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Special Issue "Remote Sensing of Night Lights – Beyond DMSP"

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

Deadline for manuscript submissions: closed (30 September 2018).

Special Issue Editors

Guest Editor
Prof. Noam Levin

Department of Geography, The Hebrew University of Jerusalem, Mount Scopus, Jerusalem 91905, Israel
Website | E-Mail
Interests: remote sensing; GIS; light pollution; conservation planning; conflict research
Guest Editor
Dr. Christopher Kyba

German Research Center for Geoscience (GFZ), Telegrafenberg, 14473 Potsdam, Germany
Website | E-Mail
Interests: artificial light; light pollution
Guest Editor
Dr. Qingling Zhang

Center for Geo-spatial Information, Institute of Advanced Computing and Digital Engineering Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences 1068 Xueyuan Avenue, Shenzhen 518055, China
Website | E-Mail
Interests: global urbanization and sustainability; remote sensing of land-use and land-cover change; crop watching with satellites; big remote sensing data mining

Special Issue Information

Dear Colleagues,

Remote sensing of night-time lights offers a unique ability to monitor human activity from space. Since the 1990s, many studies have taken advantage of the DMSP/OLS sensor, to monitor artificial lights from space and to quantify the relationships between human activity and socio-economic variables and night-time brightness. In the last decade, new avenues have opened to advance the study of remote sensing of night lights, with the availability of new sensors, offering better spatial, temporal and radiometric resolution than DMSP/OLS. This special issue aims to highlight novel research on remote sensing of night lights going beyond the DMSP/OLS. We especially aim for studies covering the following topics:

  • The potential of new sensors (such as VIIRS/DNB, astronaut photos from the International Space Station (ISS), EROS-B, cubesats or other spaceborne and airborne sensors) to quantify night-time brightness at fine spatial and temporal resolutions.
  • Generation of products from the VIIRS/DNB sensor (e.g., stable lights, gas flares, wildfires, etc.), and the correction of atmospheric and lunar effects on the measured signal.
  • The correspondence between ground observations of artificial lights and light pollution and space borne measurements of night time brightness.
  • Remote sensing studies focusing on the spectral and directional properties of artificial lights.
  • Applications of night-time observations for estimating ecological light pollution and human health impacts.

Assoc. Prof. Noam Levin
Dr. Christopher Kyba
Dr. Qingling Zhang
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 semimonthly 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 1800 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

  • Night-time lights
  • Light pollution
  • VIIRS/DNB
  • Urban
  • International Space Station (ISS)

Published Papers (13 papers)

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Editorial

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Open AccessEditorial
Remote Sensing of Night Lights—Beyond DMSP
Remote Sens. 2019, 11(12), 1472; https://doi.org/10.3390/rs11121472
Received: 8 June 2019 / Accepted: 17 June 2019 / Published: 21 June 2019
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Abstract
Remote sensing of night lights differs from other sources of remote sensing in its ability to directly observe human activity from space as well as in informing us on a new type of anthropogenic threat, that of light pollution. This special issue focuses [...] Read more.
Remote sensing of night lights differs from other sources of remote sensing in its ability to directly observe human activity from space as well as in informing us on a new type of anthropogenic threat, that of light pollution. This special issue focuses on studies which used newer sensors than the Defense Meteorological Satellite Program - Operational Line-Scan System (DMSP/OLS). Most of the analyses focused on data from the Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime sensor (also called the Day/Night Band, or VIIRS/DNB in short), for which the first instrument in the series was launched in 2011. In this editorial, we provide an overview of the 12 papers published in this special issue, and offer suggestions for future research directions in this field, both with respect to the remote sensing platforms and algorithms, and with respect to the development of new applications. Full article
(This article belongs to the Special Issue Remote Sensing of Night Lights – Beyond DMSP)

