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Nighttime Light Remote Sensing Products for Urban Applications
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Nighttime Light Remote Sensing Products for Urban Applications

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

Deadline for manuscript submissions: 10 June 2025 | Viewed by 5488

Special Issue Editors

Dr. Zihao Zheng

E-Mail Website
Guest Editor
School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
Interests: nighttime lighting remote sensing; regional habitats assessment; spatial mapping
Special Issues, Collections and Topics in MDPI journals
Dr. Qiming Zheng

E-Mail Website
Guest Editor
Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
Interests: nighttime light imagery; urbanization; climate change mitigation; time series analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past decades, urbanization has developed rapidly, and urban nighttime light (NTL) has become an indispensable part of the urban landscape. By using Earth observation systems, the remote sensing of NTL undoubtedly gives us a unique perspective that allows us to better understand NTL patterns, population density distribution, and the spatial patterns of urban development in cities, which provides new knowledge and insights for urban planning, resource management, and environmental monitoring.

Recently, the spatial and spectral resolution of NTL remote sensing products has improved, making their application in urban planning and management more precise and effective. These new products provide more dimensional data support for urban research. Moreover, the introduced technologies, such as artificial intelligence and machine learning, make the data processing and analysis of NTL products more intelligent and automated, greatly facilitating knowledge mining and scenario application based on NTL data.

Therefore, this Special Issue intends to stimulate more research and applications on urban NTL remote sensing, bring together the latest research results on NTL products in urban applications, promote exchanges and collaborations among researchers, promote innovations in the field of urban NTL, and fulfill the goal of smarter and more sustainable urban development. Submissions including, but not limited to, the following topics are welcome: NTL data products, urban applications of NTL data, NTL image processing algorithms, etc.

Dr. Zihao Zheng
Dr. Qiming Zheng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All 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 light remote sensing
  • urbanization
  • urban light pollution
  • ecological impacts of artificial lights
  • disaster assessments
  • DMSP-OLS
  • NPP-VIIRS
  • SDGSAT-1
  • ISS night photographs

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Published Papers (6 papers)

