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Application of Nighttime Remote Sensing in Achieving the Sustainable Development Goals

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

Deadline for manuscript submissions: 10 October 2024 | Viewed by 4603

Special Issue Editors

School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China
Interests: urban expansion; cellular automata; land use planning; land management; artificial intelligence; urban sustainability; nighttime light; remote sensing
Special Issues, Collections and Topics in MDPI journals
School of Geography and Planning, Sun Yat-sen University, Guangzhou, China
Interests: carbon emission; urban sustainability; land use change; remote sensing; geographic information science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Sustainable Development Goals (SDGs) are the blueprint to achieve a better and more sustainable future for all. Remote sensing communities are committed to achieving SDGs because remote sensing techniques are essential tools to make sustainable development a reality at the local level. In particular, China has successfully launched a Sustainable Development Science Satellite (SDGSAT-1) – the world’s first scientific satellite towards SDGs. SDGSAT-1 is promising for a variety of SDG applications. Therefore, this Special Issue aims to discuss the latest theories and advanced methods of nighttime remote sensing in achieving SDGs. We would like to invite you to submit original research that fits the aims and scope of this Special Issue. We look forward to receiving your well-prepared research. Potential subtopics include, but are not limited to:

  • Quantification methods of SDG indicators;
  • Scenario simulation towards SDGs;
  • Artificial intelligence in achieving SDGs;
  • Urban carbon emission and energy conservation;
  • Sustainable urban form for climate change adaption;
  • Implications of land use/cover changes on the environment;
  • Urban resilience and vulnerability against COVID-19;
  • Smart growth of land use and ecological conservation.

Dr. Jinyao Lin
Dr. Jinpei Ou
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

  • sustainable development goals
  • social indicators
  • land use planning
  • environmental conservation
  • artificial intelligence

Published Papers (4 papers)

