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Author (up) Bennett, M.M.; Smith, L.C.
Title Advances in using multitemporal night-time lights satellite imagery to detect, estimate, and monitor socioeconomic dynamics Type Journal Article
Year 2017 Publication Remote Sensing of Environment Abbreviated Journal Remote Sensing of Environment
Volume 192 Issue Pages 176-197
Keywords Remote Sensing
Abstract Since the late 1990s, remotely sensed night-time lights (NTL) satellite imagery has been shown to correlate with socioeconomic parameters including urbanization, economic activity, and population. More recent research demonstrates that multitemporal NTL data can serve as a reliable proxy for change over time in these variables whether they are increasing or decreasing. Time series analysis of NTL data is especially valuable for detecting, estimating, and monitoring socioeconomic dynamics in countries and subnational regions where reliable official statistics may be lacking. Until 2012, multitemporal NTL imagery came primarily from the Defense Meteorological Satellite Program – Operational Linescan System (DMSP-OLS), for which digital imagery is available from 1992 to 2013. In October 2011, the launch of NASA/NOAA's Suomi National Polar-orbiting Partnership satellite, whose Visible Infrared Imaging Radiometer Suite (VIIRS) sensor has a Day/Night Band (DNB) specifically designed for capturing radiance from the Earth at night, marked the start of a new era in NTL data collection and applications. In light of these advances, this paper reviews progress in using multitemporal DMSP-OLS and VIIRS imagery to analyze urbanization, economic, and population dynamics across a range of geographic scales. An overview of data corrections and processing for comparison of multitemporal NTL imagery is provided, followed by a meta-analysis and integrative synthesis of these studies. Figures are included that visualize the capabilities of DMSP-OLS and VIIRS to capture socioeconomic change in the post-Soviet Russian Far East and war-torn Syria, respectively. Finally, future directions for NTL research are suggested, particularly in the areas of determining the fundamental causes of observed light and in leveraging VIIRS' superior sensitivity and spatial and radiometric resolution.
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ISSN 0034-4257 ISBN Medium
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Call Number GFZ @ kyba @ Serial 2024
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Author (up) Cao, X.; Chen, J.; Imura, H.; Higashi, O.
Title A SVM-based method to extract urban areas from DMSP-OLS and SPOT VGT data Type Journal Article
Year 2009 Publication Remote Sensing of Environment Abbreviated Journal Remote Sensing of Environment
Volume 113 Issue 10 Pages 2205-2209
Keywords Remote Sensing
Abstract Mapping urban areas at regional and global scales has become an urgent task because of the increasing pressures from rapid urbanization and associated environmental problems. Satellite imaging of stable anthropogenic lights from DMSP-OLS provides an accurate, economical, and straightforward way to map the global distribution of urban areas. To address problems in the thresholding methods that use empirical strategies or manual trial-and-error procedures, we proposed a support vector machine (SVM)-based region-growing algorithm to semi-automatically extract urban areas from DMSP-OLS and SPOT NDVI data. Several simple criteria were used to select SVM training sets of urban and non-urban pixels, and an iterative classification and training procedure was adopted to identify the urban pixels through region growing. The new method was validated using the extents of 25 Chinese cities, as classified by Landsat ETM+ images, and then compared with two common thresholding methods. The results showed that the SVM-based algorithm could not only achieve comparable results to the local-optimized threshold method, but also avoid its tedious trial-and-error procedure, suggesting that the new method is an easy and simple alternative for extracting urban extent from DMSP-OLS and SPOT NDVI data.
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ISSN 0034-4257 ISBN Medium
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Call Number GFZ @ kyba @ Serial 2041
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Author (up) Cao, X.; Hu, Y.; Zhu, X.; Shi, F.; Zhuo, L.; Chen, J.
Title A simple self-adjusting model for correcting the blooming effects in DMSP-OLS nighttime light images Type Journal Article
Year 2019 Publication Remote Sensing of Environment Abbreviated Journal Remote Sensing of Environment
Volume 224 Issue Pages 401-411
Keywords Remote Sensing
Abstract Night-time light (NTL) data from the Defense Meteorological Satellite Program (DMSP) Operation Linescan System (OLS) provide important observations of human activities; however, DMSP-OLS NTL data suffer from problems such as saturation and blooming. This research developed a self-adjusting model (SEAM) to correct blooming effects in DMSP-OLS NTL data based on a spatial response function and without using any ancillary data. By assuming that the pixels adjacent to the background contain no lights (i.e., pseudo light pixels, PLPs), the blooming effect intensity, a parameter in the SEAM model, can be estimated by pixel-based regression using PLPs and their neighboring light sources. SEAM was applied to all of China, and its performance was assessed for twelve cities with different population sizes. The results show that SEAM can largely reduce the blooming effect in the original DMSP-OLS dataset and enhance its quality. The images after blooming effect correction have higher spatial similarity with Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) images and higher spatial variability than the original DMSP-OLS data. We also found that the average effective blooming distance is approximately 3.5 km in China, which may be amplified if the city is surrounded by water surfaces, and that the blooming effect intensity is positively correlated to atmospheric quality. The effectiveness of the proposed model will improve the capacity of DMSP-OLS images for mapping the urban extent and modeling socioeconomic parameters.
