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Application of Nighttime Remote Sensing in Achieving the Sustainable Development Goals

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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

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Keywords

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

Published Papers (2 papers)

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25 pages, 4919 KiB

Open AccessArticle

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 - 08 Jan 2024

Viewed by 746

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) 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

(This article belongs to the Special Issue Application of Nighttime Remote Sensing in Achieving the Sustainable Development Goals)

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20 pages, 7483 KiB

Open AccessArticle

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 3 | Viewed by 1283

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, 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

(This article belongs to the Special Issue Application of Nighttime Remote Sensing in Achieving the Sustainable Development Goals)

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Application of Nighttime Remote Sensing in Achieving the Sustainable Development Goals

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