Operational Linescan System Research Articles

Assessing Light Pollution in China Based on Nighttime Light Imagery

Rapid urbanization and economic development inevitably lead to light pollution, which has become a universal environmental issue. In order to reveal the spatiotemporal patterns and evolvement rules of light pollution in China, images from 1992 to 2012 were selected from the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) and systematically corrected to ensure consistency. Furthermore, we employed a linear regression trend method and nighttime light index method to demonstrate China’s light pollution characteristics across national, regional, and provincial scales, respectively. We found that: (1) China’s light pollution expanded significantly in provincial capital cities over the past 21 years and hot-spots of light pollution were located in the eastern coastal region. The Yangtze River Delta, Pearl River Delta, and Beijing–Tianjin–Hebei regions have formed light pollution stretch areas; (2) China’s light pollution was mainly focused in areas of north China (NC) and east China (EC), which, together, accounted for over 50% of the light pollution for the whole country. The fastest growth of light pollution was observed in northwest China (NWC), followed by southwest China (SWC). The growth rates of east China (EC), central China (CC), and northeast China (NEC) were stable, while those of north China (NC) and south China (SC) declined; (3) Light pollution at the provincial scale was mainly located in the Shandong, Guangdong, and Hebei provinces, whereas the fastest growth of light pollution was in Tibet and Hainan. However, light pollution levels in the developed provinces (Hong Kong, Macao, Shanghai, and Tianjin) were higher than those of the undeveloped provinces. Similarly, the light pollution heterogeneities of Taiwan, Beijing, and Shanghai were higher than those of undeveloped western provinces.

Delineation and classification of rural–urban fringe using geospatial technique and onboard DMSP–Operational Linescan System

This study aims to analyse the processes and patterns of peri-urbanization using diurnal earth observation data-sets from onboard DMSP–Operational Linescan System. In this study, multiple correlation, simple and conditional linear regression are used to find out the degree of relationship and spatial behavioural pattern of the factors responsible for the urbanization. All the factors are standardized using the Analytical Hierarchy Process (AHP) coupled fuzzy membership functions. AHP is used to derive the weighting of the factors to produce the urbanity index. In total three functional zones – urban, rural and urban shadow are generated based on factor standardization and spatial contiguity index. Urban fringe is sharing ≥ 60% of Urbanity Index followed by rural fringe (39.50–60% of urbanity index) and urban shadow <39.50% of urbanity index. Shape index indicates that the city is going through unplanned development following cross to star shape growth.

New urban map of Eurasia using MODIS and multi-source geospatial data

Urban areas are of paramount significance to both the individuals and communities at local and regional scales. However, the rapid growth of urban areas exerts effects on climate, biodiversity, hydrology, and natural ecosystems worldwide. Therefore, regular and up-to-date information related to urban extent is necessary to monitor the impacts of urban areas at local, regional, and potentially global scales. This study presents a new urban map of Eurasia at 500 m resolution using multi-source geospatial data, including Moderate Resolution Imaging Spectroradiometer (MODIS) data of 2013, population density of 2012, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime lights of 2012, and constructed Impervious Surface Area (ISA) data of 2010. The Eurasian urban map was created using the threshold method for these data, combined with references of fine resolution Landsat and Google Earth imagery. The resultant map was compared with nine global urban maps and was validated using random sampling method. Results of the accuracy assessment showed high overall accuracy of the new urban map of 94%. This urban map is one product of the 20 land cover classes of the next version of Global Land Cover by National Mapping Organizations.

The luminous intensity of regional ‘night-light’ output can predict the growing volume of published scientific research by ‘luminaries’ in developing countries

Global scientific research output has experienced continuous and rapid growth during the last 20 years. The spatial and temporal variations of the international papers at the national and regional scales were analyzed by combining the remotely sensed nighttime light data from the Defense Meteorological Satellite Program's Operational Linescan System. The findings indicate that the publication of international-circulation scientific papers in most of the countries examined have experienced a trend of exponential increase which can be positively correlated with nighttime light in those counties or regions. Furthermore, the developing countries have higher correlation coefficients than the developed countries. Thus, literal nighttime light data can potentially be used in future to better predict the number of publications of the research of figurative `luminaries' residing in developing countries.

