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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 June 2023) | Viewed by 22738

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Dr. Johannes Puschnig

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Department of Physics and Astronomy, Uppsala University, 75236 Uppsala, Sweden
Interests: light pollution; night sky brightness; atmospheric modeling measurements; astrophysics
Special Issues, Collections and Topics in MDPI journals

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Dear Colleagues,

Over the last decade, it has become clear that constraining long-term trends from photometric night sky brightness measurements is a complex problem that involves many aspects, such as the quantification of instrumental effects (e.g., temporal change in sensor sensitivity and spectral composition), atmospheric composition (e.g., impact of clouds and aerosols) and the environment (e.g., terrain, vegetation, and albedo). While the impact of some of these aspects has been studied recently, to date, no physical framework exists that takes into account the coupling of several of these parameters. As a result, despite the numerous monitoring campaigns that have been established and are still operating, the temporal change in the night sky brightness assessed in these efforts remains on open question.

This Special Issue aims to address this knowledge gap through several pathways, such as combining state-of-the-art (atmospheric) modeling with existing empirical datasets concerned with light pollution, the atmosphere and meteorological conditions. Moreover, authors are invited to contribute with the further assessment of sensor aging effects, using novel or existing methods.

Articles may address, but are not limited to, the following topics:

Long-term trend assessment of light pollution;
Impact of atmosphere on photometric night sky brightness measurements (models and empirical);
Impact of terrain, vegetation and other meteorological conditions on photometric night sky brightness measurements (models and empirical);
Source characterization;
Instrumental effects, such as “sensor aging”.

Dr. Johannes Puschnig
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

night sky brightness
light pollution
light scattering
long-term trends of light pollution
photometric measurements
atmospheric modeling

Related Special Issue

Latest Developments and Applications in Remote Sensing with Nighttime Lights II in Remote Sensing (2 articles)

Published Papers (10 papers)

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Research

26 pages, 7434 KiB

Open AccessArticle

Laboratory Characterisation of a Commercial RGB CMOS Camera for Measuring Night Sky Brightness

by Pietro Fiorentin, Andrea Bertolo, Stefano Cavazzani and Sergio Ortolani

Remote Sens. 2023, 15(17), 4196; https://doi.org/10.3390/rs15174196 - 26 Aug 2023

Viewed by 903

Abstract

The use of RGB cameras in photometric applications has grown over the last few decades in many fields such as industrial applications, light engineering and the analysis of the quality of the night sky. In this last field, they are often used in conjunction with a Sky Quality Meter (SQM), an instrument used for the measurement of night sky brightness (NSB), mainly when there is a significant amount of artificial light at night (ALAN). The performances of these two instruments are compared here. A simple source composed of nine narrowband LEDs in an integrating sphere was used to excite the two instruments and therefore measure the spectral responsivity of the SQM and of the three channels of the camera. The estimated uncertainties regarding spectral responsivity were less than 10%. A synthetic instrument approximating the SQM’s responsivity can be created using a combination of the R, G and B channels. The outputs of the two instruments were compared by measuring the spectral radiance of the night sky. An evaluation of the spectral mismatch between the two instruments completed the analysis of their spectral sensitivity. Finally, the measurements of real SQMs in four sites experiencing different levels of light pollution were compared with the values obtained by processing the recorded RGB images. Overall, the analysis shows that the two instruments have significantly different levels of spectral responsivity, and the alignment of their outputs requires the use of a correction which depends on the spectral distribution of the light coming from the sky. A synthetic SQM will always underestimate real SQM measures; an average correction factor was evaluated considering nine sky spectra under low and medium levels of light pollution; this was determined to be 1.11 and, on average, compensated for the gap. A linear correction was also supposed based on the correlation between the NSB levels measured by the two instruments; the mean squared error after the correction was 0.03 mag arcsec⁻². Full article

(This article belongs to the Special Issue Latest Developments and Applications in Remote Sensing with Nighttime Lights)

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18 pages, 6193 KiB

Open AccessArticle

A Spatiotemporally Constrained Interpolation Method for Missing Pixel Values in the Suomi-NPP VIIRS Monthly Composite Images: Taking Shanghai as an Example

by Qingyun Liu, Junfu Fan, Jiwei Zuo, Ping Li, Yunpeng Shen, Zhoupeng Ren and Yi Zhang

