Nighttime Light Data Research Articles

A Comprehensive Assessment of the Pansharpening of the Nighttime Light Imagery of the Glimmer Imager of the Sustainable Development Science Satellite 1

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.

Does new airport investment promote urban economic development?: Global evidence from nighttime light data

This study aims to provide global evidence on the net impact of new airport investment on urban economic development by analyzing cities with/without new airport construction in a quasi-experimental design framework. A two-way fixed difference-in-differences (DID) method along with Mahalanobis distance matching (MDM) is applied to scrutinize the causal association of new airport investment with urban economic development. This empirical analysis assumes nighttime light intensity at a grid-cell scale of 15 arc-second as an economic indicator. We regress nighttime light intensity from 2015 to 2019 for 27 treatment cities with new airport construction from 2015 to 2017 and other control cities selected from 13,038 cities in the world. The estimation results indicate that new airport construction increases nighttime light intensity by (1) about 10.8 % in cities that had demonstrated relatively low nighttime light intensity and (2) about 9.5 % in cities that had introduced their first airports. This study presents empirical evidence on the positive economic impacts of new airport investment for relatively small cities in emerging economies, wherein economic statistics are insufficient or unavailable.

A study on siting of emergency shelters for dam failure floods considering population distribution and weather effects

In recent years, dam failures have occurred frequently because of extreme weather, posing a significant threat to downstream residents. The establishment of emergency shelters is crucial for reducing casualties. The selection of suitable shelters depends on key information such as the number and distribution of affected people, and the effective capacity and accessibility of the shelters. However, previous studies on siting shelters did not fully consider population distribution differences at a finer scale. This limitation hinders the accuracy of estimating the number of affected people. In addition, most studies ignored the impact of extreme rainfall on the effective capacity and accessibility of shelters, leading to a low applicability of the shelter selection results. Therefore, in this study, land-use and land-cover change (LUCC) and nighttime lighting data were used to simulate population distribution and determine the number and distribution of affected people. Qualified candidate shelters were obtained based on screening criteria, and their effective capacity and accessibility information under different weather conditions were quantified. Considering factors such as population transfer efficiency, construction cost and shelter capacity constraints, a multi-objective siting model was established and solved using the non-dominated sorting genetic algorithm II (NSGA- II) to obtain the final siting scheme. The method was applied to the Dafangying Reservoir, and the results showed the following: (1) The overall mean relative error (MRE) of the population in the 35 downstream streets was 11.16 %, with good fitting accuracy. The simulation results truly reflect the population distribution. (2) Normal weather screening generated 352 qualified candidate shelters, whereas extreme rainfall weather screening generated 266 candidate shelters. (3) Based on the population distribution and weather factors, four scenarios were set up, with 63, 106, 73, and 131 shelters selected. These two factors have a significant impact on the selection of shelters and the allocation of evacuees, and should be considered in the event of a dam-failure floods.

Identification of key areas for ecological restoration of territorial space based on ecological security pattern: a case study of Kangbao County

Accurate identification of key areas for ecological restoration is an important foundation for orderly promotion of ecological restoration of territorial space. This study identified the key areas for ecological restoration in Kangbao County according to the research paradigm of “source identification – resistance surface construction – corridor extraction”. First, ecological source sites were identified and screened based on habitat quality modeling, morphological spatial pattern analysis and landscape connectivity analysis. Then, the base resistance surface was constructed using ArcGIS software and nighttime light data. Thereafter, ecological corridors were extracted and ecological pinch points and ecological barrier points identified with the Linkage Mapper plug-in. Forty ecological source sites with a total area of 68.06 km2 were identified in Kangbao County. These sites were dominated by woodland and grassland, and the integrity of these ecological source sites still needed to be improved. The 96 ecological corridors (total length of 743.81 km) were identified, which were densely distributed in the south and east parts of the study area, and there is still much room for improvement of the habitat connectivity. Additionally, 75 ecological pinch points (total area of 31.72 km2) and 69 ecological obstacles (total area of 16.42 km2) were identified. The key areas to be restored were mainly distributed in Yan Yufang, Har Chimega, Tuchengzi, Zhangji, and Danchenghe Townships. This study can lay a firm foundation for the ecological restoration in Kangbao County.