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ISSN 0034-4257 ISBN Medium
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Call Number GFZ @ kyba @ Serial 2239
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Author (up) Elvidge, C. D.; Baugh, K. E.; Dietz, J. B.; Bland, T.; Sutton, P. C.; Kroehl, H. W.
Title Radiance calibration of DMSP-OLS low-light imaging data of human settlements. Type Journal Article
Year 1999 Publication Remote Sensing of Environment Abbreviated Journal
Volume 68 Issue 1 Pages 77-88
Keywords Remote Sensing; DMSP; DMSP-OLS; satellite; night lights; light pollution
Abstract Nocturnal lighting is a primary method for enabling human activity. Outdoor lighting is used extensively worldwide in residential, commercial, industrial, public facilities, and roadways. A radiance calibrated nighttime lights image of the United States has been assembled from Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS). The satellite observation of the location and intensity of nocturnal lighting provide a unique view of humanities presence and can be used as a spatial indicator for other variables that are more difficult to observe at a global scale. Examples include the modeling of population density and energy related greenhouse gas emissions.
Address NOAA National Geophysical Data Center, Boulder, CO USA
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language English Summary Language English Original Title
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Call Number LoNNe @ kagoburian @ Serial 930
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Author (up) Gong, P.; Li, X.; Wang, J.; Bai, Y.; Chen, B.; Hu, T.; Liu, X.; Xu, B.; Yang, J.; Zhang, W.; Zhou, Y.
Title Annual maps of global artificial impervious area (GAIA) between 1985 and 2018 Type Journal Article
Year 2020 Publication Remote Sensing of Environment Abbreviated Journal Remote Sensing of Environment
Volume 236 Issue Pages in press
Keywords Remote Sensing
Abstract Artificial impervious areas are predominant indicators of human settlements. Timely, accurate, and frequent information on artificial impervious areas is critical to understanding the process of urbanization and land use/cover change, as well as of their impacts on the environment and biodiversity. Despite their importance, there still lack annual maps of high-resolution Global Artificial Impervious Areas (GAIA) with longer than 30-year records, due to the high demand of high performance computation and the lack of effective mapping algorithms. In this paper, we mapped annual GAIA from 1985 to 2018 using the full archive of 30-m resolution Landsat images on the Google Earth Engine platform. With ancillary datasets, including the nighttime light data and the Sentinel-1 Synthetic Aperture Radar data, we improved the performance of our previously developed algorithm in arid areas. We evaluated the GAIA data for 1985, 1990, 1995, 2000, 2005, 2010, and 2015, and the mean overall accuracy is higher than 90%. A cross-product comparison indicates the GAIA data are the only dataset spanning over 30 years. The temporal trend in GAIA agrees well with other datasets at the local, regional, and global scales. Our results indicate that the GAIA reached 797,076 km2 in 2018, which is 1.5 times more than that in 1990. China and the United States (US) rank among the top two in artificial impervious area, accounting for approximately 50% of the world's total in 2018. The artificial impervious area of China surpassed that of the US in 2015. By 2018, the remaining eight among the top ten countries are India, Russia, Brazil, France, Italy, Germany, Japan, and Canada. The GAIA dataset can be freely downloaded from http://data.ess.tsinghua.edu.cn.
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Call Number GFZ @ kyba @ Serial 2756
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Author (up) Imhoff, M.
Title Using nighttime DMSP/OLS images of city lights to estimate the impact of urban land use on soil resources in the United States Type Journal Article
Year 1997 Publication Remote Sensing of Environment Abbreviated Journal Remote Sensing of Environment
Volume 59 Issue 1 Pages 105-117
Keywords Remote Sensing
Abstract Nightime “city light” footprints derived from DMSP/OLS satellite images were merged with census data and a digital soils map in a continental-scale test of a remote sensing and geographic information system methodology for approximating the extent of built-up land and its potential impact on soil resources in the United States. Using image processing techniques and census data, we generated maps where the “city lights” class represented mean population densities of 947 persons km−2 and 392 housing units km−2, areas clearly not available to agriculture. By our analysis, such “city lights” representing urban areas accounted for 2.7% of the surface area in the United States, an area approximately equal to the State of Minnesota or one half the size of California. Using the UN/FAO Fertility Capability Classification System to rank soils, results for the United States show that development appears to be following soil resources, with the better agricultural soils being the most urbanized. Some unique soil types appear to be on the verge of being entirely coopted by “urban sprawl.” Urban area figures derived from the DMSP/OLS imagery compare well to those derived from statistical sources. Further testing and refinement of the methodology remain but the technique shows promise for possible extension to global evaluations of urbanization, population and even global productivity.
