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Application of Nighttime Remote Sensing in Achieving the Sustainable Development Goals

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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 April 2025) | Viewed by 13745

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Dr. Jinyao Lin

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Guest Editor

School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China
Interests: land use; urban flood; green infrastructure; urban expansion; waterlogging
Special Issues, Collections and Topics in MDPI journals

Dr. Jinpei Ou

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Guest Editor

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

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Keywords

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

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Published Papers (10 papers)

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26 pages, 11812 KiB

Open AccessArticle

Mapping Gridded GDP Distribution of China Based on Remote Sensing Data and Machine Learning Methods

by Saimiao Liu, Wenliang Liu, Yi Zhou, Shixin Wang, Futao Wang and Zhenqing Wang

Remote Sens. 2025, 17(10), 1709; https://doi.org/10.3390/rs17101709 - 13 May 2025

Viewed by 252

Abstract

The gridded spatial distribution data of Gross Domestic Product (GDP) has a wide range of application values in many fields, such as regional economic analysis, urban planning, sustainable utilization of resources, and disaster risk assessment. However, currently the publicly accessible GDP grid datasets face limitations in terms of temporal coverage, spatial extent, and accuracy. Therefore, based on the remote sensing data of land use and nighttime light, this study developed two methods: the factor averaging method (FAM) and grid averaging method (GAM), and used Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) algorithms to jointly construct the spatial model of GDP, so as to produce China’s 1 km gridded GDP in 2020. The experimental results show the following: (1) The GAM yields higher R² values than the FAM in modeling the three industries, and therefore, it is adopted as the basis for GDP spatialization modeling. (2) XGBoost achieves higher R² values than RF in modeling primary and secondary industries, but lower R² values in modeling tertiary industry. Consequently, both methods are combined to construct the overall GDP spatialization model. (3) The accuracy of the GDP spatialization results is evaluated based on town-level GDP statistics, with an R² value of 0.78, indicating its reliable predictive capability. (4) Compared with publicly available GDP datasets, our dataset exhibits consistent spatial distribution patterns and aggregation trends. Furthermore, our GDP dataset provides a more detailed depiction of variations within county-level administrative units. Therefore, the method proposed in this study offers a valuable option for generating a gridded GDP dataset, visually displaying the uneven economic development across various regions in China. It helps to uncover economic disparities among regions and provides data support for formulating differentiated support policies, so as to promote balanced regional development among regions. Furthermore, it contributes to promoting sustained, inclusive, and sustainable economic growth (SDG 8) and reducing inequalities within and among countries (SDG 10), thereby providing strong support for urban planning and sustainable development. 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, 7589 KiB

Open AccessArticle

GDP Estimation by Integrating Qimingxing-1 Nighttime Light, Street-View Imagery, and Points of Interest: An Empirical Study in Dongguan City

by Zejia Chen, Chengzhi Zhang, Suixuan Qiu and Jinyao Lin

Remote Sens. 2025, 17(7), 1127; https://doi.org/10.3390/rs17071127 - 21 Mar 2025

Viewed by 391

Abstract

In the context of economic globalization, the issue of imbalanced regional development has become increasingly prominent. Misreporting in traditional economic censuses has made it difficult to accurately reflect economic conditions, increasing the demand for precise GDP estimation. While nighttime light data, point of interest (POI) data, and street-view imagery (SVI) have been utilized in economic research, each data source has limitations when used independently. Furthermore, previous studies have rarely used high-resolution (over 30 m) nighttime light data. To address these limitations, we constructed both random forest and decision tree models and compared different indicator combinations for estimating GDP at the town scale in Dongguan: (1) Qimingxing-1 nighttime light data only; (2) Qimingxing-1 nighttime light and SVI data; and (3) Qimingxing-1 nighttime light, SVI, and POI data. The random forest model performed better than the decision tree, with its correlation coefficient improving from 0.9604 (nighttime light only) to 0.9710 (nighttime light and SVI) and reaching 0.9796 with full integration. Moreover, the Friedman test and SHAP values further demonstrated the reliability of our model. These findings indicate that the integrated model provides a more accurate reflection of economic development levels and offers a more effective tool for regional economic estimation. Full article

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

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18 pages, 2634 KiB

Open AccessArticle

Monitoring Fine-Scale Urban Shrinkage Space with NPP-VIIRS Imagery

by Shili Chen and Cheng Cheng

Remote Sens. 2025, 17(4), 688; https://doi.org/10.3390/rs17040688 - 18 Feb 2025

Viewed by 441

Abstract

Urban shrinkage is a significant challenge to sustainable urban development. To date, the existing research has yet to analyze urban shrinkage at a fine-scale level. This study addresses this gap by employing nighttime light (NTL) data, which, due to its strong correlation with human activity and high spatial–temporal resolution, offers a robust approach for micro-scale population estimation. This paper aims to explore the characteristics and formation mechanisms of urban shrinkage spaces in Guangzhou, using NTL data and applying ordinary least squares (OLS) and geographically weighted regression (GWR) models. The correlational analysis reveals a marked improvement in model fit with GWR (R² = 0.91) compared with OLS (R² = 0.63), confirming the predictive power of NTL-based GWR for population mapping and the spatial delineation of urban shrinkage. We demonstrate that urban shrinkage spaces in Guangzhou are predominantly distributed in the outer suburbs, while urban growth is concentrated within the urban core area and inner suburbs. The formation of urban shrinkage in Liwan District examined as a case study, is primarily influenced by market factors, government actions, and regulatory constraints—a constellation of factors likely generalizable with other contexts of urban shrinkage. A comprehensive understanding of urban shrinkage at a fine-scale level is imperative for policy makers to optimize urban land use planning and mitigate the factors contributing to shrinkage space, thereby promoting sustainable urban development. 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, 11014 KiB

