Relation between satellite observed visible-near infrared emissions,population, economic activity and electric power consumption

The area lit by anthropogenic visible-near infrared emissions (i.e., lights) has been estimated for 21 countries using night-time data from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS). The area lit is highly correlated to gross domestic product and electric power consumption. Significant outliers exist in the relation between area lit and population. The results indicate that the local level of economic development must be factored into the apportionment of population across the land surface based on DMSP-OLS observed lights.     

基于夜间灯光数据的城市建成区提取方法评价与应用

DMSP/OLS夜间灯光数据已被广泛应用于城市建成区的提取。目前主要存在4类提取方法:经验阈值法、突变检测法、统计数据法和较高分辨率影像数据空间比较法。以上海为例,在2000年、2003年、2006年夜间灯光数据的基础上,利用4种方法完成了城市建成区的提取。通过对不同年份数据提取结果的比较,证明了相同的灰度分割阈值对不同年份的夜间灯光数据中不存在通用性;在对2003年夜间灯光数据的提取中,4类方法所得结果精确度从高到低依次为统计数据法、突变检测法、经验阈值法和较高分辨率影像数据空间比较法,相对误差分别为1.3%、2.1%、5.1%和11.2%,在对4种方法的便捷性和可实现性进行分析与评价后,使用突变检测法完成了上海市2000~2006年城市建成区的提取。     

Quantifying urban land use and impact on soils in Egypt using diurnal satellite imagery of the Earth surface

Conversion of agricultural land to urban use represents a potential loss of agricultural productivity, especially in areas where arable land is in short supply. Using derived products from both daytime (Landsat sensor data) and night-time imaging systems (U.S. Air Force Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS)) we examined the impacts of urbanization on soils in Egypt; a country with very limited agricultural land. We concluded that urban land cover types to occupy 3.7% of the total area of Egypt and that over 30% of the soils most suitable for agriculture are under urban land cover. Analysis of multiyear historical DMSP/OLS data sets (digitized from paper images) proved unreliable for long-term urban growth estimates.     

Can night-time light images play a role in evaluating the Syrian Crisis?

This study investigates whether night-time light images acquired from the Defense Meteorological Satellite Program’s Operational Linescan System provide spatial and temporal insight with regard to the humanitarian aspects of the Syrian crisis. Evaluating the ongoing crisis in Syria is challenging since reliable witness reports are hard to gather in a war zone. Therefore satellite images, as one of the few sources of objective information, are potentially of great importance. We used 38 monthly Defense Meteorological Satellite Program’s Operational Linescan System composites covering the period between January 2008 and February 2014. The results indicate that night-time light and lit area in Syria declined by about 74% and 73%, respectively, between March 2011 and February 2014. In 12 of 14 provinces, night-time light declined by >60%. Damascus and Quneitra are the exceptions, with night-time light having declined only by about 35%. Notably, the number of internally displaced persons (IDPs) of each province shows a linear correlation with night-time light loss, with an R² value of 0.52. Through clustering the time series images, we found that the international border of Syria represents a distinct boundary between regions of differing night-time light spatiotemporal patterns. The contrast across the border increases as the region studied encompasses a wider zone on either side of the border. These findings lend support to the hypothesis that night-time light can be a useful source for monitoring humanitarian crises such as that unfolding in Syria.     

Assessing the impact of urban land development on net primary productivity in the southeastern United States

