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Special Issue "Recent Advances in Remote Sensing with Nighttime Lights"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 May 2017)

Special Issue Editors

Guest Editor
Prof. Dr. Bailang Yu

School of Geographic Sciences, Key Lab. of Geographic Information Science (Ministry of Education), East China Normal University, 500 Dongchuan Rd, Shanghai 200241, China
Website | E-Mail
Phone: +86-21-54341172
Fax: +86-21-5434-1172
Interests: nighttime light remote sensing; urban remote sensing; object-oriented analysis for remotely sensed images; LiDAR (Light Detection and Ranging)
Guest Editor
Dr. Yuyu Zhou

Dept. of Geological & Atmospheric Sciences, Iowa State University, 3019 Agronomy Hall, Ames, IA 50011, USA
Website | E-Mail
Phone: +1-515-2942842
Interests: nighttime lights remote sensing; urbanization; urban heat island; building energy use; renewable energy; fossil-fuel emissions; climate change
Guest Editor
Dr. Chunyang He

State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, Beijing 100875, China
Website | E-Mail
Phone: +86-10-58804498
Interests: applications of nighttime lights data; urbanization; land use/cover change detection; landscape modeling; landscape sustainability
Guest Editor
Dr. Xiaofeng Li

National Oceanic and Atmospheric Administration, NCWCP E/RA3, 5830 University Research Ct. Office #3216, College Park, MD 20740-3818, USA
Website | E-Mail
Phone: +1-301-683-3314
Interests: ocean remote sensing; physical oceanography; boundary layer meteorology; synthetic aperture radar imaging mechanism; multiple-polarization radar applications; satellite image classification and segmentation

Special Issue Information

Dear Colleagues,

Nighttime lights satellite images provide a unique perspective for observing our planet. Given that most of the nocturnal lights are closely related to human activities, nighttime lights images have been widely used in mapping urban settlements, estimating socioeconomic indicators (e.g., population, GDP, energy consumption, and house vacancy rate), evaluating events (e.g., Conflicts and wars), and other studies, such as fisheries, gas flaring, light pollution, wildfire, and aurora. Nighttime lights remote sensing has become a research frontier and is drawing increasing attention from scholars of remote sensing, GIS, urban planning, economy, and other research communities.

In 2014, Remote Sensing had published a Special Issue of “Remote Sensing with Nighttime Lights” (http://www.mdpi.com/journal/remotesensing/special_issues/nightimelights) edited by Dr. Christopher D. Elvidge, a pioneer of nighttime lights remote sensing study. This very successful Special Issue published 23 papers, and some of these papers have very high citations. During the past two years after publication of this special issue, nighttime lights remote sensing experienced new and fast developments, which are indicated by new sensors and satellites, especially some small satellites (e.g., Jilin-1 from China), methods (e.g., fusion with other data), and applications (e.g., pollution mapping and event detection).

This Special Issue stands on the shoulders of last successful special issue and aims to publish original manuscripts of latest innovative research in recent advances in nighttime lights remote sensing. Comprehensive reviews of this research field are also welcome. The potential topics include, but are not limited to:

  • State-of-the-art remote sensing technologies for capturing nighttime lights
  • Methods for processing nighttime lights remotely sensed data
  • Fusion of nighttime lights with other remote sensing data, and/or other data (e.g., social media data, traffic data, and in situ data)
  • Applications of nighttime light remotely sensed data (e.g., urban and population mapping, human activities analysis, events detection, and environmental and ecological impacts analysis)
  • Evaluations and/or applications of new nighttime lights remotely sensed data

Authors are required to check and follow the specific Instructions to Authors, http://www.mdpi.com/journal/remotesensing/instructions.

Dr. Bailang Yu
Dr. Yuyu Zhou
Dr. Chunyang He
Dr. Xiaofeng Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Nighttime light
  • Light pollution
  • Urban area mapping
  • Human activities
  • Socio-economic indicators
  • DMSP-OLS
  • NPP-VIIRS
  • EROS-B
  • Jilin-1

Published Papers (20 papers)

