Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.9
5.0
Journals
Remote Sensing
Special Issues
Remote Sensing of Nighttime Observations
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Remote Sensing of Nighttime Observations

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 67709

Special Issue Editor

Dr. Mikhail Zhizhin

E-Mail Website
Guest Editor
1. Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines, Golden, CO 80401, USA
2. Cooperative Institute for Research in Environmental Sciences (CIRES), NOAA National Centers for Environmental Information, Asheville, NC 28801-5001, USA
Interests: multispectral remote sensing; nighttime observations; gas flares; VIIRS Nightfire; VIIRS boat detector; high performance computing; pattern recognition; computational geophysics

Special Issue Information

Dear Colleagues,

Remote sensing of night lights allows observation of human activity from space for almost 30 years. Collecting the night light data involves cross-calibration of different sensors and multiple filters for moonlit clouds and terrain, lightning, energetic particles, air glow, and auroras. This Special Issue will highlight new techniques and applications of remote sensing of night lights. The possible applications include mapping of city lights, road network and light pollution, detection of blackouts, and intensity change resulting from urban and transportation development and military conflicts. The Special Issue extends the traditional scope of the night time observations of artificial lights on land to include lights in the ocean from fishing boats and multispectral infrared signals from high temperature sources, such as gas flares. We also invite papers on new nighttime sensors, including small satellites with high resolution sensors and cubesats.

Dr. Mikhail ZHIZHIN
Guest Editor

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 submissions that pass pre-check are 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 2700 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 lights
  • Multispectral
  • JPSS/VIIRS
  • DMSP/OLS
  • LJ1-01
  • City lights
  • Fishing boats
  • Infrared heat sources
  • Gas flaring
  • Urban
  • Human activity
  • Light pollution
  • Blackouts
  • Small satellites

Published Papers (18 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

19 pages, 4063 KiB  
Article
Identifying and Classifying Shrinking Cities Using Long-Term Continuous Night-Time Light Time Series
by Baiyu Dong, Yang Ye, Shixue You, Qiming Zheng, Lingyan Huang, Congmou Zhu, Cheng Tong, Sinan Li, Yongjun Li and Ke Wang
Remote Sens. 2021, 13(16), 3142; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163142 - 08 Aug 2021
Cited by 13 | Viewed by 6993
Abstract
Shrinking cities—cities suffering from population and economic decline—has become a pressing societal issue of worldwide concern. While night-time light (NTL) data have been applied as an important tool for the identification of shrinking cities, the current methods are constrained and biased by the [...] Read more.
Shrinking cities—cities suffering from population and economic decline—has become a pressing societal issue of worldwide concern. While night-time light (NTL) data have been applied as an important tool for the identification of shrinking cities, the current methods are constrained and biased by the lack of using long-term continuous NTL time series and the use of unidimensional indices. In this study, we proposed a novel method to identify and classify shrinking cities by long-term continuous NTL time series and population data, and applied the method in northeastern China (NEC) from 1996 to 2020. First, we established a long-term consistent NTL time series by applying a geographically weighted regression model to two distinct NTL datasets. Then, we generated NTL index (NI) and population index (PI) by random forest model and the slope of population data, respectively. Finally, we developed a shrinking city index (SCI), based on NI and PI to identify and classify city shrinkage. The results showed that the shrinkage pattern of NEC in 1996–2009 (stage 1) and 2010–2020 (stage 2) was quite different. From stage 1 to stage 2, the shrinkage situation worsened as the number of shrinking cities increased from 102 to 162, and the proportion of severe shrinkage increased from 9.2% to 30.3%. In stage 2, 85.4% of the cities exhibited population decline, and 15.7% of the cities displayed an NTL decrease, suggesting that the changes in NTL and population were not synchronized. Our proposed method provides a robust and long-term characterization of city shrinkage and is beneficial to provide valuable information for sustainable urban planning and decision-making. Full article
(This article belongs to the Special Issue Remote Sensing of Nighttime Observations)
►▼ Show Figures

Graphical abstract

30 pages, 5806 KiB  
Article
Measuring Gas Flaring in Russia with Multispectral VIIRS Nightfire
by Mikhail Zhizhin, Alexey Matveev, Tilottama Ghosh, Feng-Chi Hsu, Martyn Howells and Christopher Elvidge
Remote Sens. 2021, 13(16), 3078; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163078 - 05 Aug 2021
Cited by 12 | Viewed by 4326
Abstract
According to the data reported by the international and governmental agencies, the Russian Federation remains one of the world’s major associated petroleum gas (APG) flaring nations. In the past decade, numerous studies have shown the applicability of satellite-based methods to estimate gas flaring. [...] Read more.
According to the data reported by the international and governmental agencies, the Russian Federation remains one of the world’s major associated petroleum gas (APG) flaring nations. In the past decade, numerous studies have shown the applicability of satellite-based methods to estimate gas flaring. New satellite-based observations might offer an insight in region-, company-, and site-specific gas flaring patterns, as the reported data are often incomplete. We provide a detailed catalog of the upstream and downstream gas flares and an in-depth analysis at the country, region, company and site level of the satellite monitoring results of flaring in Russia from 2012 to 2020. Our analysis is based on the VIIRS Nightfire data and validated against high-resolution daytime satellite images and geographical and geological metadata published by the oil and gas companies and the Russian government. Gas flaring volumes in Russia are estimated to average at 23 billion cubic meters (BCM) annually (15% of global flaring), with 19 BCM (82% on national scale) corresponding to the oil upstream flaring, which has been subject to heavy government regulations since 2013. Despite initially dropping, observed flaring volumes have been on the climb since 2018. We are able to monitor seasonal variations, accidents in gas processing and to track the activities to reduce gas flaring. An effect of gas composition on the flare temperature is reported for oil and gas fields in Russia. Full article
(This article belongs to the Special Issue Remote Sensing of Nighttime Observations)
►▼ Show Figures