Detecting spatiotemporal dynamics of global electric power consumption using DMSP-OLS nighttime stable light data

The rapid development of global industrialization and urbanization has resulted in a great deal of electric power consumption (EPC), which is closely related to economic growth, carbon emissions, and the long-term stability of global climate. This study attempts to detect spatiotemporal dynamics of global EPC using the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime stable light (NSL) data. The global NSL data from 1992 to 2013 were intercalibrated via a modified invariant region (MIR) method. The global EPC at 1km resolution was then modeled using the intercalibrated NSL data to assess spatiotemporal dynamics of EPC from a global scale down to continental and national scales. The results showed that the MIR method not only reduced the saturated lighted pixels, but also improved the continuity and comparability of the NSL data. An accuracy assessment was undertaken and confined that the intercalibrated NSL data were relatively suitable and accurate for estimating EPC in the world. Spatiotemporal variations of EPC were mainly identified in Europe, North America, and Asia. Special attention should be paid to China where the high grade and high-growth type of EPC covered 0.409% and 1.041% of the total country area during the study period, respectively. The results of this study greatly enhance the understanding of spatiotemporal dynamics of global EPC at the multiple scales. They will provide a scientific evidence base for tracking spatiotemporal dynamics of global EPC.

Intercalibration of DMSP/OLS by Parallel Regressions

The Defense Meteorological Satellite Program—Operational Linescan System night lights offer a unique data set, covering more than two decades. However, for a number of reasons, an intercalibration procedure has to take place in order to reduce year-to-year inconsistencies before a meaningful time series can be established. Several intercalibration procedures have been proposed in the literature. However, they are rarely compared to each other. One difficulty in the comparison has been the lack of reference data in most, if not all, of the countries. Nevertheless, some metrics have been proposed, primarily based on quantifying year-to-year variation and comparing with auxiliary data sets. The objective of this letter is to introduce a new and more efficient intercalibration approach and compare it with commonly used procedures. The results show that the proposed method significantly improves intercalibration with respect to both reducing year-to-year fluctuations as well as improving correlation with auxiliary data sets.

On the application of a concentric zone model (CZM) for classifying and extracting urban boundaries using night-time stable light data in Urumqi of Xinjiang, China

ABSTRACTNight-time satellite imagery provided by the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP−OLS) is widely used in monitoring urban construction area. However, there are still large uncertainties in validating such applications across different regions. In this study, we use the concentric zone model (CZM) to classify the night-time light emission data to define the urban area. Using this new classification method, we have revealed the urban development in Urumqi, a large city in the newly proposed Silk Road Economic Development Belt. Our results show a range of thresholds for defining Urumqi boundary. Comparing the city boundaries from 2002 to 2012, the city did not show significant expansion during the period of 2002–2007 except for a northward expansion. From 2007 to 2012, the city has expanded outward by about 6 km and the surrounding terrain appears not to be a major obstacle to its development. In this study, we have demonstrated that through the analysis of the layer structure of the night-time light emission data, the evolution of the Urumqi city has been clearly demonstrated. Therefore, using CZM to classify night-time stable light data is a practical method for studying urbanization.