Correction: Yang et al. Spatial Diffusion Waves of Human Activities: Evidence from Harmonized Nighttime Light Data during 1992–2018 in 234 Cities of China. Remote Sens. 2023, 15, 1426

In the original publication [...]

Research on the fine-scale spatial-temporal evolution characteristics of carbon emissions based on nighttime light data: A case study of Xi'an city

Carbon emissions have resulted in severe ecological damage, jeopardizing the very existence of humanity. In China, carbon emissions from urban areas contribute to 80% of the total carbon emissions, this significant contribution underscores the importance of addressing urban emissions. To gain insights into the spatial patterns of carbon emissions in urban areas at the fine-scale regional level, this study utilizes Xi'an as a case study, constructs a fine-scale regional carbon emission estimation model based on energy consumption and NPP-VIIRS-like NTL data, and quantifies multiscale carbon emissions from 2000 to 2021. Furthermore, global spatial autocorrelation, cold and hot spot analysis, and standard deviation ellipse analysis were used to investigate the spatial characteristics of carbon emissions at the street (township) level in Xi'an. This study revealed a sixfold increase in carbon emissions in Xi'an, rising from 51.35 million tons in 2000 to 281.95 million tons in 2021.The area affected by carbon emissions has expanded, with the centre of gravity of emission consistently shifting towards the south. The average street-level carbon emission density fluctuated and doubled compared to that in 2000. The total number of streets with low-carbon-emission-density decreased by 23%, indicating a shift away from low-carbon-emission-density streets. Furthermore, the global Moran's I index consistently remained within the range of 0.85–0.95, indicating significant spatial clustering, with cold spots expanding into surrounding districts and counties. Additionally, this study accurately identified carbon emissions within the city, offering the potential for the development of more precise carbon reduction strategies. Moreover, this approach can provide valuable support for the dynamic monitoring of carbon emissions in developing cities worldwide.

Transportation infrastructure and economic growth: Evidence from "new nighttime light data" in the Yangtze River Delta.

The enhancement and improvement of China's high-speed rail network plays a crucial role in promoting sustainable economic growth in the region. By utilizing "new nighttime light data" in China's Yangtze River Delta from 2003 to 2018, this paper investigates the impact of HSR on economic growth using a multiperiod difference-in-differences (DID) model. The operations of high-speed rail have a significant and positive impact on economic growth, which often becomes more apparent with a certain time delay. The operations of high-speed rail have a significant positive impact on the development of large, mega, and super-cities, with this impact becoming more pronounced as the size of the city increases. Furthermore, high-speed rail operations have a significant influence on the economic growth of cities that largely rely on secondary and tertiary industries, in contrast to the primary industry. The promotion of economic growth by high-speed rail is primarily achieved through three transmission channels: low carbon emissions, labor force agglomeration, and innovation. Over time, high-speed rail will progressively reduce economic disparities between regions and facilitate a trend towards regional economic coordination or convergence. This study makes a valuable contribution to the exploration of pathways towards achieving economic growth.

An urban-rural Fringe extraction method based on Combined Urban-rural Fringe Index (CUFI)

In this article, a multi-source data-based method for urban-rural fringe extraction is proposed to solve the problem of insufficiently accurate division of urban-rural fringe, which can conveniently and accurately realise the extraction of the area of urban-rural fringe. The method uses multi-source data such as night light remote sensing images, POI data and high-resolution remote sensing images. We constructed the Combined Urban-Rural Fringe Index (CUFI) model based on the characteristics of NDVI and NTL, POI kernel density value points that change from the city centre to the surrounding area. The calculation of CUFI involves three aspects. The initial step involves processing data, which includes reducing noise in nighttime light images and removing inaccurate information from POI data. The subsequent step involves calculating combined features of NTL and POI kernel density, which includes analyzing the kernel density of POI data, standardizing the results of POI kernel density and nighttime light data, computing NTL & POI indexes using the equal weight combined feature calculation method and reclassifying NTL & POI indexes .The third part involves rejecting misinformation using NDVI. This includes calculating the NDVI index using high-resolution satellite data, rejecting bare land information in the NDVI index using nighttime light data, and reclassifying the processed results. The reclassified results are then multiplied with the reclassification results of the NTL&POI index to obtain the CUFI calculation results. In order to verify the effectiveness and reliability of the method, Tangshan City, Hebei Province was selected as the experimental area, and the method was used to compare and analyse the accuracy with the traditional urban built-up area boundary extraction method. The results show that compared with the traditional method, the extraction accuracy of the CUFI method is higher, reaching 89.51%, which is able to effectively identify urban villages and lakes in the urban area, as well as improving the resolution of extracting Urban-Rural Fringe using NTL& POI methods, and also the urban-rural fringe which has weak night light and fewer POI points, but actually has urban-rural duality attributes Extraction.