Graphical abstract

33 pages, 81237 KiB  
Article
Research on the Spatio-Temporal Dynamic Evolution Characteristics and Influencing Factors of Electrical Power Consumption in Three Urban Agglomerations of Yangtze River Economic Belt, China Based on DMSP/OLS Night Light Data
by Yang Zhong, Aiwen Lin, Chiwei Xiao and Zhigao Zhou
Remote Sens. 2021, 13(6), 1150; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13061150 - 17 Mar 2021
Cited by 16 | Viewed by 2864
Abstract
In this paper, based on electrical power consumption (EPC) data extracted from DMSP/OLS night light data, we select three national-level urban agglomerations in China’s Yangtze River Economic Belt(YREB), includes Yangtze River Delta urban agglomerations(YRDUA), urban agglomeration in the middle reaches of the Yangtze [...] Read more.
In this paper, based on electrical power consumption (EPC) data extracted from DMSP/OLS night light data, we select three national-level urban agglomerations in China’s Yangtze River Economic Belt(YREB), includes Yangtze River Delta urban agglomerations(YRDUA), urban agglomeration in the middle reaches of the Yangtze River(UAMRYR), and Chengdu-Chongqing urban agglomeration(CCUA) as the research objects. In addition, the coefficient of variation (CV), kernel density analysis, cold hot spot analysis, trend analysis, standard deviation ellipse and Moran’s I Index were used to analyze the Spatio-temporal Dynamic Evolution Characteristics of EPC in the three urban agglomerations of the YREB. In addition, we also use geographically weighted regression (GWR) model and random forest algorithm to analyze the influencing factors of EPC in the three major urban agglomerations in YREB. The results of this study show that from 1992 to 2013, the CV of the EPC in the three urban agglomerations of YREB has been declining at the overall level. At the same time, the highest EPC value is in YRDUA, followed by UAMRYR and CCUA. In addition, with the increase of time, the high-value areas of EPC hot spots are basically distributed in YRDUA. The standard deviation ellipses of the EPC of the three urban agglomerations of YREB clearly show the characteristics of “east-west” spatial distribution. With the increase of time, the correlations and the agglomeration of the EPC in the three urban agglomerations of the YREB were both become more and more obvious. In terms of influencing factor analysis, by using GWR model, we found that the five influencing factors we selected basically have a positive impact on the EPC of the YREB. By using the Random forest algorithm, we found that the three main influencing factors of EPC in the three major urban agglomerations in the YREB are the proportion of secondary industry in GDP, Per capita disposable income of urban residents, and Urbanization rate. Full article
(This article belongs to the Special Issue Remote Sensing of Nighttime Observations)
►▼ Show Figures

Graphical abstract

28 pages, 10024 KiB  
Article
Learning from Nighttime Observations of Gas Flaring in North Dakota for Better Decision and Policy Making
by Rong Lu, Jennifer L. Miskimins and Mikhail Zhizhin
Remote Sens. 2021, 13(5), 941; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050941 - 03 Mar 2021
Cited by 3 | Viewed by 2266
Abstract
In today’s oil industry, companies frequently flare the produced natural gas from oil wells. The flaring activities are extensive in some regions including North Dakota. Besides company-reported data, which are compiled by the North Dakota Industrial Commission, flaring statistics such as count and [...] Read more.
In today’s oil industry, companies frequently flare the produced natural gas from oil wells. The flaring activities are extensive in some regions including North Dakota. Besides company-reported data, which are compiled by the North Dakota Industrial Commission, flaring statistics such as count and volume can be estimated via Visible Infrared Imaging Radiometer Suite nighttime observations. Following data gathering and preprocessing, Bayesian machine learning implemented with Markov chain Monte Carlo methods is performed to tackle two tasks: flaring time series analysis and distribution approximation. They help further understanding of the flaring profiles and reporting qualities, which are important for decision/policy making. First, although fraught with measurement and estimation errors, the time series provide insights into flaring approaches and characteristics. Gaussian processes are successful in inferring the latent flaring trends. Second, distribution approximation is achieved by unsupervised learning. The negative binomial and Gaussian mixture models are utilized to describe the distributions of field flare count and volume, respectively. Finally, a nearest-neighbor-based approach for company level flared volume allocation is developed. Potential discrepancies are spotted between the company reported and the remotely sensed flaring profiles. Full article
(This article belongs to the Special Issue Remote Sensing of Nighttime Observations)
►▼ Show Figures

