Satellite Images Of Nighttime Lights Research Articles

Estimating the relationship between ethnic inequality, conflict and voter turnout in Africa using geocoded data

To what extent is voter turnout influenced by ethnic inequality? We combine ethnolinguistic maps on sub-national locations of ethnic groups with night-time light satellite images to construct a dataset of ethnic inequality across sub-national locations in 24 African countries. Using election data from the constituency-level elections archive, we show that ethnic inequality is negatively associated with voter turnout. This relationship holds even after we control for endogeneity and geographic characteristics. We also construct a dataset of election conflict across the sub-national areas. We find that an increase in ethnic inequality is associated with an increase in conflict, which reduces voter turnout.

Nighttime light satellite images reveal uneven socioeconomic development along China's land border

China shares its board with one developed and thirteen developing countries. A timely, precise, and efficient socioeconomic study of border regions is vital for evaluating political problems and identifying potential economic prospects. Usually, conventional socioeconomic statistical data suffer from significant time lags and unequal statistical scales. This study utilized the random forest model to establish a connection between satellite-derived nighttime light data and the improved human development index (IHDI). The relationship was then applied to predict the IHDI, and differences in its strength, trend, and change pattern by bordering statistical units from 2000 to 2020 were evaluated. Our findings indicate that China's administrative units (AUCs) are more developed and have a greater development trend than their neighbors (AUNs). Except for the Tibet Autonomous Region, all AUCs are spatially more developed than AUNs, with the discrepancy widening between 2000 and 2020. Socioeconomic changes in AUCs predominantly exhibit a forward-leaping development pattern, which may be represented by a logarithmic (53%) or sigmoid (22.6%) function, whereas AUNs' socioeconomic changes exhibit either a late-leaping exponential (34.2%) or static development (18.6%) trend. The IHDI values in AUCs exhibit greater disparity as measured by the Theil index, than the AUNs, primarily due to the natural environment, resource availability, and development policies. In less developed regions, harsh natural surroundings, temperatures, and scarce natural resources hinder socioeconomic growth.

Relationship Between Urbanization And Road Networks In The Lower Northeastern Region Of Thailand Using Nighttime Light Satellite Imagery

The objective of this research on the relationship between urbanization and road networks in the lower Northeastern region of Thailand was to compare the urban area in 2006, 2013 and 2016 using nighttime light satellite images from the National Oceanic and Atmospheric Administration (NOAA), acquired by the Defense Meteorological Satellite Program (DMSP/OLS) and the Suomi National Polar-orbiting Partnership (Suomi NPP). After that the relationship between urbanization and road network was identified using nighttime light satellite images from these satellites. The nighttime light data was used to determine the urbanization levels, which were then compared with Landsat 8 Satellite images taken in 2016 in order to find the Pearson correlation coefficient. The results indicated that areas with high urbanization identified from the nighttime light satellite images taken by the Suomi NPP Satellite had a day/night band reflectance of 172-255 indicated and were located primarily along the roads. The analysis of these data suggested that urbanization has a significantly positive relationship with the road network at 0.01 level, with R2 values of 0.800 for urbanization and 0.985 for the road network.

Using satellite images of nighttime lights to predict the economic impact of COVID-19 in India

The outbreak of COVID-19 in early 2020 heralded a deep global recession not seen since the Second World War. With entire nations in lockdown, burgeoning economies of countries like India plunged into a downward spiral. The conventional instruments of estimating the short-term economic impact of a pandemic is limited, and as a result, it is challenging to implement timely monetary policies to mitigate the financial impact of such unforeseen events. This study investigates the promise of using nighttime images of lights on Earth, also known as nightlight (NTL), captured by the Visible Infrared Imaging Radiometer Suite (VIIRS) instrumentation onboard the Suomi National Polar-Orbiting Partnership (Suomi NPP) satellite mission to measure the economic cost of the pandemic in India. First, a novel data processing framework was developed for a recently released radiance dataset known as VNP46A1, part of NASA’s Black Marble suite of NTL products. Second, the elasticity of nightlight to India’s National Gross Domestic Product (GDP) was estimated using panel regression followed by machine learning to predict the Year-over-Year (YoY) change in GDP during Fiscal Year (FY) 2020Q1 (Apr-Jun, 2020). Electricity consumption, known to closely track economic output and precipitation were included as additional features to improve model performance. A strong relationship between both electricity usage and nightlight to GDP was observed. The model predicted a YoY contraction of 24% in FY2020Q1, almost identical to the official GDP decline of 23.9% later announced by the Indian Government. Based on the findings, the study concludes that nightlight along with electricity usage can be invaluable proxies for estimating the cost of short-term supply–demand shocks such as COVID-19, and should be explored further.

