Operational Linescan System Research Articles

Light At Night And Breast Cancer Incidence In The Nurses' Health Study II

Introduction: Both animal models and observational studies in humans suggest that exposure to light at night (LAN) can disrupt circadian patterns and decrease nocturnal secretion of melatonin, which is hypothesized to disturb the regulation of estrogen and increase breast cancer risk. This study examined the effect of residential outdoor LAN on breast cancer incidence using data from the US-based Nurses' Health Study II cohort. Methods: A total of 110,594 women were followed from 1989-2011. Annual LAN exposure was measured using satellite data from the US Operational Linescan System, which provides time-varying, high-dynamic range data for a composite of lights from cities, towns, and other sites with persistent lighting. These data represent relative levels of nighttime illumination to ~1 km2 scale. We confirmed incident invasive and in situ breast cancer cases by medical record review. We used Cox proportional hazard models to calculate hazard ratios (HRs) and 95% confidence intervals (CIs), adjusting for a number of breast cancer risk factors, including age, race, benign breast disease history, family history of breast cancer, age at menarche, parity, age at first birth, height, BMI, oral contraceptive use, postmenopausal hormone use, smoking status, Alternative Healthy Eating Index, physical activity, marital status, living alone, income, Census tract median home value and median income, and fine particulate matter (PM2.5) air pollution. Results: Over 2,058,737 person-years, we identified 3,196 breast cancer cases. In multivariable models, compared to women in the lowest quintile of LAN, the HRs (95% CIs) were Q2: 1.0 (0.9, 1.1); Q3: 1.1 (0.9, 1.2); Q4: 1.2 (1.1, 1.4); Q5: 1.1 (0.9, 1.2); p for trend=0.03. Results were similar in pre- and post-menopausal women. Conclusions: We observed a positive relationship between residential LAN and breast cancer in this cohort of adult women, indicating that light exposure may play a role in cancer risk.

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.

Mapping US Urban Extents from MODIS Data Using One-Class Classification Method

Urban areas are one of the most important components of human society. Their extents have been continuously growing during the last few decades. Accurate and timely measurements of the extents of urban areas can help in analyzing population densities and urban sprawls and in studying environmental issues related to urbanization. Urban extents detected from remotely sensed data are usually a by-product of land use classification results, and their interpretation requires a full understanding of land cover types. In this study, for the first time, we mapped urban extents in the continental United States using a novel one-class classification method, i.e., positive and unlabeled learning (PUL), with multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) data for the year 2010. The Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) night stable light data were used to calibrate the urban extents obtained from the one-class classification scheme. Our results demonstrated the effectiveness of the use of the PUL algorithm in mapping large-scale urban areas from coarse remote-sensing images, for the first time. The total accuracy of mapped urban areas was 92.9% and the kappa coefficient was 0.85. The use of DMSP-OLS night stable light data can significantly reduce false detection rates from bare land and cropland far from cities. Compared with traditional supervised classification methods, the one-class classification scheme can greatly reduce the effort involved in collecting training datasets, without losing predictive accuracy.

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.

Regional Urban Extent Extraction Using Multi-Sensor Data and One-Class Classification

Stable night-time light data from the Defense Meteorological Satellite Program (DMSP) Operational Line-scan System (OLS) provide a unique proxy for anthropogenic development. This paper presents a regional urban extent extraction method using a one-class classifier and combinations of DMSP/OLS stable night-time light (NTL) data, MODIS normalized difference vegetation index (NDVI) data, and land surface temperature (LST) data. We first analyzed how well MODIS NDVI and LST data quantify the properties of urban areas. Considering that urban area is the only class of interest, we applied the one-class support vector machine (OCSVM) to classify different combinations of the three datasets. We evaluated the effectiveness of the proposed method and compared with the locally optimized threshold method in regional urban extent mapping in China. The experimental results demonstrate that DMSP/OLS NTL data, MODIS NDVI and LST data provide different but complementary information sources to quantify the urban extent at a regional scale. The results also indicate that the OCSVM classification of the combination of all three datasets generally outperformed the locally optimized threshold method. The proposed method effectively and efficiently extracted the urban extent at a regional scale, and is applicable to other study areas.