The southeastern United States (SE-US) has undergone one of the highest rates of landscape changes in the country due to changing demographics and land use practices over the last few decades. Increasing evidence indicates that these changes have impacted mesoscale weather patterns, biodiversity and water resources. Since the Southeast has one of the highest rates of land productivity in the nation, it is important to monitor the effects of such changes regularly. Here, we propose a remote sensing based methodology to estimate regional impacts of urban land development on ecosystem structure and function. As an indicator of ecosystem functioning, we chose net primary productivity (NPP), which is now routinely estimated from the MODerate resolution Imaging Spectroradiometer (MODIS) data. We used the MODIS data, a 1992 Landsat-based land cover map and nighttime data derived from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) for the years 1992/1993 and 2000 to estimate the extent of urban development and its impact on NPP. The analysis based on the nighttime data indicated that in 1992/1993, urban areas amounted to 4.5% of the total land surface of the region. In the year 2000, the nighttime data showed an increase in urban development for the southeastern United States of 1.9%. Estimates derived from the MODIS data indicated that land cover changes due to urban development that took place during the 1992-2000 period reduced annual NPP of the southeastern United States by 0.4%. Despite the uncertainties in sensor fusion and the coarse resolution of the data used in this study, results show that the combination of MODIS products such as NPP with nighttime data could provide rapid assessment of urban land cover changes and their impacts on regional ecosystem resources.     

Land cover in East Asia classified using Terra MODIS and DMSP OLS products

Land cover is classified over East Asia using 250‐m Moderate Resolution Imaging Spectroradiometer (MODIS) land surface reflectance, MODIS snow cover and Operational Linescan System (OLS) human settlement data. The classification method includes a decision tree classification scheme that considers 11 kinds of land surface features derived from the OLS product and the time series of two MODIS products in 2000. The decision tree was defined manually based on the experiment because of insufficient training data, ease of tuning by visual interpretation, and extensibility to further research. The resulting classification is compared to three kinds of reference data, i.e. MODIS land cover product, Chinese digital land cover map, and Chinese census. The land cover classification can be input into a hydrological model applied to the Yellow River in China.     

Early damaged area estimation system using DMSP-OLS night-time imagery

The disaster information system, the Early Damaged Area Estimation System (EDES), was developed to estimate damaged areas of natural disaster using the night-time imagery of the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS). The system employs two estimation methods to detect the city lights loss or reduction as possible impacted areas; one is the bi-temporal images (BTI) method and the other is the time-series images (TSI) method. Both methods are based on significance tests assuming that brightness of city lights fluctuates as normal random variables, and the BTI method is simplified by introducing the assumption that the standard deviation of city lights fluctuation is constant. The validity of the estimation method is discussed based on the result of the application to the 2001 Western India earthquake disaster. The estimation results identify the damaged areas distant from the epicentre fairly well, especially when using the TSI method. The system is designed to estimate the global urban damage and to provide geographic information through the Internet within 24?h after a severe disaster event. The information is expected to support the disaster response and relief activities of governments and non-governmental organizations.     

DMSP/OLS夜间灯光数据应用研究综述

DMSP/OLS以其独特的光电放大特性与对夜间灯光的获取能力,成为人类活动监测的良好数据源。回顾了DMSP/OLS夜间灯光数据在城镇信息提取、社会经济因子估计及光污染、火灾、渔火、天然气燃烧监测等方面的应用,其中重点介绍了利用灯光数据估算城市化水平和人口的方法和步骤,并总结了DMSP/OLS夜间灯光数据在应用中的优越性与局限性,最后对其未来应用方向进行了展望。      

Intercalibration of DMSP-OLS night-time light data by the invariant region method

DMSP-OLS (Defense Meteorological Satellite Program Operational Linescan System) night-time light data can accurately reflect the scope and intensity of human activities. However, the raw data cannot be used directly for temporal analyses due to the lack of inflight calibration. There are three problems that should be addressed in intercalibration. First, because of differences between sensors, the data are not identical even when obtained in the same year. Second, different acquisition times may lead to random or systematic fluctuations in the data obtained by satellites in different orbits. Third, a pixel saturation phenomenon also exists in the urban centres of the image. Therefore, an invariant region method was used in this article, and the relative radiometric calibration and saturation correction achieved the desired results. In the meantime, intercalibration models for each satellite year of DMSP-OLS night-time light data were produced. Finally, intercalibration accuracy was evaluated, and the intercalibration results were tested with the corresponding gross domestic product (GDP) data.     

Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data

Urban areas concentrate people, economic activity, and the built environment. As such, urbanization is simultaneously a demographic, economic, and land-use change phenomenon. Historically, the remote sensing community has used optical remote sensing data to map urban areas and the expansion of urban land-cover for individual cities, with little research focused on regional and global scale patterns of urban change. However, recent research indicates that urbanization at regional scales is growing in importance for economics, policy, land use planning, and conservation. Therefore, there is an urgent need to understand and monitor urbanization dynamics at regional and global scales. Here, we illustrate the use of multi-temporal nighttime light (NTL) data from the U.S Air Force Defense Meteorological Satellites Program/Operational Linescan System (DMSP/OLS) to monitor urban change at regional and global scales. We use independently derived data on population, land use and land cover to test the ability of multi-temporal NTL data to measure regional and global urban growth over time. We apply an iterative unsupervised classification method on multi-temporal NTL data from 1992 to 2008 to map urbanization dynamics in India, China, Japan, and the United States. For two-year intervals between 1992 and 2000, India consistently experienced higher rates of urban growth than China, and both countries exceeded the urban growth rates of the United States and Japan. This is not surprising given that the populations of India and China were growing faster than those of the U.S. and Japan during those periods. For two-year intervals between 2000 and 2008, China experienced higher rates of urban growth than India. Results show that the multi-temporal NTL provides a regional and potentially global measure of the spatial and temporal changes in urbanization dynamics for countries at certain levels of GDP and population-driven growth.     

Population spatialization in China based on night-time imagery and land use data

Population is a key indicator of socioeconomic development, urban planning and environmental protection, particularly for developing countries like China. But, census data for any given area are neither always available nor adequately reflect the internal differences of population. The authors tried to overcome this problem by spatializing the population across China through utilizing integer night-time imagery (Defense Meteorological Satellite Program/Operational Linescan System, DMSP/OLS) and land-use data. In creating the population linear regression model, night-time light intensity and lit areas, under different types of land use, were employed as predictor variables, and census data as dependent variables. To improve model performance, eight zones were created using night-time imagery clustering and shortest path algorithm. The population model is observed to have a coefficient of determination (R ²) ranging from 0.80 to 0.95 in the research area, which remained the same in different years. A comparison of the results of this study with those of other researchers shows that the spatialized population density map, prepared on the basis of night-time imagery, reflects the population distribution character more explicitly and in greater detail.     

Estimating energy consumption from night-time DMPS/OLS imagery after correcting for saturation effects

A methodology is presented to accurately estimate electric power consumption from saturated night-time Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) imagery using a stable light correction. An area correction for the stable light image of DMSP/OLS for the year 1999 was performed and the build-up area rate data were used to clarify the intensity distribution characteristics of the stable light. Based on the spatial distribution characteristics of the stable light, the saturation light of the electric power supply area of Japan was corrected using a cubic regression equation. The regression between the correction calculations by the cubic regression equation and the statistical electric power consumption data was applied in Japan and also in China, India and 10 other Asian countries. The correction method was then evaluated. This study confirms that electric power consumption can be estimated with high precision from the stable light.     

Analysis of the Development Trend of Global Papers Combining the DMSP-OLS Nighttime Light Data

The multi\|subject cooperation and intersection becomes the main development tendency of the modern sciences.In this study,the remote sensed nighttime light data from the Defense Meteorological Satellite Program(DMSP)Operational Linescan System(OLS)were combined with the global international papers for obtaining the temporal and spatial distribution characteristics and dynamic change characteristics at national and regional scale.The results indicate that most of the countries or regions have the increasing tendencies with the exponential models.There are good correlation between the nighttime light data and international paper for most of the countries or regions.In the 72 calculated countries,there are 53 countries with the correlation coefficients more than 0.5 when the significance level is more than 0.01.Furthermore,the developing countries have higher correlation coefficients than the developed countries.In the top 18 countries with higher correlation coefficients,16 are the developing countries.The night\|time light data can be potentially used to better predict the numbers of countries’ papers.     

