Nighttime Light Images Research Articles

Estimation Model and Spatio-Temporal Analysis of Carbon Emissions from Energy Consumption with NPP-VIIRS-like Nighttime Light Images: A Case Study in the Pearl River Delta Urban Agglomeration of China

Urbanization is growing at a rapid pace, and this is being reflected in the rising energy consumption from fossil fuels, which is contributing significantly to greenhouse gas impacts and carbon emissions (CE). Aiming at the problems of the time delay, inconsistency, uneven spatial coverage scale, and low precision of the current regional carbon emissions from energy consumption accounting statistics, this study builds a precise model for estimating the carbon emissions from regional energy consumption and analyzes the spatio-temporal characteristics. Firstly, in order to estimate the carbon emissions resulting from energy consumption, a fixed effects model was built using data on province energy consumption and NPP-VIIRS-like nighttime lighting data. Secondly, the PRD urban agglomeration was selected as the case study area to estimate the carbon emissions from 2012 to 2020 and predict the carbon emissions from 2021 to 2023. Then, their multi-scale spatial and temporal distribution characteristics were analyzed through trends and hotspots. Lastly, the influence factors of CE from 2012 to 2020 were examined with the OLS, GWR, GTWR, and MGWR models, as well as a ridge regression to enhance the MGWR model. The findings indicate that, from 2012 to 2020, the carbon emissions in the PRD urban agglomeration were characterized by the non-equilibrium feature of “high in the middle and low at both ends”; from 2021 to 2023, the central and eastern regions saw the majority of its high carbon emission areas, the east saw the region with the highest rate of growth, the east and the periphery of the high value area were home to the area of medium values, while the southern, central, and northern regions were home to the low value areas; carbon emissions were positively impacted by population, economics, land area, and energy, and they were negatively impacted by science, technology, and environmental factors. This study could provide technical support for the long-term time-series monitoring and remote sensing inversion of the carbon emissions from energy consumption in large-scale, complex urban agglomerations.

Urban Resilience of Large Public Health Events Based on NPP-VIIRS Nighttime Light Images: A Case Study of 35 Large Cities in China

Urban Resilience of Large Public Health Events Based on NPP-VIIRS Nighttime Light Images: A Case Study of 35 Large Cities in China

Assessing urbanization dynamics using a pixel-based nighttime light indicator

Nighttime light (NTL) is a reliable indicator for measuring urban development, population density, and economic activities. This study sets out to explain the spatio-temporal pattern of urbanization and population distribution using pixel-based NTL data. Changes in NTL intensity in Puglia (Italy) were evaluated from 2014 to 2023, and how NTL is affected by various spatial metrics was investigated. Summer–winter differences at the pixel level were monitored utilizing the Normalized Nighttime Light Index (NNLI), and the relationship between this indicator and tourism was assessed. A consistent increase in NTL across the region in both summer and winter was measured, with variations among provinces. Urban areas with high population density showed greater NTL values, while inland areas showed low NTL intensity. The study also revealed that proximity to urban areas was the most influential factor in predicting NTL intensity. The analysis revealed that differences between summer and winter correlated with tourist activity, especially in coastal municipalities. Leveraging NTL images can support decision-making across various sectors and aid in developing evidence-based urban strategies tailored to local needs and challenges.

Mapping manufactured capital in mainland China with harmonized night‐time light images between 1992 and 2018

AbstractThe manufactured capital, usually denoted as material stocks from an industrial ecology perspective, has thus far received wide attention in sustainability and circularity science. Sustainable resource management should be rooted in detailed knowledge of manufactured capital accumulation in society at a high spatial resolution. Previous studies demonstrated that night‐time light (NTL) data provide a great opportunity for monitoring material stocks dynamics at a higher spatial resolution on the regional and global scale. However, the potential of historical–geographical refined material stocks has not been fully analyzed and explored because of the inconsistency of NTL images detected by the different satellites. In this study, based on a new set of material stocks data in China and harmonized NTL images (1992–2018), we map the national stocks of 13 bulk materials (including cement, gravel, wood, brick, sand, asphalt, glass, lime, plastic, rubber, copper, aluminum, and steel) at a 1 × 1 km resolution from 1992 to 2018. The results find that the total material stocks increased from 190,000 to 460,000 t/km2 between 1992 and 2018. Among the five end‐use sectors, buildings have the highest density of 430,000 t/km2, while domestic appliances have the lowest density of 140 t/km2. Four manufactured capital clusters, including the Yangtze River Delta, Pearl River Delta, Beijing–Tianjin–Hebei, and Chengdu–Chongqing agglomerations, possess 38% of the national total stocks in 2018, revealing an unbalanced distributed pattern of manufactured capital across China. Our results provide valuable support for policymakers and business decision‐makers on efficient resource management and urban mining.

