CRYSTAL: A novel and effective method to remove clouds in daily nighttime light images by synergizing spatiotemporal information

Satellite nighttime light (NTL) images offer a valuable depiction of the rapidly changing world by revealing the presence of artificial illumination. Thus, daily NTL images are increasingly applied to monitor human dynamics and environmental events. However, data gaps caused by cloud contamination and low-quality observations inevitably impair the effectiveness of such applications. Although a temporal gap-filling method is employed in recent Black Marble NTL products to produce seamless images, the filled images are unsuitable for quantitative analysis. Therefore, we developed an effective method, named as Cloud Removing bY Synergizing spatio-TemporAL information (CRYSTAL), to generate cloud-free NTL images with satisfactorily accurate pixel brightness and spatial continuity. Simulation experiments show that CRYSTAL can produce more accurate results than the temporal gap-filling method in fifteen cities worldwide, with an average RMSE reduction of 33.69%. Images generated by CRYSTAL restore temporal variances in NTL and are thus suitable for multi-temporal quantitative analysis. CRYSTAL can reconstruct daily NTL time series by filling gaps using available partially clear images. Experiments in two cities demonstrated that the reconstructed time series had 31.85% more valid values than the original time series and effectively revealed urban dynamics during the early stages of the coronavirus disease 2019 pandemic. In summary, CRYSTAL is a novel and effective gap-filling method for the restoration of invalid NTL observations and has the potential to generate high-quality NTL data for use in future applications.

Night-time Human Mobility during Pandemic in the Philippines as Observed by VIIRS Satellite

Recent research has analyzed and studied the growing literature on human mobility during quarantine periods using various methodology and techniques. There are several ways to use light pollution to assess mobility. The data from the VIIRS satellite can be used to quantify light pollution and human mobility in the Philippines during quarantine. The data utilized in this study came from NASA's EOSDIS Worldview website. The number of cases and pixels count increases from early April 2020 to late August 2020. However, the cases increased from February to April 2021. This could be attributed to the active human mobility seen between December 2020 and January 2021. Human interactions have been intense since August 2020, causing an increase in COVID cases that peaked between March and April 2021, before dropping in May 2021. Following the conclusion of this study, light pollution VIIRS satellite pictures can be used to identify possible COVID- 19 cases. There are many more factors and variables to consider when writing a comprehensive paper. With the relaxed quarantine time has been achieved beyond June 2021, additional dates may be explored since there may be a direct relationship between light pollution and COVID-19 instances. © 2022 IEEE.

Assessing the Impact of Covid-19 on Human Activities in the Greater Toronto Area by Nighttime Light Images and Active Covid-19 Cases

This paper explores the effect of COVID-19 outbreaks on human activity through nighttime light images of Greater Toronto Area (GTA), Canada. The methods used in this paper include image preprocessing, image classification, and spatial analysis. By using the nighttime light radiance data from VIIRS/NPP data products and COVID-19 cases and comparing this data from the pre-pandemic year, the impact of COVID-19 was analyzed. The result shows that during the pandemic year the monthly average nighttime light radiance has decreased about 4.3-5.0% compared to the pre-pandemic year. The classification results shows that the average percentage of changes in residential areas, public facilities, and commercial areas are 0.3%, -0.7%, and -1.2%, respectively of each corresponding month. Meanwhile, the spatial analysis results show population distribution patterns in GTA during the pandemic year. Overall, the nighttime lights (NTL) images can be used for a preliminary understanding of how COVID-19 affected human activities and is corroborated with other forms data collection used for the pandemic analysis.

China's Largest City-Wide Lockdown: How Extensively Did Shanghai COVID-19 Affect Intensity of Human Activities in the Yangtze River Delta?