Research

Jump to: Editorial

Open AccessArticle
Extending Nighttime Combustion Source Detection Limits with Short Wavelength VIIRS Data
Remote Sens. 2019, 11(4), 395; https://doi.org/10.3390/rs11040395
Received: 17 October 2018 / Revised: 29 January 2019 / Accepted: 4 February 2019 / Published: 15 February 2019
Cited by 2 | PDF Full-text (11425 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The Visible Infrared Imaging Radiometer Suite (VIIRS) collects low light imaging data at night in five spectral bands. The best known of these is the day/night band (DNB) which uses light intensification for imaging of moonlit clouds in the visible and near-infrared (VNIR). [...] Read more.
The Visible Infrared Imaging Radiometer Suite (VIIRS) collects low light imaging data at night in five spectral bands. The best known of these is the day/night band (DNB) which uses light intensification for imaging of moonlit clouds in the visible and near-infrared (VNIR). The other four low light imaging bands are in the NIR and short-wave infrared (SWIR), designed for daytime imaging, which continue to collect data at night. VIIRS nightfire (VNF) tests each nighttime pixel for the presence of sub-pixel IR emitters across six spectral bands with two bands each in three spectral ranges: NIR, SWIR, and MWIR. In pixels with detection in two or more bands, Planck curve fitting leads to the calculation of temperature, source area, and radiant heat using physical laws. An analysis of January 2018 global VNF found that inclusion of the NIR and SWIR channels results in a doubling of the VNF pixels with temperature fits over the detection numbers involving the MWIR. The addition of the short wavelength channels extends detection limits to smaller source areas across a broad range of temperatures. The VIIRS DNB has even lower detection limits for combustion sources, reaching 0.001 m2 at 1800 K, a typical temperature for a natural gas flare. Comparison of VNF tallies and DNB fire detections in a 2015 study area in India found the DNB had 15 times more detections than VNF. The primary VNF error sources are false detections from high energy particle detections (HEPD) in space and radiance saturation on some of the most intense events. The HEPD false detections are largely eliminated in the VNF output by requiring multiband detections for the calculation of temperature and source size. Radiance saturation occurs in about 1% of the VNF detections and occurs primarily in the M12 spectral band. Inclusion of the radiances affected by saturation results in temperature and source area calculation errors. Saturation is addressed by identifying the presence of saturation and excluding those radiances from the Planck curve fitting. The extremely low detection limits for the DNB indicates that a DNB fire detection algorithm could reveal vast numbers of combustion sources that are undetectable in longer wavelength VIIRS data. The caveats with the DNB combustion source detection capability is that it should be restricted to pixels that are outside the zone of known VIIRS detected electric lighting. Full article
(This article belongs to the Special Issue Remote Sensing of Night Lights – Beyond DMSP)
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Open AccessArticle
Variation of Individual Location Radiance in VIIRS DNB Monthly Composite Images
Remote Sens. 2018, 10(12), 1964; https://doi.org/10.3390/rs10121964
Received: 20 September 2018 / Revised: 26 November 2018 / Accepted: 4 December 2018 / Published: 6 December 2018
Cited by 2 | PDF Full-text (1605 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
With the growing size and use of night light time series from the Visible Infrared Imaging Radiometer Suite Day/Night Band (DNB), it is important to understand the stability of the dataset. All satellites observe differences in pixel values during repeat observations. In the [...] Read more.
With the growing size and use of night light time series from the Visible Infrared Imaging Radiometer Suite Day/Night Band (DNB), it is important to understand the stability of the dataset. All satellites observe differences in pixel values during repeat observations. In the case of night light data, these changes can be due to both environmental effects and changes in light emission. Here we examine the stability of individual locations of particular large scale light sources (e.g., airports and prisons) in the monthly composites of DNB data from April 2012 to September 2017. The radiances for individual pixels of most large light emitters are approximately normally distributed, with a standard deviation of typically 15–20% of the mean. Greenhouses and flares, however, are not stable sources. We observe geospatial autocorrelation in the monthly variations for nearby sites, while the correlation for sites separated by large distances is small. This suggests that local factors contribute most to the variation in the pixel radiances and furthermore that averaging radiances over large areas will reduce the total variation. A better understanding of the causes of temporal variation would improve the sensitivity of DNB to lighting changes. Full article