Assessment and Improvement of Urban Resilience to Flooding at a Subdistrict Level Using Multi-Source Geospatial Data: Jakarta as a Case Study

Urban resilience to natural disasters (e.g., flooding), in the context of climate change, has been becoming increasingly important for the sustainable development of cities. This paper presents a method to assess the urban resilience to flooding in terms of the recovery rate of different subdistricts in a city using all-weather synthetic aperture radar imagery (i.e., Sentinel-1A imagery). The factors that influence resilience, and their relative importance, are then determined through principal component analysis. Jakarta, a flood-prone city in Indonesia, is selected as a case study. The resilience of 42 subdistricts in Jakarta, with their gross domestic product data super-resolved using nighttime-light satellite images, was assessed. The association between resilience levels and influencing factors, such as topology, mixtures of religion, and points-of-interest density, were subsequently derived. Topographic factors, such as elevation (coefficient = 0.3784) and slope (coefficient = 0.1079), were found to have the strongest positive influence on flood recovery, whereas population density (coefficient = −0.1774) a negative effect. These findings provide evidence for policymakers to make more pertinent strategies to improve flood resilience, especially in subdistricts with lower resilience levels.

Ghost town or city of hope? The spatial spillover effects of high-speed railway stations in China

In the past decade, China has developed the most extensive high-speed rail (HSR) transportation network globally. Previous studies have shown that HSR greatly improved inter-city accessibility and that it plays an increasingly important role in facilitating urban growth. Chinese cities tend to use the spatial spillover benefits of HSR stations to promote the HSR new town development program. This study aims to measure the spatial spillover effect of HSR stations in China accurately by employing high-precision calibrated nighttime light satellite images. By utilizing a difference-in-differences estimation, it was found that the nighttime light image intensity has increased by an average of 27% in areas surrounding HSR stations. The study additionally documents a number of heterogeneities concerning these spillover effects across Chinese cities and provides an in-depth discussion regarding the factors that could affect the performance of HSR new towns.

Dogs are the main species involved in animal-vehicle collisions in southern Spain: Daily, seasonal and spatial analyses of collisions.

Animal-vehicle collisions have become a serious traffic safety issue. Collisions have steadily increased over the last few decades, as have their associated socio-economic costs. Here, we explore the spatial and temporal patterns of animal-vehicle collisions reported to authorities in the province of Seville, southern Spain. Most animal-vehicle collisions involved domestic animals (>95%), particularly dogs (>80%), a pattern that sharply contrasts with that found in other Spanish and European regions, where collisions are mostly caused by game species. Dog-vehicle collisions were related to the traffic intensity of the roads and they were more frequent around dawn and dusk, coinciding with the peaks of activity of dogs. This pattern was consistent throughout the week, although on weekends there were fewer collisions due to lower traffic density at those times. These findings suggest that the aggregation of dog-vehicle collisions around twilight likely resulted from a combined effect of the activity peaks of dogs and traffic density. Seasonally, collisions increased in autumn and winter, coinciding with the period of intense hunting activity in the region. Further, during autumn and winter, rush hour partly overlaps with twilight due to longer nights in comparison with summer and spring, which may contribute to the increased rate of dog-vehicle collisions in these seasons. Spatially, satellite images of nighttime lights showed that dog-vehicle collisions were clustered near urban areas. Overall, the high incidence of stray dogs involved in animal-vehicle collisions highlights a road safety issue with this type of animals in the region.