COVID-19 has been the most widespread and far-reaching public health emergency since the beginning of the 21st century. The Chinese COVID-19 lockdown has been the most comprehensive and strict in the world. Based on the Shanghai COVID-19 outbreak in 2022, we analyzed the heterogeneous impact of the COVID-19 lockdown on human activities and urban economy using monthly nighttime light data. We found that the impact of lockdown on human activities in the Yangtze River Delta is very obvious. The number of counties in Shanghai, Jiangsu, Zhejiang and Anhui showing a downward trend of MNLR (Mean of Nighttime Light Radiation) is 100%, 97%, 99% and 85%, respectively. Before the outbreak of COVID-19, the proportion of counties with a downward trend of MNLR was 19%, 67%, 22% and 33%, respectively. Although the MNLR of some counties also decreased in 2019, the scope and intensity was far less than 2022. Under regular containment (2020 and 2021), MNLR in the Yangtze River Delta also showed a significant increase (MNLR change > 0). According to NLRI (Nighttime Light Radiation Influence), the Shanghai lockdown has significantly affected the surrounding provinces (Average NLRI < 0). Jiangsu is the most affected province other than Shanghai. At the same time, Chengdu-Chongqing, Guangdong–Hong Kong–Macao and the Triangle of Central China have no obvious linkage effect.

Identifying Basal Nighttime Radiance Levels for Estimating Traffic Flow based on VIIRS/DNB data

The recent COVID-19 outbreak drove the attention to methods for monitoring the flow of people between human settlements, including traffic flow. Although the remote sensing of nighttime lights is a viable option to estimate traffic flow-derived indicators, changes in radiance levels at night are not all associated with traffic. This paper presents the theoretical approach proposed on the development of an algorithm able to identify spectrally unbiased control samples for regions of interest (ROI), namely roadway sections. Firstly, an experiment is presented to put in evidence the background dependency of the DNB monthly composites (vcm) radiance levels. Then, an overview of the algorithm is presented, followed by an empirical estimation of its time complexity. The results showed that the algorithm has an O(n) time complexity and that control samples and ROIs can have similar time series features, indicating that analysis without the use of control samples can lead to biased results. © 2021 Society for Industrial and Applied Mathematics.

Spatiotemporal Variation of Nitrogen Dioxide and Nighttime Light Dataset of Iranian Metropolises in the COVID-19 Outbreak

COVID-19 lockdown has caused a reduction in traffic volume and industrial activities which are the main sources of air pollution in whole of the world. As tropospheric NO2 pollutant and nighttime light (NTL) are the representative of human activities, this study focused to quantify the annual and monthly change of NO2 concentration and NTL in 14 metropolises of Iran before, during and after the lockdown months such as March, April, October and November. TROPOMI images of Sentinel-5p were used for investigation of NO2 column density in 2019, 2020 and 2021, and the variation of NTL was monitored by VIIRS images. The findings showed the majority of metropolises have an increase of NO2 concentration in March and October and a decrease in April and November in 2020 but a significant increase in 2021. The similar pattern of NTL change as NO2 was observed in the most metropolises. The correlation coefficient between NO2 concentration and NTL was calculated from 0.66 to 0.75. So, in majority of metropolises, the reduction of NO2 was observed with reduction of NTL. According to the results, reducing traffic volume as mobile source does not has an effective contribution in NO2 emission in some metropolises of Iran which the stationary sources are dominant such as Isfahan. Tehran as the capital of Iran showed the highest annual mean NO2 reduction in lockdown, this finding showed the important role of traffic volume on air quality of Tehran compared to industrial activities. The integrated application of TROPOMI and NTL data will help to better decision making for controlling and managing of air quality in country's urban area.

Assessment of Economic Recovery in Hebei Province, China, under the COVID-19 Pandemic Using Nighttime Light Data

The COVID-19 pandemic has presented unprecedented disruptions to human society worldwide since late 2019, and lockdown policies in response to the pandemic have directly and drastically decreased human socioeconomic activities. To quantify and assess the extent of the pandemic's impact on the economy of Hebei Province, China, nighttime light (NTL) data, vegetation information, and provincial quarterly gross domestic product (GDP) data were jointly utilized to estimate the quarterly GDP for prefecture-level cities and county-level cities. Next, an autoregressive integrated moving average model (ARIMA) model was applied to predict the quarterly GDP for 2020 and 2021. Finally, economic recovery intensity (ERI) was used to assess the extent of economic recovery in Hebei Province during the pandemic. The results show that, at the provincial level, the economy of Hebei Province had not yet recovered;at the prefectural and county levels, three prefectures and forty counties were still struggling to restore their economies by the end of 2021, even though these economies, as a whole, were gradually recovering. In addition, the number of new infected cases correlated positively with the urban NTL during the pandemic period, but not during the post-pandemic period. The study results are informative for local government's strategies and policies for allocating financial resources for urban economic recovery in the short- and long-term. © 2022 by the authors.