Graphical abstract

21 pages, 33913 KiB  
Article
An Optimized Approach for Extracting Urban Land Based on Log-Transformed DMSP-OLS Nighttime Light, NDVI, and NDWI
by Yuanmao Zheng, Qiang Zhou, Yuanrong He, Cuiping Wang, Xiaorong Wang and Haowei Wang
Remote Sens. 2021, 13(4), 766; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040766 - 19 Feb 2021
Cited by 11 | Viewed by 2563
Abstract
Quantitative and accurate urban land information on regional and global scales is urgently required for studying socioeconomic and eco-environmental problems. The spatial distribution of urban land is a significant part of urban development planning, which is vital for optimizing land use patterns and [...] Read more.
Quantitative and accurate urban land information on regional and global scales is urgently required for studying socioeconomic and eco-environmental problems. The spatial distribution of urban land is a significant part of urban development planning, which is vital for optimizing land use patterns and promoting sustainable urban development. Composite nighttime light (NTL) data from the Defense Meteorological Program Operational Line-Scan System (DMSP-OLS) have been proven to be effective for extracting urban land. However, the saturation and blooming within the DMSP-OLS NTL hinder its capacity to provide accurate urban information. This paper proposes an optimized approach that combines NTL with multiple index data to overcome the limitations of extracting urban land based only on NTL data. We combined three sources of data, the DMSP-OLS, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI), to establish a novel approach called the vegetation–water-adjusted NTL urban index (VWANUI), which is used to rapidly extract urban land areas on regional and global scales. The results show that the proposed approach reduces the saturation of DMSP-OLS and essentially eliminates blooming effects. Next, we developed regression models based on the normalized DMSP-OLS, the human settlement index (HSI), the vegetation-adjusted NTL urban index (VANUI), and the VWANUI to analyze and estimate urban land areas. The results show that the VWANUI regression model provides the highest performance of all the models tested. To summarize, the VWANUI reduces saturation and blooming, and improves the accuracy with which urban areas are extracted, thereby providing valuable support and decision-making references for designing sustainable urban development. Full article
(This article belongs to the Special Issue Remote Sensing of Nighttime Observations)
►▼ Show Figures

Graphical abstract

35 pages, 3358 KiB  
Article
An Improved Correction Method of Nighttime Light Data Based on EVI and WorldPop Data
by Pengfei Liu, Qing Wang, Dandan Zhang and Yongzong Lu
Remote Sens. 2020, 12(23), 3988; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12233988 - 06 Dec 2020
Cited by 11 | Viewed by 2782
Abstract
Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) data has the shortcomings of discontinuous and pixel saturation effect. It was also incompatible with the Soumi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) data. In view those shortcomings, this research put forward [...] Read more.
Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) data has the shortcomings of discontinuous and pixel saturation effect. It was also incompatible with the Soumi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) data. In view those shortcomings, this research put forward the WorldPop and the enhanced vegetation index (EVI) adjusted nighttime light (WEANTL) using EVI and WorldPop data to achieve intercalibration and saturation correction of DMSP/OLS data. A long time series of nighttime light images of china from 2001 to 2018 was constructed by fitting the DMSP/OLS data and NPP/VIIRS data. Corrected nighttime light images were examined to discuss the estimation ability of gross domestic product (GDP) and electric power consumption (EPC) on national and provincial scales, respectively. The results indicated that, (1) after correction, the nighttime light (NTL) data can guarantee the growth trend on national and regional scales, and the interannual volatility of the corrected NTL data is lower than that of the uncorrected NTL data; (2) on the national scale, compared with the established model of NTL data and GDP data (NTL-GDP), the determination coefficient (R2) and the mean absolute relative error (MARE) are 0.981 and 8.518%. The R2 and MARE of the established model of NTL data and EPC data (NTL-EPC) were 0.990 and 4.655%; (3) on the provincial scale, the R2 and MARE of NTL-GDP model under the provincial units are 0.7386 and 38.599%. The R2 value and MARE of NTL-EPC model are 0.8927 and 29.319%; (4) on the provincial scale, the R2 and MARE of NTL-GDP model on time series are 0.9667 and 10.877%. The R2 and MARE of NTL-GDP model on time series are 0.9720 and 6.435%; the established TNL-GDP and TNL-EPC models with 30 provinces data all passed the F-test at the 0.001 level; (5) the prediction accuracy of GDP and EPC on time series was nearly 100%. Therefore, the correction method provided in this research can be applied in estimating the GDP and EPC on multiple scales reliably and accurately. Full article
(This article belongs to the Special Issue Remote Sensing of Nighttime Observations)
►▼ Show Figures