Impact of nighttime light data saturation correction on the application of carbon emissions spatialization: A comparative study of the correction effect and application effect based on five methods in China

Nighttime light (NTL) remote sensing data have attracted wide attention in monitoring carbon dioxide (CO2) emissions of fossil fuel consumption in recent years due to its unique perspective of observing human activity and low cost. However, saturation errors in NTL data are believed to limit the accuracy of the application. Therefore, this study compared the desaturation ability and the effect of spatializing CO2 emissions in China based on five saturation correction methods of Letu + Tong, SARMRC (the saturation correction method based on a regression model and radiance-calibrated NTL data), VANUI (index-based vegetation adjusted NTL urban index), EANTLI (enhanced vegetation index (EVI)-adjusted NTL index) and LERNCI (land surface temperature (LST)-and-EVI-regulated-NTL-city index). Based on this, we explored the influence of saturation correction on the spatialization of CO2 emissions. The main findings of this study include: (1) The saturation correction effect is limited by the bias of the NTL image itself in some regions, the auxiliary data, and the regional differences of the fitting models, which fail to reach the best effect in spatializing CO2 emissions at the provincial level. However, as downscaling to the prefectural level, the Letu + Tong image with the advantage of desaturation has a better effect on spatializing CO2 emissions. (2) The saturation correction effect of Letu + Tong and EANTLI images is relatively better with advantages in three evaluation criteria than other images. In most provinces, the original NTL and SARMRC images are better at spatializing CO2 emissions. (3) Implementing inter-calibration was found to be generally beneficial in enhancing the effects of saturation correction. This study provides an important basis for improving the quality of NTL data and helps to reflect the spatial pattern of CO2 emissions more accurately and efficiently.

The differential impacts of the spatiotemporal vertical and horizontal expansion of megacity Dhaka on ecosystem services

The relationship between ecosystem services (ES) and urbanization is crucial for sustainable development. Rapid urbanization threatens the natural capital of Dhaka city, affecting the delivery of ES through changes in land use and land cover. We used vertical growth (VG), nighttime light data (NTL), and population density (PD) as standard urbanization indicators alongside technomass, a three-dimensional indicator, to evaluate the degree of urbanization as a continuous spatial process. We modeled the spatiotemporal relationships between urbanization degrees and ES using the ecosystem service value (ESV) dataset applied in Dhaka, with regional modified value coefficients. Results from the geographically weighted regression (GWR) model showed that technomass emerged as a more appropriate indicator to analyze urbanization for ESV analysis (r > 0.61), followed by NTL (r > 0.56), and PD (r > 0.54) across all zones from 2000 to 2021. We observed a 68.34% decrease in net ESV, equivalent to $245.88 million (in 2021 USD), from 2000 to 2021. This decline was driven by the conversion of water bodies (−70.93%), agricultural land (−60.08%), forest and vegetation (−70.18%) into urban built-up areas and other uses. In contrast, net technomass increased by 243.11% due to the city's vertical growth. The digital building height (DBH) model revealed that the built-up area had expanded by 94.94% over the study period, with an average annual growth rate of 4.52%. Significant correlations (p < 0.05) were observed between ES and urbanization. The rural-urban fringe area exhibited the most significant increase in urbanization (r > 0.90), along with a 440.47% growth in technomass. Our results provide insights into the impact of urbanization on ES, particularly at the regional scale, and have highlighted the importance of integrating VG and technomass for urbanization analysis. These findings could be useful for environmental management, policymaking, spatial planning, and coordinating future ES protection and urban development.