Spatio-temporal variations of night-time lights at early stages of the COVID-19 epidemic in the United States

The COVID-19 epidemic swept the world and continued to spread. Without effective medical treatments and vaccine during the early stage of the pandemic, local governments in various countries had to lock down cities and adopt non-pharmaceutical interventions (NPIs), such as the stay-at-home order, social distancing, and so on. NPIs against the COVID-19 epidemic have significantly changed socioeconomic activities in cities. However, characteristics and patterns of urban socio-economic activities under this influence are still unclear. Benefiting from the development of earth observation technologies, such large-scale changes in socioeconomic activities are enough to be captured by satellites through remotely sensed night-time lights (NTL). In this study, we selected 20 major cities in the United States including New York, Chicago and Los Angeles to analyze spatio-temporal variations of NTL caused by the lockdown of cities. The first round of COVID-19 epidemic occurred in the United States in mid-March 2020. Since March 2020, American cities have successively issued stay-at-home orders, but there are differences in the time and strictness of policy implementation. Large cities have a higher population density and a higher intensity of social activities, so they are more susceptible to infectious diseases. The diversity of lockdown dates and strictness of lockdowns in cities in the United States are conducive to investigating the spatio-temporal variations of NTL. We acquired monthly averaged NPP VIIRS products of February, March and April, 2020, which are from Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) onboard the Suomi National Polar-orbiting Platform (NPP). We further analyzed the spatial pattern, distance decay and disparities in land use types of changes in NTL. Results show that NTL generally dimmed by 5-8% in U.S. cities caused by the lockdown of cities. There are 6 cities where the luminous brightness has dropped by more than 10%: Chicago, Dallas, Denver, Detroit, Minneapolis, and St. Louis. Among them, Minneapolis has the largest decrease in luminous brightness, with a decrease of about 40% in March. The spatial change of NTL shows obvious "core-periphery" pattern that the reduction of NTL declines with the distance from the city center. This is mainly because the central area of the city is a concentrated commercial area. After the closure of the city, commercial activities have dropped significantly, resulting in an obvious reduction in NTL around city centers. The reduction of NTL varies among diverse urban land use types. In New York, NTL decreased the most on land for residence and aviation facilities by 12% and 11%, respectively. In Chicago, NTL generally decreased by 20% in all types of urban land, and NTL recovered after one month of the lockdown of cities in other urban land except sports facilities land. This study only analyzes the spatio-temporal changes of NTL. In the future, it can be combined with multi-source data to explain the driving force of NTL changes. Nighttime light remote sensing effectively reflects urban socio-economic dynamics with an important application in monitoring and assessing socio-economic impacts of emergencies. © 2022 National Remote Sensing Bulletin. All rights reserved.

Quantifying Nighttime Light Changes in KL, Putrajaya, and Selangor from Human Activities Impact of Pandemic COVID-19

The Malaysian government has implemented extensive physical distancing measures to prevent and control virus transmission in response to the pandemic COVID-19. Particularly in the Kuala Lumpur, Putrajaya, and Selangor regions, quantitative, spatially disaggregated information about the population-scale shifts in an activity caused by these measures is extremely rare. A next-generation space-borne low-light imager called the Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS-DNB) can monitor changes in human activities. However, a cross-country examination of COVID-19 replies has not yet utilized the potential. To understand how communities have complied with COVID-19 measures in the two years since the pandemic. This study aims to quantify nighttime light (NTL) before and during COVID-19 using multi-year (2019-2021) monthly time series data derived from VIIRS nighttime light (NTL) products covering urban areas in Selangor, Putrajaya, and Kuala Lumpur. The NTL was processed in the Google Earth Engine (GEE) platform. NTL data has documented the link between curfew orders, nationwide closures, and the uneven response to control measures between and within the areas. Our findings demonstrate satellite images from VIIRS DNB can examine public opinion regarding national curfews and lockdowns, laws, and the sociocultural elements that influence their effectiveness, particularly in unstable and sparsely populated areas. Statistical T-test analysis revealed that the p-value for Kuala Lumpur was 0.01687, and less than 0.05 meant a significant difference between NTL reduction before and during COVID-19. Petaling showed a p-value of 0.0034 and less than 0.05, indicating a significant difference between NTL reduction before and during COVID-19. However, for area Putrajaya, the p-value is 0.0957, and more than 0.05 means there is no significant difference between the reduction of NTL before and during COVID-19. © 2022 IEEE.