(This article belongs to the Special Issue Remote Sensing of Night Lights – Beyond DMSP)
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Open AccessArticle
Modeling the Census Tract Level Housing Vacancy Rate with the Jilin1-03 Satellite and Other Geospatial Data
Remote Sens. 2018, 10(12), 1920; https://doi.org/10.3390/rs10121920
Received: 16 October 2018 / Revised: 20 November 2018 / Accepted: 23 November 2018 / Published: 30 November 2018
Cited by 2 | PDF Full-text (4610 KB) | HTML Full-text | XML Full-text
Abstract
The vacant house is an essential phenomenon of urban decay and population loss. Exploration of the correlations between housing vacancy and some socio-environmental factors is conducive to understanding the mechanism of urban shrinking and revitalization. In recent years, rapidly developing night-time remote sensing, [...] Read more.
The vacant house is an essential phenomenon of urban decay and population loss. Exploration of the correlations between housing vacancy and some socio-environmental factors is conducive to understanding the mechanism of urban shrinking and revitalization. In recent years, rapidly developing night-time remote sensing, which has the ability to detect artificial lights, has been widely applied in applications associated with human activities. Current night-time remote sensing studies on housing vacancy rates are limited by the coarse spatial resolution of data. The launch of the Jilin1-03 satellite, which carried a high spatial resolution (HSR) night-time imaging camera, provides a new supportive data source. In this paper, we examined this new high spatial resolution night-time light dataset in housing vacancy rate estimation. Specifically, a stepwise multivariable linear regression model was engaged to estimate the housing vacancy rate at a very fine scale, the census tract level. Three types of variables derived from geospatial data and night-time image represent the physical environment, landuse (LU) structure, and human activities, respectively. The linear regression models were constructed and analyzed. The analysis results show that (1) the HVRs estimating model using the Jilin1-03 satellite and other ancillary geospatial data fits well with the Census statistical data (adjusted R2 = 0.656, predicted R2 = 0.603, RMSE = 0.046) and thus is a valid estimation model; (2) the Jilin1-03 satellite night-time data contributed a 28% (from 0.510 to 0.656) fitting accuracy increase and a 68% (from 0.359 to 0.603) predicting accuracy increase in the estimate model of the housing vacancy rate. Reflecting socio-economic conditions, the luminous intensity of commercial areas derived from the Jilin1-03 satellite is the most influential variable to housing vacancy. Land use structure indirectly and partially demonstrated that the social environment factors in the community have strong correlations with residential vacancy. Moreover, the physical environment factor, which depicts vegetation conditions in the residential areas, is also a significant indicator of housing vacancy. In conclusion, the emergence of HSR night light data opens a new door to future microscopic scale study within cities. Full article
(This article belongs to the Special Issue Remote Sensing of Night Lights – Beyond DMSP)
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Open AccessFeature PaperArticle
Mapping Fishing Activities and Suitable Fishing Grounds Using Nighttime Satellite Images and Maximum Entropy Modelling
Remote Sens. 2018, 10(10), 1604; https://doi.org/10.3390/rs10101604
Received: 13 August 2018 / Revised: 27 September 2018 / Accepted: 28 September 2018 / Published: 9 October 2018
Cited by 2 | PDF Full-text (7222 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Fisheries surveys over broad spatial areas are crucial in defining and delineating appropriate fisheries management areas. Yet accurate mapping and tracking of fishing activities remain largely restricted to developed countries with sufficient resources to use automated identification systems and vessel monitoring systems. For [...] Read more.