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Research

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Open AccessArticle Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data
Remote Sens. 2017, 9(8), 862; doi:10.3390/rs9080862
Received: 21 June 2017 / Revised: 5 August 2017 / Accepted: 17 August 2017 / Published: 21 August 2017
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Abstract
The accurate and timely monitoring of regional urban extent is helpful for analyzing urban sprawl and studying environmental issues related to urbanization. This paper proposes a classification scheme for large-scale urban extent mapping by combining the Day/Night Band of the Visible Infrared Imaging
[...] Read more.
The accurate and timely monitoring of regional urban extent is helpful for analyzing urban sprawl and studying environmental issues related to urbanization. This paper proposes a classification scheme for large-scale urban extent mapping by combining the Day/Night Band of the Visible Infrared Imaging Radiometer Suite on the Suomi National Polar-orbiting Partnership Satellite (NPP-VIIRS DNB) and the Normalized Difference Vegetation Index from the Moderate Resolution Imaging Spectroradiometer products (MODIS NDVI). A Back Propagation (BP) neural network based one-class classification method, the Present-Unlabeled Learning (PUL) algorithm, is employed to classify images into urban and non-urban areas. Experiments are conducted in mainland China (excluding surrounding islands) to detect urban areas in 2012. Results show that the proposed model can successfully map urban area with a kappa of 0.842 on the pixel level. Most of the urban areas are identified with a producer’s accuracy of 79.63%, and only 10.42% the generated urban areas are misclassified with a user’s accuracy of 89.58%. At the city level, among 647 cities, only four county-level cities are omitted. To evaluate the effectiveness of the proposed scheme, three contrastive analyses are conducted: (1) comparing the urban map obtained in this paper with that generated by the Defense Meteorological Satellite Program/Operational Linescan System Nighttime Light Data (DMSP/OLS NLD) and MODIS NDVI and with that extracted from MCD12Q1 in MODIS products; (2) comparing the performance of the integration of NPP-VIIRS DNB and MODIS NDVI with single input data; and (3) comparing the classification method used in this paper (PUL) with a linear method (Large-scale Impervious Surface Index (LISI)). According to our analyses, the proposed classification scheme shows great potential to map regional urban extents in an effective and efficient manner. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Open AccessArticle An Improved Vegetation Adjusted Nighttime Light Urban Index and Its Application in Quantifying Spatiotemporal Dynamics of Carbon Emissions in China
Remote Sens. 2017, 9(8), 829; doi:10.3390/rs9080829
Received: 30 May 2017 / Revised: 27 July 2017 / Accepted: 9 August 2017 / Published: 11 August 2017
PDF Full-text (11349 KB) | HTML Full-text | XML Full-text
Abstract
Nighttime Light (NTL) has been widely used as a proxy of many socio-environmental issues. However, the limited range of sensor radiance of NTL prevents its further application and estimation accuracy. To improve the performance, we developed an improved Vegetation Adjusted Nighttime light Urban
[...] Read more.
Nighttime Light (NTL) has been widely used as a proxy of many socio-environmental issues. However, the limited range of sensor radiance of NTL prevents its further application and estimation accuracy. To improve the performance, we developed an improved Vegetation Adjusted Nighttime light Urban Index (VANUI) through fusing multi-year NTL with population density, the Normalized Difference Vegetation Index and water body data and applied it to fine-scaled carbon emission analysis in China. The results proved that our proposed index could reflect more spatial variation of human activities. It is also prominent in reducing the carbon modeling error at the inter-city level and distinguishing the emission heterogeneity at the intra-city level. Between 1995 and 2013, CO2 emissions increased significantly in China, but were distributed unevenly in space with high density emissions mainly located in metropolitan areas and provincial capitals. In addition to Beijing-Tianjin-Hebei, the Yangzi River Delta and the Pearl River Delta, the Shandong Peninsula has become a new emission hotspot that needs special attention in carbon mitigation. The improved VANUI and its application to the carbon emission issue not only broadened our understanding of the spatiotemporal dynamics of fine-scaled CO2 emission, but also provided implications for low-carbon and sustainable development plans. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Open AccessEditor’s ChoiceArticle Ongoing Conflict Makes Yemen Dark: From the Perspective of Nighttime Light
Remote Sens. 2017, 9(8), 798; doi:10.3390/rs9080798
Received: 31 May 2017 / Revised: 27 July 2017 / Accepted: 1 August 2017 / Published: 3 August 2017
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Abstract
The Yemen conflict has caused a severe humanitarian crisis. This study aims to evaluate the Yemen crisis by making use of time series nighttime light images from the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite sensor (NPP-VIIRS). We develop a process
[...] Read more.
The Yemen conflict has caused a severe humanitarian crisis. This study aims to evaluate the Yemen crisis by making use of time series nighttime light images from the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite sensor (NPP-VIIRS). We develop a process flow to correct NPP-VIIRS nighttime light from April 2012 to March 2017 by employing the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) stable nighttime light image. The time series analyses at national scales show that there is a sharp decline in the study period from February 2015 to June 2015 and that the total nighttime light (TNL) of Yemen decreased by 71.60% in response to the decline period. The nighttime light in all provinces also showed the same decline period, which indicates that the Saudi-led airstrikes caused widespread and severe humanitarian crisis in Yemen. Spatial pattern analysis shows that the areas of declining nighttime light are mainly concentrated in Sana’a, Dhamar, Ibb, Ta’izz, ’Adan, Shabwah and Hadramawt. According to the validation with high-resolution images, the decline in nighttime light in Western cities is caused by the damage of urban infrastructure, including airports and construction; moreover, the reason for the decline in nighttime light in eastern cities is the decrease in oil exploration. Using nighttime light remote sensing imagery, our findings suggest that war made Yemen dark and provide support for international humanitarian assistance organizations. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Open AccessArticle The Uncertainty of Nighttime Light Data in Estimating Carbon Dioxide Emissions in China: A Comparison between DMSP-OLS and NPP-VIIRS
Remote Sens. 2017, 9(8), 797; doi:10.3390/rs9080797
Received: 31 May 2017 / Revised: 19 July 2017 / Accepted: 28 July 2017 / Published: 2 August 2017
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Abstract
Nighttime light data can characterize urbanization, economic development, population density, energy consumption and other human activities. Additionally, carbon dioxide (CO2) emissions are closely related to the scope and intensity of human activities. In this study, we assess the utility of nighttime