An improved method of night-time light saturation reduction based on EVI

Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) night-time light (NTL) data have been widely applied to studies on anthropogenic activities and their interactions with the environment. Due to limitations of the OLS sensor, DMSP NTL data suffer from a saturation problem in central urban areas, which further affects studies based on nocturnal lights. Recently, the vegetation-adjusted NTL urban index (VANUI) has been developed based on the inverse correlation of vegetation and urban surfaces. Despite its simple implementation and ability to effectively increase variations in NTL data, VANUI does not perform well in certain rapidly growing cities. In this study, we propose a new index, denoted enhanced vegetation index (EVI)-adjusted NTL index (EANTLI), that was developed by reforming the VANUI algorithm and utilizing the EVI. Comparisons with radiance-calibrated NTL (RCNTL) and the new Visible Infrared Imager Radiometer Suite (VIIRS) data for 15 cities worldwide show that EANTLI reduces saturation in urban cores and mitigates the blooming effect in suburban areas. EANTLI’s similarity to RCNTL and VIIRS is consistently higher than VANUI’s similarity to RCNTL and VIIRS in both spatial distribution and latitudinal transects. EANTLI also yields better results in the estimation of electric power consumption of 166 Chinese prefecture-level cities. In conclusion, EANTLI can effectively reduce NTL saturation in urban centres, thus presenting great potential for wide-range applications.     

Detection of rural electrification in Africa using DMSP-OLS night lights imagery

We report on the first systematic ground-based validation of the US Air Force Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) night lights imagery to detect rural electrification in the developing world. Drawing upon a unique survey of villages in Senegal and Mali, this study compares night-time light output from the DMSP-OLS against ground-based survey data on electricity use in 232 electrified villages and additional administrative data on 899 unelectrified villages. The analysis reveals that electrified villages are consistently brighter than unelectrified villages across annual composites, monthly composites, and a time series of nightly imagery. Electrified villages appear brighter because of the presence of streetlights, and brighter villages tend to have more streetlights. By contrast, the correlation of light output with household electricity use and access is low. We further demonstrate that a detection algorithm using data on night-time light output and the geographic location of settlements can accurately classify electrified villages. This research highlights the potential to use night lights imagery for the planning and monitoring of ongoing efforts to connect the 1.4 billion people who lack electricity around the world.     

Spatial improvement of human population distribution based on multi-sensor remote-sensing data: an input for exposure assessment

A spatial mismatch of hazard data and exposure data (e.g. population) exists in risk analysis. This article provides an integrated approach for a rapid and accurate estimation of population distribution on a per-pixel basis, through the combined use of medium and coarse spatial resolution remote-sensing data, namely the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) night-time imagery, enhanced vegetation index (EVI), and digital elevation model (DEM) data. The DMSP/OLS night-time light data have been widely used for the estimation of population distribution because of their free availability, global coverage, and high temporal resolution. However, given its low-radiometric resolution as well as the overglow effects, population distribution cannot be estimated accurately. In the present study, the DMSP/OLS data were combined with EVI and DEM data to develop an elevation-adjusted human settlement index (EAHSI) image. The model for population density estimation, developed based on the significant linear correlation between population and EAHSI, was implemented in Zhejiang Province in southeast China, and a spatialized population density map was generated at a resolution of 250 m?×?250 m. Compared with the results from raw human settlement index (59.69%) and single night-time lights (35.89%), the mean relative error of estimated population by EAHSI has been greatly reduced (17.74%), mainly due to the incorporation of elevation information. The accurate estimation of population density can be used as an input for exposure assessment in risk analysis on a regional scale and on a per-pixel basis.     