Impact analysis of COVID-19 pandemic control measures on nighttime light and air quality in cities.

The COVID-19 pandemic has profoundly affected human society on a global scale. COVID-19 pandemic control measures have led to significant changes in nighttime light (NTL) and air quality. Four cities that were severely impacted by the pandemic and that implemented different pandemic control measures, namely, Wuhan (China), Delhi (India), New York (United States), and Rome (Italy), were selected as study areas. The Visible Infrared Imaging Radiometer Suite (VIIRS) and air quality data were used to study the variation characteristics of NTL and air quality in the four cities in 2020. NTL brightness in Wuhan, Delhi, New York, and Rome decreased by 8.88%, 17.18%, 8.21%, and 6.33%, respectively, compared with pre-pandemic levels; in the resumption phase Wuhan and Rome NTL brightness recovered by 13.74% and 3.38%, but Delhi and New York decreased by 16.23% and 4.99%. Nitrogen dioxide (NO2) concentrations in the lockdown periods of Wuhan, Delhi, New York, and Rome decreased by 65.07%, 68.75%, 55.59%, and 56.81%, respectively; PM2.5 decreased by 49.25%, 69.40%, 52.54%, and 66.67%. Air quality improved, but ozone (O3) concentrations increased significantly during the lockdown periods. The methods presented herein can be used to investigate the impact of pandemic control measures on urban lights and air quality.

Rapid assessment of disaster damage and economic resilience in relation to the flooding in Zhengzhou, China in 2021

The timely and accurate assessment of flooding disasters and economic resilience is significant for post-disaster reconstruction and recovery. In July 2021, the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) daily data were explored as a proxy to assess the flooding damage caused by heavy rainfall in Zhengzhou City, China. A combination of the night-time light (NTL) changes and the radiation normalization method was used to rapidly identify affected areas and extract populations following the flooding disaster. A daily gross domestic product (GDP) prediction model was developed to evaluate the economic resilience of Zhengzhou City using multi-temporal DNB daily and monthly NTL data. The severity of the disaster was estimated by the extent of power outages, flooding crisis regions, and affected populations. It has been predicted that the Zhengzhou economy is unlikely to be restored to its normal level before the end of 2021 owing to the dual impact of the coronavirus outbreak and flooding disaster;the revised recovery-time prediction is late April 2022. We concluded that our NTL data provided new, simple, and effective insights into the post-flooding assessment of the affected areas, populations, GDP forecast, and economic recovery.

Illuminated border: Spatiotemporal analysis of COVID-19 pressure in the Sino-Burma border from the perspective of nighttime light.

The emergence of mutant strains such as Omicron has increased the uncertainty of COVID-19, and all countries have taken strict measures to prevent the spread of the disease. The spread of the disease between countries is of particular concern. However, most COVID-19 research focuses mainly on the country or community, and there is less research on the border areas between two countries. In this study, we analyzed changes in the total nighttime light intensity (TNLI) and total nighttime lit area (TNLA) along the Sino-Burma border and used the data to construct an epidemic pressure input index (PII) model in reference to the Shen potential model. The results show that, as the epidemic became more severe, TNLI on both sides of the border at the Ruili border port increased, while that in areas far from the port decreased. At the same time, increases and decreases in TNLA occurred in areas far from the port, and PII can indicate the areas where imported cases are likely to occur. Along the Sino-Burma border, the PII model showed low PII in the north and south and high PII in the central region. The areas between Dehong and Lincang, especially the Ruili, Wanding, Nansan, and Qingshuihe border ports, had high PII. The results of this study offer a reference for public health officials and decision makers when determining resource allocation and the implementation of stricter quarantine rules. With updated epidemic statistics, PII can be recalculated to support timely monitoring of COVID-19 in border areas.