High-speed rail and urban expansion: An empirical study using a time series of nighttime light satellite data in China

Transportation, especially high-speed rail (HSR), plays a key role in urban development. Although many empirical studies have substantiated the impact of HSR on urban economic outcomes, the role of HSR in urban spatial expansion is seldom empirically measured. Previous studies have shown that the improvement of transportation infrastructure and technology can promote the expansion of cities, mostly on the construction land area announced by the government. This study, which is based on high-precision calibrated nighttime light satellite images, accurately measured the impact of HSR on urban expansion. We estimated a difference-in-differences model after eliminating selection bias by propensity score matching. HSR has a positive effect on urban expansion with an elasticity of 0.12–0.13. In addition, HSR is almost twice as successful at promoting urban expansion in the underdeveloped central and western cities as in the developed eastern cities in China. Governments should consider the varying responses of cities at different levels of development when using HSR to promote urban development. For future HSR line planning in China, the governments should appropriately focus on the central and western regions in order to achieve the most efficient use of resources and balanced regional development.

Exploring regional and urban clusters and patterns in Europe using satellite observed lighting

We explore regional and urban clusters and patterns in Europe by using satellite images of nighttime lights and by employing Exploratory Spatial Data Analysis. We map Defense Meteorological Satellite Program nighttime lights data onto the nomenclature of territorial units for statistics III, Local Administrative Units II and pixel (i.e. 1 km2grid cell system of Europe) level and apply global and local statistics of spatial association. Under the assumption that nighttime light data are a good proxy for economic activity, the analysis at regional level shows that the regions of global cities and megacities and their surrounding areas are hot spots of high economic activity levels. The regional analysis also reveals the polycentric hierarchical structure of Europe. Using the case studies of the regions of London and Île -de -France, the analysis at the urban level reveals the different urban structure of these two global regions and identifies the functional urban areas of London and Paris.

Characterizing Light Pollution Trends across Protected Areas in China Using Nighttime Light Remote Sensing Data

Protected areas (PAs) with natural, ecological, and cultural value play important roles related to biological processes, biodiversity, and ecosystem services. Over the past four decades, the spatial range and intensity of light pollution in China has experienced an unprecedented increase. Few studies have been documented on the light pollution across PAs in China, especially in regions that provide a greater amount of important biodiversity conservation. Here, nighttime light satellite images from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) were selected to characterize light pollution trends across PAs using nighttime light indexes and hot spot analysis, and then the light pollution changes in PAs were classified. Furthermore, the causes of light pollution changes in PAs were determined using high-resolution satellite images and statistical data. The results showed the following: (1) Approximately 57.30% of PAs had an increasing trend from 1992 to 2012, and these PAs were mainly located in the eastern region, the central region, and a small part of the western region of China. Hot spot analysis showed that the patterns of change for the total night light and night light mean had spatial agglomeration characteristics; (2) The PAs affected by light pollution changes were divided into eight classes, of which PAs with stable trends accounted for 41%, and PAs with high increasing trends accounted for 10%. PAs that had high increasing trends with low density accounted for the smallest amount, i.e., only 1%; (3) The factors influencing light pollution changes in PAs included the distance to urban areas, mineral exploitation, and tourism development and the migration of residents. Finally, based on the status of light pollution encroachment into PAs, strategies to control light pollution and enhance the sustainable development of PAs are recommended.

Top Lights - Bright Cities and Their Contribution to Economic Development

The commonly-used satellite images of nighttime lights fail to capture the true brightness of most cities. We show that night lights are a reliable proxy for economic activity at the city level, provided they are first corrected for top-coding. We present a stylized model of urban luminosity and empirical evidence which both suggest that these ‘top lights’ follow a Pareto distribution. We then propose a simple correction procedure which recovers the full distribution of city lights. Applying this approach to cities in Sub-Saharan Africa, we find that primate cities are outgrowing secondary cities but are changing from within.

NIGHT TIME LIGHT SATELLITE DATA FOR EVALUATING THE SOCIOECONOMICS IN CENTRAL ASIA

Abstract. Using nighttime lights data combined with LandScan population counts and socioeconomic statistics, dynamic change was monitored in the social economy of the five countries in Central Asia, from 1993 to 2012. In addition, the spatial pattern of regional historical development was analyzed, using this data. The countries included in this study were Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan and Turkmenistan. The economic development in these five Central Asian countries, the movement of the economic center, the distribution of poor areas and the night light development index (NLDI) were studied at a relatively fine spatial scale. In addition, we studied the relationship between the per capita lighting and per capita GDP at the national scale, finding that the per capital lighting correlated with per capita GDP. The results of this study reflect the socioeconomic development of Central Asia but more importantly, show that nighttime light satellite images are an effective tool for monitoring spatial and temporal social economic parameters.