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Notes Approved no
Call Number LoNNe @ christopher.kyba @ Serial 496
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Author (up) Katz, Y.; Levin, N.
Title Quantifying urban light pollution -- A comparison between field measurements and EROS-B imagery Type Journal Article
Year 2016 Publication Remote Sensing of Environment Abbreviated Journal Remote Sensing of Environment
Volume 177 Issue Pages 65-77
Keywords Remote Sensing; Skyglow
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Call Number LoNNe @ kyba @ Serial 1359
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Author (up) Kotarba, A.Z.; Aleksandrowicz, S.
Title Impervious surface detection with nighttime photography from the International Space Station Type Journal Article
Year 2016 Publication Remote Sensing of Environment Abbreviated Journal Remote Sensing of Environment
Volume 176 Issue Pages 295-307
Keywords Remote Sensing
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.
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Call Number LoNNe @ kyba @ Serial 1356
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Author (up) Kuechly, H.U.; Kyba, C.C.M.; Ruhtz, T.; Lindemann, C.; Wolter, C.; Fischer, J.; Hölker, F.
Title Aerial survey and spatial analysis of sources of light pollution in Berlin, Germany Type Journal Article
Year 2012 Publication Remote Sensing of Environment Abbreviated Journal Remote Sensing of Environment
Volume 126 Issue Pages 39-50
Keywords Light pollution; Artificial lighting; Urban analysis; Remote sensing; GIS; Darkness; Spatial analysis; Light at night
Abstract Aerial observations of light pollution can fill an important gap between ground based surveys and nighttime satellite data. Terrestrially bound surveys are labor intensive and are generally limited to a small spatial extent, and while existing satellite data cover the whole world, they are limited to coarse resolution. This paper describes the production of a high resolution (1 m) mosaic image of the city of Berlin, Germany at night. The dataset is spatially analyzed to identify the major sources of light pollution in the city based on urban land use data. An area-independent ‘brightness factor’ is introduced that allows direct comparison of the light emission from differently sized land use classes, and the percentage area with values above average brightness is calculated for each class. Using this methodology, lighting associated with streets has been found to be the dominant source of zenith directed light pollution (31.6%), although other land use classes have much higher average brightness. These results are compared with other urban light pollution quantification studies. The minimum resolution required for an analysis of this type is found to be near 10 m. Future applications of high resolution datasets such as this one could include: studies of the efficacy of light pollution mitigation measures, improved light pollution simulations, economic and energy use, the relationship between artificial light and ecological parameters (e.g. circadian rhythm, fitness, mate selection, species distributions, migration barriers and seasonal behavior), or the management of nightscapes. To encourage further scientific inquiry, the mosaic data is freely available at Pangaea: http://dx.doi.org/10.1594/PANGAEA.785492.
Address Freie Universität Berlin, Department of Earth Sciences, Institute for Space Sciences, Carl-Heinrich-Becker-Weg 6‐10, 12165 Berlin, Germany
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Call Number IDA @ john @ Serial 188
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Author (up) L.Imhoff, M.; Lawrence, W.T.; Stutzer, D.C.; Elvidge, C.D.
Title A technique for using composite DMSP/OLS “City Lights” satellite data to map urban area Type Journal Article
Year 1997 Publication Remote Sensing of Environment Abbreviated Journal Remote Sensing of Environment
Volume 61 Issue 3 Pages 361-370
Keywords Remote Sensing
Abstract A Tresholding technique was used to convert a prototype “city lights” data set from the National Oceanic and Atmospheric Administration's National Geophysical Data Center (NOAAINGDC) into a map of “urban areas” for the continental United States. Thresholding was required to adapt the Defense Meteorological Satellite Program's Operational Linescan System (DMSPIOLS)-based NGDC data set into an urban map because the values reported in the prototype represent a cumulative percentage lighted for each pixel extracted from hundreds of nighttime cloud screened orbits, rather than any suitable land-cover classification. The cumulative percentage lighted data could not be used alone because the very high gain of the OLS nighttime photomultiplier configuration can. lead to a pixel (2.7X2.7 km) appearing “lighted” even with very low intensity, nonurban light sources. We found that a threshold of %89% yielded the best results, removing ephemeral light sources and “blooming” of light onto water when adjacent to cities while still leaving the dense urban core intact. This approach gave very good results when compared with the urban areas as defined by the 1990 U. S. Census; the “urban” area from our analysis being only 5% less than that of the Census. The Census was also used to derive population.- and housing-density statistics for the continent-wide “city lights” analysis; these averaged 1033 persons/km2 and 426 housing units/ king, respectively. The use of a nighttime sensor to determine the location and estimate the density of population based on light sources has proved feasible in this exploratory effort. However, issues concerning the use of census data as a benchmark for evaluating the accuracy of remotely sensed imagery are discussed, and potential improvements in the sensor regarding spatial resolution, instrument gain, and pointing accuracy are addressed.
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Call Number GFZ @ kyba @ Serial 2220
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