Monitoring Resumption of Work and Production in Wuhan Built-up Area during COVID-19 Based on NPP-VIIRS and Landsat 8 Data

At the beginning of 2020, an outbreak of the Corona Virus Disease 2019 (COVID-19) broke out in Wuhan, Hubei Province, China. With the effective control of the epidemic, all enterprises gradually resumed work and production. The advantage of remote sensing is that it can obtain a large range of data in a short time, which is conducive to the dynamic monitoring of land surface changes. Therefore, using remote sensing technology to monitor the resumption of work and production in Wuhan built-up area is of great significance to economic and social development. This study presented a Vegetation and Building Adjusted NTL Urban Index (VBANUI) based on NPP-VIIRS and Landsat 8 data to extract the built-up of Wuhan. The extraction accuracy of VBANUI was 5.1%, which was better than the traditional method (based on Nighttime Light (NTL): 9.4% and based on Vegetation Adjusted NTL Urban Index (VANUI): 6.5%). The average value of nighttime light intensity and the proportion of high nighttime light intensity in Wuhan built-up areas from April to June were larger than those from January to March in 2020, increasing by 2.8 nW/cm2/sr and 2.6%, respectively. In general, the nighttime light intensity in the built-up area of Wuhan increased gradually with the resumption of work and production. © 2021 IEEE.

An Industrial Heat Source Extraction Method: BP Neural Network Using Temperature Feature Template

Aiming at the problem of insufficient quantity and spatial refinement in the extraction of industrial heat source from annual scale thermal anomaly data, a neural network industrial heat source extraction method based on temperature feature template is proposed by using VIIRS active fire data. This study took Beijing-Tianjin-Hebei and its surrounding areas as the study area, Firstly, according to the spatial aggregation characteristics of industrial heat sources, the heat source objects were divided by the OPTICS algorithm. Secondly, according to the thermal radiation characteristics of the heat sources, the temperature characteristic template of industrial heat sources and non-industrial heat sources were constructed. Finally, the BP neural network was used to extract industrial heat source objects using the temperature feature template and heat source statistical characteristics as parameters. The results show that: (1) the extraction precision of industrial heat source of the neural network algorithm of temperature feature template proposed in this paper reached 96.31%. Compared with time filtering and logistic regression methods, the extraction precision of industrial heat sources was improved by 8.45% and 7.53%, respectively;(2) From 2015 to 2020, the number of industrial heat sources in the six provinces and cities in Beijing-Tianjin-Hebei and its surrounding areas decreased by 27.46%. The number of industrial heat source objects and heat anomalies in Hebei Province decreased by 8.06% and 7.44% annually, respectively, which was the largest decrease compared with other provinces and cities. The concentration of industrial heat sources in Shandong and Tianjin increased by 25.72% and 86.64%, respectively, indicating that the industrial transformation and upgrade policies in the two places have achieved remarkable results;(3) Tangshan, Handan, Lvliang, and Changzhi accounted for 31.37% of the total industrial heat sources in the study area, which are the main cities in Beijing-Tianjin-Hebei and its surrounding areas. The degree of industrial heat source accumulation and energy consumption in seven cities such as Linfen and Taiyuan was higher than those in other cities;The degree of industrial heat source accumulation and energy consumption in 11 cities such as Beijing and Zhoukou was lower than those in other cities;(4) From January to May 2020, the number of industrial heat anomalies in Beijing-Tianjin-Hebei and its surrounding areas remained unchanged or increased compared with the same period in 2019 and 2021. The COVID-19 had no significant impact on the industrial heat source in the study area. The number of industrial heat anomalies in Wuhan in January and February 2020 decreased by more than 66.67% compared with that in the same period in 2019 and 2021, the number of industrial heat anomalies from March to May 2020 was lower than that in the same period of 2019. The COVID-19 has had a significant impact on industrial heat sources in Wuhan from January to May 2020. This study reflects the current situation and trend of industrial heat source development in Beijing-Tianjin-Hebei and its surrounding areas, which provides a valuable reference for the formulation and adjustment of relevant policies such as reducing energy consumption and improving secondary industry concentration. © 2022, Science Press. All right reserved.

