Nighttime Light Data Research Articles

Which Nighttime Lights Data Better Represent India’s Economic Activities and Regional Inequality?

This paper demonstrates how the estimation of economic activities and inequality for the Indian economy based on nighttime lights data from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite outperforms those obtained using traditional Defense Meteorological Satellite Program Operational Line-Scan System (DMSP-OLS) sources. In the extant literature, nighttime lights data have been frequently used as a proxy for economic activities, especially at granular geographical levels where gross domestic product data are otherwise unavailable. Since the traditional DMSP-OLS-based nighttime lights data suffer from measurement errors due to blurring and top coding, VIIRS-based nighttime lights data, which are devoid of such limitations, are gaining popularity. We show that at the subnational or district level in India, VIIRS satellite-based nighttime lights data are indeed a better proxy for economic activities and inequality. Our robust findings imply that all future scholarly works that employ nighttime lights data for the Indian economy should use VIIRS-based data for greater accuracy compared to other similar data sources.

Modeling the spatiotemporal dynamics of electric power consumption in China from 2000 to 2020 based on multisource remote sensing data and machine learning

China's rising electricity power consumption (EPC) is strongly correlated with carbon emissions. Timely and accurate analysis of the spatiotemporal dynamics of EPC is important to the realization of carbon peaking and carbon neutrality goals in China. However, due to data quality problems and limitations of modeling methods, the estimation accuracy warrants further improvement. In this study, the EPC in China from 2000 to 2020 was estimated based on multisource remote sensing data using the Random Forest (RF) model. Compared with previous studies, the accuracy of this study was improved by 39%–47%. The reasons were combining multisource remote sensing data can mitigate the quality issues of nighttime light (NTL) data, and the RF can capture the nonlinear relations between remote sensing data and EPC. In addition, the spatial pattern of the average EPC in China was dominated by the low-level EPC, as well as showing an obvious increasing trend. We also found that in the middle reaches of the Yellow River and northern coastal China, the low-speed increase in EPC led to high carbon emissions and emission intensity. We suggest optimizing the fuel fix of energy and adjusting the industrial structure, combining them with scientific and rational spatial planning.

Mapping the Time-Series of Essential Urban Land Use Categories in China: A Multi-Source Data Integration Approach

Accurate, detailed, and long-term urban land use mapping is crucial for urban planning, environmental assessment, and health evaluation. Despite previous efforts, mapping essential urban land use categories (EULUCs) across multiple periods remains challenging, primarily due to the scarcity of enduring consistent socio-geographical data, such as the widely used Point of Interest (POI) data. Addressing this issue, this study presents an experimental method for mapping the time-series of EULUCs in Dalian city, China, utilizing Local Climate Zone (LCZ) data as a substitute for POI data. Leveraging multi-source geospatial big data and the random forest classifier, we delineate urban land use distributions at the parcel level for the years 2000, 2005, 2010, 2015, 2018, and 2020. The results demonstrate that the generated EULUC maps achieve promising classification performance, with an overall accuracy of 78% for Level 1 and 71% for Level 2 categories. Features derived from nighttime light data, LCZ, Sentinel-2 satellite imagery, and topographic data play leading roles in our land use classification process. The importance of LCZ data is second only to nighttime light data, achieving comparable classification accuracy to that when using POI data. Our subsequent correlation analysis reveals a significant correlation between POI and LCZ data (p = 0.4), which validates the rationale of the proposed framework. These findings offer valuable insights for long-term urban land use mapping, which can facilitate effective urban planning and resource management in the near future.

Spatial spillover effect and driving factors of urban carbon emissions in the Yellow River Basin using nighttime light data

Yellow River Basin (YRB) is a pivotal region for energy consumption and carbon emissions (CEs) in China, with cities emerging as the main sources of regional CEs. This highlights their critical role in achieving regional sustainable development and China’s carbon neutrality. Consequently, there is a pressing need for a detailed exploration of the urban spillover effects and an in-depth analysis of the complex determinants influencing CEs within the YRB. Remote sensing data provide optimal conditions for conducting extensive studies across large geographical areas and extended time periods. This study integrates DMSP/OLS and NPP/VIIRS nighttime light datasets for a longitudinal analysis of urban CEs in the YRB. Using a harmonized dataset from DMSP/OLS and NPP/VIIRS nighttime light from 2007 to 2021, this study quantifies CEs of 58 prefecture-level cities in the YRB. By combining ESDA, STIRPAT model and spatial econometric model, this investigation further clarifies empirically the spatial spillover effects and driving factors of urban CEs. The analysis delineates a phase-wise augmentation in urban CEs, converging towards a distinct spatial distribution characterized by “lower reach > middle reach > upper reach”. The spatial autocorrelation tests unravel a complex interplay between agglomeration and differentiation patterns within urban CEs, underscored by pronounced spatial lock-in phenomena. Significantly, this study demonstrates that urbanization, economic development, energy consumption structure, green coverage rate, industrial structure, population, technological progress, and FDI each exhibit varied direct and indirect effect on urban CEs. Furthermore, it elaborates on potential policy implications and future research directions, offering crucial insights for formulating CEs mitigation strategies to advance sustainable development.