Study on radiometric intercalibration methods for DMSP-OLS night-time light imagery

ABSTRACTThe Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) night-time light image sets has been successfully used in various fields to monitor temporal and spatial distributions, such as urbanization and socioeconomic activities. However, the radiometric calibration of night-time light images has long been a problem that limits the application of night-time light image sets in multi-temporal analyses. The key to an intercalibration model is to automatically extract the reference pixels with stable lights and to simultaneously remove unstable (or variant) light pixels viewed as outliers (or gross error). This paper systemically compares five weighted least squares regression (WLSR) algorithms for radiometric intercalibration to determine the method with the highest accuracy, including (1) classic methods: empirical rule (ER), Danish method (DM), and posterior variance estimation (PVE); and (2) state-of-the-art methods: random sample consensus (RANSAC) and least median of squares (LMedS). Moreover, a more objective adjusted root mean square error (RMSE) method is proposed to evaluate accuracy. Through the experiments, we systemically analyse the performance of different estimators and propose recommendations for optimizing intercalibration for specific applications. Moreover, the study reveals that gross error approximatively obeys the stochastic or expansion model in the radiometric intercalibration of DMSP-OLS image sets. Overall, LMedS works best and is proposed to intercalibrate the radiometric values of night-time light image sets.

Monitoring Urban Dynamics in the Southeast U.S.A. Using Time-Series DMSP/OLS Nightlight Imagery

The Defense Meteorological Satellite Program (DMSP)’s Operational Line-scan System (OLS) stable nighttime light (NTL) imagery offers a good opportunity for characterizing the extent and dynamics of urban development at the global and regional scales. However, their ability to characterize intra-urban variation is limited due to saturation and blooming of the data values. In this study, we adopted the methods of Mann-Kendall and linear regression to analyze urban dynamics from time series Vegetation Adjusted NTL Urban Index (VANUI) data from 1992 to 2013 in the Southeast United States of America (U.S.A.), which is one of the fastest growing regions in the nation. The newly built urban areas were effectively detected based on the trend analysis. In addition, the VANUI-derived urban areas with an optimal threshold method were found highly consistent with the Landsat-derived National Land Cover Database. The total urbanized areas in large metropolitan areas in southeastern U.S.A. increased from 8524 km2 in 1992 to 14,684 km2 in 2010, accounting for 5% and 9% of the total area, respectively. The results further showed that urban expansion in the region cannot be purely explained by population growth. Our results suggested that the VANUI time series provided an effective method for characterizing the spatiotemporal dynamics of urban extent at the regional scale.

Artificial Outdoor Nighttime Lights Associate with Altered Sleep Behavior in the American General Population.

Our study aims to explore the associations between outdoor nighttime lights (ONL) and sleep patterns in the human population. Cross-sectional telephone study of a representative sample of the general US population age 18 y or older. 19,136 noninstitutionalized individuals (participation rate: 83.2%) were interviewed by telephone. The Sleep-EVAL expert system administered questions on life and sleeping habits; health; sleep, mental and organic disorders (Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision; International Classification of Sleep Disorders, Second Edition; International Classification of Diseases, 10(th) Edition). Individuals were geolocated by longitude and latitude. Outdoor nighttime light measurements were obtained from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS), with nighttime passes taking place between 19:30 and 22:30 local time. Light data were correlated precisely to the geolocation of each participant of the general population sample. Living in areas with greater ONL was associated with delayed bedtime (P < 0.0001) and wake up time (P < 0.0001), shorter sleep duration (P < 0.01), and increased daytime sleepiness (P < 0.0001). Living in areas with greater ONL also increased the dissatisfaction with sleep quantity and quality (P < 0.0001) and the likelihood of having a diagnostic profile congruent with a circadian rhythm disorder (P < 0.0001). Although they improve the overall safety of people and traffic, nighttime lights in our streets and cities are clearly linked with modifications in human sleep behaviors and also impinge on the daytime functioning of individuals living in areas with greater ONL.