Dynamic assessment of the COVID-19 lockdown in the Xinjiang region using night-time light remote sensing

ABSTRACT China used to have the world’s most stringent prevention policies during the COVID-19 pandemic. The strict lockdown in Xinjiang region in 2022 was considered to be the longest in the process of COVID-19 pandemic prevention. Therefore, we examined the dynamic effects of the COVID-19 lockdown in Xinjiang region using night-time light data. The findings showed that before the outbreak of COVID-19, the night-time light in Xinjiang region still maintained a significant growth. However, after the lockdown of COVID-19 in August 2022, the night-time light showed a significant decrease, especially in August. Urumqi's night-time light decreased by at least 25% compared to the non-lockdown, while that in other major cities was between 5% and 15%. After the lockdown was lifted in November 2022, the night-time light in Urumqi increased by at least 50% compared to the lockdown, while that in other major cities was generally less than 30%. The regions where night-time light decreased in Urumqi were mainly within a radius of 40 km, whereas that in other cities were mainly concentrated within a radius of 6 km. The changes in statistical data and night-time light indicated that the economic activities in Xinjiang region were significantly affected by the COVID-19 lockdown.

Human activities along southwest border of China: Findings based on DMSP/OLS Nighttime light data

Human activities along southwest border of China exert significant influences on sustainable development of regional economy, politics, and environment among countries Vietnam, Laos, and Myanmar. However, related empirical studies remain very limited due to the low availability and comparability of small-scale statistic data in that region. Fortunately, Nighttime light (NTL) images provide uniform, consistent and valuable data sources. Using NTL data from 1992 to 2013, this article seeks to contribute the literature by investigating the trend of relative intensity of human activities between China and her southwest neighborhoods. We find that the human activities intensity of Chinese borderland maintained advantage over her neighborhoods, and the trend of this advantage is nonlinear. Regional development policy launched by Chinese government is considered to be a possible explanation.

Using Nighttime Light Data to Estimate Sub-national GDP and Growth in Pakistan

Using Nighttime Light Data to Estimate Sub-national GDP and Growth in Pakistan

Spatial Downscaling of NPP/VIIRS DNB Nighttime Light Data Based on Deep Learning

Spatial Downscaling of NPP/VIIRS DNB Nighttime Light Data Based on Deep Learning

An improved cross-sensor calibration approach for DMSP-OLS and NPP-VIIRS nighttime light data

An improved cross-sensor calibration approach for DMSP-OLS and NPP-VIIRS nighttime light data

Satellite Nighttime Light and Digital Elevation Data to Assess Urban Expansion in Floodplains of Dhaka City

This study aims to evaluate the potential of DMSP/OLS nighttime light data to investigate human presence and activity in Dhaka City, Bangladesh. The study explores the sensor's ability to detect and monitor urban expansion patterns and human presence and activity at a regional scale. Thematic land cover maps revealed a rapid expansion of built-up areas within the 43-year study period, increasing from 35 km² in 1972 to 378 km² in 2015, representing a net gain of approximately 980% and an average annual growth rate of 6%. Notably, this growth rate was significantly higher in peripheral areas (2903% and a yearly expansion rate of 8%) compared to the central city area (Dhaka City Corporation) (460% and an annual expansion rate of 4%). This substantial urbanisation has primarily occurred in Dhaka's northern, northwestern, and southwestern sectors, transforming previously agricultural land, vegetation cover, wetlands, and water bodies. The findings demonstrate the utility of DMSP/OLS imagery for detecting human activities and presence in the Dhaka region, providing valuable insights into urban expansion patterns and their implications for environmental and social dynamics.

Temporal and Spatial Evolution Analysis and Correlation Measurement of Urban–Rural Fringes Based on Nighttime Light Data

Rural–urban fringe areas serve as crucial transitional zones within urban structures, and their spatiotemporal evolution holds significant reference value for scientifically planning urban configurations. The existing research predominantly focuses on large cities, overlooking the spatiotemporal evolution mechanisms of small- to medium-sized cities. This study employs nighttime light data as the data source to ensure continuous and consistent data, overcoming administrative boundaries. Taking Taizhou City as a case study, a combination of the threshold method and an improved Mann–Kendall algorithm is employed to reveal the evolution process of fringe areas. And a gravity model is utilized to unearth the interaction relationships among regions. The results indicate that from 2010 to 2020, the urban area expanded from 1097 km2 to 2791 km2, with fringe areas experiencing initial contraction followed by gradual expansion. The central urban areas of Jiaojiang, Luqiao, and Huangyan gradually merged, forming a concentrated region. Linhai maintained a high level of attraction, while spatial gravity weakened in other areas. This study quantitatively analyzes the expansion trends of fringe areas in small- to medium-sized cities, elucidating the attractive effects of time–cost distance and land resources on development and providing valuable support for subsequent spatial planning and governance.