Detecting surface oil slicks using VIIRS nighttime imagery under moon glint: a case study in the Gulf of Mexico

Using data collected over the Gulf of Mexico during night between May 2012 and September 2013 by the Visible Infrared Imager Radiometer Suite (VIIRS), we demonstrate a new application from its day-and-night band (DNB). Under cloud free and moon glint conditions, the DNB revealed surface oil slicks from natural oil seeps. This is despite the fact that the signal-to-noise ratio (SNR) of this wide band (505–890 nm) under moon glint is much lower (30:1–50:1) and its resolution is also coarser (750 m) than the VIIRS imaging bands (375 m) under daytime solar illumination. The DNB was designed to map light sources at night. Similar to its predecessor, the Defense Meteorological Satellite Program Operational Linescan System (OLS), the VIIRS DNB should be suitable to identifying bioluminescence at night. However, with its finer resolution and higher SNR than OLS, the VIIRS DNB is demonstrated here to be also able to complement other sensors in the detection and mapping of oil spills.

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.

A dynamic scaling algorithm for the optimized digital display of VIIRS Day/Night Band imagery

The VIIRS Day/Night Band (DNB) is a visible and near-infrared sensor that is sensitive to a broad range of light intensities ranging from daylight down to airglow at night. The on-board calibration of the DNB allows for the quantification of radiance values over the full range of the instrument’s sensitivity, unlike the heritage Operational Linescan System (OLS). For scenes that span the day/night terminator, observed DNB radiance values may vary by up to eight orders of magnitude. Consequently, it is impractical to display the full range of radiance values in a single digital image. In this work, an algorithm is presented that scales the observed radiance values between expected maximum and minimum values that are a function of solar and lunar zenith angles as well as the fraction of the lunar disc that is illuminated by the Sun. This dynamic scaling algorithm preserves scene contrast over the full range of solar and lunar illumination conditions, similar to the Near Constant Contrast (NCC) imagery product. Unlike the NCC algorithm, however, the ‘erf-dynamic scaling’ algorithm (so-called for its likeness to the Gauss error function) presented here requires no ancillary information outside of what is included in the DNB data distributed according to the Joint Polar Satellite System (JPSS) program file standards. Results indicate that this algorithm has improved performance over simple methods for displaying DNB imagery and, in some instances, may exceed the performance of the NCC product itself. This algorithm is expected to replace many ad hoc methods of displaying DNB imagery and may serve as a substitute for operational users that do not have access to the NCC product.

Global Trends in Exposure to Light Pollution in Natural Terrestrial Ecosystems

The rapid growth in electric light usage across the globe has led to increasing presence of artificial light in natural and semi-natural ecosystems at night. This occurs both due to direct illumination and skyglow - scattered light in the atmosphere. There is increasing concern about the effects of artificial light on biological processes, biodiversity and the functioning of ecosystems. We combine intercalibrated Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) images of stable night-time lights for the period 1992 to 2012 with a remotely sensed landcover product (GLC2000) to assess recent changes in exposure to artificial light at night in 43 global ecosystem types. We find that Mediterranean-climate ecosystems have experienced the greatest increases in exposure, followed by temperate ecosystems. Boreal, Arctic and montane systems experienced the lowest increases. In tropical and subtropical regions, the greatest increases are in mangroves and subtropical needleleaf and mixed forests, and in arid regions increases are mainly in forest and agricultural areas. The global ecosystems experiencing the greatest increase in exposure to artificial light are already localized and fragmented, and often of particular conservation importance due to high levels of diversity, endemism and rarity. Night time remote sensing can play a key role in identifying the extent to which natural ecosystems are exposed to light pollution.