ASSESSMENT OF LIGHT POLLUTION IMPACT ON PROTECTED AREAS IN CHINA

Abstract. The Protected Areas (PAs) with natural, ecological and cultural value play important role in biological processes, biodiversity and ecosystem service. During the past years of rapid urban expansion in China, the spatial range and intensity of light pollution unprecedented increase. Historically, optical remote sensing and field survey data had been used to reveal that human activities impacted on PAs for individual areas and few papers documented the issue of light pollution impact on PAs at national scale. Here, time series night-time light satellite images of Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) were selected to assess the light pollution impacted on PAs in China. The method we proposed can be effectively applied to assess the impact of light pollution on PAs and the percent of dark PAs decreased by 35.38 % from 1992 to 2012 at nationwide. The trend of light pollution of most PAs in stable, however, light pollution of the local area is increase significantly, especially in northern Xinjiang, Gansu, Xizang, Yunnan, Jiangsu and Shandong. Considering the current status of light pollution encroach into PAs, two strategies of appropriate buffer zone and wide measured for light pollution are also recommend.

Evaluating the Use of DMSP/OLS Nighttime Light Imagery in Predicting PM2.5 Concentrations in the Northeastern United States

Degraded air quality by PM2.5 can cause various health problems. Satellite observations provide abundant data for monitoring PM2.5 pollution. While satellite-derived products, such as aerosol optical depth (AOD) and normalized difference vegetation index (NDVI), have been widely used in estimating PM2.5 concentration, little research was focused on the use of remotely sensed nighttime light (NTL) imagery. This study evaluated the merits of using NTL satellite images in predicting ground-level PM2.5 at a regional scale. Geographically weighted regression (GWR) was employed to estimate the PM2.5 concentration and analyze its relationships with AOD, meteorological variables, and NTL data across the New England region. Observed data in 2013 were used to test the constructed GWR models for PM2.5 prediction. The Vegetation Adjusted NTL Urban Index (VANUI), which incorporates Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI into NTL to overcome the defects of NTL data, was used as a predictor variable for final PM2.5 prediction. Results showed that Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) NTL imagery could be an important dataset for more accurately estimating PM2.5 exposure, especially in urbanized and densely populated areas. VANUI data could obviously improve the performance of GWR for the warm season (GWR model with VANUI performed 17% better than GWR model without NDVI and NTL data and 7.26% better than GWR model without NTL data in terms of RMSE), while its improvements were less obvious for the cold season (GWR model with VANUI performed 3.6% better than the GWR model without NDVI and NTL data and 1.83% better than the GWR model without NTL data in terms of RMSE). Moreover, the spatial distribution of the estimated PM2.5 levels clearly revealed patterns consistent with those densely populated areas and high traffic areas, implying a close and positive correlation between VANUI and PM2.5 concentration. In general, the DMSP/OLS NTL satellite imagery is promising for providing additional information for PM2.5 monitoring and prediction.

Analysis of urban growth and estimating population density using satellite images of nighttime lights and land-use and population data

We investigated the spatiotemporal dynamics of urban expansion in Japan from 1990 to 2006 by using gridded land-use data, population census data, and satellite images of nighttime lights. First, we mapped Defense Meteorological Satellite Program (DMSP) nighttime lights and land-use data onto the 1 km2 grid cell system of Japan to determine the proportional areas of DMSP and urban land use within each grid cell. Then, we investigated the relationships among population density, DMSP, and urban area. The urban/built-up area was strongly positively correlated with population density, and rapid expansion of the urban/built-up area around megacities was associated with population increases. In contrast, population density dropped steeply in rural areas and in small towns. Statistical analysis showed that correlation coefficients between population density and DMSP increased as the DMSP nighttime lights brightness value increased. We next estimated population density in the Hokkaido region using an ordinary least squares (OLS) regression model. Numerical evaluation of the results showed that the combination of land-use data and DMSP could be used to predict the population density. Finally, we compared OLS and geographically weighted regression (GWR) model for Sapporo city, Hokkaido. Compared with the OLS, the GWR can improve predictions of population density.