Enhancing nighttime light remote Sensing: Introducing the nighttime light background value (NLBV) for urban applications

Artificial light at night, as captured by nighttime light (NTL) remote sensing, typically consists of two components: static urban lighting facilities and dynamic outdoor human activities. Separating these components can improve our understanding of the mechanism underlying NTL remote sensing and broaden its applications. In this paper, we introduce the concept of Nighttime Light Background Value (NLBV) to represent NTL emitted solely by static urban lighting facilities, excluding the influence of outdoor human activities. By utilizing a random forest method, we derived the pixel-level NLBV for Shanghai from NTL data. Comparative analysis demonstrates that NLBV exhibits a stronger correlation with building density and road density compared to the original NTL data. Our empirical findings demonstrate that the definition and application of NLBV can significantly enhance NTL-based applications for extracting urban physical attributes and estimating socioeconomic variables. Firstly, the urban built-up area extracted based on NLBV outperforms the original NTL data, especially in highly urbanized. Secondly, separating static urban lighting and dynamic human activity enables a more accurate estimation of socioeconomic variables with different contributions. Moreover, our results highlight the significant potential of incorporating NLBV in NTL-based applications across various disciplines. Overall, this study demonstrates the significance of NLBV in improving the accuracy and applicability of NTL data, opening up new opportunities for research and practical applications across various domains.

Identification of illumination source types using nighttime light images from SDGSAT-1

ABSTRACT The constant need for decarbonization has led to the replacement of artificial light at night (ALAN) with light-emitting diodes (LEDs), inducing blue light pollution and its consequent adverse effects. As a result, there is an urgent need for the development of a technique for the rapid, accurate, and large-scale discrimination of the various illumination sources. The newly launched Sustainable Development Science Satellite-1 (SDGSAT-1) can play this role by supplementing the existing nighttime light data with multispectral and high-resolution features. Along these lines, in this work, a novel approach to identify various types of illumination sources using machine learning in SDGSAT-1 images was proposed, taking Beijing as a worked example. The results indicate that: (1) The method can effectively distinguish the various types of light sources with an overall accuracy of 0.92 for ALAN and 0.95 for streetlights. (2) The illumination patterns can be clearly depicted, indicating distinct spatial heterogeneity in ALAN along Beijing’s 5th Ring Road. (3) Statistically significant disparities between road classes and streetlight types were detected, with a notable increase in LED streetlight usage as the road class diminishes. This work emphasizes the crucial role of SDGSAT-1 in analysing ALAN, providing valuable insights in urban lighting management.

Identifying Urban Built-Up Areas Based on Spatial Coupling between Nighttime Light Data and POI: A Case Study of Changchun

Nighttime light (NTL) data and points of interest (POI) data offer precise visual indications of the distributional characteristics of urban spatial structures. This study employed nighttime light data and point of interest data to investigate spatial distribution patterns in Changchun, a selected study area. The built-up area was extracted from the nighttime light data, while kernel density analysis was utilized to examine the distribution of point of interest data. The processing outcomes of both datasets were gridded with spatially resolved resolution. Afterward, the fishnet tool was employed to conduct two-factor integrated mapping and visual analysis, which helped identify shared or divergent spatial coupling relationships. The results indicated a high degree of consistency in the distribution of both NTL and POI across Changchun, with 84.58% of the coupling demonstrating a concordant pattern. The spatial analysis conducted in this study showed that the heterogeneities of the coupling relationship within each administrative borough expanded outward from the center of the borough. POI provided a more accurate depiction of the spatial distribution of urban built-up areas compared to NTL, leading to a more precise representation of spatial patterns of human activity intensity. Changchun has undergone zoning adjustments, resulting in the emergence of multiple urban centers in both the central city and the surrounding administrative districts. These urban centers are gradually merging into each other. The study found that the level of spatial coupling was much higher in the central area compared to the surrounding administrative districts. This has contributed to the formation of multiple urban centers and the gradual expansion of the urban built-up area beyond the main city, indicating a trend towards regional integration and development. This study provides a more detailed and accurate description of the current distribution of urbanization and spatial structural characteristics of Changchun by investigating the spatial coupling between POI and NTL. The findings contribute to a better understanding of the urban development patterns in the region and provide insights for future urban planning and management.