A Simulation Study on the Urban Population of China Based on Nighttime Light Data Acquired from DMSP/OLS

The urban population (UP) measure is one of the most direct indicators that reflect the urbanization process and the impacts of human activities. The dynamics of UP is of great importance to studying urban economic, social development, and resource utilization. Currently, China lacks long time series UP data with consistent standards and comparability over time. The nighttime light images from the Defense Meteorological Satellite Program’s (DMSP) Operational Linescan System (OLS) allow the acquisition of continuous and highly comparable long time series UP information. However, existing studies mainly focus on simulating the total population or population density level based on the nighttime light data. Few studies have focused on simulating the UP in China. Based on three regression models (i.e., linear, power function, and exponential), the present study discusses the relationship between DMSP/OLS nighttime light data and the UP and establishes optimal regression models for simulating the UPs of 339 major cities in China from 1990 to 2010. In addition, the present study evaluated the accuracy of UP and non-agricultural population (NAP) simulations conducted using the same method. The simulation results show that, at the national level, the power function model is the optimal regression model between DMSP/OLS nighttime light data and UP data for 1990–2010. At the provincial scale, the optimal regression model varies among different provinces. The linear regression model is the optimal regression model for more than 60% of the provinces. In addition, the comparison results show that at the national, provincial, and city levels, the fitting results of the UP based on DMSP/OLS nighttime light data are better than those of the NAP. Therefore, DMSP/OLS nighttime light data can be used to effectively retrieve the UP of a large-scale region. In the context of frequent population flows between urban and rural areas in China and difficulty in obtaining accurate UP data, this study provides a timely and effective method for solving this problem.

Mapping and evaluation the process, pattern and potential of urban growth in China

In this paper, the night-time light (NTL hereafter) data obtained by the Defence Meteorological Satellite Programme's Operational Line scan System (DMSP/OLS) were used to extract urban light index (ULI) for analysing urban growth and patterns in China. A unit circle mode was established to perform a comprehensive analysis and calculation of the variation of various types of urban land covers. In order to verify the ULI ability in extracting urban spatial patterns, eight typical cities were selected to make a comparison therein using Landsat TM/ETM + data. The objectives of this research include: 1) mapping temporal process of the urban growth; 2) exploring spatial characteristics of eight typical cities; 3) analysing spatial distribution pattern of primary urban agglomerations, and 4) assessing potential and tendency for urbanization in the future. The research results indicate that: the spatial pattern features of ULI generally agree with those of the features of urban pattern extracted from Landsat TM data. The morphological change of the urban light space can reflect the objective characteristics of the evolution of urban space. The study further illustrated that the NTL Data are applicable for extracting urban space information and have the potential to deepen understanding of urban space expansion.

Feasibility of a new-generation nighttime light data for estimating in-use steel stock of buildings and civil engineering infrastructures

Regional scale estimation of in-use steel stock (IUSS) using satellite observation of nighttime lights (NTL) is essential for urban resource consumption management and resource recovery monitoring. S-NPP VIIRS (Suomi National Polar-orbiting Partnership’s Visible Infrared Imaging Radiometer Suite) NTL product is newly released in 2013, which has enhancements in both spatial and radiometric resolutions compared with the DMSP-OLS (Defense Meteorological Satellite Program’s Operational Linescan System) NTL products. To evaluate whether these enhancements can improve the estimations of IUSS of buildings (IUSSB) and civil engineering infrastructure (IUSSCE), the S-NPP VIIRS NTL products were used to estimate IUSSB and IUSSCE at both sub-national level of Japan and national level of world, and their performances were compared with those of using DMSP-OLS radiance calibrated (RC) NTL data. Our results indicate that the S-NPP VIIRS NTL data can offer more accurate estimates of IUSSB and IUSSCE (R2 values of 0.952 and 0.909) than those of DMSP-OLS RC NTL (R2 of 0.929 and 0.884) at prefectural level of Japan. Similar to DMSP-OLS RC NTL product, urban NTL of S-NPP VIIRS data has a stronger relationship with IUSSB, and IUSSCE was more closely related to total NTL. At national level, S-NPP VIIRS NTL also showed better estimations of IUSSB and IUSSCE. We confirmed that dividing the world into different regional groups is also required for estimating IUSSB and IUSSCE from S-NPP VIIRS NTL. For estimation of IUSSCE, two classifications including Asia region and Non-Asia region are appropriate when using S-NPP VIIRS NTL product, which is superior to DMSP-OLS RC NTL. For estimation of IUSSB, more classifications may be still required which is similar to DMSP-OLS RC NTL. Overall, our study indicated that S-NPP VIIRS data have greater capability in modeling IUSSB and IUSSCE than DMSP-OLS RC data. Our results are critical for ever-improvement of policy making on urban resource management.