Figure 1

21 pages, 8902 KiB  
Article
A Relative Radiation Normalization Method of ISS Nighttime Light Images Based on Pseudo Invariant Features
by Shengrong Wei, Weili Jiao, Tengfei Long, Huichan Liu, Lu Bi, Wei Jiang, Boris A. Portnov and Ming Liu
Remote Sens. 2020, 12(20), 3349; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12203349 - 14 Oct 2020
Cited by 6 | Viewed by 2318
Abstract
The International Space Station (ISS) offers a unique view from space that provides nighttime light (NTL) images of many parts of the globe. Compared with other NTL remote sensing data, ISS NTL multispectral images taken by astronauts with commercial digital single-lens reflex (DSLR) [...] Read more.
The International Space Station (ISS) offers a unique view from space that provides nighttime light (NTL) images of many parts of the globe. Compared with other NTL remote sensing data, ISS NTL multispectral images taken by astronauts with commercial digital single-lens reflex (DSLR) cameras have the characteristics of free access, high spatial resolution, abundant data and no light saturation, so it plays a unique advantage in the research of small-scale urban planning, optimization of lighting resource allocation and blue light pollution. In order to improve the radiation consistency of ISS NTL images, a relative radiation normalization method of ISS NTL images is proposed in this paper. Pseudo invariant features (PIF) were identified in the cloud-free Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) time series NTL remote sensing annual composite product, and then they were used to derive the relative radiation normalization model of ISS NTL images. The results show that the radiation brightness of ISS NTL images in different regions is normalized to the same gray level with that of DMSP/OLS NTL remote sensing images in the same year, which improves the radiation brightness comparability between different regions of ISS NTL images. This method is universally applicable to all ISS NTL images, which is beneficial to the NTL comparability of ISS NTL image in the regional horizontal and temporal vertical. Full article
(This article belongs to the Special Issue Remote Sensing of Nighttime Observations)
►▼ Show Figures

Graphical abstract

17 pages, 4014 KiB  
Article
The Dimming of Lights in India during the COVID-19 Pandemic
by Tilottama Ghosh, Christopher D. Elvidge, Feng-Chi Hsu, Mikhail Zhizhin and Morgan Bazilian
Remote Sens. 2020, 12(20), 3289; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12203289 - 10 Oct 2020
Cited by 31 | Viewed by 4932
Abstract
The monthly Suomi National Polar-orbiting (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) composite reveals the dimming of lights as an effect of the lockdown enforced by the government of India in response to the COVID-19 pandemic. The changes in lighting [...] Read more.
The monthly Suomi National Polar-orbiting (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) composite reveals the dimming of lights as an effect of the lockdown enforced by the government of India in response to the COVID-19 pandemic. The changes in lighting are examined by creating difference maps of a pre-pandemic pair and comparing it with two pandemic pairs. The visual raster difference maps are substantiated with quantitative analysis showing the proportion of population affected by the changes in the lighting brightness levels. In the pre-pandemic images of February and March 2019, 60% of the population lived in administrative units that became brighter in March 2019. However, in the first pandemic pair, 87% of the population lived in administrative units that became dimmer in March 2020 after the lockdown in comparison to February 2020. The nightly DNB profile at the airport in Delhi illustrate how the dimming of lights coincide with the date of the onset of the lockdown (in March 2020). The study shows the usefulness of the DNB nightly and monthly composites in examining economic impacts of the pandemic as countries throughout the world go through economic declines and move towards recovery. Full article
(This article belongs to the Special Issue Remote Sensing of Nighttime Observations)
►▼ Show Figures

Graphical abstract

25 pages, 8492 KiB  
Article
Analyzing Spatiotemporal Variation Modes and Industry-Driving Force Research Using VIIRS Nighttime Light in China
by Xiaoke Song, Yunhao Chen and Kangning Li
Remote Sens. 2020, 12(17), 2785; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12172785 - 27 Aug 2020
Cited by 3 | Viewed by 2821
Abstract
Urbanization is a complex process closely involving the economy, society, and population. While monitoring urban development and exploring the industry-driving force in a real-time and effective way are the prerequisites for optimizing industry structure, narrowing the urban development gap, and achieving sustainable development. [...] Read more.
Urbanization is a complex process closely involving the economy, society, and population. While monitoring urban development and exploring the industry-driving force in a real-time and effective way are the prerequisites for optimizing industry structure, narrowing the urban development gap, and achieving sustainable development. Nighttime light is an effective tool to monitor urban development from a macro perspective. However, the systematic research of nighttime light spatiotemporal variation modes and the industry-driving force of urban nighttime light are still unknown. Considering these issues, this paper analyzes the spatiotemporal variation modes of the average light index (ALI) and investigates the industry-driving force of ALI in 100 major prefecture-level cities across China mainland based on National Polar-Orbiting Partnership Satellite Visible Infrared Imaging Radiometer Suite (NPP VIIRS). The conclusions are as following three aspects. First, ALI is observed a funnel pattern among four regions in spatial dimension, with low in center and high in the surrounding, and it shows 5 variation modes (“W,” “√,” “Exponent,” “Logarithm,” and “N”) in temporal dimension, of which the “√” mode accounts for the highest proportion (60%). Second, the industry structure is closely related to ALI. Besides, the factor analysis result illustrates that the secondary and tertiary industry are the driving industries of ALI. Third, the classification result based on the industry contribution rate indicates that cities driven by different industries show significant spatial distribution differences. The three major industry-driving cities are mainly distributed in central and western regions, the secondary and tertiary industry-driving cities are evenly distributed, and the tertiary industry-driving cities are mainly distributed in provincial capitals. From 2013 to 2018, the fluctuation of city distribution driven by different industries changes obviously. The number of tertiary industry-driving cities increases steadily and the three major industry-driving cities are distributed wider spatially. Additionally, the impacts of location and raw coal on ALI are discussed. In general, these findings are essential to further research urban development mode and can be considered as the reference to narrow urban development gap. Full article
(This article belongs to the Special Issue Remote Sensing of Nighttime Observations)
►▼ Show Figures