[...] Read more.
Nighttime light data can characterize urbanization, economic development, population density, energy consumption and other human activities. Additionally, carbon dioxide (CO2) emissions are closely related to the scope and intensity of human activities. In this study, we assess the utility of nighttime light data as a powerful tool to reflect CO2 emissions from energy consumption, analyze the uncertainty associated with different nighttime light data for modeling CO2 emissions, and provide guidance and a reference for modeling CO2 emissions based on nighttime light data. In this paper, Mainland China was taken as a case study, and nighttime light datasets (the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime light data and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data) as well as a global gridded CO2 emissions dataset (PKU-CO2) were used to perform simple regressions at provincial, prefectural and 0.1° × 0.1° grid levels, respectively. The analyses are aimed at exploring the accuracy and uncertainty of DMSP-OLS and NPP-VIIRS nighttime light data in modeling CO2 emissions at different spatial scales. The improvement of nighttime light index and the potential factors influencing the effects of modeling CO2 emissions based on nighttime light datasets were also explored. The results show that DMSP-OLS is superior to NPP-VIIRS in modeling CO2 emissions at all spatial scales, and the bigger the scale, the more evident the advantages of DMSP-OLS. When modeling CO2 emissions with nighttime light datasets, not only the total amount of lights within a given statistical unit but also the agglomeration degree of lights should be taken into account. Furthermore, the geographical location and socio-economic conditions at the study site, such as gross regional product per capita (GRP per capita), population, and urbanization were shown to have an impact on the regression effect of the nighttime lights-CO2 emissions model. The regression effect was found to be better at higher latitude and longitude areas with higher GRP per capita and higher urbanization, while population showed little effect on the regression effect of the nighttime lights - CO2 emissions model. The limitation of this study is that the thresholds of potential factors are unclear and the quantitative guidance is insufficient. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Open AccessArticle Transferability of Economy Estimation Based on DMSP/OLS Night-Time Light
Remote Sens. 2017, 9(8), 786; doi:10.3390/rs9080786
Received: 29 May 2017 / Revised: 14 July 2017 / Accepted: 28 July 2017 / Published: 31 July 2017
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Abstract
Despite the fact that economic data are of great significance in the assessment of human socioeconomic development, the application of this data has been hindered partly due to the unreliable and inefficient economic censuses conducted in developing countries. The night-time light (NTL) imagery
[...] Read more.
Despite the fact that economic data are of great significance in the assessment of human socioeconomic development, the application of this data has been hindered partly due to the unreliable and inefficient economic censuses conducted in developing countries. The night-time light (NTL) imagery from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) provides one of the most important ways to evaluate an economy with low cost and high efficiency. However, little research has addressed the transferability of the estimation across years. Based on the entire DN series from 0 to 63 of NTL data and GDP data in 31 provinces of mainland China from 2000 to 2012, this paper aims to study the transferability of economy estimation across years, with four linear and non-linear data mining methods, including the Multiple Linear Regression (MLR), Local Weighted Regression (LWR), Partial Least Squares Regression (PLSR), and Support Vector Machine Regression (SVMR). We firstly built up the GDP estimation model based on the NTL data in each year with each method respectively, then applied each model to the other 12 years for the evaluation of the time series transferability. Results revealed that the performances of models differ greatly across years and methods: PLSR (mean of ) and SVMR (mean of ) are superior to MLR (mean of ) and LWR (mean of ) for model calibration; only PLSR (mean of , mean of ) holds a strong transferability among different years; the frequency of three DN sections of (0–1), (4–16), and (57–63) are especially important for economy estimation. Such results are expected to provide a more comprehensive understanding of the NTL, which can be used for economy estimation across years. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Open AccessArticle A New Urban Index for Expressing Inner-City Patterns Based on MODIS LST and EVI Regulated DMSP/OLS NTL
Remote Sens. 2017, 9(8), 777; doi:10.3390/rs9080777
Received: 25 May 2017 / Revised: 18 July 2017 / Accepted: 27 July 2017 / Published: 29 July 2017
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Abstract
With the rapid pace of urban expansion, comprehensively understanding urban spatial patterns, built environments, green-spaces distributions, demographic distributions, and economic activities becomes more meaningful. Night Time Lights (NTL) images acquired through the Operational Linescan System of the US Defense Meteorological Satellite Program (DMSP/OLS
[...] Read more.
With the rapid pace of urban expansion, comprehensively understanding urban spatial patterns, built environments, green-spaces distributions, demographic distributions, and economic activities becomes more meaningful. Night Time Lights (NTL) images acquired through the Operational Linescan System of the US Defense Meteorological Satellite Program (DMSP/OLS NTL) have long been utilized to monitor urban areas and their expansion characteristics since this system detects variation in NTL emissions. However, the pixel saturation phenomenon leads to a serious limitation in mapping luminance variations in urban zones with nighttime illumination levels that approach or exceed the pixel saturation limits of OLS sensors. Consequently, we propose an NTL-based city index that utilizes the Moderate-resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and Enhanced Vegetation Index (EVI) images to regulate and compensate for desaturation on NTL images acquired from corresponding urban areas. The regulated results achieve good performance in differentiating central business districts (CBDs), airports, and urban green spaces. Consequently, these derived imageries could effectively convey the structural details of urban cores. In addition, compared with the Vegetation Adjusted NTL Urban Index (VANUI), LST-and-EVI-regulated-NTL-city index (LERNCI) reveals superior capability in delineating the spatial structures of selected metropolis areas across the world, especially in the large cities of developing countries. The currently available results indicate that LERNCI corresponds better to city spatial patterns. Moreover, LERNCI displays a remarkably better “goodness-of-fit” correspondence with both the Version 1 Nighttime Visible Infrared Imaging Radiometer Suite Day/Night Band Composite (NPP/VIIRS DNB) data and the WorldPop population-density data compared with the VANUI imageries. Thus, LERNCI can act as a helpful indicator for differentiating and classifying regional economic activities, population aggregations, and energy-consumption and city-expansion patterns. LERNCI can also serve as a valuable auxiliary reference for decision-making processes that concern subjects such as urban planning and easing the central functions of metropolis. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Open AccessArticle Mapping Development Pattern in Beijing-Tianjin-Hebei Urban Agglomeration Using DMSP/OLS Nighttime Light Data