Remote Sens. 2023, 15(9), 2480; https://doi.org/10.3390/rs15092480 - 08 May 2023

Cited by 1 | Viewed by 1616

Abstract

The Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS/DNB) nighttime light data is a powerful remote sensing data source. However, due to stray light pollution, there is a lack of VIIRS data in mid-high latitudes during the summer, resulting in the absence of high-precision spatiotemporal continuous datasets. In this paper, we first select nine-time series interpolation methods to interpolate the missing images. Second, we construct image pixel-level temporal continuity constraints and spatial correlation constraints and remove the pixels that do not meet the constraints, and the eliminated pixels are filled with the focal statistics tool. Finally, the accuracy of the time series interpolation method and the spatiotemporally constrained interpolation method (STCIM) proposed in this paper are evaluated from three aspects: the number of abnormal pixels (NP), the total light brightness value (TDN), and the absolute value of the difference (ADN). The results show that the images simulated by the STCIM are more accurate than the nine selected time series interpolation methods, and the image interpolation accuracy is significantly improved. Relevant research results have improved the quality of the VIIRS dataset, promoted the application research based on the VIIRS DNB long-time series night light remote sensing image, and enriched the night light remote sensing theory and method system. Full article

(This article belongs to the Special Issue Latest Developments and Applications in Remote Sensing with Nighttime Lights)

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24 pages, 7790 KiB

Open AccessArticle

Boba Shop, Coffee Shop, and Urban Vitality and Development—A Spatial Association and Temporal Analysis of Major Cities in China from the Standpoint of Nighttime Light

by Yuquan Zhou, Xiong He and Bahram Zikirya

Remote Sens. 2023, 15(4), 903; https://doi.org/10.3390/rs15040903 - 06 Feb 2023

Cited by 8 | Viewed by 4206

Abstract

Nighttime light (NTL) is a critical indicator of urban vitality and development. Using NTL as a representation of urban vitality and development, the study explores how different fresh-made beverage shops, namely boba and coffee shops, proxy various facets of urban vitality and development in four megacities in China. Existing studies mostly discuss urban vitality as a broad concept and seldom investigate the diverse urban vitality and development represented by different indicators. This study selects Beijing, Shanghai, Guangzhou, and Shenzhen as case study regions and explores (1) their urban vitality pattern represented by NTL. (2) the heterogeneous spatial distribution of boba and coffee shops; (3) how boba and coffee shops represent urban vitality differently; and (4) how boba and coffee shops portray the economy and population growth aspect of urban development differently. We acquired NTL data from remote sensing images to measure urban vitality and development. Cross-sectionally, the majority of urban vitality and development represented by NTL concentrates in urban centers. The distribution of coffee shops assimilates the spatial pattern of urban vitality represented by NTL while boba shops have a greater spatial extent in metropolitan fringes. Longitudinally, from 2012 to 2020, the global and local bivariate Moran’s I analysis between NTL and beverage shops shows that the coffee shops capture urban vitality and development better than boba shops in Beijing, while the pattern is reversed in Guangzhou and Shenzhen. Examining the evolving spatial dynamics between beverage shops’ growth and urban development using bivariate Moran’s I and Getis–Ord Gi/Mann–Kendall emerging hot spot analysis, we found that the locations with the most intense economic growth have seen the most spatial expansion of coffee shops. In contrast, those with the fastest population growth have seen the greatest spatial development of boba businesses. These results indicate that coffee shops represent the economic aspect of urban vitality while boba shops emphasize the population growth aspects. By examining the dynamic spatio–temporal relationship between small beverage shops and urban vitality and development represented by NTL data, this study broadens the usage of remote sensing data in urban studies and expands on previous research and offers insights for urban planners and geographers to reference when choosing indicators of urban vitality and growth. Full article

(This article belongs to the Special Issue Latest Developments and Applications in Remote Sensing with Nighttime Lights)

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24 pages, 3216 KiB

Open AccessArticle

Estimating Nighttime PM_2.5 Concentration in Beijing Based on NPP/VIIRS Day/Night Band

by Jianqiong Deng, Shi Qiu, Yu Zhang, Haodong Cui, Kun Li, Hongjia Cheng, Zhaoyan Liu, Xianhui Dou and Yonggang Qian