Fisheries surveys over broad spatial areas are crucial in defining and delineating appropriate fisheries management areas. Yet accurate mapping and tracking of fishing activities remain largely restricted to developed countries with sufficient resources to use automated identification systems and vessel monitoring systems. For many countries, the spatial extent and boundaries of fishing grounds are not completely known. We used satellite images at night to detect fishing grounds in the Philippines for fishing gears that use powerful lights to attract coastal pelagic fishes. We used nightly boat detection data, extracted by U.S. NOAA from the Visible Infrared Imaging Radiometer Suite (VIIRS), for the Philippines from 2012 to 2016, covering 1713 nights, to examine spatio-temporal patterns of fishing activities in the country. Using density-based clustering, we identified 134 core fishing areas (CFAs) ranging in size from 6 to 23,215 km2 within the Philippines’ contiguous maritime zone. The CFAs had different seasonal patterns and range of intensities in total light output, possibly reflecting differences in multi-gear and multi-species signatures of fishing activities in each fishing ground. Using maximum entropy modeling, we identified bathymetry and chlorophyll as the main environmental predictors of spatial occurrence of these CFAs when analyzed together, highlighting the multi-gear nature of the CFAs. Applications of the model to specific CFAs identified different environmental drivers of fishing distribution, coinciding with known oceanographic associations for a CFA’s dominant target species. This case study highlights nighttime satellite images as a useful source of spatial fishing effort information for fisheries, especially in Southeast Asia. Full article
(This article belongs to the Special Issue Remote Sensing of Night Lights – Beyond DMSP)
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Open AccessArticle
NPP-VIIRS DNB Daily Data in Natural Disaster Assessment: Evidence from Selected Case Studies
Remote Sens. 2018, 10(10), 1526; https://doi.org/10.3390/rs10101526
Received: 28 June 2018 / Revised: 14 September 2018 / Accepted: 18 September 2018 / Published: 23 September 2018
Cited by 3 | PDF Full-text (7229 KB) | HTML Full-text | XML Full-text
Abstract
Whereas monthly and annual nighttime light (NTL) composite datasets are being increasingly used to estimate socioeconomic status, use of the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) daily data has been limited for detecting and assessing the impact [...] Read more.
Whereas monthly and annual nighttime light (NTL) composite datasets are being increasingly used to estimate socioeconomic status, use of the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) daily data has been limited for detecting and assessing the impact of short-term disastrous events. This study explores the application of daily NPP-VIIRS DNB data in assessing the impact of three types of natural disasters: earthquakes, floods, and storms. Daily DNB images one month prior to and 10 days after a disastrous event were collected and a Percent of Normal Light (PNL) image was produced as the ratio of the mean DNB radiance of the pre- and post-disaster images. Areas with a PNL value lower than one were considered as being affected by the event. The results were compared with the damaged proxy map and the flood proxy map generated using synthetic aperture radar data as well as the reported power outage rates. Our analyses show that overall NPP-VIIRS DNB daily data are useful for detecting damages and power outages caused by earthquake, storm, and flood events. Cloud coverage was identified as a major limitation in using the DNB daily data; rescue activities, traffic, and socioeconomic status of the areas also affect the use of DNB daily data in assessing the impact of natural disasters. Our findings offer new insight into the use of the daily DNB data and provide a practical guide for researchers and practitioners who may consider using such data in different situations or regions. Full article
(This article belongs to the Special Issue Remote Sensing of Night Lights – Beyond DMSP)
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Open AccessArticle
Night-Time Light Dynamics during the Iraqi Civil War
Remote Sens. 2018, 10(6), 858; https://doi.org/10.3390/rs10060858
Received: 29 March 2018 / Revised: 24 May 2018 / Accepted: 28 May 2018 / Published: 1 June 2018
Cited by 2 | PDF Full-text (3457 KB) | HTML Full-text | XML Full-text
Abstract
In this study, we analyzed the night-time light dynamics in Iraq over the period 2012–2017 by using Visible Infrared Imaging Radiometer Suite (VIIRS) monthly composites. The data quality of VIIRS images was improved by repairing the missing data, and the Night-time Light Ratio [...] Read more.