Remote Sens. 2017, 9(7), 760; doi:10.3390/rs9070760
Received: 29 May 2017 / Revised: 7 July 2017 / Accepted: 17 July 2017 / Published: 23 July 2017
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Abstract
Spatial inequality of urban development may cause problems like inequality of living conditions and the lack of sustainability, drawing increasing academic interests and societal concerns. Previous studies based on statistical data can hardly reveal the interior mechanism of spatial inequality due to the
[...] Read more.
Spatial inequality of urban development may cause problems like inequality of living conditions and the lack of sustainability, drawing increasing academic interests and societal concerns. Previous studies based on statistical data can hardly reveal the interior mechanism of spatial inequality due to the limitation of statistical units, while the application of remote sensing data, such as nighttime light (NTL) data, provides an effective solution. In this study, based on the DMSP/OLS NTL data, the urbanization type of all towns in the Beijing-Tianjin-Hebei urban agglomeration was analyzed from the aspects of development level and speed. Meanwhile, spatial cluster analysis of development level by local Moran’s I was used to explore spatial inequality, and the trend was discussed by comparing the development characteristics on both sides of the transition line of different development levels (inequality boundary). The results showed that the development level of the whole region increased dramatically as the mean DN value increased by 65.99%, and 83.72% of the towns showed a positive development during 2000–2012. The spatial distribution of urbanization types showed that Beijing and Tianjin were at a high urbanization level with rapid speed of development, with the southern region having a medium development level and the northwestern region lagging behind. The spatial cluster analysis also revealed a gradually intensifying trend of inequality as the number of towns with balanced development reduced by 319 during 2000–2012, while the towns in the high-high areas increased by 99 and those in the low-low areas increased by 229. Moreover, the development speed inside the inequality boundary was obviously higher than that outside, indicating an increasingly serious situation for spatial inequality of urban development in the whole region. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Open AccessArticle Intercalibration and Gaussian Process Modeling of Nighttime Lights Imagery for Measuring Urbanization Trends in Africa 2000–2013
Remote Sens. 2017, 9(7), 713; doi:10.3390/rs9070713
Received: 7 February 2017 / Revised: 3 May 2017 / Accepted: 5 July 2017 / Published: 11 July 2017
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Abstract
Sub-Saharan Africa currently has the world’s highest urban population growth rate of any continent at roughly 4.2% annually. A better understanding of the spatiotemporal dynamics of urbanization across the continent is important to a range of fields including public health, economics, and environmental
[...] Read more.
Sub-Saharan Africa currently has the world’s highest urban population growth rate of any continent at roughly 4.2% annually. A better understanding of the spatiotemporal dynamics of urbanization across the continent is important to a range of fields including public health, economics, and environmental sciences. Nighttime lights imagery (NTL), maintained by the National Oceanic and Atmospheric Administration, offers a unique vantage point for studying trends in urbanization. A well-documented deficiency of this dataset is the lack of intra- and inter-annual calibration between satellites, which makes the imagery unsuitable for temporal analysis in their raw format. Here we have generated an ‘intercalibrated’ time series of annual NTL images for Africa (2000–2013) by building on the widely used invariant region and quadratic regression method (IRQR). Gaussian process methods (GP) were used to identify NTL latent functions independent from the temporal noise signals in the annual datasets. The corrected time series was used to explore the positive association of NTL with Gross Domestic Product (GDP) and urban population (UP). Additionally, the proportion of change in ‘lit area’ occurring in urban areas was measured by defining urban agglomerations as contiguously lit pixels of >250 km2, with all other pixels being rural. For validation, the IRQR and GP time series were compared as predictors of the invariant region dataset. Root mean square error values for the GP smoothed dataset were substantially lower. Correlation of NTL with GDP and UP using GP smoothing showed significant increases in R2 over the IRQR method on both continental and national scales. Urban growth results suggested that the majority of growth in lit pixels between 2000 and 2013 occurred in rural areas. With this study, we demonstrated the effectiveness of GP to improve conventional intercalibration, used NTL to describe temporal patterns of urbanization in Africa, and detected NTL responses to environmental and humanitarian crises. The smoothed datasets are freely available for further use. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Open AccessFeature PaperArticle A Stepwise Calibration of Global DMSP/OLS Stable Nighttime Light Data (1992–2013)
Remote Sens. 2017, 9(6), 637; doi:10.3390/rs9060637
Received: 16 May 2017 / Revised: 19 June 2017 / Accepted: 19 June 2017 / Published: 21 June 2017
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Abstract
The Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) stable nighttime light (NTL) data provide a wide range of potentials for studying global and regional dynamics, such as urban sprawl and electricity consumption. However, due to the lack of on-board calibration, it requires
[...] Read more.
The Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) stable nighttime light (NTL) data provide a wide range of potentials for studying global and regional dynamics, such as urban sprawl and electricity consumption. However, due to the lack of on-board calibration, it requires inter-annual calibration for these practical applications. In this study, we proposed a stepwise calibration approach to generate a temporally consistent NTL time series from 1992 to 2013. First, the temporal inconsistencies in the original NTL time series were identified. Then, a stepwise calibration scheme was developed to systematically improve the over- and under- estimation of NTL images derived from particular satellites and years, by making full use of the temporally neighbored image as a reference for calibration. After the stepwise calibration, the raw NTL series were improved with a temporally more consistent trend. Meanwhile, the magnitude of the global sum of NTL is maximally maintained in our results, as compared to the raw data, which outperforms previous conventional calibration approaches. The normalized difference index indicates that our approach can achieve a good agreement between two satellites in the same year. In addition, the analysis between the calibrated NTL time series and other socioeconomic indicators (e.g., gross domestic product and electricity consumption) confirms the good performance of the proposed stepwise calibration. The calibrated NTL time series can serve as useful inputs for NTL related dynamic studies, such as global urban extent change and energy consumption. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Open AccessArticle Modeling the Spatiotemporal Dynamics of Gross Domestic Product in China Using Extended Temporal Coverage Nighttime Light Data