Remote Sens. 2023, 15(2), 349; https://doi.org/10.3390/rs15020349 - 06 Jan 2023

Cited by 1 | Viewed by 1571

Abstract

Nighttime PM_2.5 detection by remote sensing can expand understanding of PM_2.5 spatiotemporal patterns due to wider coverage compared to ground monitors and by supplementing traditional daytime detection. However, using remote sensing data to invert PM_2.5 at night is still challenging. Compared with daytime detection, which operates on sunlight, nighttime detection operates on much weaker moonlight and artificial light sources, complicating signal extraction. Moreover, as the attempts to sense PM_2.5 remotely using satellite data are relatively recent, the existing nighttime models are still not mature, overlooking many important factors such as stray light, seasonality in meteorological effects, and observation angle. This paper attempts to improve the accuracy of nighttime PM_2.5 detection by proposing an inversion model that takes these factors into consideration. The Visible Infrared Imaging Radiometer Suite/Day/Night Band (VIIRS/DNB) on board the polar-orbiting Suomi National Polar-orbiting Partnership (Suomi NPP) and National Oceanic Atmospheric Administration-20 (NOAA-20) was used to establish a nighttime PM_2.5 inversion model in the Beijing area from 1 March 2018 to 28 February 2019. The model was designed by first studying the effects of these factors through a stepwise regression, then building a multivariate regression model to compensate for these effects. The results showed that the impact of satellite viewing zenith angle (VZA) was strongest, followed by seasonality and moonlight. Total accuracy was measured using correlation coefficient (R) compared to ground measurements, achieving 0.87 over the urban area and 0.74 over the suburbs. Specifically, the proposed method works efficiently at subsatellite points, which in this case correspond to VZA from 0 and 5°. In spring, summer, autumn, and winter, the R reached 0.95, 0.93, 0.94, and 0.97 at subsatellite points in the urban area, while it was 0.88, 0.82, 0.85, and 0.77 in the suburbs. Full article

(This article belongs to the Special Issue Latest Developments and Applications in Remote Sensing with Nighttime Lights)

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21 pages, 33385 KiB

Open AccessArticle

Assessment of Economic Recovery in Hebei Province, China, under the COVID-19 Pandemic Using Nighttime Light Data

by Feng Li, Jun Liu, Meidong Zhang, Shunbao Liao and Wenjie Hu

Remote Sens. 2023, 15(1), 22; https://doi.org/10.3390/rs15010022 - 21 Dec 2022

Cited by 3 | Viewed by 1790

Abstract

The COVID-19 pandemic has presented unprecedented disruptions to human society worldwide since late 2019, and lockdown policies in response to the pandemic have directly and drastically decreased human socioeconomic activities. To quantify and assess the extent of the pandemic’s impact on the economy of Hebei Province, China, nighttime light (NTL) data, vegetation information, and provincial quarterly gross domestic product (GDP) data were jointly utilized to estimate the quarterly GDP for prefecture-level cities and county-level cities. Next, an autoregressive integrated moving average model (ARIMA) model was applied to predict the quarterly GDP for 2020 and 2021. Finally, economic recovery intensity (ERI) was used to assess the extent of economic recovery in Hebei Province during the pandemic. The results show that, at the provincial level, the economy of Hebei Province had not yet recovered; at the prefectural and county levels, three prefectures and forty counties were still struggling to restore their economies by the end of 2021, even though these economies, as a whole, were gradually recovering. In addition, the number of new infected cases correlated positively with the urban NTL during the pandemic period, but not during the post-pandemic period. The study results are informative for local government’s strategies and policies for allocating financial resources for urban economic recovery in the short- and long-term. Full article

(This article belongs to the Special Issue Latest Developments and Applications in Remote Sensing with Nighttime Lights)

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19 pages, 6077 KiB

Open AccessArticle

Monitoring and Analysis of Population Distribution in China from 2000 to 2020 Based on Remote Sensing Data

by Fei Teng, Yanjun Wang, Mengjie Wang and Linqi Wang

Remote Sens. 2022, 14(23), 6019; https://doi.org/10.3390/rs14236019 - 28 Nov 2022