Exploring economic activity from outer space: A Python notebook for processing and analyzing satellite nighttime lights

Nighttime lights (NTL) data are widely recognized as a useful proxy for monitoring national, subnational, and supranational economic activity. These data offer advantages over traditional economic indicators such as GDP, including greater spatial granularity, timeliness, lower cost, and comparability between regions regardless of statistical capacity or political interference. However, despite these benefits, the use of NTL data in regional science has been limited. This is in part due to the lack of accessible methods for processing and analyzing satellite images. To address this issue, this paper presents a user-friendly geocomputational notebook that illustrates how to process and analyze satellite NTL images. First, the notebook introduces a cloud-based Python environment for visualizing, analyzing, and transforming raster satellite images into tabular data. Next, it presents interactive tools to explore the space-time patterns of the tabulated data. Finally, it describes methods for evaluating the usefulness of NTL data in terms of their cross-sectional predictions, time-series predictions, and regional inequality dynamics.

Generation of monthly VIIRS nighttime lights time-series (1992–2013) images using deep learning (cGAN) technique

Satellite-based nighttime lights (NTL) are often used as indicators of the socioeconomic dynamics of an area. The Earth Observation Group at Colorado School of Mines provides monthly NTL products from the Day Night Band (DNB) sensor on board the Visible and Infrared Imaging Suite (VIIRS) satellite (April 2012 onwards) and from Operational Linescan System (OLS) sensor onboard the Defense Meteorological Satellite Program (DMSP) satellites (April 1992–December 2013). VIIRS-DNB (hereinafter referred to as VIIRS) and DMSP-OLS (hereinafter referred to as DMSP) NTL images have 15-arc second and 30-arc second spatial resolution, respectively. In the current study, an attempt has been made to generate synthetic (fine-resolution) monthly VIIRS-like products of 1992–2012 from (coarse-resolution) DMSP products, using a deep learning-based image translation network. Initially, the defects of the 216 monthly DMSP images (1992–2013) were corrected to remove geometric errors, background noise, and radiometric errors. Correction on monthly VIIRS imagery to remove background noise and ephemeral lights was done by thresholding. Improved monthly NTL images of DMSP-OLS and VIIRS from April 2012–December 2013 were used in a conditional generative adversarial network (cGAN) along with the land cover, as auxiliary input, to generate VIIRS-like monthly imagery from 1992 to 2012. The modelled imagery was aggregated annually, which showed an R2 of 0.94 with the results of other annual-scale VIIRS-like imagery products (2000–2020) over India; R2 of 0.85 w.r.t GDP; and R2 of 0.69 w.r.t population. Regression analysis of the generated VIIRS-like products with the actual VIIRS images for the years 2012 and 2013 over India indicated a good approximation with an R2 of 0.64 and 0.67 respectively, while the spatial density relation depicted an under-estimation of the brightness values by the model at extremely high radiance values with an R2 of 0.56 and 0.53 respectively. Qualitative analysis is also performed on both country and State scales. Visual analysis over 1992–2013 confirms a gradual increase in the brightness of the nighttime lights indicating that the cGAN model images closely represent the actual pattern followed by the nighttime lights. Finally, synthetically generated monthly VIIRS-like products are delivered to the research community which will be useful for studying the changes in socio-economic dynamics over time.

VIIRS Night-Time Light Image in the Spatial Structure Identification of Banyuwangi Regency

A spatial structure is a hierarchical arrangement of settlement centers and a network system of infrastructure and facilities that support the community’s socio-economic activities. Spatial structure is important because the city’s rapid growth requires regional service center planning. In general, the method for determining the spatial structure uses the Centrality Index (IS) with a variable number of facilities, infrastructure, and people to obtain a hierarchical arrangement of regional service centers. However, this method is often constrained by data limitations, so in this study remote sensing technology was used through night light imagery in identifying potential centers. This study used the Visible and Infrared Imaging Radiometer Suite (VIIRS) sensor, one of the sensors carried by the Suomi-NPP (National Polar-orbiting Operational Environmental Satellite System Preparatory Project) satellite. Identification is done by overlaying the night light image with the existing spatial structure to produce potential centers. Based on the analysis results, eight classifications are based on the light emission interval class, where the higher the class, the higher the light emission value, which indicates the potential for a new center. The Pesanggaran sub-district is in class eight, the Tegaldimo sub-district is in class six, and the Banyuwangi sub-district is in class one.