Use of an inside buffer method to extract the extent of urban areas from DMSP/OLS nighttime light data in North China

Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) nighttime imagery provides a valuable data source for mapping urban areas. However, the spatial extents of large cities are often overestimated because of the effect of over-glow from nighttime light if a fixed thresholding technique is used. In the work reported here, an inside buffer method was developed to solve this issue. The method is based on the fact that the area overestimated is proportional to the extent of the lit area if a fixed threshold is used to extract urban areas in a region/county. Using this method, the extents of urban areas in North China were extracted and validated by interpretations from Landsat Thematic Mapper images. The results showed that the lit areas had a significant linear relationship with the urban areas for 120 representative cities in North China in 2000, with an R2 value of over 0.95. This demonstrates that the inside buffer method can be used to extract urban areas. The validation results showed that the inside buffer model developed in 2000 can be directly used to extract the extent of urban areas using more recent nighttime light imagery. This is of great value for the timely updating of urban area databases in large regions or countries.

Impervious surface detection with nighttime photography from the International Space Station

Abstract For over two decades nighttime satellite imagery from the Operational Linescan System (OLS) has been used to detect impervious surfaces. However, OLS-based maps suffer from the sensor's coarse resolution (2.7 km/pixel), overglow, and saturation in urban areas, resulting in inaccurate estimates of the extent and degree of impervious surfaces. In order to provide more reliable estimates of impervious surface extent, we used high resolution (~ 10 m/pixel) nighttime photography from the International Space Station (ISS). Focusing on the city of Berlin in Germany, we produced a map of the extent of impervious surfaces. Our classification was 85% accurate for both user and producer measures. Impervious surfaces omitted by ISS photography were mainly transit roads and airport runways, while green areas and water bodies within the city were falsely identified. An analysis based on ISS imagery classified 55.7% of the study area as impervious, which is only 3.9% less than ground truth (while the OLS-based estimate was 40% higher than ground truth). ISS imagery failed to provide reliable information about the degree of imperviousness for individual pixels (± 20% errors); nevertheless it accurately estimated the spatially-averaged degree of imperviousness for the whole study area (30.2% vs. the reference value of 30.1%). These results show that ISS photography is an important source of nighttime imagery for mapping the extent of impervious surfaces, and represents a considerable improvement over OLS capabilities.

Modeling spatiotemporal CO2 (carbon dioxide) emission dynamics in China from DMSP-OLS nighttime stable light data using panel data analysis

China’s rapid industrialization and urbanization have resulted in a great deal of CO2 (carbon dioxide) emissions, which is closely related to its sustainable development and the long term stability of global climate. This study proposes panel data analysis to model spatiotemporal CO2 emission dynamics at a higher resolution in China by integrating the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime stable light (NSL) data with statistic data of CO2 emissions. Spatiotemporal CO2 emission dynamics were assessed from national scale down to regional and urban agglomeration scales. The evaluation showed that there was a true positive correlation between NSL data and statistic CO2 emissions in China at the provincial level from 1997 to 2012, which could be suitable for estimating CO2 emissions at 1km resolution. The spatiotemporal CO2 emission dynamics between different regions varied greatly. The high-growth type and high-grade of CO2 emissions were mainly distributed in the Eastern region, Shandong Peninsula and Middle south of Liaoning, with clearly lower concentrations in the Western region, Central region and Sichuan–Chongqing. The results of this study will enhance the understanding of spatiotemporal variations of CO2 emissions in China. They will provide a scientific basis for policy-making on viable CO2 emission mitigation policies.