A new method for extracting built-up urban areas using DMSP-OLS nighttime stable lights: a case study in the Pearl River Delta, southern China

The nighttime stable light (NSL) images on board the Operational Line-scan System (OLS) of the Defense Meteorological Satellite Program (DMSP) are useful for extracting large-scale built-up urban areas. However, most NSL-based studies are presently empirical threshold-based approaches. They always overestimate the areas of built-up land in urban regions because of the ‘blooming’ effect of NSL; and overlook small patches in developing towns where the NSL is much lower. In this study, a neighborhood statistics analysis (NSA) method is developed on the basis of the relative spatial variations between neighboring built-up and non-built-up pixels in DMSP-OLS images. It is applied to extract the built-up areas of eight cities in the Pearl River Delta in 1996, 2000, 2005, and 2009. The validations indicate that the total areas of the NSA-mapped results are highly correlated with those from Landsat TM/ETM+ data (R2 = 0.94; p < 0.001). The comparison results, which are evaluated by landscape indices (the landscape shape index (LSI), the contiguity index (CONTIG), and the perimeter area ratio (PARA)), also show good correlations (R2 > 0.46; p < 0.001). In addition, the total NSL of the built-up urban areas is significantly correlated with the statistical population data (R2 = 0.62; p < 0.001), which indirectly confirms the effectiveness of our proposed method.

Dynamics of Urbanization Levels in China from 1992 to 2012: Perspective from DMSP/OLS Nighttime Light Data

The authenticity and reliability of urbanization levels measured by different indicators in China have not reached a consensus, which may impede our understanding of the process of urbanization and its impacts on the environment. The objective of this study was to describe a reliable method of estimating urbanization level based on the Operational Line-scan System (OLS) on the Defense Meteorological Satellite Program (DMSP) nighttime light data and to analyze the dynamics of urbanization levels in China from 1992 to 2012. We calculated the comprehensive urbanization level at the national, provincial, and county scales using a compounded night light index (CNLI) and compared the change rate of CNLI with those of the other two conventional urbanization level indicators, proportion of the nonagricultural population and proportion of built-up area. Our results showed that CNLI derived from the DMSP/OLS data set provided a relatively reliable and accurate measure of the comprehensive urbanization level in China. During the last two decades, China has experienced continued and rapid urbanization with large regional variations. The CNLI increased 3.12 times, from 1.72 × 10−3 to 7.09 × 10−3. The annual increases of CNLI in eastern provinces were much faster than those in western provinces. In addition, we found that the rates of change in these three indicators were consistent for most provinces with the exception of the four municipalities (Beijing, Tianjin, Shanghai, and Chongqing) and a few eastern coastal provinces (Jiangsu, Zhejiang, Fujian, and Guangdong). Because the imbalance among population growth, urban expansion and socioeconomic development may affect cities’ sustainable development, we should pay more attention to these regions with large disparities between different indicators.

Utilization of the Suomi National Polar-Orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band for Arctic Ship Tracking and Fisheries Management

Maritime ships operating on-board illumination at night appear as point sources of light to highly sensitive low-light imagers on-board environmental satellites. Unlike city lights or lights from offshore gas platforms, whose locations remain stationary from one night to the next, lights from ships typically are ephemeral. Fishing boat lights are most prevalent near coastal cities and along the thermal gradients in the open ocean. Maritime commercial ships also operate lights that can be detected from space. Such observations have been made in a limited way via U.S. Department of Defense satellites since the late 1960s. However, the Suomi National Polar-orbiting Partnership (S-NPP) satellite, which carries a new Day/Night Band (DNB) radiometer, offers a vastly improved ability for users to observe commercial shipping in remote areas such as the Arctic. Owing to S-NPP’s polar orbit and the DNB’s wide swath (~3040 km), the same location in Polar Regions can be observed for several successive passes via overlapping swaths—offering a limited ability to track ship motion. Here, we demonstrate the DNB’s improved ability to monitor ships from space. Imagery from the DNB is compared with the heritage low-light sensor, the Operational Linescan System (OLS) on board the Defense Meteorological Support Program (DMSP) satellites, and is evaluated in the context of tracking individual ships in the Polar Regions under both moonlit and moonless conditions. In a statistical sense, we show how DNB observations of ship lights in the East China Sea can be correlated with seasonal fishing activity, while also revealing compelling structures related to regional fishery agreements established between various nations.