Remote Sens. 2017, 9(6), 626; doi:10.3390/rs9060626
Received: 12 May 2017 / Revised: 12 June 2017 / Accepted: 15 June 2017 / Published: 18 June 2017
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Abstract
Nighttime light data derived from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) in conjunction with the Soumi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) possess great potential for measuring the dynamics of Gross Domestic Product (GDP) at large scales.
[...] Read more.
Nighttime light data derived from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) in conjunction with the Soumi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) possess great potential for measuring the dynamics of Gross Domestic Product (GDP) at large scales. The temporal coverage of the DMSP-OLS data spans between 1992 and 2013, while the NPP-VIIRS data are available from 2012. Integrating the two datasets to produce a time series of continuous and consistently monitored data since the 1990s is of great significance for the understanding of the dynamics of long-term economic development. In addition, since economic developmental patterns vary with physical environment and geographical location, the quantitative relationship between nighttime lights and GDP should be designed for individual regions. Through a case study in China, this study made an attempt to integrate the DMSP-OLS and NPP-VIIRS datasets, as well as to identify an optimal model for long-term spatiotemporal GDP dynamics in different regions of China. Based on constructed regression relationships between total nighttime lights (TNL) data from the DMSP-OLS and NPP-VIIRS data in provincial units (R2 = 0.9648, P < 0.001), the temporal coverage of nighttime light data was extended from 1992 to the present day. Furthermore, three models (the linear model, quadratic polynomial model and power function model) were applied to model the spatiotemporal dynamics of GDP in China from 1992 to 2015 at both the country level and provincial level using the extended temporal coverage data. Our results show that the linear model is optimal at the country level with a mean absolute relative error (MARE) of 11.96%. The power function model is optimal in 22 of the 31 provinces and the quadratic polynomial model is optimal in 7 provinces, whereas the linear model is optimal only in two provinces. Thus, our approach demonstrates the potential to accurately and timely model long-term spatiotemporal GDP dynamics using an integration of DMSP-OLS and NPP-VIIRS data. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Open AccessArticle Evaluating the Use of DMSP/OLS Nighttime Light Imagery in Predicting PM2.5 Concentrations in the Northeastern United States
Remote Sens. 2017, 9(6), 620; doi:10.3390/rs9060620
Received: 3 May 2017 / Revised: 1 June 2017 / Accepted: 13 June 2017 / Published: 16 June 2017
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Abstract
Degraded air quality by PM2.5 can cause various health problems. Satellite observations provide abundant data for monitoring PM2.5 pollution. While satellite-derived products, such as aerosol optical depth (AOD) and normalized difference vegetation index (NDVI), have been widely used in estimating PM
[...] Read more.
Degraded air quality by PM2.5 can cause various health problems. Satellite observations provide abundant data for monitoring PM2.5 pollution. While satellite-derived products, such as aerosol optical depth (AOD) and normalized difference vegetation index (NDVI), have been widely used in estimating PM2.5 concentration, little research was focused on the use of remotely sensed nighttime light (NTL) imagery. This study evaluated the merits of using NTL satellite images in predicting ground-level PM2.5 at a regional scale. Geographically weighted regression (GWR) was employed to estimate the PM2.5 concentration and analyze its relationships with AOD, meteorological variables, and NTL data across the New England region. Observed data in 2013 were used to test the constructed GWR models for PM2.5 prediction. The Vegetation Adjusted NTL Urban Index (VANUI), which incorporates Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI into NTL to overcome the defects of NTL data, was used as a predictor variable for final PM2.5 prediction. Results showed that Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) NTL imagery could be an important dataset for more accurately estimating PM2.5 exposure, especially in urbanized and densely populated areas. VANUI data could obviously improve the performance of GWR for the warm season (GWR model with VANUI performed 17% better than GWR model without NDVI and NTL data and 7.26% better than GWR model without NTL data in terms of RMSE), while its improvements were less obvious for the cold season (GWR model with VANUI performed 3.6% better than the GWR model without NDVI and NTL data and 1.83% better than the GWR model without NTL data in terms of RMSE). Moreover, the spatial distribution of the estimated PM2.5 levels clearly revealed patterns consistent with those densely populated areas and high traffic areas, implying a close and positive correlation between VANUI and PM2.5 concentration. In general, the DMSP/OLS NTL satellite imagery is promising for providing additional information for PM2.5 monitoring and prediction. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Open AccessArticle Characterizing the Growth Patterns of 45 Major Metropolitans in Mainland China Using DMSP/OLS Data
Remote Sens. 2017, 9(6), 571; doi:10.3390/rs9060571
Received: 12 April 2017 / Revised: 25 May 2017 / Accepted: 4 June 2017 / Published: 7 June 2017
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Abstract
Understanding growth patterns at the metropolitan level is instructive for better planning and policy making on sustainable urban development. Using DMSP/OLS data from 1992 to 2013, this article aims to investigate growth patterns of major metropolitans in Mainland China from the aspects of
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Understanding growth patterns at the metropolitan level is instructive for better planning and policy making on sustainable urban development. Using DMSP/OLS data from 1992 to 2013, this article aims to investigate growth patterns of major metropolitans in Mainland China from the aspects of intensification and expansion. We start by calibrating the DMSP/OLS data and selecting 45 major metropolitans. On intensification, results suggest that aggregately, metropolitans displayed cyclical pattern over time and large metropolitans tended to have higher levels of intensification than moderate or small ones. Individually, metropolitans with similar intensification over time could be clustered together using Dendrogram, and evolution pattern of the clusters exhibited similarity to the aggregated one. On expansion, results show that aggregately metropolitans displayed a decreasing trend over time, and moderate or small metropolitans tended to have higher levels of expansion than large ones. Particularly, moderate metropolitans were more likely to expand adjacently, and small ones were more likely to experience scatter or corridor expansion. Each metropolitan can be represented by a mixed expansion model over time, which might tell where and how much expansion occurred in the current year. Furthermore, intensification is highly correlated with expansion over time for small metropolitans, but they are poorly correlated for large or moderate ones. Lastly, the high correlation of intensification and expansion with the change of GDP in each year indicates the reliability of our work. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Open AccessArticle Evaluation of Urbanization Dynamics and its Impacts on Surface Heat Islands: A Case Study of Beijing, China