Cited by 5 | Viewed by 2477

Abstract

Accurately and precisely grasping the spatial distribution and changing trends of China’s regional population is of great significance in new urbanization, economic development, public health, disaster assessment, and ecological environmental protection. To monitor and evaluate the long-term spatiotemporal characteristics of the population distribution in China, a population monitoring estimation model was proposed. Based on remote sensing data such as nighttime light (NTL) images, land use data, and data from the fifth, sixth, and seventh censuses of China, the population spatiotemporal distribution in China from 2000 to 2020 was analyzed with a random forest algorithm. This study obtained spatial distribution maps of population density at a 1 km x 1 km resolution in 2000, 2010, and 2020. The results revealed the trend of the spatiotemporal pattern of population change from 2000 to 2020. It shows that: the accuracy assessment using the 2020 census population of townships/streets as a reference shows an R² of 0.67 and a mean relative error (MRE) of 0.44. The spatial pattern of the population in 2000 and 2010 is generally unchanged. In 2020, population agglomeration is evident in the east, with a slight increase in the proportion of the population in the west. The patterns of population agglomeration and urbanization also change over time. The population spatiotemporal distribution obtained in this study can provide a scientific reference for urban sustainable development and promote the rational allocation of urban resources. Full article

(This article belongs to the Special Issue Latest Developments and Applications in Remote Sensing with Nighttime Lights)

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23 pages, 4090 KiB

Open AccessFeature PaperArticle

Long-Time Trends in Night Sky Brightness and Ageing of SQM Radiometers

by Pietro Fiorentin, Renata Binotto, Stefano Cavazzani, Andrea Bertolo, Sergio Ortolani and Ivo Saviane

Remote Sens. 2022, 14(22), 5787; https://doi.org/10.3390/rs14225787 - 16 Nov 2022

Cited by 2 | Viewed by 1825

Abstract

A very wide-used instrument for the measurement of the Night Sky Brightness (NSB) is the Sky Quality Meter (SQM). One of its important issues is tracking NSB for long time and connecting its variations to changes in outdoor lighting. The stability of these radiometers is fundamental; variation on the instrument behaviour could be confused with changes of the sky brightness. The SQMs of the network of the Veneto Region (Italy) and the SQM installed at La Silla (Chile) are analysed by using the twilight method considering both sunset and dawn measurements, which allows to compensate for shifts in the SQM internal clock. The slope of the observed long-term trends ranges between 29 ± 5 and 86 ± 22 mmag_SQM arcsec⁻² year⁻¹. These high values require a correction of the measurements to continue to track NSB by those instruments. The correction is presented for an Italian site, for example: raw measures show an apparent trend towards darker sky (30 ± 5 mmag_SQM arcsec⁻² year⁻¹), after the correction a clear tendency towards a brighter polluted sky appears (−21 ± 8 mmag_SQM arcsec⁻² year⁻¹), in agreement with the estimated trend of the installed luminous flux of outdoor lighting for that area. Full article

(This article belongs to the Special Issue Latest Developments and Applications in Remote Sensing with Nighttime Lights)

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15 pages, 3800 KiB

Open AccessArticle

Estimates of Power Shortages and Affected Populations during the Initial Period of the Ukrainian-Russian Conflict

by Zihao Zheng, Zhifeng Wu, Zheng Cao, Qifei Zhang, Yingbiao Chen, Guanhua Guo, Zhiwei Yang, Cheng Guo, Xin Wang and Francesco Marinello

Remote Sens. 2022, 14(19), 4793; https://doi.org/10.3390/rs14194793 - 26 Sep 2022

Cited by 7 | Viewed by 2584

Abstract

Since the outbreak of the Ukrainian-Russian conflict on 24 February 2022, Ukraine’s economy, society, and cities have been devastated and struck on multiple fronts, with large numbers of refugees fleeing to neighboring countries. The lighting systems in Ukrainian cities have been severely restricted due to Russian missile bombing and curfew policies. The power shortages adversely affected the livelihoods of the Ukrainian residents dramatically. For a timely assessment of the power shortages’ extent and the affected population in Ukraine, this study tracked the dynamics of nighttime light emissions in Ukraine based on the newly developed daily Black Marble product (VNP46A2) from NASA. The results show that the average light radiance in Ukrainian urban areas has decreased by about 37% since the eruption of the war, with Kiev city being the most dramatic region, having a post-conflict decrease of about 51%. In addition, by introducing near-real-time population data, we have implemented a survey of the affected population in Ukraine suffering from war-induced power shortages. Estimates show that about 17.3 million Ukrainian residents were affected by power shortages. In more detail, the number of children under 10 years old was about 2.35 million (about 5.24% of the total population), while the number of elderly people over 60 years old was about 3.53 million (about 7.86% of the total population). Generally, the results of this study could contribute positively to the timely assessment of the impact of the conflict and the implementation of humanitarian relief. Full article