Graphical abstract

19 pages, 2005 KiB  
Article
Exploring the Factors Controlling Nighttime Lights from Prefecture Cities in Mainland China with the Hierarchical Linear Model
by Tao Jia, Kai Chen and Xin Li
Remote Sens. 2020, 12(13), 2119; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12132119 - 02 Jul 2020
Cited by 6 | Viewed by 2277
Abstract
Nighttime light data have been proven to be valuable for socioeconomic studies. However, they are not only affected by anthropogenic factors but also by physical factors, and previous studies have rarely examined these diverse variables in a systematic way that explains differences in [...] Read more.
Nighttime light data have been proven to be valuable for socioeconomic studies. However, they are not only affected by anthropogenic factors but also by physical factors, and previous studies have rarely examined these diverse variables in a systematic way that explains differences in nighttime lights across different cities. In this paper, hierarchical linear models at two levels of city and province were developed to investigate the nighttime lights effect on cross-level factors. An experiment was conducted for 281 prefecture cities in Mainland China using orbital satellite data in 2016. (1) There exist significant differences among city average lights, of which 49.9% is caused at the provincial level, indicating the factors at the provincial level cannot be ignored. (2) Economy-energy-infrastructure and demography factors have a significant positive lights effect. Meanwhile, industry-information and living-standard factors at the provincial level can further significantly increase these differences by 18.30% and 29.01%, respectively. (3) The natural-greenness factor displayed a significant negative lights effect, and its interaction with natural-ecology will continue to decrease city lights by 11.99%. However, artificial-greenness is an unreliable city-level factor explaining lights variations. (4) As for the negative lights effect of elevation and latitude, these become significant in a multivariate context and contribute lights indirectly. (5) The two-level hierarchical linear models are statistically significant at the level of 10%, and compared with the null model, the explained variances on city lights can be improved by 70% at the city level and 90% at the provincial level in the final mixed effect model. Full article
(This article belongs to the Special Issue Remote Sensing of Nighttime Observations)
►▼ Show Figures

Graphical abstract

21 pages, 6052 KiB  
Article
Post-Earthquake Night-Time Light Piecewise (PNLP) Pattern Based on NPP/VIIRS Night-Time Light Data: A Case Study of the 2015 Nepal Earthquake
by Shengjun Gao, Yunhao Chen, Long Liang and Adu Gong
Remote Sens. 2020, 12(12), 2009; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12122009 - 23 Jun 2020
Cited by 13 | Viewed by 2983
Abstract
Earthquakes are unpredictable and potentially destructive natural disasters that take a long time to recover from. Monitoring post-earthquake human activity (HA) is of great significance to recovery and reconstruction work. There is a strong correlation between night-time light (NTL) and HA, which aid [...] Read more.
Earthquakes are unpredictable and potentially destructive natural disasters that take a long time to recover from. Monitoring post-earthquake human activity (HA) is of great significance to recovery and reconstruction work. There is a strong correlation between night-time light (NTL) and HA, which aid in the study of spatiotemporal changes in post-earthquake human activities. However, seasonal and noise impact from National Polar-Orbiting Partnership Satellite Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) data greatly limits their application. To tackle these issues, random noise and seasonal fluctuation of NPP/VIIRS from January 2014 to December 2018 is removed by adopting the seasonal-trend decomposition procedure based on loess (STL). Based on the theory of post-earthquake recovery model, a post-earthquake night-time light piecewise (PNLP) pattern is explored by employing the National Polar-Orbiting Partnership Satellite Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) monthly data. PNLP indicators, including pre-earthquake development rate (kp), recovery rate (kr1), reconstruction rate (kr2), development rate (kd), relative reconstruction rate (krp) and loss (S), are defined to describe the PNLP pattern. Furthermore, the 2015 Nepal earthquake is chosen as a case study and the spatiotemporal changes in different areas are analyzed. The results reveal that: (1) STL is an effective algorithm for obtaining HA trend from the time series of denoising NTL; (2) the PNLP pattern, divided into four phases, namely the emergency phase (EP), recovery phase (RP-1), reconstruction phase (RP-2), and development phase (DP), aptly describes the variation in post-earthquake HA; (3) PNLP indicators are capable of evaluating the recovery differences across regions. The main socio-economic factors affecting the PNLP pattern and PNLP indicators are energy source for lighting, type of building, agricultural economy, and human poverty index. Based on the NPP/VIIRS data, the PNLP pattern can reflect the periodical changes of HA after earthquakes and provide an effective means for the analysis and evaluation of post-earthquake recovery and reconstruction. Full article
(This article belongs to the Special Issue Remote Sensing of Nighttime Observations)
►▼ Show Figures