In this study, we analyzed the night-time light dynamics in Iraq over the period 2012–2017 by using Visible Infrared Imaging Radiometer Suite (VIIRS) monthly composites. The data quality of VIIRS images was improved by repairing the missing data, and the Night-time Light Ratio Indices (NLRIs), derived from urban extent map and night-time light images, were calculated for different provinces and cities. We found that when the Islamic State of Iraq and Syria (ISIS) attacked or occupied a region, the region lost its light rapidly, with the provinces of Al-Anbar, At-Ta’min, Ninawa, and Sala Ad-din losing 63%, 73%, 88%, and 56%, of their night-time light, respectively, between December 2013 and December 2014. Moreover, the light returned after the Iraqi Security Forces (ISF) recaptured the region. In addition, we also found that the night-time light in the Kurdish Autonomous Region showed a steady decline after 2014, with the Arbil, Dihok, and As-Sulaymaniyah provinces losing 47%, 18%, and 31% of their night-time light between December 2013 and December 2016 as a result of the economic crisis in the region. The night-time light in Southern Iraq, the region controlled by Iraqi central government, has grown continuously; for example, the night-time light in Al Basrah increased by 75% between December 2013 and December 2017. Regions formerly controlled by ISIS experienced a return of night-time light during 2017 as the ISF retook almost all this territory in 2017. This indicates that as reconstruction began, electricity was re-supplied in these regions. Our analysis shows the night-time light in Iraq is directly linked to the socioeconomic dynamics of Iraq, and demonstrates that the VIIRS monthly night-time light images are an effective data source for tracking humanitarian disasters in that country. Full article
(This article belongs to the Special Issue Remote Sensing of Night Lights – Beyond DMSP)
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Open AccessArticle
Delineating Urban Boundaries Using Landsat 8 Multispectral Data and VIIRS Nighttime Light Data
Remote Sens. 2018, 10(5), 799; https://doi.org/10.3390/rs10050799
Received: 5 March 2018 / Revised: 11 May 2018 / Accepted: 17 May 2018 / Published: 21 May 2018
Cited by 4 | PDF Full-text (15120 KB) | HTML Full-text | XML Full-text
Abstract
Administering an urban boundary (UB) is increasingly important for curbing disorderly urban land expansion. The traditionally manual digitalization is time-consuming, and it is difficult to connect UB in the urban fringe due to the fragmented urban pattern in daytime data. Nighttime light (NTL) [...] Read more.
Administering an urban boundary (UB) is increasingly important for curbing disorderly urban land expansion. The traditionally manual digitalization is time-consuming, and it is difficult to connect UB in the urban fringe due to the fragmented urban pattern in daytime data. Nighttime light (NTL) data is a powerful tool used to map the urban extent, but both the blooming effect and the coarse spatial resolution make the urban product unable to meet the requirements of high-precision urban study. In this study, precise UB is extracted by a practical and effective method using NTL data and Landsat 8 data. Hangzhou, a megacity experiencing rapid urban sprawl, was selected to test the proposed method. Firstly, the rough UB was identified by the search mode of the concentric zones model (CZM) and the variance-based approach. Secondly, a buffer area was constructed to encompass the precise UB that is near the rough UB within a certain distance. Finally, the edge detection method was adopted to obtain the precise UB with a spatial resolution of 30 m. The experimental results show that a good performance was achieved and that it solved the largest disadvantage of the NTL data-blooming effect. The findings indicated that cities with a similar level of socio-economic status can be processed together when applied to larger-scale applications. Full article
(This article belongs to the Special Issue Remote Sensing of Night Lights – Beyond DMSP)
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Open AccessArticle
Delineating Spatial Patterns in Human Settlements Using VIIRS Nighttime Light Data: A Watershed-Based Partition Approach
Remote Sens. 2018, 10(3), 465; https://doi.org/10.3390/rs10030465
Received: 7 February 2018 / Revised: 14 March 2018 / Accepted: 14 March 2018 / Published: 15 March 2018
Cited by 5 | PDF Full-text (30158 KB) | HTML Full-text | XML Full-text
Abstract