How does sprawl differ across cities in China? A multi-scale investigation using nighttime light and census data

Timely and accurate measurement of urban sprawl is indispensable for assessing its environmental and societal impacts. However, few studies have examined urban sprawl in cities across China. In this study, we quantified urban sprawl in different cities across China from 1990 to 2010 by combining the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) nighttime light images and national census data. We found that the annual growth rate of urban areas was 2.45% greater than that of the urban population in China from 1990 to 2010, indicating an overall trend of urban sprawl. Small and medium cities in west China had the highest level of sprawl over time. We conclude that much more attention should be given to the prevention of excessive urban expansion in small and medium cities in west China.

World energy consumption pattern as revealed by DMSP-OLS nighttime light imagery

Remotely sensed nighttime light (NTL) from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) provides a spatially consistent and cost-effective mean to estimate energy consumption pattern. While previous researches have documented the application of NTL to predict electric power consumption (EPC) with varying degrees of success, few have systematically studied the possible factors affecting the EPC-NTL relationship. Moreover, no substantial research effort has been made to relate overall energy consumption (OEC) to NTL. This study investigated key factors governing the EPC/OEC-NTL relationship by examining the influences of affluence, urbanization, technology, temperature, and NTL pattern. Results show that EPC increased with higher per capital GDP, urbanization rate, and high-technology exports, and lower agricultural development, both globally and regionally. Meanwhile, EPC generally reduced with higher temperature and more agglomerate human activities. A strong OEC-NTL relationship was found; but the influencing factors to the OEC-NTL relationship varied across regions due to the natures of energy use. These factors must be considered especially for the studies of less-affluent regions where NTL was undetectable by the DMSP-OLS sensor.

Combining DMSP/OLS Nighttime Light with Echo State Network for Prediction of Daily PM2.5 Average Concentrations in Shanghai, China

The objective of this study is to investigate the potential of nighttime light data, acquired with Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) owned by National Oceanic and Atmospheric Administration (NOAA), in predicting urban daily particulate matter (PM)2.5 with an aerodynamic diameter of less than 2.5 µm average concentrations. To achieve the purpose, we firstly extracted two night light indices, the Nighttime Light Intensity Index (NLII) and the Nighttime Saturated Light Area Index (NSLAI) from DMSP/OLS images. Through Gaussian fitting of the relationship between the indices and the daily PM2.5 concentrations data released by the government, we found that the intraday nighttime light indices were all more relevant with the PM2.5 average concentrations of the next day in Shanghai. Therefore, the 56 sets of data, the light indices were collected from 3 November 2013 to 28 December 2013, the daily PM2.5 concentrations data were collected from 4 November 2013 to 29 December 2013, and these were divided into two parts. The first 40 sets were used for training the model of echo state network (ESN). The last 16 sets were used for testing. The value of R2 of predicted results was as high as 0.6318. In summary, the effectiveness of nighttime light data that used for the prediction of urban daily PM2.5 average concentrations was verified in this study.

Mapping Impervious Surface Distribution with Integration of SNNP VIIRS-DNB and MODIS NDVI Data

Data from the U.S. Defense Meteorological Satellite Program’s Operational Line-scan System are often used to map impervious surface area (ISA) distribution at regional and global scales, but its coarse spatial resolution and data saturation produce high inaccuracy in ISA estimation. Suomi National Polar-orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite’s Day/Night Band (VIIRS-DNB) with its high spatial resolution and dynamic data range may provide new insights but has not been fully examined in mapping ISA distribution. In this paper, a new variable—Large-scale Impervious Surface Index (LISI)—is proposed to integrate VIIRS-DNB and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data for mapping ISA distribution. A regression model was established, in which LISI was used as an independent variable and the reference ISA from Landsat images was a dependent variable. The results indicated a better estimation performance using LISI than using a single VIIRS-DNB or MODIS NDVI variable. The LISI-based approach provides accurate spatial patterns from high values in core urban areas to low values in rural areas, with an overall root mean squared error of 0.11. The LISI-based approach is recommended for fractional ISA estimation in a large area.