Special Issue "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: 30 September 2021.

Special Issue Editor

Dr. Mikhail ZHIZHIN
Website
Guest Editor
University of Colorado, 325 Broadway St, E/GC2, Boulder CO 80305, USA;
Cooperative Institute for Research in Environmental Sciences (CIRES), NOAA National Centers for Environmental Information, 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

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 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 (9 papers)

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Open AccessArticle
Exploring the Factors Controlling Nighttime Lights from Prefecture Cities in Mainland China with the Hierarchical Linear Model
Remote Sens. 2020, 12(13), 2119; https://doi.org/10.3390/rs12132119 - 02 Jul 2020
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)
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Open AccessArticle
Post-Earthquake Night-Time Light Piecewise (PNLP) Pattern Based on NPP/VIIRS Night-Time Light Data: A Case Study of the 2015 Nepal Earthquake
Remote Sens. 2020, 12(12), 2009; https://doi.org/10.3390/rs12122009 - 23 Jun 2020
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)
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Open AccessArticle
City Grade Classification Based on Connectivity Analysis by Luojia I Night-Time Light Images in Henan Province, China
Remote Sens. 2020, 12(11), 1705; https://doi.org/10.3390/rs12111705 - 27 May 2020
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)
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Open AccessArticle
National Scale Spatial Variation in Artificial Light at Night
Remote Sens. 2020, 12(10), 1591; https://doi.org/10.3390/rs12101591 - 16 May 2020
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)
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Open AccessArticle
An Approach of Identifying and Extracting Urban Commercial Areas Using the Nighttime Lights Satellite Imagery
Remote Sens. 2020, 12(6), 1029; https://doi.org/10.3390/rs12061029 - 23 Mar 2020
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)
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Open AccessArticle
Constructing a New Inter-Calibration Method for DMSP-OLS and NPP-VIIRS Nighttime Light
Remote Sens. 2020, 12(6), 937; https://doi.org/10.3390/rs12060937 - 13 Mar 2020
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)
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Open AccessArticle
Modeling China’s Prefecture-Level Economy Using VIIRS Imagery and Spatial Methods
Remote Sens. 2020, 12(5), 839; https://doi.org/10.3390/rs12050839 - 05 Mar 2020
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)
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Open AccessArticle
A New Fusion Approach for Extracting Urban Built-up Areas from Multisource Remotely Sensed Data
Remote Sens. 2019, 11(21), 2516; https://doi.org/10.3390/rs11212516 - 28 Oct 2019
Cited by 5
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)
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Open AccessLetter
Identifying and Correcting Signal Shift in DMSP-OLS Data
Remote Sens. 2020, 12(14), 2219; https://doi.org/10.3390/rs12142219 - 10 Jul 2020
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)
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