As an informative proxy measure for a range of urbanization and socioeconomic variables, satellite-derived nighttime light data have been widely used to investigate diverse anthropogenic activities in human settlements over time and space from the regional to the national scale. With a higher [...] Read more.
As an informative proxy measure for a range of urbanization and socioeconomic variables, satellite-derived nighttime light data have been widely used to investigate diverse anthropogenic activities in human settlements over time and space from the regional to the national scale. With a higher spatial resolution and fewer over-glow and saturation effects, nighttime light data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument with day/night band (DNB), which is on the Suomi National Polar-Orbiting Partnership satellite (Suomi-NPP), may further improve our understanding of spatiotemporal dynamics and socioeconomic activities, particularly at the local scale. Capturing and identifying spatial patterns in human settlements from VIIRS images, however, is still challenging due to the lack of spatially explicit texture characteristics, which are usually crucial for general image classification methods. In this study, we propose a watershed-based partition approach by combining a second order exponential decay model for the spatial delineation of human settlements with VIIRS-derived nighttime light images. Our method spatially partitions the human settlement into five different types of sub-regions: high, medium-high, medium, medium-low and low lighting areas with different degrees of human activity. This is primarily based on the local coverage of locally maximum radiance signals (watershed-based) and the rank and magnitude of the nocturnal radiance signal across the whole region, as well as remotely sensed building density data and social media-derived human activity information. The comparison results for the relationship between sub-regions with various density nighttime brightness levels and human activities, as well as the densities of different types of interest points (POIs), show that our method can distinctly identify various degrees of human activity based on artificial nighttime radiance and ancillary data. Furthermore, the analysis results across 99 cities in 10 urban agglomerations in China reveal inter-regional variations in partition thresholds and human settlement patterns related to the urban size and form. Our partition method and relative results can provide insight into the further application of VIIRS DNB nighttime light data in spatially delineated urbanization processes and socioeconomic activities in human settlements. Full article
(This article belongs to the Special Issue Remote Sensing of Night Lights – Beyond DMSP)
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Open AccessArticle
A Genetic Algorithm-Based Urban Cluster Automatic Threshold Method by Combining VIIRS DNB, NDVI, and NDBI to Monitor Urbanization
Remote Sens. 2018, 10(2), 277; https://doi.org/10.3390/rs10020277
Received: 9 December 2017 / Revised: 5 February 2018 / Accepted: 8 February 2018 / Published: 11 February 2018
Cited by 8 | PDF Full-text (13631 KB) | HTML Full-text | XML Full-text
Abstract
Accurate and timely information related to quantitative descriptions and spatial distributions of urban areas is crucial to understand urbanization dynamics and is also helpful to address environmental issues associated with rapid urban land-cover changes. Thresholding is acknowledged as the most popular and practical [...] Read more.
Accurate and timely information related to quantitative descriptions and spatial distributions of urban areas is crucial to understand urbanization dynamics and is also helpful to address environmental issues associated with rapid urban land-cover changes. Thresholding is acknowledged as the most popular and practical way to extract urban information from nighttime lights. However, the difficulty of determining optimal threshold remains challenging to applications of this method. In order to address the problem of selecting thresholds, a Genetic Algorithm-based urban cluster automatic threshold (GA-UCAT) method by combining Visible-Infrared Imager-Radiometer Suite Day/Night band (VIIRS DNB), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI) is proposed to distinguish urban areas from dark rural background in NTL images. The key point of this proposed method is to design an appropriate fitness function of GA by means of integrating between-class variance and inter-class variance with all these three data sources to determine optimal thresholds. In accuracy assessments by