Remote Sens. 2017, 9(5), 453; doi:10.3390/rs9050453
Received: 23 January 2017 / Revised: 24 April 2017 / Accepted: 5 May 2017 / Published: 7 May 2017
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Abstract
As the capital of China, Beijing has experienced a continued and rapid urbanization process in the past few decades. One of the key environmental impacts of rapid urbanization is the effect of urban heat island (UHI). The objective of this study was to
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As the capital of China, Beijing has experienced a continued and rapid urbanization process in the past few decades. One of the key environmental impacts of rapid urbanization is the effect of urban heat island (UHI). The objective of this study was to estimate the urbanization indexes of Beijing from 1992 to 2013 based on the stable nighttime light (NTL) data derived from the Defense Meteorological Satellite Program’s Operational Line Scanner System (DMSP/OLS), which has became a widely used remote sensing database after decades of development. The annual average value nighttime light Digital Number (NTL-DN), and the total lit number and urban area proportion within Beijing’s boundary were calculated and compared with social-economic statistics parameters to estimate the correlation between them. Four Landsat thematic mapper (TM) images acquired in 1995 and 2009 were applied to estimate the normalized difference vegetation index (NDVI) and normalized land surface temperature (LSTnor), and spatial correlation analysis was then carried out to investigate the relationship between the urbanization level and NDVI and LSTnor. Our results showed a strong negative linear relationship between the NTL-DN value and NDVI; however, in contrast, a strong positive linear relationship between existed between the NTL-DN value and LSTnor. By conducting a spatial comparison analysis of 1995 and 2009, the vegetation coverage change and surface temperature difference were calculated and compared with the NTL-DN difference. Our result revealed that the regions of fast urbanization resulted in a decrease of NDVI and increase of LSTnor. In addition, choropleth maps showing the spatial pattern of urban heat island zones were produced based on different temperatures, and the analysis result indicated that the spatial distribution of surface temperature was closely related with the NTL-DN and NDVI. These findings are helpful for understanding the urbanization process as well as urban ecology, which both have significant implications for urban planning and minimize the potential environmental impacts of urbanization in Beijing. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Open AccessArticle Improving Fractional Impervious Surface Mapping Performance through Combination of DMSP-OLS and MODIS NDVI Data
Remote Sens. 2017, 9(4), 375; doi:10.3390/rs9040375
Received: 12 January 2017 / Revised: 29 March 2017 / Accepted: 13 April 2017 / Published: 17 April 2017
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Abstract
Impervious surface area (ISA) is an important parameter for many studies such as urban climate, urban environmental change, and air pollution; however, mapping ISA at the regional or global scale is still challenging due to the complexity of impervious surface features. The Defense
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Impervious surface area (ISA) is an important parameter for many studies such as urban climate, urban environmental change, and air pollution; however, mapping ISA at the regional or global scale is still challenging due to the complexity of impervious surface features. The Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) data have been used for ISA mapping, but high uncertainty existed due to mixed-pixel and data-saturation problems. This paper presents a new index called normalized impervious surface index (NISI), which is an integration of DMSP-OLS and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data, in order to reduce these problems. Meanwhile, this newly developed index is compared with previously used indices—Human Settlement Index (HSI) and Vegetation Adjusted Nighttime light Urban Index (VANUI)—in ISA mapping performance. We selected China as an example to map fractional ISA distribution through a support vector regression approach based on the relationship between the index and Landsat-derived ISA data. The results indicate that the proposed NISI provided better ISA estimation accuracy than HSI and VANUI, especially when the fractional ISA in a pixel is relatively large (i.e., >0.6) or very small (i.e., <0.2). This approach can be used to rapidly update ISA datasets at regional and global scales. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Open AccessArticle Synergistic Use of Nighttime Satellite Data, Electric Utility Infrastructure, and Ambient Population to Improve Power Outage Detections in Urban Areas
Remote Sens. 2017, 9(3), 286; doi:10.3390/rs9030286
Received: 22 December 2016 / Revised: 13 March 2017 / Accepted: 14 March 2017 / Published: 17 March 2017
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Abstract
Natural and anthropogenic hazards are frequently responsible for disaster events, leading to damaged physical infrastructure, which can result in loss of electrical power for affected locations. Remotely-sensed, nighttime satellite imagery from the Suomi National Polar-orbiting Partnership (Suomi-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS)
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Natural and anthropogenic hazards are frequently responsible for disaster events, leading to damaged physical infrastructure, which can result in loss of electrical power for affected locations. Remotely-sensed, nighttime satellite imagery from the Suomi National Polar-orbiting Partnership (Suomi-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) can monitor power outages in disaster-affected areas through the identification of missing city lights. When combined with locally-relevant geospatial information, these observations can be used to estimate power outages, defined as geographic locations requiring manual intervention to restore power. In this study, we produced a power outage product based on Suomi-NPP VIIRS DNB observations to estimate power outages following Hurricane Sandy in 2012. This product, combined with known power outage data and ambient population estimates, was then used to predict power outages in a layered, feedforward neural network model. We believe this is the first attempt to synergistically combine such data sources to quantitatively estimate power outages. The VIIRS DNB power outage product was able to identify initial loss of light following Hurricane Sandy, as well as the gradual restoration of electrical power. The neural network model predicted power outages with reasonable spatial accuracy, achieving Pearson coefficients (r) between 0.48 and 0.58 across all folds. Our results show promise for producing a continental United States (CONUS)- or global-scale power outage monitoring network using satellite imagery and locally-relevant geospatial data. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Open AccessArticle The Impact of Energy Consumption on the Surface Urban Heat Island in China’s 32 Major Cities
