban population increased.

Nighttime-Lights-Derived Fossil Fuel Carbon Dioxide Emission Maps and Their Limitations

Brightness of nighttime lights has been used as an indicator for spatial disaggregation of CO2 emission based on an assumed linear relationship between the digital number (DN) values of nighttime light imagery and the amount of CO2 emissions. However, reliability of the linear relationship of these variables has not been thoroughly examined. In this study we find that the actual overall correlations are exponential rather than linear. More specific analyses showed that the DN values of nighttime light imagery first behaves linearly (from 3 to 50) and then exponentially (from 51 to 63), correlating to the amount of CO2 emissions. Regardless of the use of a linear or piecewise function, some featured limitations are evident as we developed the methodology. Among significant limitations, CO2 emissions were not visualized in unlit areas and a lack of variation existed in regions with the same DN values of nighttime light imagery. Lastly CO2 emissions in urban core areas were grossly under-estimated.

Correcting Incompatible DN Values and Geometric Errors in Nighttime Lights Time-Series Images

The Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime lights imagery has proven to be a powerful remote sensing tool to monitor urbanization and assess socioeconomic activities at large scales. However, the existence of incompatible digital number (DN) values and geometric errors severely limit application of nighttime light image data on multiyear quantitative research. In this paper, we extend and improve previous studies on intercalibrating nighttime lights image data to obtain more compatible and reliable nighttime lights time-series (NLT) image data for China and the U.S. through four steps, namely, intercalibration, geometric correction, steady-increase adjustment, and population data correction. We then use gross domestic product (GDP) data to test the processed NLT image data indirectly and find that sum light (summed DN value of pixels in a nighttime light image) maintains apparent increase trends with relatively large GDP growth rates but does not increase or decrease with relatively small GDP growth rates. As nighttime light is a sensitive indicator for economic activity, the temporally consistent trends between sum light and GDP growth rate imply that brightness of nighttime lights on the ground is correctly represented by the processed NLT image data. Finally, through analyzing the corrected NLT image data from 1992 to 2008, we find that China experienced apparent nighttime lights development in 1992–1997 and 2001–2008, respectively, and the U.S. showed nighttime lights decay in large areas after 2001.

Night-time light derived estimation of spatio-temporal characteristics of urbanization dynamics using DMSP/OLS satellite data

Understanding the spatio-temporal dynamics of urban development at regional and global scales is increasingly important for urban planning, policy decision making and resource use and conservation. Continuous satellite derived observations of anthropogenic lighting signal at night provide consistent and efficient proxy measures of demographic and socioeconomic dynamics in the urbanization process. Previous studies have demonstrated significant positive correlations between the nocturnal light brightness, mainly derived from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS), and population and economic variables. Quantitative measurements of artificial lighting emissions at night therefore can be indicative of the overall degree of socioeconomic development at regional to country levels. The spatio-temporal characteristics of anthropogenic night-time lighting, potentially connected to the dynamic patterns of spatially expanding human settlement and economic activities during the urban expansion process, however, has received less attention largely because of diversity of both socioeconomic activity and urban forms. Based upon the quadratic relationship between the pixel-level night-time light radiance and corresponding brightness gradient (i.e. the rate of maximum local change) at the local scale, we here proposed a spatially explicit approach for partitioning DMSP/OLS night-time light images into five types of night-time lighting areas for individual cities: low, medium-low, medium, medium-high and high, generally associated with urban sub-areas experienced distinctly different forms and human activity. At the country scale, our findings suggest that significant rises are commonly found in these five types of night-time lighting areas with different growth rates across 271 China's cities from 1992 to 2012. At the urban scale, however, five types of night-time lighting areas show various trends for individual cities in relation to the urban size and development levels. The marked increase in high night-time lighting area is highly prevalent in most of China's cities with rapid urbanization over the past 21years while significantly decreased low and medium-low night-time lighting areas are most likely to occur in large and extra-large cities. Moreover, the transition between different types of night-time lighting areas could further portray the spatio-temporal characteristics of urban development. Analyzing results indicate that the spatial expansions of gradually intensified night-time light brightness correspond geographically with the rural–urban gradients following a stepwise transition of night-time light brightness during the urban expansion.

Characterizing Spatio-Temporal Dynamics of Urbanization in China Using Time Series of DMSP/OLS Night Light Data

Stable nighttime light (NTL) data, derived from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS), are typically considered a proxy measure of the dynamics of human settlements and have been extensively used to quantitative estimates of demographic variables, economic activity, and land-use change in previous studies at both regional and global scales. The utility of DMSP data for characterizing spatio-temporal trends in urban development at a local scale, however, has received less attention. In this study, we utilize a time series of DMSP data to examine the spatio-temporal characteristics of urban development in 285 Chinese cities from 1992 to 2009, at both the local and national levels. We compare linear models and piecewise linear models to identify the turning points of nighttime lights and calculate the trends in nighttime light growth at the pixel level. An unsupervised classification is applied to identify the patterns in the nighttime light time series quantitatively. Our results indicate that nighttime light brightness in most areas of China exhibit a positive, multi-stage process over the last two decades; however, the average trends in nighttime light growth differ significantly. Through the piecewise linear model, we identify the saturation of nighttime light brightness in the urban center and significant increases in suburban areas. The maps of turning points indicate the greater the distance to the city center or sub-center, the later the turning point occurs. Six patterns derived from the classification illustrate the various characteristics of the nighttime light time series from the local to the national level. The results portray spatially explicit patterns and conspicuous temporal trends of urbanization dynamics for individual Chinese cities from 1992 to 2009.