Graphical abstract

19 pages, 5105 KiB  
Article
City Grade Classification Based on Connectivity Analysis by Luojia I Night-Time Light Images in Henan Province, China
by Zongze Zhao, Gang Cheng, Cheng Wang, Shuangting Wang and Hongtao Wang
Remote Sens. 2020, 12(11), 1705; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12111705 - 27 May 2020
Cited by 8 | Viewed by 2634
Abstract
City classification can provide important data and technical support for city planning and government decision-making. Traditional city classification mainly relies on the accumulation and analysis of census data, which requires a large time period and relies heavily on historical and statistical data. This [...] Read more.
City classification can provide important data and technical support for city planning and government decision-making. Traditional city classification mainly relies on the accumulation and analysis of census data, which requires a large time period and relies heavily on historical and statistical data. This paper mainly utilizes Luojia I Night-Time Light (NTL) images to analyze the rank classification of cities in Henan Province, China. Intensity values can be expressed as the mathematical surface of continuous human activities, and the basic characteristics of urban structures are determined by analogy with the topography of the earth. A connectivity analysis method for NTL images is proposed to analyze the connected regions of images at different intensity levels. By constructing a tree structure, different cities can be analyzed “crosswise” and “lengthwise” to generate a series of parametric information from connected regions of NTL images. Based on these parameters, 18 cities in Henan Province were classified and analyzed. The results show that these attribute information can be well used for city center detection and grade classification, and can meet the requirements of application analysis. Full article
(This article belongs to the Special Issue Remote Sensing of Nighttime Observations)
►▼ Show Figures

Graphical abstract

17 pages, 2573 KiB  
Article
National Scale Spatial Variation in Artificial Light at Night
by Daniel T.C. Cox, Alejandro Sánchez de Miguel, Simon A. Dzurjak, Jonathan Bennie and Kevin J. Gaston
Remote Sens. 2020, 12(10), 1591; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12101591 - 16 May 2020
Cited by 17 | Viewed by 4946
Abstract
The disruption to natural light regimes caused by outdoor artificial nighttime lighting has significant impacts on human health and the natural world. Artificial light at night takes two forms, light emissions and skyglow (caused by the scattering of light by water, dust and [...] Read more.
The disruption to natural light regimes caused by outdoor artificial nighttime lighting has significant impacts on human health and the natural world. Artificial light at night takes two forms, light emissions and skyglow (caused by the scattering of light by water, dust and gas molecules in the atmosphere). Key to determining where the biological impacts from each form are likely to be experienced is understanding their spatial occurrence, and how this varies with other landscape factors. To examine this, we used data from the Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band and the World Atlas of Artificial Night Sky Brightness, to determine covariation in (a) light emissions, and (b) skyglow, with human population density, landcover, protected areas and roads in Britain. We demonstrate that, although artificial light at night increases with human density, the amount of light per person decreases with increasing urbanization (with per capita median direct emissions three times greater in rural than urban populations, and per capita median skyglow eleven times greater). There was significant variation in artificial light at night within different landcover types, emphasizing that light pollution is not a solely urban issue. Further, half of English National Parks have higher levels of skyglow than light emissions, indicating their failure to buffer biodiversity from pressures that artificial lighting poses. The higher per capita emissions in rural than urban areas provide different challenges and opportunities for mitigating the negative human health and environmental impacts of light pollution. Full article
(This article belongs to the Special Issue Remote Sensing of Nighttime Observations)
►▼ Show Figures