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Research

20 pages, 5997 KiB  
Article
Adaptive Nighttime-Light-Based Building Stock Assessment Framework for Future Environmentally Sustainable Management
by Zhiwei Liu, Jing Guo, Ruirui Zhang, Yuya Ota, Sota Nagata, Hiroaki Shirakawa and Hiroki Tanikawa
Remote Sens. 2024, 16(13), 2495; https://doi.org/10.3390/rs16132495 - 8 Jul 2024
Viewed by 517
Abstract
The accumulation of artificially built environment stock during urbanization processes has been actively involved in altering the material and energy use pattern of human societies. Therefore, an accurate assessment of built environment stock can provide insights for decision makers to implement appropriate environmentally [...] Read more.
The accumulation of artificially built environment stock during urbanization processes has been actively involved in altering the material and energy use pattern of human societies. Therefore, an accurate assessment of built environment stock can provide insights for decision makers to implement appropriate environmentally sustainable retrofitting strategies. This study presents a building stock estimation enhancement framework (BSEEF) that leverages nighttime light (NTL) to accurately assess and spatially map building stocks. By innovatively integrating a region classification module with a hybrid region-specified self-optimization module, BSEEF adaptively enhances the estimation accuracy across diverse urban landscapes. A comparative case study of Japan demonstrated that BSEEF significantly outperformed a traditional linear regression model, with improvements ranging from 1.81% to 16.75% across different metrics used for assessment, providing more accurate building stock estimates. BSEEF enhances environment/sustainability studies by enabling precise spatial analysis of built environment stocks, offering a versatile and robust framework that adapts to technological changes and achieves superior accuracy without extensive reliance on complex datasets. These advances will make BSEEF an indispensable tool in strategic planning for urban development, promoting sustainable and resilient communities globally. Full article
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17 pages, 18154 KiB  
Article
Integrating NTL Intensity and Building Volume to Improve the Built-Up Areas’ Extraction from SDGSAT-1 GLI Data
by Shaoyang Liu, Congxiao Wang, Bin Wu, Zuoqi Chen, Jiarui Zhang, Yan Huang, Jianping Wu and Bailang Yu
Remote Sens. 2024, 16(13), 2278; https://doi.org/10.3390/rs16132278 - 21 Jun 2024
Viewed by 460
Abstract
Urban built-up areas are the main space carrier of population and urban activities. It is of great significance to accurately identify urban built-up area for monitoring urbanization dynamics and their impact on Sustainable Development Goals. Using only nighttime light (NTL) remote sensing data [...] Read more.
Urban built-up areas are the main space carrier of population and urban activities. It is of great significance to accurately identify urban built-up area for monitoring urbanization dynamics and their impact on Sustainable Development Goals. Using only nighttime light (NTL) remote sensing data will lead to omission phenomena in urban built-up area extraction, especially for SDGSAT-1 glimmer imager (GLI) data with high spatial resolution. Therefore, this study proposed a novel nighttime Lights integrate Building Volume (LitBV) index by integrating NTL intensity information from SDGSAT-1 GLI data and building volume information from Digital Surface Model (DSM) data to extract built-up areas more accurately. The results indicated that the LitBV index achieved remarkable results in the extraction of built-up areas, with the overall accuracy of 81.25%. The accuracy of the built-up area extraction based on the LitBV index is better than the results based on only NTL data and only building volume. Moreover, experiments at different spatial resolutions (10 m, 100 m, and 500 m) and different types of NTL data (SDGSAT-1 GLI data, Luojia-1 data, and NASA’s Black Marble data) showed that the LitBV index can significantly improve the extraction accuracy of built-up areas. The LitBV index has a good application ability and prospect for extracting built-up areas with high-resolution SDGSAT-1 GLI data. Full article
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25 pages, 4919 KiB  
Article
A Comprehensive Assessment of the Pansharpening of the Nighttime Light Imagery of the Glimmer Imager of the Sustainable Development Science Satellite 1
by Hui Li, Linhai Jing, Changyong Dou and Haifeng Ding
Remote Sens. 2024, 16(2), 245; https://doi.org/10.3390/rs16020245 - 8 Jan 2024
Viewed by 1045
Abstract
The Sustainable Development Science Satellite 1 (SDGSAT-1) satellite, launched in November 2021, is dedicated to providing data detailing the “traces of human activities” for the implementation of the United Union’s 2030 Agenda for Sustainable Development and global scientific research. The glimmer imager (GI) [...] Read more.
The Sustainable Development Science Satellite 1 (SDGSAT-1) satellite, launched in November 2021, is dedicated to providing data detailing the “traces of human activities” for the implementation of the United Union’s 2030 Agenda for Sustainable Development and global scientific research. The glimmer imager (GI) that is equipped on SDGSAT-1 can provide nighttime light (NL) data with a 10 m panchromatic (PAN) band and red, green, and blue (RGB) bands of 40 m resolution, which can be used for a wide range of applications, such as in urban expansion, population studies of cities, and economics of cities, as well as nighttime aerosol thickness monitoring. The 10 m PAN band can be fused with the 40 m RGB bands to obtain a 10 m RGB NL image, which can be used to identify the intensity and type of night lights and the spatial distribution of road networks and to improve the monitoring accuracy of sustainable development goal (SDG) indicators related to city developments. Existing remote sensing image fusion algorithms are mainly developed for daytime optical remote sensing images. Compared with daytime optical remote sensing images, NL images are characterized by a large amount of dark (low-value) pixels and high background noises. To investigate whether daytime optical image fusion algorithms are suitable for the fusion of GI NL images and which image fusion algorithms are the best choice for GI images, this study conducted a comprehensive evaluation of thirteen state-of-the-art pansharpening algorithms in terms of quantitative indicators and visual inspection using four GI NL datasets. The results showed that PanNet, GLP_HPM, GSA, and HR outperformed the other methods and provided stable performances among the four datasets. Specifically, PanNet offered UIQI values ranging from 0.907 to 0.952 for the four datasets, whereas GSA, HR, and GLP_HPM provided UIQI values ranging from 0.770 to 0.856. The three methods based on convolutional neural networks achieved more robust and better visual effects than the methods using multiresolution analysis at the original scale. According to the experimental results, PanNet shows great potential in the fusion of SDGSAT-1 GI imagery due to its robust performance and relatively short training time. The quality metrics generated at the degraded scale were highly consistent with visual inspection, but those used at the original scale were inconsistent with visual inspection. Full article
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20 pages, 7483 KiB  
Article
Grid-Scale Poverty Assessment by Integrating High-Resolution Nighttime Light and Spatial Big Data—A Case Study in the Pearl River Delta
by Minying Li, Jinyao Lin, Zhengnan Ji, Kexin Chen and Jingxi Liu
Remote Sens. 2023, 15(18), 4618; https://doi.org/10.3390/rs15184618 - 20 Sep 2023
Cited by 6 | Viewed by 1555
Abstract
Poverty is a social issue of global concern. Although socioeconomic indicators can easily reflect poverty status, the coarse statistical scales and poor timeliness have limited their applications. While spatial big data with reasonable timeliness, easy access, and wide coverage can overcome such limitations, [...] Read more.
Poverty is a social issue of global concern. Although socioeconomic indicators can easily reflect poverty status, the coarse statistical scales and poor timeliness have limited their applications. While spatial big data with reasonable timeliness, easy access, and wide coverage can overcome such limitations, the integration of high-resolution nighttime light and spatial big data for assessing relative poverty is still limited. More importantly, few studies have provided poverty assessment results at a grid scale. Therefore, this study takes the Pearl River Delta, where there is a large disparity between the rich and the poor, as an example. We integrated Luojia 1-01, points of interest, and housing prices to construct a big data poverty index (BDPI). To evaluate the performance of the BDPI, we compared this new index with the traditional multidimensional poverty index (MPI), which builds upon socioeconomic indicators. The results show that the impoverished counties identified by the BDPI are highly similar to those identified by the MPI. In addition, both the BDPI and MPI gradually decrease from the center to the fringe of the study area. These two methods indicate that impoverished counties were mainly distributed in ZhaoQing, JiangMen and HuiZhou Cities, while there were also several impoverished parts in rapidly developing cities, such as CongHua and HuaDu Counties in GuangZhou City. The difference between the two poverty assessment results suggests that the MPI can effectively reveal the poverty status in old urban areas with convenient but obsolete infrastructures, whereas the BDPI is suitable for emerging-development areas that are rapidly developing but still lagging behind. Although BDPI and MPI share similar calculation procedures, there are substantial differences in the meaning and suitability of the methodology. Therefore, in areas lacking accurate socioeconomic statistics, the BDPI can effectively replace the MPI to achieve timely and fine-scale poverty assessment. Our proposed method could provide a reliable reference for formulating targeted poverty-alleviation policies. Full article
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