A dynamic model for population mapping: a methodology integrating a Monte Carlo simulation with vegetation-adjusted night-time light images

Population is attracting increasing attention as a driver of resource overexploitation, environmental degradation, loss of biodiversity, and other environmental challenges. Timely and accurately updating maps of population distribution are thus urgently needed. Images of night-time lights from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) have been used for years in population mapping as an alternative to human settlement distribution. The capacity of night-time light images for gridding populations, however, is compromised by the dual effects of saturation and overglow. Static models of the human settlement index (HSI), elevation-adjusted human settlement index (EAHSI), and vegetation-adjusted night-time light urban index (VANUI) have been developed to counteract these negative effects by using constant coefficients. The static models, however, retain disadvantages due to the negative effects of the high variation of socio-economic backgrounds in different study areas. In this study, we integrate Monte Carlo simulation with the above three static indices and propose the dynamic model VANUI Supported by Monte Carlo Simulation (VANUIMCS) for mapping the population of Liaoning Province, China. We assess the accuracy of the simulation using data for 60 counties and 1251 townships. The VANUIMCS improve the accuracy of population mapping, with the mean percentage errors of 19.43% at the county level and 43.19% at the township level.     

Brazil's 2001 energy crisis monitored from space

Data sensed by the US Air Force Defence Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) during the years 2000 and 2001 in Brazil were tested as a tool to monitor reduction of nocturnal lighting. This particular timing was examined as the Brazilian population and industry were forced to reduce electric power consumption by 20% during 2001, in relation to 2000, for a period of several months, starting officially on 1 June 2001. Large urban agglomerates were compelled to switch off city lights by at least the same amount. The Distrito Federal (DF), including the Brazilian capital, Brasilia, was one of the primary areas where the government actively sought electric power consumption reductions. Using the DF as a study case, we demonstrate that the mean grey levels derived from averaging DMSP-OLS data acquired over urban centres appear to be a useful index to monitor relative oscillations in energy consumption.     

VIIRS constant spatial-resolution advantages

The Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imager Radiometer Suite (VIIRS) was launched on 28 October 2011, nearly 20 years after the conceptual definition began at the Hughes Aircraft Company's Santa Barbara Research Center. Constrained off-nadir pixel growth, producing constant or near-constant VIIRS spatial resolution over the entire scan swath, is a patented design feature that dramatically improves imaging radiometry compared to VIIRS predecessors. VIIRS ground-projected east–west (across the orbit track) and north–south (along the orbit track) pixel dimensions are constrained to within a factor of two from nadir to ±1500 km off-nadir (edge of scan) in all 22 VIIRS spectral bands. The capability is a valuable improvement to previous systems' six-fold across-track pixel growth over narrower swaths, while improving signal-to-noise ratio (SNR) without larger optics. The technique allows the VIIRS day/night band (DNB) to offer nearly 9- to over 50-fold finer and truly constant area spatial resolution with enhanced sensitivity and dynamic range compared with the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS). This article reviews constant resolution from concept to VIIRS implementation and compares several VIIRS applications to similar applications of systems VIIRS replaces to demonstrate advantages of the new capability.     

Mapping spatio-temporal changes of Chinese electric power consumption using night-time imagery

China's rapid economic development in the last 20 years has resulted in increased demand for electricity and ensuing shortages in electric power supply. It is necessary to derive accurate and timely information regarding changing spatio-temporal patterns and trends of electric power consumption to inform future electricity allocation. Night-time annual image composites for 1995–2005 were obtained from the Defense Meteorological Satellite Program's Operational Linescan System and were inter-calibrated. The inter-calibrated night-time image composites were used in conjunction with the LandScan 2008 population data to estimate the amounts of electric power consumption in 1995, 2000 and 2005 for China at the province level. The estimated amounts of electric power consumption were then disaggregated to the pixel level. A pixel-based map was produced to show the spatio-temporal changes of electric power consumption from 1995 to 2005, in which 11 regional agglomerations with large increases of electric power consumption had emerged. During the process of producing this spatio-temporal change map, some errors were generated because of the use of single-year LandScan population data, imperfect reference regions for inter-calibration and a single threshold value for delimiting urban areas. However, we believe these errors are limited and acceptable, so we present this method of estimation and disaggregation to show the increases in electric consumption.