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Research

17 pages, 2477 KiB  
Article
Quantifying Night Sky Brightness as a Stressor for Coastal Ecosystems in Moreton Bay, Queensland
by Noam Levin, Rachel Madeleine Cooper and Salit Kark
Remote Sens. 2024, 16(20), 3828; https://doi.org/10.3390/rs16203828 - 15 Oct 2024
Viewed by 613
Abstract
Growing light pollution is increasingly studied in terrestrial environments. However, research on night lights in coastal ecosystems is limited. We aimed to complement spaceborne remote sensing with ground-based hemispheric photos to quantify the exposure of coastal habitats to light pollution. We used a [...] Read more.
Growing light pollution is increasingly studied in terrestrial environments. However, research on night lights in coastal ecosystems is limited. We aimed to complement spaceborne remote sensing with ground-based hemispheric photos to quantify the exposure of coastal habitats to light pollution. We used a calibrated DSLR Canon camera with a fisheye lens to photograph the night sky in 24 sites in the rapidly developing area of Moreton Bay, Queensland, Australia, extracting multiple brightness metrics. We then examined the use of the LANcubeV2 photometer and night-time satellite data from SDGSAT-1 for coastal areas. We found that the skies were darker in less urbanized areas and on islands compared with the mainland. Sky brightness near the zenith was correlated with satellite observations only at a coarse spatial scale. When examining light pollution horizontally above the horizon (60–80° degrees below the zenith), we found that the seaward direction was brighter than the landward direction in most sites due to urban glow on the seaward side. These findings emphasize the importance of ground measurements of light pollution alongside satellite imagery. In order to reduce the exposure of coastal ecosystems to light pollution, actions need to go beyond sites with conservation importance and extend to adjacent urban areas. Full article
(This article belongs to the Special Issue Nighttime Light Remote Sensing Products for Urban Applications)
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20 pages, 33767 KiB  
Article
Multi-Source Data-Driven Extraction of Urban Residential Space: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area Urban Agglomeration
by Xiaodie Yuan, Xiangjun Dai, Zeduo Zou, Xiong He, Yucong Sun and Chunshan Zhou
Remote Sens. 2024, 16(19), 3631; https://doi.org/10.3390/rs16193631 - 29 Sep 2024
Viewed by 1019
Abstract
The accurate extraction of urban residential space (URS) is of great significance for recognizing the spatial structure of urban function, understanding the complex urban operating system, and scientific allocation and management of urban resources. The traditional URS identification process is generally conducted through [...] Read more.
The accurate extraction of urban residential space (URS) is of great significance for recognizing the spatial structure of urban function, understanding the complex urban operating system, and scientific allocation and management of urban resources. The traditional URS identification process is generally conducted through statistical analysis or a manual field survey. Currently, there are also superpixel segmentation and wavelet transform (WT) processes to extract urban spatial information, but these methods have shortcomings in extraction efficiency and accuracy. The superpixel wavelet fusion (SWF) method proposed in this paper is a convenient method to extract URS by integrating multi-source data such as Point of Interest (POI) data, Nighttime Light (NTL) data, LandScan (LDS) data, and High-resolution Image (HRI) data. This method fully considers the distribution law of image information in HRI and imparts the spatial information of URS into the WT so as to obtain the recognition results of URS based on multi-source data fusion under the perception of spatial structure. The steps of this study are as follows: Firstly, the SLIC algorithm is used to segment HRI in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) urban agglomeration. Then, the discrete cosine wavelet transform (DCWT) is applied to POI–NTL, POI–LDS, and POI–NTL–LDS data sets, and the SWF is carried out based on different superpixel scale perspectives. Finally, the OSTU adaptive threshold algorithm is used to extract URS. The results show that the extraction accuracy of the NLT–POI data set is 81.52%, that of the LDS–POI data set is 77.70%, and that of the NLT–LDS–POI data set is 90.40%. The method proposed in this paper not only improves the accuracy of the extraction of URS, but also has good practical value for the optimal layout of residential space and regional planning of urban agglomerations. Full article
(This article belongs to the Special Issue Nighttime Light Remote Sensing Products for Urban Applications)
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33 pages, 24631 KiB  
Article
Assessment of Systematic Errors in Mapping Electricity Access Using Night-Time Lights: A Case Study of Rwanda and Kenya
by Tunmise Raji, Jay Taneja and Nathaniel Williams
Remote Sens. 2024, 16(19), 3561; https://doi.org/10.3390/rs16193561 - 25 Sep 2024
Cited by 1 | Viewed by 827
Abstract
Remotely sensed nighttime light data have become vital for electrification mapping in data-scarce regions. However, uncertainty persists regarding the veracity of these electrification maps. This study investigates how characteristics of electrified areas influence their detectability using nighttime lights. Utilizing a dataset comprising the [...] Read more.
Remotely sensed nighttime light data have become vital for electrification mapping in data-scarce regions. However, uncertainty persists regarding the veracity of these electrification maps. This study investigates how characteristics of electrified areas influence their detectability using nighttime lights. Utilizing a dataset comprising the locations, installation date, and electricity purchase history of thousands of electric meters and transformers from utilities in Rwanda and Kenya, we present a systematic error assessment of electrification maps produced with nighttime lights. Descriptive analysis is employed to offer empirical evidence that the likelihood of successfully identifying an electrified nighttime light pixel increases as characteristics including the time since electrification, the number of meters within a pixel, and the total annual electricity purchase of meters in a pixel increase. The performance of models trained on various temporal aggregations of nighttime light data (annual, quarterly, monthly, and daily) was compared, and it was determined that aggregation at the monthly level yielded the best results. Additionally, we investigate the transferability of electrification models across locations. Our findings reveal that models trained on data from Rwanda demonstrate strong transferability to Kenya, and vice versa, as indicated by balanced accuracies differing by less than 5% when additional data from the test location are included in the training set. Also, models developed with data from the centralized grid in East Africa were found to be useful for detecting areas electrified with off-grid systems in West Africa. This research provides valuable insight into the characterization of sources of nighttime lights and their utility for mapping electrification. Full article
(This article belongs to the Special Issue Nighttime Light Remote Sensing Products for Urban Applications)
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19 pages, 20474 KiB  
Article
Evaluation of Urban Microscopic Nighttime Light Environment Based on the Coupling Observation of Remote Sensing and UAV Observation
by Baogang Zhang, Ming Liu, Ruicong Li, Jie Liu, Lie Feng, Han Zhang, Weili Jiao and Liang Lang
Remote Sens. 2024, 16(17), 3288; https://doi.org/10.3390/rs16173288 - 4 Sep 2024
Viewed by 658
Abstract
The urban canopy refers to the spatial area at the average height range of urban structures. The light environment of the urban canopy not only influences the ecological conditions of the canopy layer region but also serves as an indicator of the upward [...] Read more.
The urban canopy refers to the spatial area at the average height range of urban structures. The light environment of the urban canopy not only influences the ecological conditions of the canopy layer region but also serves as an indicator of the upward light influx of artificial nighttime light in the urban environment. Previous research on urban nighttime light environment mainly focused on the urban surface layer and urban night sky layer, lacking attention to the urban canopy layer. This study observes the urban canopy layer with the flight and photography functions of an unmanned aerial vehicle (UAV) and combines color band remote sensing data with ground measurement data to explore the relationship between the three levels of the urban nighttime light environment. Furthermore, a three–dimensional observation method is established for urban nighttime light environments based on a combination of three observation methods. The research results indicate that there is a good correlation between drone aerial photography data and remote sensing data (R2 = 0.717), as well as between ground–measured data and remote sensing data (R2 = 0.876). It also shows that UAV images can serve as a new path for the observation of urban canopy nighttime light environments because of the accuracy and reliability of UAV aerial data. Meanwhile, the combination of UAV photography, ground measurement, and remote sensing data provides a new method for the monitoring and control of urban nighttime light pollution. Full article
(This article belongs to the Special Issue Nighttime Light Remote Sensing Products for Urban Applications)
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23 pages, 10796 KiB  
Article
Production of Annual Nighttime Light Based on De-Difference Smoothing Algorithm
by Shuyan Zhang, Yong Ma, Erping Shang, Wutao Yao, Ke Qiao, Jian Peng, Jin Yang and Chun Feng
Remote Sens. 2024, 16(16), 3013; https://doi.org/10.3390/rs16163013 - 16 Aug 2024
Viewed by 630
Abstract
Nighttime light (NTL) remote sensing has emerged as a powerful tool in various fields such as urban expansion, socio-economic estimation, light pollution, and energy domains. However, current annual NTL products suffer from several critical limitations, including poor consistency, severe background noise, and limited [...] Read more.
Nighttime light (NTL) remote sensing has emerged as a powerful tool in various fields such as urban expansion, socio-economic estimation, light pollution, and energy domains. However, current annual NTL products suffer from several critical limitations, including poor consistency, severe background noise, and limited comparability. These issues have significantly interfered with the research of long-term NTL trends and diminished the accuracy of related findings. Therefore, this study developed a de-difference smoothing algorithm for producing high-quality annual NTL products based on monthly National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data. It enabled the construction of a continuous global high-quality NTL dataset, named the De-Difference Smoothed Nighttime Light (DDSNL), covering the period from 2012 to 2023. Comparative analyses were conducted to validate the accuracy and availability of the DDSNL product against the benchmark EOG NPP-VIIRS and NPP-VIIRS-like NTL datasets. The results showed that DDSNL products had strong correlation with the NTL distribution of EOG NPP-VIIRS, but little correlation with NPP-VIIRS-like. Notably, DDSNL demonstrated better background noise reduction and higher separability between NTL and non-NTL areas compared to EOG NPP-VIIRS NTL. In contrast to the complete exclusion of background in NPP-VIIRS-Like, the retention of background values in DDSNL leads to more reasonable representation in the urban fringes. In the analysis of NTL changes matching impervious surface changes, the DDSNL product demonstrated the least interference from noise, resulting in the smallest segmentation threshold and the highest matching accuracy. This indirectly demonstrates the spatial and temporal consistency of the annual DDSNL product, ensuring its reliability in change detection-related studies. The annual DDSNL product developed in this research exhibits high fidelity, strong consistency, and improved comparability, and can provide reliable data reference for applications in electrification and urban studies. Full article
(This article belongs to the Special Issue Nighttime Light Remote Sensing Products for Urban Applications)
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Graphical abstract