Remote Sens. 2017, 9(3), 250; doi:10.3390/rs9030250
Received: 30 December 2016 / Revised: 23 February 2017 / Accepted: 6 March 2017 / Published: 8 March 2017
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Abstract
Supported by the rapid economic development in the last few decades, China has become the largest energy consumer in the world. Alongside this, the effect of the anthropogenic heat released from energy consumption is increasingly apparent. We quantified the daytime and nighttime surface
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Supported by the rapid economic development in the last few decades, China has become the largest energy consumer in the world. Alongside this, the effect of the anthropogenic heat released from energy consumption is increasingly apparent. We quantified the daytime and nighttime surface urban heat island intensity (SUHII) for the 32 major cities in mainland China, using MODIS land surface temperature data from 2008 to 2012, and estimated the energy consumption intensity (ECI) based on the correlation between energy consumption and the sum of nighttime lights. On this basis, the impact of energy consumption on the surface urban heat island in China’s 32 major cities was analyzed, by directly examining the relationship between SUHII and the urban-suburban difference in ECI. The results show that energy consumption has a significantly positive correlation with the nighttime SUHII, but no correlation with the daytime SUHII. It indicates that the cities with a larger urban-suburban difference in ECI have a far greater impact on SUHII during the nighttime. Therefore, the statistical analysis of the historical observation data in this study provides evidence for a long-held hypothesis that the anthropogenic heat released from energy consumption is an important contributor to the urban thermal environment. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Open AccessEditor’s ChoiceArticle Urban Land Extraction Using VIIRS Nighttime Light Data: An Evaluation of Three Popular Methods
Remote Sens. 2017, 9(2), 175; doi:10.3390/rs9020175
Received: 15 December 2016 / Revised: 29 January 2017 / Accepted: 15 February 2017 / Published: 20 February 2017
Cited by 3 | PDF Full-text (3711 KB) | HTML Full-text | XML Full-text
Abstract
Timely and accurate extraction of urban land area using the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data is important for urban studies. However, a comprehensive assessment of the existing methods for extracting urban land using VIIRS nighttime
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Timely and accurate extraction of urban land area using the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data is important for urban studies. However, a comprehensive assessment of the existing methods for extracting urban land using VIIRS nighttime light data remains inadequate. Therefore, we first reviewed the relevant methods and selected three popular methods for extracting urban land area using nighttime light data. These methods included local-optimized thresholding (LOT), vegetation-adjusted nighttime light urban index (VANUI), integrated nighttime lights, normalized difference vegetation index, and land surface temperature support vector machine classification (INNL-SVM). Then, we assessed the performance of these methods for extracting urban land area based on the VIIRS nighttime light data in seven evaluation areas with various natural and socioeconomic conditions in China. We found that INNL-SVM had the best performance with an average kappa of 0.80, which was 6.67% higher than the LOT and 2.56% higher than the VANUI. The superior performance of INNL-SVM was mainly attributed to the integration of information on nighttime light, vegetation cover, and land surface temperature. This integration effectively reduced the commission and omission errors arising from the overflow effect and low light brightness of the VIIRS nighttime light data. Additionally, INNL-SVM can extract urban land area more easily. Thus, we suggest that INNL-SVM has great potential for effectively extracting urban land with VIIRS nighttime light data at large scales. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Open AccessArticle Analyzing Parcel-Level Relationships between Urban Land Expansion and Activity Changes by Integrating Landsat and Nighttime Light Data
Remote Sens. 2017, 9(2), 164; doi:10.3390/rs9020164
Received: 17 December 2016 / Revised: 8 February 2017 / Accepted: 14 February 2017 / Published: 16 February 2017
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Abstract
Urban growth is a process that imposes profound physical and socioeconomic restructuring on cities. Urban land expansion as an immediate physical manifestation of urban growth has been extensively studied using a variety of remote sensing methods. However, little research addresses the interactions between
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Urban growth is a process that imposes profound physical and socioeconomic restructuring on cities. Urban land expansion as an immediate physical manifestation of urban growth has been extensively studied using a variety of remote sensing methods. However, little research addresses the interactions between urban land expansion and corresponding activity changes, especially at local scales. We propose an innovative analytical framework that integrates Landsat and nighttime light data to capture the parcel-level relationships between urban land expansion and activity changes. The urban land data are acquired based on the classification of Landsat images, whereas the activity changes are approximated by the nighttime light data. Using the Local Indicator of Spatial Association (LISA) (local Moran’s I) approach, four types of local relationships between land expansion and activity changes are defined at the parcel level. The proposed analytical framework is applied in Guangzhou, China, as a case study. The results reveal the mismatched growth between urban land and activity intensity at the parcel level, where the increase in urban land area outpaces the increase of activity intensity. Such results are expected to provide a more comprehensive understanding of urban growth, and can be used to assist urban planning and related decision-making. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Open AccessArticle Assessing Light Pollution in China Based on Nighttime Light Imagery
Remote Sens. 2017, 9(2), 135; doi:10.3390/rs9020135
Received: 19 November 2016 / Revised: 17 January 2017 / Accepted: 24 January 2017 / Published: 6 February 2017
Cited by 3 | PDF Full-text (9710 KB) | HTML Full-text | XML Full-text
Abstract
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
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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. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Review