comparing with ground truth—Landsat 8 OLI images, this new method has been validated and results with OA (Overall Accuracy) ranging from 0.854 to 0.913 and Kappa ranging from 0.699 to 0.722 show that the GA-UCAT approach is capable of describing spatial distributions and giving detailed information of urban extents. Additionally, there is discussion on different classifications of rural residential spots in Landsat remote sensing images and nighttime light (NTL) and evaluations of spatial-temporal development patterns of five selected Chinese urban clusters from 2012 to 2017 on utilizing this proposed method. The new method shows great potential to map global urban information in a simple and accurate way and to help address urban environmental issues. Full article
(This article belongs to the Special Issue Remote Sensing of Night Lights – Beyond DMSP)
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Open AccessArticle
An Object Similarity-Based Thresholding Method for Urban Area Mapping from Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) Data
Remote Sens. 2018, 10(2), 263; https://doi.org/10.3390/rs10020263
Received: 18 November 2017 / Revised: 27 January 2018 / Accepted: 6 February 2018 / Published: 8 February 2018
Cited by 3 | PDF Full-text (4722 KB) | HTML Full-text | XML Full-text
Abstract
Nighttime light data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) provides a unique data source for mapping and monitoring urban areas at regional and global scales. This study proposes an object similarity-based thresholding method using VIIRS DNB data to [...] Read more.
Nighttime light data from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) provides a unique data source for mapping and monitoring urban areas at regional and global scales. This study proposes an object similarity-based thresholding method using VIIRS DNB data to map urban areas. The threshold for a target potential urban object was determined by comparing its similarity with all reference urban objects with known optimal thresholds derived from Landsat data. The proposed method includes four major steps: potential urban object generation, threshold optimization for reference urban objects, object similarity comparison, and urban area mapping. The proposed method was evaluated using VIIRS DNB data of China and compared with existing mapping methods in terms of threshold estimation and urban area mapping. The results indicated that the proposed method estimated thresholds and mapped urban areas accurately and generally performed better than the cluster-based logistic regression method. The correlation coefficients between the estimated thresholds and the reference thresholds were 0.9201–0.9409 (using Euclidean distance as similarity measure) and 0.9461–0.9523 (using Mahalanobis distance as similarity measure) for the proposed method and 0.9435–0.9503 for the logistic regression method. The average Kappa Coefficients of the urban area maps were 0.58 (Euclidean distance) and 0.57 (Mahalanobis distance) for the proposed method and 0.51 for the logistic regression method. The proposed method shows potential to map urban areas at a regional scale effectively in an economic and convenient way. Full article
(This article belongs to the Special Issue Remote Sensing of Night Lights – Beyond DMSP)
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Open AccessArticle
Regional Inequality in China Based on NPP-VIIRS Night-Time Light Imagery
Remote Sens. 2018, 10(2), 240; https://doi.org/10.3390/rs10020240
Received: 11 December 2017 / Revised: 29 January 2018 / Accepted: 4 February 2018 / Published: 5 February 2018
Cited by 8 | PDF Full-text (5993 KB) | HTML Full-text | XML Full-text
Abstract
Regional economic inequality is a persistent problem for all nations. Meanwhile, satellite-derived night-time light (NTL) data have been extensively used as an efficient proxy measure for economic activity. This study firstly proposes a new method for correction of the NTL data derived from [...] Read more.
