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Remote Sensing of Night-Time Light II
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Remote Sensing of Night-Time Light II

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

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 4698

Special Issue Editors

Dr. Ran Goldblatt

E-Mail Website
Guest Editor
New Light Technologies Inc., Washington, DC, USA
Interests: remote sensing; image classification; economic development; disaster management; night-time lights; built-up land cover
Special Issues, Collections and Topics in MDPI journals
Dr. Steven Louis Rubinyi

E-Mail Website
Guest Editor
The World Bank, Washington, DC 20433, USA
Interests: remote sensing; built environment; natural environment; population modeling; spatial economics; GIS
Special Issues, Collections and Topics in MDPI journals
Dr. Hogeun Park

E-Mail Website
Guest Editor
The World Bank, Washington, DC, USA
Interests: urbanization; land use and land cover change; urban economy; spatial analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Since the early 1990s, with the launch of DMSP-OLS, remotely sensed observations of night-time lights have been a significant tool in understanding almost every aspect related to human activity on Earth without being filtered through national data agencies that are potentially inefficient or biased. Night-time lights can indicate the characteristics of a wide range of human-related aspects, from economic activity and development, urbanization processes, changes in GDP, migration patterns, the economic impacts of conflicts, or the impacts of natural hazards on vulnerable populations. Newer sensors, such as VIIRS/DNB, provide night-time light data even at a higher spatial resolution, enabling us to understand variations in human activity and its relation to the natural and the human environment at a much higher granularity. With advances in the availability and quality of night-time light data, improvements in data storage capabilities, and the development of new methods and workflows for analysing data, there has been an increase in the number of scientific applications that exploit remotely sensed night-time lights to better understand our world. This second volume of the Special Issue ‘Remote Sensing of Night-Time Light’ in Remote Sensing will stimulate progress in the remote sensing research domain related to the utilization of night-time lights in a wide range of scientific fields, including economics, social sciences, disaster management, environmental sciences, ecology, urban research, and more. This issue will continue bringing together original and novel studies demonstrating the applications of remotely sensed night-time lights in wide-ranging multidisciplinary and interdisciplinary domains. Review contributions are also welcomed.

Dr. Ran Goldblatt
Dr. Steven Louis Rubinyi
Dr. Hogeun Park
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

  • night-time lights
  • VIIRS
  • DMSP-OLS
  • economic development
  • economic activity
  • data fusion
  • urbanization processes
  • GDP
  • poverty
  • electrification

Published Papers (4 papers)