Graphical abstract

18 pages, 4056 KiB  
Article
An Approach of Identifying and Extracting Urban Commercial Areas Using the Nighttime Lights Satellite Imagery
by Xuzhe Duan, Qingwu Hu, Pengcheng Zhao, Shaohua Wang and Mingyao Ai
Remote Sens. 2020, 12(6), 1029; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12061029 - 23 Mar 2020
Cited by 9 | Viewed by 3039
Abstract
Urban commercial areas can reflect the spatial distribution of business activities. However, the scope of urban commercial areas cannot be easily detected by traditional methods because of difficulties in data collection. Considering the positive correlation between business scale and nighttime lighting, this paper [...] Read more.
Urban commercial areas can reflect the spatial distribution of business activities. However, the scope of urban commercial areas cannot be easily detected by traditional methods because of difficulties in data collection. Considering the positive correlation between business scale and nighttime lighting, this paper proposes a method of urban commercial areas detection based on nighttime lights satellite imagery. First, an imagery preprocess model is proposed to correct imageries and improve efficiency of cluster analysis. Then, an exploratory spatial data analysis and hotspots clustering method is employed to detect commercial areas by geographic distribution metric with urban commercial hotspots. Furthermore, four imageries of Wuhan City and Shenyang City are selected as an example for urban commercial areas detection experiments. Finally, a comparison is made to find out the time and space factors that affect the detection results of the commercial areas. By comparing the results with the existing map data, we are convinced that the nighttime lights satellite imagery can effectively detect the urban commercial areas. The time of image acquisition and the vegetation coverage in the area are two important factors affecting the detection effect. Harsh weather conditions and high vegetation coverage are conducive to the effective implementation of this method. This approach can be integrated with traditional methods to form a fast commercial areas detection model, which can then play a role in large-scale socio-economic surveys and dynamic detection of commercial areas evolution. Hence, a conclusion can be reached that this study provides a new method for the perception of urban socio-economic activities. Full article
(This article belongs to the Special Issue Remote Sensing of Nighttime Observations)
►▼ Show Figures

Figure 1

15 pages, 5190 KiB  
Article
Constructing a New Inter-Calibration Method for DMSP-OLS and NPP-VIIRS Nighttime Light
by Jinji Ma, Jinyu Guo, Safura Ahmad, Zhengqiang Li and Jin Hong
Remote Sens. 2020, 12(6), 937; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12060937 - 13 Mar 2020
Cited by 52 | Viewed by 9446
Abstract
The anthropogenic nighttime light (NTL) data that are acquired by satellites can characterize the intensity of human activities on the ground. It has been widely used in urban development assessment, socioeconomic estimate, and other applications. However, currently, the two main sensors, Defense Meteorological [...] Read more.
The anthropogenic nighttime light (NTL) data that are acquired by satellites can characterize the intensity of human activities on the ground. It has been widely used in urban development assessment, socioeconomic estimate, and other applications. However, currently, the two main sensors, Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and Suomi National Polar-orbiting Partnership Satellite’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS), provide inconsistent data. Hence, the application of NTL for long-term analysis is hampered. This study constructed a new inter-calibration method for DMSP-OLS and NPP-VIIRS nighttime light to solve this problem. First, NTL data were processed to obtain vicarious site across China. By comparing different candidate models, it is discovered the Biphasic Dose Response (BiDoseResp) model, which is a weighted combination of sigmoid functions, can best perform the regression between DMSP-OLS and logarithmically transformed NPP-VIIRS. The coefficient of determination of BiDoseResp model reaches 0.967. It’s residual sum of squares is 6.136 × 10 5 , which is less than 6.199 × 10 5 of Logistic function. After obtaining the BiDoseResp-calibrated VIIRS (BDRVIIRS), we smoothed it by a filter with optimal parameters to maximize the consistency. The result shows that the consistency of NTL data is greatly enhanced after calibration. In 2013, the correlation coefficient between DMSP-OLS and original NPP-VIIRS data in the China region is only 0.621, while that reaches to 0.949 after calibration. Finally, a consistent NTL dataset of China from 1992 to 2018 was produced. When compared with the existing methods, our method is applicable to the full dynamic range of DMSP-OLS. Besides, it is more suitable for country or larger scale areas. It is expected that this method can greatly facilitate the development of research that is based on the historical NTL archive. Full article
(This article belongs to the Special Issue Remote Sensing of Nighttime Observations)
►▼ Show Figures

Figure 1

19 pages, 3103 KiB  
Article
Modeling China’s Prefecture-Level Economy Using VIIRS Imagery and Spatial Methods
by Jiping Cao, Yumin Chen, John P. Wilson, Huangyuan Tan, Jiaxin Yang and Zhiqiang Xu
Remote Sens. 2020, 12(5), 839; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12050839 - 05 Mar 2020
Cited by 9 | Viewed by 3245
Abstract
Nighttime light (NTL) data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS), carried by the Suomi National Polar Orbiting Partnership (NPP) satellite, has been widely used to evaluate gross domestic product (GDP). Nevertheless, due to the monthly VIIRS data fluctuation and missing [...] Read more.
Nighttime light (NTL) data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS), carried by the Suomi National Polar Orbiting Partnership (NPP) satellite, has been widely used to evaluate gross domestic product (GDP). Nevertheless, due to the monthly VIIRS data fluctuation and missing data (excluded by producers) over high-latitude regions, the suitability of VIIRS data for longitudinal city-level economic estimation needs to be examined. While GDP distribution in China is always accompanied by regional disparity, previous studies have hardly considered the spatial autocorrelation of the GDP distribution when using NTL imagery. Thus, this paper aims to enhance the precision of the longitudinal GDP estimation using spatial methods. The NTL images are used with road networks and permanent resident population data to estimate the 2013, 2015, and 2017 3-year prefecture-level (342 regions) GDP in mainland China, based on eigenvector spatial filtering (ESF) regression (mean R2 = 0.98). The ordinary least squares (OLS) (mean R2 = 0.86) and spatial error model (SEM) (mean pseudo R2 = 0.89) were chosen as reference models. The ESF regression exhibits better performance for root-mean-square error (RMSE), mean absolute relative error (MARE), and Akaike information criterion (AIC) than the reference models and effectively eliminated the spatial autocorrelation in the residuals in all 3 years. The results indicate that the spatial economic disparity, as well as population distribution across China’s prefectures, is decreasing. The ESF regression also demonstrates that the population is crucial to the local economy and that the contribution of urbanization is growing. Full article
(This article belongs to the Special Issue Remote Sensing of Nighttime Observations)
►▼ Show Figures