Brightness of Nighttime Lights as a Proxy for Freight Traffic: A Case Study of China

Since economic reforms in 1978, China's huge growth has led to a rapid increase in demand for freight traffic. Timely assessments of past and current amounts of freight traffic are basis for predicting future demands of freight traffic and appropriately allocating transportation resources. Sum lights (summed digital number (DN) value of pixels of nighttime light imagery) for years 2000, 2004, and 2008 respectively are extracted from corresponding Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) stable lights annual image composites. The sum lights are then regressed on total freight traffic (TFT), railway freight traffic (RFT), and highway freight traffic (HFT), respectively, at the province level. Results show that sum light strongly correlates to TFT and HFT, so sum light can be used as a proxy for TFT and HFT at the province level. However, due to lack of strong correlations between RFT and GDP, sum light is not appropriate to be as a proxy of RFT. Finally we disaggregate each province/municipality's HFT to each pixel in proportion to the DN value of the pixel of the nighttime light image to produce a Chinese HFT map of 2008 with 1 km × 1 km resolution. Compared to traditional census-based freight traffic data, the freight traffic data derived from the nighttime light imagery contain more spatial information.

Quantitative estimation of urbanization dynamics using time series of DMSP/OLS nighttime light data: A comparative case study from China's cities

Urbanization process involving increased population size, spatially extended land cover and intensified economic activity plays a substantial role in anthropogenic environment changes. Remotely sensed nighttime lights datasets derived from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) provide a consistent measure for characterizing trends in urban sprawl over time (Sutton, 2003). The utility of DMSP/OLS imagery for monitoring dynamics in human settlement and economic activity at regional to global scales has been widely verified in previous studies through statistical correlations between nighttime light brightness and demographic and economic variables (Elvidge et al., 2001; Ghosh et al., 2010). The quantitative relationship between long-term nighttime light signals and urbanization variables, required for extensive application of DMSP/OLS data for estimating and projecting the trajectory of urban development, however, are not well addressed for individual cities at a local scale. We here present analysis results concerning quantitative responses of stable nighttime lights derived from time series of DMSP/OLS imagery to changes in urbanization variables during 1994–2009 for more than 200 prefectural-level cities and municipalities in China. To identify the best-fitting model for nighttime lights-based measurement of urbanization processes with different development patterns, we comparatively use three regression models: linear, power-law and exponential functions to quantify the long-term relationships between nighttime weighted light area and four urbanization variables: population, gross domestic product (GDP), built-up area and electric power consumption. Our results suggest that nighttime light brightness could be an explanatory indicator for estimating urbanization dynamics at the city level. Various quantitative relationships between urban nighttime lights and urbanization variables may indicate diverse responses of DMSP/OLS nighttime light signals to anthropogenic dynamics in urbanization process in terms of demographic and economic variables. At the city level, growth in weighted lit area may take either a linear, concave (exponential) or convex (power law) form responsive to expanding human population and economic activities during urbanization. Therefore, in practice, quantitative models for using DMSP/OLS data to estimate urbanization dynamics should vary with different patterns of urban development, particularly for cities experiencing rapid urban growth at a local scale.

Using remotely sensed night-time light as a proxy for poverty in Africa

BackgroundPopulation health is linked closely to poverty. To assess the effectiveness of health interventions it is critical to monitor the spatial and temporal changes in the health indicators of populations and outcomes across varying levels of poverty. Existing measures of poverty based on income, consumption or assets are difficult to compare across geographic settings and are expensive to construct. Remotely sensed data on artificial night time lights (NTL) have been shown to correlate with gross domestic product in developed countries.MethodsUsing national household survey data, principal component analysis was used to compute asset-based poverty indices from aggregated household asset variables at the Administrative 1 level (n = 338) in 37 countries in Africa. Using geographical information systems, mean brightness of and distance to NTL pixels and proportion of area covered by NTL were computed for each Administrative1 polygon. Correlations and agreement of asset-based indices and the three NTL metrics were then examined in both continuous and ordinal forms.ResultsAt the Administrative 1 level all the NTL metrics distinguished between the most poor and least poor quintiles with greater precision compared to intermediate quintiles. The mean brightness of NTL, however, had the highest correlation coefficient with the asset-based wealth index in continuous (Pearson correlation = 0.64, p < 0.01) and ordinal (Spearman correlation = 0.79, p < 0.01; Kappa = 0.64) forms.ConclusionMetrics of the brightness of NTL data offer a robust and inexpensive alternative to asset-based poverty indices derived from survey data at the Administrative 1 level in Africa. These could be used to explore economic inequity in health outcomes and access to health interventions at sub-national levels where household assets data are not available at the required resolution.