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Research

22 pages, 7027 KiB  
Article
Urban Growth and Its Ecological Effects in China
by Hanqian Chen, Shuyu Deng, Shunxue Zhang and Yao Shen
Remote Sens. 2024, 16(8), 1378; https://doi.org/10.3390/rs16081378 - 13 Apr 2024
Viewed by 386
Abstract
As the largest developing nation, China is currently experiencing rapid urban growth. Conducting a thorough scientific assessment of this expansion and its ecological consequences is of paramount importance for advancing China’s ecological civilization and aligning with the United Nations’ Sustainable Development Goals. This [...] Read more.
As the largest developing nation, China is currently experiencing rapid urban growth. Conducting a thorough scientific assessment of this expansion and its ecological consequences is of paramount importance for advancing China’s ecological civilization and aligning with the United Nations’ Sustainable Development Goals. This study employs multi-source remote sensing data to investigate the spatiotemporal trends in Chinese urban development and explore its impact on the ecological environment. From 2013 to 2021, the findings indicate an increasing trend in China’s total nocturnal light, with the southern and central regions exhibiting higher growth rates. This suggests a decade-long expansion of Chinese cities, especially in the southern and central regions. However, the impact of urban expansion on ecological quality varies. Beijing, Shenyang, and Xi’an have witnessed improved environmental quality, while Kunming and Shenzhen have experienced minimal changes, and Hefei and Wuhan have encountered a decline. The observed spatial heterogeneity underscores the intricate relationship between urban expansion and ecological shifts. This study reveals the spatiotemporal dynamics of China’s urban expansion and its ecological impact, providing valuable insights and policy recommendations for fostering the harmonized development of urbanization and ecological preservation. Full article
(This article belongs to the Special Issue Remote Sensing of Night-Time Light II)
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21 pages, 6631 KiB  
Article
Deciphering China’s Socio-Economic Disparities: A Comprehensive Study Using Nighttime Light Data
by Tianyu Chen, Yuke Zhou, Dan Zou, Jingtao Wu, Yang Chen, Jiapei Wu and Jia Wang
Remote Sens. 2023, 15(18), 4581; https://doi.org/10.3390/rs15184581 - 18 Sep 2023
Cited by 2 | Viewed by 1228
Abstract
Achieving equitable and harmonized socio-economic development is a vital gauge of national progress, particularly in geographically extensive nations such as China. This study, employing nighttime lights as a socio-economic development indicator and remote sensing vegetation indices, investigates spatial variations in wealth distribution across [...] Read more.
Achieving equitable and harmonized socio-economic development is a vital gauge of national progress, particularly in geographically extensive nations such as China. This study, employing nighttime lights as a socio-economic development indicator and remote sensing vegetation indices, investigates spatial variations in wealth distribution across China’s eastern and western regions, delineated by the Hu Huanyong Line. It uncovers the balance between economic growth and green space preservation and discrepancies in development and green space allocation. A thorough county-level analysis using this nighttime light (NTL) and vegetation index exposes the dynamic shifts in socio-economic focal points. The Gini coefficient, assessing inequality and spatial autocorrelation within the index ratio, enriches our regional development understanding. The findings depict a heterogeneous yet rapid economic expansion, primarily within a 30 km coastal buffer zone. Despite a decrease in Gini coefficients in both eastern and western regions, the potential for inland development escalates as coastal illumination approaches saturation. This study unveils enduring, yet lessening, economic disparities between eastern and western China, underscoring the necessity for green preservation in eastern development plans. Moreover, inland regions emerge as potential areas for accelerated development. This study offers crucial insights for formulating balanced, sustainable regional development strategies in China. Full article
(This article belongs to the Special Issue Remote Sensing of Night-Time Light II)
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23 pages, 21155 KiB  
Article
Developing a Pixel-Scale Corrected Nighttime Light Dataset (PCNL, 1992–2021) Combining DMSP-OLS and NPP-VIIRS
by Shijie Li, Xin Cao, Chenchen Zhao, Na Jie, Luling Liu, Xuehong Chen and Xihong Cui
Remote Sens. 2023, 15(16), 3925; https://doi.org/10.3390/rs15163925 - 08 Aug 2023
Cited by 3 | Viewed by 1479
Abstract
The spatial extent and values of nighttime light (NTL) data are widely used to reflect the scope and intensity of human activities, such as extracting urban boundaries, spatializing population density, analyzing economic development levels, etc. DMSP-OLS and NPP-VIIRS are widely used global NTL [...] Read more.
The spatial extent and values of nighttime light (NTL) data are widely used to reflect the scope and intensity of human activities, such as extracting urban boundaries, spatializing population density, analyzing economic development levels, etc. DMSP-OLS and NPP-VIIRS are widely used global NTL datasets, but their severe inconsistencies hinder long-time series studies. At present, global coverage, long time series, and public NTL products are still rare and have room for improvement in terms of pixel-scale correction, temporal and spatial consistency, etc. We proposed a set of inter-correction methods for DMSP-OLS and NPP-VIIRS based on two corrected DMSP-OLS and NPP-VIIRS products, i.e., CCNL-DMSP and VNL-VIIRS, with the goal of temporal and spatial consistency at the pixel-scale. A pixel-scale corrected nighttime light dataset (PCNL, 1992–2021) that met the needs of pixel-scale studies was developed through outlier removal, resampling, masking, regression, and calibration processes, optimizing spatial and temporal consistency. To examine the quality of PCNL, we compared it with two existing global long time series NTL products, i.e., LiNTL and ChenNTL, in terms of overall accuracy, spatial consistency, temporal consistency, and applicability in the socio-economic field. PCNL demonstrates great overall accuracy at both the pixel-scale (R2: 0.93) and the city scale (R2: 0.98). In developing, developed, and war regions, PCNL shows excellent spatial consistency. At global, national, urban, and pixel-scales, PCNL has excellent temporal consistency and can portray stable trends in stable developing regions and abrupt changes in areas experiencing sudden development or disaster. Globally, PCNL has a high correlation coefficient with GDP (r: 0.945) and population (r: 0.971). For more than half of the countries, the correlation coefficients of PCNL with GDP and population are higher than the results of ChenNTL and LiNTL. PCNL can analyze the dynamic changes in socio-economic characteristics over the past 30 years at global, regional, and pixel-scales. Full article
(This article belongs to the Special Issue Remote Sensing of Night-Time Light II)
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21 pages, 4732 KiB  
Article
Snow Cover Mapping Based on SNPP-VIIRS Day/Night Band: A Case Study in Xinjiang, China
by Baoying Chen, Xianfeng Zhang, Miao Ren, Xiao Chen and Junyi Cheng
Remote Sens. 2023, 15(12), 3004; https://doi.org/10.3390/rs15123004 - 08 Jun 2023
Cited by 1 | Viewed by 1090
Abstract
Detailed snow cover maps are essential for estimating the earth’s energy balance and hydrological cycle. Mapping the snow cover across spatially extensive and topographically complex areas with less or no cloud obscuration is challenging, but the SNPP-VIIRS Day/Night Band (DNB) nighttime light data [...] Read more.
Detailed snow cover maps are essential for estimating the earth’s energy balance and hydrological cycle. Mapping the snow cover across spatially extensive and topographically complex areas with less or no cloud obscuration is challenging, but the SNPP-VIIRS Day/Night Band (DNB) nighttime light data offers a potential solution. This paper aims to map snow cover distribution at 750 m resolution across the diverse 1,664,900 km2 of Xinjiang, China, based on SNPP-VIIRS DNB radiance. We implemented a swarm intelligent optimization technique Krill Herd algorithm, which finds the optimal threshold value by taking Otsu’s method as the objective function. We derived SNPP-VIIRS DNB snow maps of 14 consecutive scenes in December 2021, compared our snow-covered area estimations with those from MODIS and AMSR2 standard snow cover products, and generated composite snow maps by merging MODIS and SNPP-VIIRS DNB data. Results show that SNPP-VIIRS DNB snow maps are capable of providing reliable snow cover maps superior to MODIS and AMSR2, with an overall accuracy level of 84.66%. The composite snow maps at 500 m spatial resolution provided 55.85% more information on snow cover distribution than standard MODIS products and achieved an overall accuracy of 84.69%. Our study demonstrated the feasibility of snow cover detection in Xinjiang based on SNPP-VIIRS DNB, which can serve as a supplementary dataset for MODIS estimations where clouded pixels are present. Full article
(This article belongs to the Special Issue Remote Sensing of Night-Time Light II)
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