Figure 1

17 pages, 7268 KiB  
Article
A New Fusion Approach for Extracting Urban Built-up Areas from Multisource Remotely Sensed Data
by Xiaolong Ma, Chengming Li, Xiaohua Tong and Sicong Liu
Remote Sens. 2019, 11(21), 2516; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11212516 - 28 Oct 2019
Cited by 24 | Viewed by 3368
Abstract
Recent advances in the fusion technology of remotely sensed data have led to an increased availability of extracted urban information from multiple spatial resolutions and multi-temporal acquisitions. Despite the existing extraction methods, there remains the challenging task of fully exploiting the characteristics of [...] Read more.
Recent advances in the fusion technology of remotely sensed data have led to an increased availability of extracted urban information from multiple spatial resolutions and multi-temporal acquisitions. Despite the existing extraction methods, there remains the challenging task of fully exploiting the characteristics of multisource remote sensing data, each of which has its own advantages. In this paper, a new fusion approach for accurately extracting urban built-up areas based on the use of multisource remotely sensed data, i.e., the DMSP-OLS nighttime light data, the MODIS land cover product (MCD12Q1) and Landsat 7 ETM+ images, was proposed. The proposed method mainly consists of two components: (1) the multi-level data fusion, including the initial sample selection, unified pixel resolution and feature weighted calculation at the feature level, as well as pixel attribution determination at decision level; and (2) the optimized sample selection with multi-factor constraints, which indicates that an iterative optimization with the normalized difference vegetation index (NDVI), the modified normalized difference water index (MNDWI), and the bare soil index (BSI), along with the sample training of the support vector machine (SVM) and the extraction of urban built-up areas, produces results with high credibility. Nine Chinese provincial capitals along the Silk Road Economic Belt, such as Chengdu, Chongqing, Kunming, Xining, and Nanning, were selected to test the proposed method with data from 2001 to 2010. Compared with the results obtained by the traditional threshold dichotomy and the improved neighborhood focal statistics (NFS) method, the following could be concluded. (1) The proposed approach achieved high accuracy and eliminated natural elements to a great extent while obtaining extraction results very consistent to those of the more precise improved NFS approach at a fine scale. The average overall accuracy (OA) and average Kappa values of the extracted urban built-up areas were 95% and 0.83, respectively. (2) The proposed method not only identified the characteristics of the urban built-up area from the nighttime light data and other daylight images at the feature level but also optimized the samples of the urban built-up area category at the decision level, making it possible to provide valuable information for urban planning, construction, and management with high accuracy. Full article
(This article belongs to the Special Issue Remote Sensing of Nighttime Observations)
►▼ Show Figures

Graphical abstract

Other

Jump to: Research

11 pages, 1004 KiB  
Letter
Identifying and Correcting Signal Shift in DMSP-OLS Data
by Konstantin Ash and Kevin Mazur
Remote Sens. 2020, 12(14), 2219; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12142219 - 10 Jul 2020
Cited by 3 | Viewed by 2373
Abstract
Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) nighttime light data has become a key tool of the environmental and social scientific fields, but suffers from several validity problems. We highlight one such problem—shifts in the digital number position in DMSP-OLS composites in [...] Read more.
Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) nighttime light data has become a key tool of the environmental and social scientific fields, but suffers from several validity problems. We highlight one such problem—shifts in the digital number position in DMSP-OLS composites in the same satellite. We present techniques for identifying the problem, using moving window raster correlation and visual inspection, and for solving the problem, by assigning control points and manually shifting raster positions. To illustrate the importance of accounting for signal shift, we re-examine a recent analysis of the relationship between public goods provision and patterns of violence in the 2011 Syrian uprising and ensuing civil war. We find the statistical results change considerably when correcting for signal shift. We attribute this change to the systematic undercounting of light intensity in heavily populated areas. We close by identifying the types of research that would most benefit from our correction and suggest future refinements to our technique through automation. Full article
(This article belongs to the Special Issue Remote Sensing of Nighttime Observations)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-18
Back to TopTop