Aladdin’s Magic Lamp: Active Target Calibration of the DMSP OLS

Nighttime satellite imagery from the Defense Meteorological Satellite Programs’ Operational Linescan System (DMSP OLS) is being used for myriad applications including population mapping, characterizing economic activity, disaggregate estimation of CO2 emissions, wildfire monitoring, and more. Here we present a method for in situ radiance calibration of the DMSP OLS using a ground based light source as an active target. We found that the wattage of light used by our active target strongly correlates with the signal measured by the DMSP OLS. This approach can be used to enhance our ability to make intertemporal and intersatellite comparisons of DMSP OLS imagery. We recommend exploring the possibility of establishing a permanent active target for the calibration of nocturnal imaging systems.

Spatiotemporal patterns of outdoor artificial nighttime lights exposure in the Republic of Korea between 1995 and 2010

Outdoor light at night (LAN) would be a public concern potentially associated with adverse health outcomes mainly in developed countries, but it might also be an environmental health issue in developing countries. However, there have been few studies reporting the spatial and temporal aspects of LAN level changes incorporated with population exposure in such nations. Therefore, we analyzed the spatiotemporal patterns of outdoor LAN exposure changes across the Republic of Korea (ROK), a developing country in Asia, between 1995 and 2010. In addition, we incorporated the population and its demographic characteristics of light exposure in this research. We utilized Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) observations as a spatial proxy of LAN and the ROK census data. This research observed 57.6% (≈57,800 km2) of land areas in the country to experience increases in outdoor artificial illumination at nights. The highest levels of LAN were found to affect about 58% of the total ROK population with major impacts on young and middle-aged people in recent years. Taking this research into account, it would require taking appropriate measures in controlling and abating outdoor artificial lights at night for both developing and developed countries. We anticipate that those efforts could prevent adverse health outcomes, for example, female breast cancer, potentially associated with LAN exposure.

On the correlation between innovation performance and DMSP-OLS nighttime stable lights: evidence from the US

Innovation has been widely acknowledged as one of the key driving forces for regional economic development. Since nighttime lights based on Defense Meteorological Satellite Program Operational Line-scan System has been used as an effective tool to estimate economic growth, a potential correlation between nighttime stable lights (NTSL) and innovation performance has been strongly indicated. To this end, this study examined the relationship between NTSL and innovation performance of 3105 counties in the United States of America through grey relation analysis. In order to test the validity of NTSL mapping of the innovation performance, all the sample data of NTSL and innovation performance were separately grouped into five categories using k-means clustering method, and the deviation between two classifications was calculated. Overall, NTSL shows a noticeable connection to innovation performance with a grey relation grade of 0.76, indicating that NTSL could properly simulate innovation performance with slight deviation, which is mainly caused by differences of industrial structure and natural environment. It is demonstrated that NTSL data could be used as a proxy for regional innovation performance. This study contributes to a better understanding of the relation between NTSL and innovation performance as well as offering an efficient approach to detect the differences in regional innovation performance.

How Dynamics of Urbanization Affect Physical and Mental Health in Urban China

AbstractUsing a 2011 national survey of urban residents, irrespective of their officialhukoustatus, and the 2000–2009 night-time light data from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS), this paper goes beyond the simple dichotomy of migrant versus non-migrant or rural versus urbanhukouto disentangle the processes of urbanization and migration and their complex associations with health, and assesses the impact of various levels and speed of urbanization on the physical and mental health of current residents in a city or town. By disaggregating urbanization into three discrete dimensions at sub-provincial levels, we find that while a higher absolute level of urbanization at the county level negatively impacted self-reported physical health, faster and accelerating urbanization had a positive impact which could be attributed to the demand-pull effect underlying the healthy migrant phenomenon. By contrast, all three dimensions of urbanization were associated with greater depressive distress and thus had an adverse effect on residents' mental health. Beyond demonstrating how variation in the process and location of urbanization affects individual health, we also illustrate more broadly the value of modelling locational parameters in analyses of individual outcomes based on national samples.