(This article belongs to the Special Issue Latest Developments and Applications in Remote Sensing with Nighttime Lights)

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17 pages, 4365 KiB

Open AccessArticle

GDP Forecasting Model for China’s Provinces Using Nighttime Light Remote Sensing Data

by Yan Gu, Zhenfeng Shao, Xiao Huang and Bowen Cai

Remote Sens. 2022, 14(15), 3671; https://doi.org/10.3390/rs14153671 - 31 Jul 2022

Cited by 12 | Viewed by 2697

Abstract

In order to promote the economic development of China’s provinces and provide references for the provinces to make effective economic decisions, it is urgent to investigate the trend of province-level economic development. In this study, DMSP/OLS data and NPP/VIIRS data were used to predict economic development. Based on the GDP data of China’s provinces from 1992 to 2016 and the nighttime light remote sensing (NTL) data of corresponding years, we forecast GDP via the linear model (LR model), ARIMA model, ARIMAX model, and SARIMA model. Models were verified against the GDP records from 2017 to 2019. The experimental results showed that the involvement of NTL as exogenous variables led to improved GDP prediction. Full article

(This article belongs to the Special Issue Latest Developments and Applications in Remote Sensing with Nighttime Lights)

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26 pages, 19477 KiB

Open AccessArticle

Using Multi-Source Geospatial Information to Reduce the Saturation Problem of DMSP/OLS Nighttime Light Data

by Qifei Zhang, Zihao Zheng, Zhifeng Wu, Zheng Cao and Renbo Luo

Remote Sens. 2022, 14(14), 3264; https://doi.org/10.3390/rs14143264 - 06 Jul 2022

Cited by 4 | Viewed by 1790

Abstract

The DMSP/OLS Nighttime light (NTL) data directly reflect the spatial distribution and light intensity of artificial lighting from the Earth’s surface at night, and has become an emerging instrument for urbanization research, including in the monitoring of urban expansion, assessment of socio-economic vitality, and estimation of energy consumption and population. However, due to the imperfect sensor design of DMSP/OLS, the dynamic range of the digital number (DN) of NTL is limited (0, 63), leading to a significant saturation problem when describing the actual light intensity, especially in dense urban areas with high light intensity. This saturation problem masks spatial differences in light intensity and weakens the reliability of DMSP/OLS NTL data. Therefore, this study proposes a novel desaturation indicator that combines NDBI and POI, the Building and POI Density-Adjusted Nighttime Light Index (BPANTLI), to regulate the DMSP/OLS NTL saturation problem based on the spatial characteristics of urban structures and human activity intensity. The proposed method is applied to three urban agglomerations with the most severe light saturation issues in China. The geographical detector model is firstly utilized to quantify the effectiveness of NDBI and POI in reflecting the difference in light intensity distribution from the NTL potential saturation region (NTL DN value (53, 63)) and NTL unsaturation region (NTL DN value (0, 52)), so as to clarify the feasibility of developing the BPANTLI. The applicability of BPANTLI is validated through three aspects—comparison of the desaturation capacity and the performance of delineating light intensity; verification of the consistency of BPANTLI with radiometric calibration nighttime light product (RCNTL) and NPP/VIIRS data; and assessing the accuracy of the BPANTLI in estimating socio-economic parameters (GDP, electricity consumption, population density). The results indicate that the BPANTLI possesses superior capability in regulating the NTL saturation problem, achieving good performance in distinguishing inner-urban structures. The regulated results reveal a remarkably improved correspondence with the RCNTL and NPP/VIIRS data, providing a more realistic picture of the light intensity distribution. It is worth noting that, given the advantages of NDBI and POI vector data in spatial resolution, the BPANTLI established in this study can overcome the limitation of the spatial resolution of DMSP/OLS nighttime lighting data and achieve dynamic transformation of the spatial resolution. The higher spatial resolution desaturation results allow for a better characterization of the light intensity distribution. Moreover, the BPANTLI-regulated light intensity significantly improves the accuracy of estimating electricity consumption, GDP, and population density, which provides a valuable reference for urban socio-economic activity assessment. Thus, the BPANTLI proposed in this study can be considered as a reasonable desaturation method with a high application value. Full article

(This article belongs to the Special Issue Latest Developments and Applications in Remote Sensing with Nighttime Lights)

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