Estimating the relationship between ethnic inequality, conflict and voter turnout in Africa using geocoded data

To what extent is voter turnout influenced by ethnic inequality? We combine ethnolinguistic maps on sub-national locations of ethnic groups with night-time light satellite images to construct a dataset of ethnic inequality across sub-national locations in 24 African countries. Using election data from the constituency-level elections archive, we show that ethnic inequality is negatively associated with voter turnout. This relationship holds even after we control for endogeneity and geographic characteristics. We also construct a dataset of election conflict across the sub-national areas. We find that an increase in ethnic inequality is associated with an increase in conflict, which reduces voter turnout.

Spatiotemporal Analysis of Carbon Emissions Based on Night-time Light Data in Western Provinces of China

This study focuses on the fusion and calibration of DMSP–OLS and NPP–VIIRS night-time light data, sourced from the National Geophysical Data Centre in the United States. A long-time series night-time light dataset, spanning from 1999 to 2019, for the western provinces of China is created. In conjunction with carbon emission statistics from these 11 western provinces, an estimation model is constructed to analyse the changes in the spatiotemporal pattern of carbon emissions within the region, revealing the characteristics of carbon emissions caused by human night-time economic activities in western China, and provides theoretical reference for the formulation of energy conservation and emission reduction policies. Results show that: – A strong correlation coefficient of 0.9067 exists between carbon emissions and the total digital number (DN) value of night-time light in the western provinces, with a negligible average relative error, and this result indicates the effectiveness of the estimation model; – The study reveals an increasing trend in carbon emissions across all 11 provinces from 1999 to 2019, and this growth forms a radial expansion pattern centred around the provincial capitals of Sichuan, Shaanxi, and Inner Mongolia; – By integrating night-time light images and calculated carbon emissions through the estimation model, a significantly positive spatial correlation of carbon emissions is discernible. This outcome presents as a high carbon agglomeration area in the Inner Mongolia Autonomous Region and a low carbon agglomeration area in Qinghai Province. On the basis of these findings, the study proposes transformation measures to promote low carbon emissions in China’s western provinces. These practical suggestions provide a point of reference for China as it aims to meet its “carbon neutrality” and “peak carbon emissions” targets.

Enhancing SDGSAT-1 night light images using a panchromatic guidance denoising algorithm

The Glimmer Imager on board the Sustainable Development Science Satellite 1 (SDGSAT-1) is capable of providing high-resolution nighttime light images. However, the presence of stripe noise and dark current noise in L4A images affects the authenticity of image information, thus limiting its scientific applications. This study proposes a panchromatic guidance denoising algorithm to enhance the quality of nighttime light images based on the spectral consistency properties between the panchromatic and RGB channels. This algorithm utilizes two low-noise panchromatic bands to obtain a denoised light area and subsequently removes a significant amount of noise from non-light areas in the RGB bands based on this light area. Our results indicate that this algorithm can effectively remove noise from SDGSAT-1 nighttime light images, achieving a noise removal rate greater than 99.8% in the selected dark ocean area. It also preserves more reasonable radiometric characteristics and spatial details of high-spatial resolution nighttime light images when compared to other popular denoising algorithms. The perturbation caused by moonlight can be effectively eliminated using this algorithm, which further improves the comparability and application of SDGSAT-1 nighttime light images under different lunar phases.

CG-CFPANet: a multi-task network for built-up area extraction from SDGSAT-1 and Sentinel-2 remote sensing images

ABSTRACT Accurate extraction of built-up areas is helpful to urban development and map updating. Nighttime light (NTL) data can capture the lighting signal of ground objects. However, most built-up area extraction is conducted on public limited coarse spatial resolution NTL images. The Sustainable Development Science Satellite-1 (SDGSAT-1) provides 10 m spatial resolution panchromatic NTL images, making it possible to map detailed urban lighting structures. In urban extraction, the boundaries of urban areas are easily confused with background objects due to the similar spectral and textual features. To address this problem, we proposed a multi-task deep learning model, CG-CFPANet, to extract illuminated built-up areas by synthesizing SDGSAT-1 NTL data and optical remote sensing images. In CG-CFPANet, a convolutional feature pyramid attention (CFPA) module for better contextual recognition and a concatenation group (CG) module to merge the two remote sensing images are developed. Our proposed CG-CFPANet achieved 1.3% higher precision in built-up area extraction than ten other recently proposed network structures: UNet, UNet++, PSPNet, DeeplabV3, FCN, ExtremeC3Net, SegNet, BiseNet, Res2-UNet, and CBRNet. It shows higher applicability for large-scale built-up area extraction.

Road extraction through Yangwang-1 nighttime light data: A case study in Wenzhou, China.