Object-based spatial cluster analysis of urban landscape pattern using nighttime light satellite images: a case study of China

Previous studies have demonstrated urban built-up areas can be derived from nighttime light satellite (DMSP-OLS) images at the national or continent scale. This paper presents a novel object-based method for detecting and characterizing urban spatial clusters from nighttime light satellite images automatically. First, urban built-up areas, derived from the regionally adaptive thresholding of DMSP-OLS nighttime light data, are represented as discrete urban objects. These urban objects are treated as basic spatial units and quantified in terms of geometric and shape attributes and their spatial relationships. Next, a spatial cluster analysis is applied to these basic urban objects to form a higher level of spatial units – urban spatial clusters. The Minimum Spanning Tree (MST) is used to represent spatial proximity relationships among urban objects. An algorithm based on competing propagation of objects is proposed to construct the MST of urban objects. Unlike previous studies, the distance between urban objects (i.e., the boundaries of urban built-up areas) is adopted to quantify the edge weight in MST. A Gestalt Theory-based method is employed to partition the MST of urban objects into urban spatial clusters. The derived urban spatial clusters are geographically delineated through mathematical morphology operation and construction of minimum convex hull. A series of landscape ecologic and statistical attributes are defined and calculated to characterize these clusters. Our method has been successfully applied to the analysis of urban landscape of China at the national level, and a series of urban clusters have been delimited and quantified.

Explaining Seasonal Fluctuations of Measles in Niger Using Nighttime Lights Imagery

Measles epidemics in West Africa cause a significant proportion of vaccine-preventable childhood mortality. Epidemics are strongly seasonal, but the drivers of these fluctuations are poorly understood, which limits the predictability of outbreaks and the dynamic response to immunization. We show that measles seasonality can be explained by spatiotemporal changes in population density, which we measure by quantifying anthropogenic light from satellite imagery. We find that measles transmission and population density are highly correlated for three cities in Niger. With dynamic epidemic models, we demonstrate that measures of population density are essential for predicting epidemic progression at the city level and improving intervention strategies. In addition to epidemiological applications, the ability to measure fine-scale changes in population density has implications for public health, crisis management, and economic development.

Estimation of Steel Stock in Building and Civil Construction by Satellite Images

Steel is the most widely used material in the world and numerous studies analyzing the material flow of steel have been conducted for Japan, Asia and the world. While top-down and bottom-up approaches have been employed in material flow analysis and steel stock accounting, the applicability of these approaches is largely dependent on data availability. To overcome this problem of limited data, we proposed a method for estimating steel stock based on satellite images. Previous studies have shown that satellite images of nighttime lights are closely correlated with human activities, such as electricity consumption, CO2 emissions, and GDP, etc., which are also linked to the amount of steel stock. Therefore, in this study, images of nighttime lights were used in conjunction with land cover data to estimate the building and civil construction steel stock in Japan and other Asian countries. We initially performed the analysis for each prefecture of Japan and then applied the combined result to Japan, China, South Korea and Taiwan. Building steel stock was highly correlated with urban nighttime lights, while the steel stock used for civil construction structures was more closely correlated with total nighttime lights.

Steel Stock Estimation for Building and Civil Construction by Satellite Image

Steel is the most widely used metal in the world. Many studies analyzing the flow and stock of steels have been conducted for Japan, Asian countries and the world. While top-down and bottom-up approaches have been used in these material flow and stock analyses, the applicability of these approaches is dependent on data availability. To overcome this problem, we proposed a methodology to estimate steel stock based on satellite images. Previous studies have shown that satellite images of nighttime lights are closely correlated with human activities, such as electricity consumption, CO 2 emissions, and GDP, which can also be linked to the amount of steel stock. In this study, images of nighttime lights were used in conjunction with land cover data to estimate the building and civil construction steel stock in Japan and other Asian countries. Analysis was first performed for each prefecture in Japan, and then the results were applied to China, South Korea and Taiwan. Building steel stock was highly correlated with urban nighttime lights, while the steel stock used for civil construction structures correlated more closely with total nighttime lights. This work is considered as revised version of the authors’ previous study by updating some of the nighttime light data.