Impact of Covid-19 Pandemic on Electricity Consumption and Nighttime Lights Based on NPP-VIIRS DNB Image Products

This paper discusses an early investigation of the impact of the Covid-19 on the socio-economic activities by using the electricity consumption and the satellite nighttime light approach. This method was implemented in the island of Bali, a well-known tourism destination. The electricity consumption trend was investigated during a period of 2019-2020 and the result then was confirmed with the satellite image of nighttime light. The analysis results have shown that the declined electricity energy usage correlates with the reduced brightness level of the night time light. It was also found that the night time light image (NTL) from remote sensing data has successfully determined the areas with a significantly decreased socio-economic activities. In contrast, the image has also uncovered some areas with an increased in night time light brightness, which indicates an increased in socio-economic activities. © 2021 IEEE.

An Impact Analysis of Artificial Light at Night (ALAN) on Bats. A Case Study of the Historic Monument and Natura 2000 Wisloujscie Fortress in Gdansk, Poland.

The artificial light at night (ALAN) present in many cities and towns has a negative impact on numerous organisms that live alongside humans, including bats. Therefore, we investigated if the artificial illumination of the historic Wisloujscie Fortress in Gdansk, Poland (part of the Natura 2000 network), during nighttime events, which included an outdoor electronic dance music (EDM) festival, might be responsible for increased light pollution and the decline in recent years of the pond bat (Myotis dasycneme). An assessment of light pollution levels was made using the methods of geographical information system (GIS) and free-of-charge satellite remote sensing (SRS) technology. Moreover, this paper reviewed the most important approaches for environmental protection of bats in the context of ecological light pollution, including International, European, and Polish regulatory frameworks. The analysis of this interdisciplinary study confirmed the complexity of the problem and highlighted, too, the need for better control of artificial illumination in such sensitive areas. It also revealed that SRS was not the best light pollution assessment method for this particular case study due to several reasons listed in this paper. As a result, the authors' proposal for improvements also involved practical recommendations for devising suitable strategies for lighting research and practice in the Natura 2000 Wisloujscie Fortress site located adjacent to urban areas to reduce the potential negative impact of ALAN on bats and their natural habitats.

Changes in turbidity and human activities along Haihe River Basin during lockdown of COVID-19 using satellite data.

During the outbreak of the COVID-19, China implemented an urban lockdown in the first period. These measures not only effectively curbed the spread of the virus but also brought a positive impact on the ecological environment. The water quality of urban inland river has a significant impact on urban ecology and public health. This study uses Sentinel-2 visible and near-infrared band reflectance and the Normalized Difference Turbidity Index (NDTI) to analyze the water quality of the Haihe River Basin during the control period of COVID-19. It is found that during the lockdown period, the river water quality was significantly improved compared to the same period in 2019. The average NDTI of the Haihe River Basin in March decreased by 0.27, a decrease of 219.06%; in April, it increased by 0.07, that is 38.38%. Further exploration using VIIRS lights found that the brightness of the lights in the main urban area was significantly lower in February, the beginning of the lockdown. However, as the city was unblocked, the lights rose sharply in March and then recovered to normal. There is obvious asynchrony in changes between river turbidity and light. The results can help understand the impact of human activities on the natural environment.

Quantifying Spatiotemporal Changes in Human Activities Induced by COVID-19 Pandemic Using Daily Nighttime Light Data.

The COVID-19 pandemic caused drastic changes in human activities and nighttime light (NTL) at various scales, providing a unique opportunity for exploring the pattern of the extreme responses of human community. This study used daily NTL data to examine the spatial variations and temporal dynamics of human activities under the influence of COVID-19, taking Chinese mainland as the study area. The results suggest that the change in the intensity of NTL is not correlated to the number of confirmed cases, but reflects the changes in human activities and the intensity of epidemic prevention and control measures within a region. During the outbreak period, the major provincial capitals and urban agglomerations were affected by COVID-19 more than smaller cities. During the recovery, different regions showed different recovery processes. The cities in West and Northeast China recovered steadily while the recovery in coastal cities showed relatively greater fluctuations due to an increase in imported cases. Wuhan, the most seriously affected city in China, did not recover until the end of March. Nevertheless, as of 31 March, the overall NTL across China had recovered to an 89.5% level of the same period in the previous year. The high consistency between the big data of travel intensity and NTL further proved the validity of the results of this study. These findings imply that daily NTL data are effective for rapidly monitoring the dynamic changes in human activities, and can help evaluate the effects of control measures on human activities during major public health events.