The extraction of roadways from remote sensing imagery constitutes a pivotal task, with far-reaching implications across diverse domains such as urban planning, management of transportation systems, emergency response initiatives, and environmental monitoring endeavors. Satellite images captured during daytime have customarily served as the primary resource for this extraction process. However, the emergence of Nighttime Light (NTL) remote sensing data introduces an innovative dimension to this arena. The exploration of NTL data for road extraction remains in its nascent stage, and this study seeks to bridge this gap. We present a refined U-Net model (CA U-Net) integrated with Cross-Attention Mechanisms, meticulously designed to extract roads from Yangwang-1 NTL images. This model incorporates several enhancements, thereby improving its proficiency in identifying and delineating road networks. Through extensive experimentation conducted in the urban landscape of Wenzhou City, the model delivers highly accurate results, achieving an F1 score of 84.46%. These outcomes significantly surpass the performance benchmarks set by Support Vector Machines (SVM) and the Optimal Threshold (OT) method. This promising development paves the way towards maximizing the utility of NTL data for comprehensive mapping and analysis of road networks. Furthermore, the findings underscore the potential of utilizing Yangwang-1 data as a reliable source for road extraction and reaffirm the viability of deploying deep learning frameworks for road extraction tasks utilizing NTL data.

Application note: evaluation of the Gini coefficient at the county level in mainland China based on Luojia 1-01 nighttime light images

The Luojia 1–01 (LJ1-01) night lighting satellite's superior spatial information capture capability provides conditions for accurate assessment of regional wealth distribution inequality (RWDI) at a small scale. This paper evaluated the wealth Gini coefficient (WGC) of 2,853 counties and 31 provinces in mainland China to establish a comprehensive picture of inequalities at county-level regions in China as a whole, using data from LJ1-01 and the Suomi National Polar Orbiter Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). The WGC values (LJ-Gini) calculated by the LJ1-01 data are always higher than those (NPP-Gini) based on NPP-VIIRS, and the mean of the ratio between them is 1.7. Compared with NPP-Gini, LJ-Gini showed sensitivity to low RWDI areas. The average county and provincial LJ-Gini are statistically consistent, 0.77 and 0.78; County LJ-Gini’s volatility is significantly higher than that of the provincial LJ-Gini, with standard deviations (SD) 0.13 and 0.096. The differences of RWDI in the regions within some provinces are more significant than in other provinces. For example, the SD of Tibet is 0.31, while all provinces' average SD is 0.13. In addition, this paper establishes a grading criterion based on the normal distribution abstracted from provincial LJ-Gini to reflect the corresponding relationship between the LJ-Gini value and the five inequality ranks. Totally, RWDI demonstrates heterogeneity at various spatial scales and regions, and it correlates negatively with economic development. The superior performance of LJ1-01 data in evaluating county-level RWDI demonstrates its potential to evaluate RWDI on a smaller scale, such as communities and streets.

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.

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.

Use of Nighttime Light Images in Evaluating Refugee Settlement

Use of Nighttime Light Images in Evaluating Refugee Settlement

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.

GIS INTEGRATION OF LAND COVER WITH NIGHT-TIME LIGHTS FOR SPATIOTEMPORAL EVALUATION OF URBAN EXPANSION

Abstract. Monitoring the urbanisation process requires accurate knowledge of the present and historical spatial extents of built-up area. Understanding the quantification of urban growth is a crucial component of urban and environmental planning. The current research is focussed on evaluating the spatiotemporal change evaluation of built-up area by using a combination Landsat 8 and NPP-VIIRS datasets with GIS integration. Firstly, spatial change analysis of the built-up area was done by employing the Land Use and Land Cover (LULC) classification methodology for the years 2014 and 2022. This was done using the Random Forest (RF) classifier within the Google Earth Engine (GEE) platform. Secondly, the change in built-up area was evaluated with respect to the NTL data. The night-time light (NTL) images detect artificial lights being radiated from cities at night. The NTL data was categorised into three classes and later used to categorise the built-up area into ‘core urban’, ‘peri-urban’ and ‘rural’. The proportion of 'core urban' pixels experienced substantial growth, increasing from 9.8% to 15.5% of the total pixels between 2014 and 2022. This represents a notable increase of 58.75%. Conversely, the 'peri-urban' pixels saw a smaller increase of 4.6%, while the 'rural' pixels witnessed a decline of 8.7%. The average built-up density of the 'core urban' area exhibited an increase from 0.385 in 2014 to 0.398 in 2022. These changes can be attributed to the progressive conversion of 'rural' pixels into 'peri-urban' areas, and subsequently, the transformation of 'peri-urban' areas into 'core urban' regions.

Critical Disaster Indicators (CDIs): Deriving the Duration, Damage Degree, and Recovery Level from Nighttime Light Image Time Series

Critical Disaster Indicators (CDIs): Deriving the Duration, Damage Degree, and Recovery Level from Nighttime Light Image Time Series