Open AccessArticle

Mapping Spatiotemporal Dynamic Changes in Urban CO₂ Emissions in China by Using the Machine Learning Method and Geospatial Big Data

by Wei Guo, Yongxing Li, Ximin Cui, Xuesheng Zhao, Yongjia Teng and Andreas Rienow

Remote Sens. 2025, 17(4), 611; https://doi.org/10.3390/rs17040611 - 11 Feb 2025

Viewed by 693

Abstract

Accurately and objectively evaluating the spatiotemporal dynamic changes in CO₂ emissions is significant for human sustainable development. However, traditional CO₂ emissions estimates, typically derived from national or provincial energy statistics, often lack spatial information. To develop a more accurate spatiotemporal model for estimating CO₂ emissions, this research innovatively incorporates nighttime light data, vegetation cover data, land use data, and geographic big data into the study of pixel-level urban CO₂ emissions estimation in China. The proposed method significantly improves the precision of CO₂ emissions estimation, achieving an average accuracy of 83.76%. This study reveals that the type of decoupling varies according to different scales, with more negative decoupling occurring in northern cities. Factors such as the per capita GDP and urbanization contribute to the increase in CO₂ emissions, while the structure of industry and energy consumption play a crucial role in reducing them. The findings in this study could potentially be used to develop tailored carbon reduction strategies for different spatial scales in China. Full article

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

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21 pages, 14898 KiB

Open AccessArticle

Analysis of Economic Vitality and Development Equilibrium of China’s Three Major Urban Agglomerations Based on Nighttime Light Data

by Saimiao Liu, Wenliang Liu, Yi Zhou, Shixin Wang, Zhenqing Wang, Zhuochen Wang, Yanchao Wang, Xinran Wang, Luoyao Hao and Futao Wang

Remote Sens. 2024, 16(23), 4571; https://doi.org/10.3390/rs16234571 - 6 Dec 2024

Viewed by 1297

Abstract

Eliminating poverty, reducing inequality, and achieving balanced development are one of the United Nations Sustainable Development Goals. Objectively and accurately measuring regional economic vitality and development equilibrium is a pressing scientific issue that needs to be addressed in order to achieve common prosperity. Nighttime light (NTL) remote sensing data have been proven to be a good proxy variable for socio-economic development, and are widely used due to their advantages of convenient access and wide spatial coverage. Based on multi-source data, this study constructs an Economic Development Index (EDI) that comprehensively reflects regional economic vitality from two aspects, economic quality and development potential, combines the Nighttime Light Development Index (NLDI) as the evaluation indicators to measure the economic vitality and development equilibrium, analyzes the economic vitality and development equilibrium of 300 district and county units in China’s three major urban agglomerations from 2000 to 2020 and their temporal and spatial variation characteristics, and discusses the connotation of EDI and its availability. The results show the following: (1) From 2000 to 2020, the average growth rate of EDI in China’s three major urban agglomerations reached 36.32%, while the average decrease rate of NLDI reached 38.75%; both economic vitality and the development equilibrium have been continuously enhanced. Among them, the Yangtze River Delta (YRD) urban agglomeration experienced the fastest economic growth, while the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) exhibited the strongest economic strength. (2) Both economic vitality and the development equilibrium in these three urban agglomerations exhibited distinct spatial agglomeration characteristics, namely center-surrounding distribution, coastal–inland distribution, and radial belt–pole distribution, respectively. (3) Over the past two decades, the economic development of these three urban agglomerations has progressed towards the pattern of regional coordinated development, pole-driven development and urban–rural integrated development. The research results can provide new research perspectives and scientific support for promoting regional balanced development, achieving sustainable development goals, and reducing inequality. 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, 5997 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 1594

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 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

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

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17 pages, 18154 KiB

Open AccessArticle

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

Cited by 2 | Viewed by 1545

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 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

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

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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 - 8 Jan 2024

Cited by 4 | Viewed by 2192

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 11 | Viewed by 2642

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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11 pages, 2174 KiB

Open AccessTechnical Note

Using Night-Time Drone-Acquired Thermal Imagery to Monitor Flying-Fox Productivity—A Proof of Concept

by Jessica Meade, Eliane D. McCarthy, Samantha H. Yabsley, Sienna C. Grady, John M. Martin and Justin A. Welbergen

Remote Sens. 2025, 17(3), 518; https://doi.org/10.3390/rs17030518 - 3 Feb 2025

Viewed by 915

Abstract

Accurate and precise monitoring of species abundance is essential for determining population trends and responses to environmental change. Species, such as bats, that have slow life histories, characterized by extended lifespans and low reproductive rates, are particularly vulnerable to environmental changes, stochastic events, and human activities. An accurate assessment of productivity can improve parameters for population modelling and provide insights into species’ capacity to recover from population perturbations, yet data on reproductive output are often lacking. Recently, advances in drone technology have allowed for the development of a drone-based thermal remote sensing technique to accurately and precisely count the numbers of flying-foxes (Pteropus spp.) in their tree roosts. Here, we extend that method and use a drone-borne thermal camera flown at night to count the number of flying-fox pups that are left alone in the roost whilst their mothers are out foraging. We show that this is an effective method of estimating flying-fox productivity on a per-colony basis, in a standardized fashion, and at a relatively low cost. When combined with a day-time drone flight used to estimate the number of adults in a colony, this can also provide an estimate of female reproductive performance, which is important for assessments of population health. These estimates can be related to changes in local food availability and weather conditions (including extreme heat events) and enable us to determine, for the first time, the impacts of disturbances from site-specific management actions on flying-fox population trajectories. Full article

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

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