Jump to: Research

Open AccessReview A Scientometric Visualization Analysis for Night-Time Light Remote Sensing Research from 1991 to 2016
Remote Sens. 2017, 9(8), 802; doi:10.3390/rs9080802
Received: 31 May 2017 / Revised: 20 July 2017 / Accepted: 1 August 2017 / Published: 4 August 2017
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Abstract
In this paper, we conducted a scientometric analysis based on the Night-Time Light (NTL) remote sensing related literature datasets retrieved from Science Citation Index Expanded and Social Science Citation Index in Web of Science core collection database. Using the methods of bibliometric and
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In this paper, we conducted a scientometric analysis based on the Night-Time Light (NTL) remote sensing related literature datasets retrieved from Science Citation Index Expanded and Social Science Citation Index in Web of Science core collection database. Using the methods of bibliometric and Social Network Analysis (SNA), we drew several conclusions: (1) NTL related studies have become a research hotspot, especially after 2011 when the second generation of NTL satellites, the Suomi National Polar-orbiting Partnership (S-NPP) Satellite with the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor was on board. In the same period, the open-access policy of the long historical dataset of the first generation satellite Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) started. (2) Most related studies are conducted by authors from USA and China, and the USA takes the lead in the field. We identified the biggest research communities constructed by co-authorships and the related important authors and topics by SNA. (3) By the visualization and analysis of the topic evolution using the co-word and co-cited reference networks, we can clearly see that: the research topics change from hardware oriented studies to more real-world applications; and from the first generation of the satellite DMSP/OLS to the second generation of satellite S-NPP. Although the Day Night Band (DNB) of the S-NPP exhibits higher spatial and radiometric resolution and better calibration conditions than the first generation DMSP/OLS, the longer historical datasets in DMSP/OLS are still important in long-term and large-scale human activity analysis. (4) In line with the intuitive knowledge, the NTL remote sensing related studies display stronger connections (such as interpretive frame, context, and academic purpose) to the social sciences than the general remote sensing discipline. The citation trajectories are visualized based on the dual-maps, thus the research preferences for combining the environmental, ecological, economic, and political science disciplines are clearly exhibited. Overall, the picture of the NTL remote sensing research is presented from the scientist-level, topic-level, and discipline-level interactions. Based on these analyses, we also discuss the possible trends in the future work, such as combining NTL studies with social science research and social media data. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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