Improved VIIRS Day/Night Band Imagery With Near-Constant Contrast

The Suomi-NPP Visible Infrared Imager Radiometer Suite (VIIRS) instrument provides the next generation of visible/infrared imaging including the day/night band (DNB) with nominal bandwidth from 500 to 900 nm. Previous to VIIRS, the Defense Meteorological Satellite Program Operational Linescan System (OLS) measured radiances that spanned over seven orders of magnitude, using an onboard gain adjustment to provide the capability to image atmospheric features across the solar terminator, to observe nighttime light emissions over the globe, and to monitor the global distribution of clouds. The VIIRS DNB detects radiances that span over eight orders of magnitude, and because it has 13-14-b quantization (compared with 6 b for OLS) with three gain stages, the DNB has its full dynamic range at every part of the scan. One process that is applied to the VIIRS DNB radiances is a solar/lunar zenith angle dependent gain adjustment to create near-constant contrast (NCC) imagery. The at-launch NCC algorithm was designed to reproduce the OLS capability and, thus, was constrained to solar and lunar angles from 0° to 105°. This limitation has, in part, lead to suboptimal imagery due to the assumption that DNB radiances fall off exponentially beyond twilight. The VIIRS DNB ultrasensitivity in low-light conditions enables it to detect faint emissions from a phenomenon called airglow, thus invalidating the exponential fall-off assumption. Another complication to the NCC imagery algorithm is the stray light contamination that contaminates the DNB radiances in the astronomical twilight region. We address these issues and develop a solution that leads to high-quality imagery for all solar and lunar conditions.

Evaluating Saturation Correction Methods for DMSP/OLS Nighttime Light Data: A Case Study from China’s Cities

Remotely sensed nighttime lights (NTL) datasets derived from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) have been identified as a good indicator of the urbanization process and have been widely used to study such demographic and economic variables as population distribution and density, electricity consumption, and carbon emission. However, one issue must be considered in the application of NTL data, i.e., saturation in the bright cores of urban centers. In this study, we evaluate four correction methods in China’s cities: the linear regression model and the cubic regression model at the regional level, and the Human Settlement Index (HSI) and the Vegetation Adjusted NTL Urban Index (VANUI) at a pixel level. The results suggest that both correction methods at the regional level improve the correlation between NTL data and socioeconomic variables. However, since the methods can only be used on saturated pixels, the correction effects are limited, as the saturated area in Chinese cities is rather small. HSI and VANUI increase the inter-urban variability within certain cities, especially when their vegetation health and abundance is negatively correlated with NTL. However, the indices may induce bias when applied in a large region with a diverse natural environment and vegetation, and the application of HSI with a relatively high sensitivity of HSI to NDVI may be limited as NTL approaches maximum. Proper methods for reducing saturation effects should thus vary with different study areas and research purposes.

Tracking Electrification in Vietnam Using Nighttime Lights

We report on a systematic ground-based validation of DMSP-OLS night lights imagery to detect rural electrification in Vietnam. Based on an original survey of village-level units in Vietnam, this study compares nighttime light output from the U.S. Air Force Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) against ground-based survey data on electrical infrastructure and electricity use in 200 electrified villages. Monthly and annual composites record a one-point increase in brightness along DMSP-OLS’s 63-point brightness scale for every 60–70 additional streetlights or 240–270 electrified homes. Using a time series of 90 nightly images, the data show a one-point increase in brightness for every 125–200 additional streetlights, or 550–700 additional electrified homes. The results highlight the potential to use night lights imagery to support efforts to connect the 1.2 billion people who lack electricity around the world.

Quantifying Contribution of Land Use Types to Nighttime Light Using an Unmixing Model

In this study, a model is developed to quantify the land use contribution to nighttime light using coarse-resolution nighttime light imagery and fine-resolution land use data. We assumed that the nighttime light of a region can be represented by a linear combination of land use areas, with its nighttime light intensity (NLI) as coefficient. Based on an unmixing strategy, the NLI of each land use type was estimated. The Berlin City and MA State were used as study areas. For the Berlin City, we made use of nighttime light imagery from the Suomi National Polar-orbiting Partnership and the land use maps with 52 classes as data sets for analysis, and we used a nighttime aerial photograph to derive reference data. For the MA State, we made use of nighttime light imagery from the Defense Meteorological Satellite Program's Operational Linescan System and the land use maps with 33 classes as data sets for analysis, and we used a nighttime photograph from the International Space Station to derive reference data. The reference NLI data were correlated with the estimated NLI data, and the $R^{2}$ values of Berlin and Massachusetts were 0.7277 and 0.7982, respectively, proving that the proposed model is effective.