Regional economic inequality is a persistent problem for all nations. Meanwhile, satellite-derived night-time light (NTL) data have been extensively used as an efficient proxy measure for economic activity. This study firstly proposes a new method for correction of the NTL data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite and then applies the corrected NTL data to estimate gross domestic product (GDP) at a multi-scale level in China from 2014 to 2017. Secondly, incorporating the two-stage nested Theil decomposition method, multi-scale level regional inequalities are investigated. Finally, by using scatter plots, this paper identifies the relationship between the regional inequality and the level of economic development. The results indicate that: (1) after correction, the NPP-VIIRS NTL data show a statistically positive correlation with GDP, which proves that our correction method is scientifically effective; (2) from 2014 to 2017, overall inequality, within-province inequality, and between-region inequality all declined, However, between-province inequality increased slightly. As for the contributions to overall regional inequality, the within-province inequality was the highest, while the between-province inequality was the lowest; (3) further analysis of within-province inequality reveals that economic inequalities in coastal provinces in China are smaller than in inland provinces; (4) China’s economic development plays an important role in affecting regional inequality, and the extent of influence of economic development on regional inequality is varied across provinces. Full article
(This article belongs to the Special Issue Remote Sensing of Night Lights – Beyond DMSP)
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Open AccessArticle
Parameterizing Anthropogenic Heat Flux with an Energy-Consumption Inventory and Multi-Source Remote Sensing Data
Remote Sens. 2017, 9(11), 1165; https://doi.org/10.3390/rs9111165
Received: 16 October 2017 / Revised: 1 November 2017 / Accepted: 10 November 2017 / Published: 13 November 2017
Cited by 5 | PDF Full-text (7338 KB) | HTML Full-text | XML Full-text
Abstract
Anthropogenic heat (AH) generated by human activities is an important factor affecting the urban climate. Thus, refined AH parameterization of a large area can provide data support for regional meteorological research. In this study, we developed a refined anthropogenic heat flux (RAHF) parameterization [...] Read more.
Anthropogenic heat (AH) generated by human activities is an important factor affecting the urban climate. Thus, refined AH parameterization of a large area can provide data support for regional meteorological research. In this study, we developed a refined anthropogenic heat flux (RAHF) parameterization scheme to estimate the gridded anthropogenic heat flux (AHF). Firstly, the annual total AH emissions and annual mean AHF of Beijing municipality in the year 2015 were estimated using a top-down, energy-consumption inventory method, which was derived based on socioeconomic statistics and energy consumption data. The heat released from industry, transportation, buildings (including both commercial and residential buildings), and human metabolism were taken into account. Then, the county-scale AHF estimation model was constructed based on multi-source remote sensing data, such as Suomi national polar-orbiting partnership (Suomi-NPP) visible infrared imaging radiometer suite (VIIRS) nighttime light (NTL) data and moderate resolution imaging spectroradiometer (MODIS) data. This model was applied to estimate the annual mean AHF of the counties in the Beijing–Tianjin–Hebei region. Finally, the gridded AHF data with 500-m resolution was obtained using a RAHF parameterization scheme. The results indicate that the annual total AH emissions of Beijing municipality in the year 2015 was approximately 1.704 × 1018 J. Of this, the buildings contribute about 34.5%, followed by transportation and industry with about 30.5% and 30.1%, respectively, and human metabolism with only about 4.9%. The annual mean AHF value of the Beijing–Tianjin–Hebei region is about 6.07 W·m−2, and the AHF in urban areas is about in the range of 20 W·m−2 and 130 W·m−2. The maximum AHF value is approximately 130.84 W·m−2, mostly in airports, railway stations, central business districts, and other densely-populated areas. The error analysis of the county-scale AHF results showed that the residual between the model estimation and energy consumption statistics is less than 1%. In addition, the spatial distribution of RAHF results is generally centered on urban area and gradually decreases towards suburbs. The spatial pattern of the RAHF results within urban areas corresponds well to the distribution of population density, building density, and the industrial district. The spatial heterogeneity of AHF within urban areas is well-reflected through the RAHF results. The RAHF results can be used in meteorological and environmental modeling for the Beijing–Tianjin–Hebei region. The results of this study also highlight the superiority of Suomi-NPP VIIRS NTL data for AHF estimation. Full article
(This article belongs to the Special Issue Remote Sensing of Night Lights – Beyond DMSP)
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