26 pages, 9131 KiB  
Article
SDG 11.3 Assessment of African Industrial Cities by Integrating Remote Sensing and Spatial Cooperative Simulation: With MFEZ in Zambia as a Case Study
by Yuchen Huang and Dongping Ming
Remote Sens. 2024, 16(16), 2995; https://doi.org/10.3390/rs16162995 - 15 Aug 2024
Viewed by 802
Abstract
Urban areas in sub-Saharan Africa are facing significant developmental challenges due to rapid population growth and urban expansion, this study aims to predict urban growth and assess the SDG 11.3.1 indicator in the Chambishi multi-facility economic zone (CFEMZ) in Zambia through the integration [...] Read more.
Urban areas in sub-Saharan Africa are facing significant developmental challenges due to rapid population growth and urban expansion, this study aims to predict urban growth and assess the SDG 11.3.1 indicator in the Chambishi multi-facility economic zone (CFEMZ) in Zambia through the integration of remote sensing data and spatial cooperative simulation so as to realize sustainable development goals (SDGs). The study utilized DMSP-OLS and VIIRS nighttime light data between 2000 and 2020 to extract the urban built-up area by applying the Pseudo-Invariant Features (PIFs) method to determine thresholds. The land-use and population changes under several development scenarios in 2030 were simulated in the study using the Spatial Cooperative Simulation (SCS) approach. The changes in SDG 11.3.1 indicators were also calculated in the form of a spatialized kilometer grid. The findings show a substantial rise in the built-up area and especially indicate a most notable increase in Chambishi. The primary cause of this growth is the development of industrial parks, which act as the region’s principal engine for urban expansion. Under the natural scenario, the land-use distribution in the study area presents an unplanned state that will make it difficult to realize SDGs. The results of the spatialization form of the SDG 11.3.1 indicator demonstrate the areas and problems of imbalance between urban construction and population growth in the CMFEZ. This study demonstrates the importance of remote sensing of nighttime lighting and spatial simulation in urban planning to achieve SDG 11.3.1 for sustainable urbanization in industrial cities. Full article
(This article belongs to the Special Issue Nighttime Light Remote Sensing Products for Urban Applications)
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