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.

How do storm surge disaster losses affect economic development?: Perspectives from disaster prevention and mitigation capacity

Storm surge disasters have caused devastating losses to coastal areas, making disaster prevention and mitigation capacity (DPMC) critical in promoting high-quality and sustainable economic development. In this paper, the systematic construction of a comprehensive index of storm surge disaster losses (SSDLs) and DPMC in China is described respectively. Then, panel fixed effect and threshold models are established to explore the relationship between SSDLs and economic growth, in particular, the moderating effect of DPMC. Our results reveal that, from 2006 to 2019, SSDLs exhibit an inverted N-shaped trend, with losses gradually decreasing from south to north. DPMC has been increasing but has visible spatial differences. Notably, SSDLs have a significant negative impact on economic development in China's coastal areas, however, DPMC can play an effective role in mitigating and regulating these negative shocks. With the continuous improvement of China's disaster prevention and mitigation system, SSDLs can be largely offset. Heterogeneity analysis shows that DPMC is most effective in the sample with a high SSDL, low DPMC, and in the southern marine economic circle in China. Further, the robustness tests are ensured by replacing measurement method, replacing GDP per capita with night-time lighting data, and replacing econometric models. Importantly, our study highlights the crucial non-linear role of DPMC in reducing the losses caused by storm surge disasters and promoting sustainable economic development. These findings provide valuable insights for policymakers to improve capacity building and subjectivity, regional cooperation and ensure the economic resilience of coastal areas in the face of storm surge disasters.

Leveraging context-adjusted nighttime light data for socioeconomic explanations of global urban resilience

Urban resilience, crucial for achieving sustainable development goals, entails the adaptation and recovery of urban systems from external shocks, such as the recent global pandemic. To investigate the response of urban systems to COVID-19, this study employs context-adjusted nighttime light data to model global urban resilience by combining an evolving urban resilience metric (EURm) with the shape similarity of resilience curves. Random effect analysis and counterfactual explanations are then implemented to explore the socioeconomic impacts on urban resilience during the pandemic, providing strategic insights for improvement. Our results delineate five diverse urban resilience patterns, each characterized by distinct phases of downturn, minimum, and recovery, with examples from Malaysia, Japan, the United States, China, and South Sudan. We also find notable correlations between socioeconomic factors and urban resilience, highlighting that stringent measures may reduce resilience, whereas proactive health and containment strategies could bolster resilience. Meanwhile, economic stress reflected by inflation adversely affects resilience. Furthermore, we delve into strategic socioeconomic modifications to enhance urban resilience using counterfactual explanations, underlining the importance of customized interpretation for varying countries. Overall, this study advances our understanding of urban resilience during global crises, guiding context-specific resilience strategies in urban planning and policy-making.

Integrating supply and benefits of ecosystem services into ecological security pattern: A spatial flow perspective

Ecological security pattern (ESP) is an effective way to alleviate the conflict between social development and ecological conservation. Previous studies on ESP mostly identified ecological sources derived from local supply and demand of diverse ecosystem services (ESs), overlooking the spatial flow of ESs and thus limiting its significance in both local and regional scales. This study proposed a framework integrating ESs supply and benefit from a spatial flow perspective for ecological sources identification. After modifying resistance surface by nighttime light data and extracting ecological corridors by MCR model, the ESP was finally constructed and optimized for sustainable land use management in the Yangtze River Delta (YRD) in 2020. The findings indicated that ecological sources were predominantly concentrated in the southwestern forestland and central Taihu Lake region, accounting for 18.47 % of the total area in the YRD. Totally 1035 ecological corridors were extracted, including 45 main corridors with a distance of 4284.47 km, overall exhibiting a “one vertical and three horizontals” shape distribution. Finally, an ESP of “four areas and four lines” was constructed after optimization, and targeted suggestions were proposed for regional sustainability. This study introduced spatial flow perspective and ESs benefit into ESP construction, which provides a scientific basis for sustainable land use management in a regional integration perspective.

Money Laundering Observation from Outer Space

This paper examines the potential of nighttime light (NTL) data as an alternative data source to predict the number of money laundering events. The study is based on the assumption that money laundering as one of financial crime categories is linked to economic development, and previous research has explored the relationship between NTL and both economic data and crime. Panel regression analysis with random effects was used to investigate the potential of NTL data to estimate money laundering activity, which was measured using Suspicious Transaction Reports (STRs) data as a proxy variable. The results suggest that NTL data can be a promising tool for estimating money laundering activity, providing new insights into the use of alternative data sources in predicting this illegal activity. The findings of this research could also contribute to the development of more effective anti-money laundering strategies by law enforcement and policymakers.

Multi-Scale Dynamics and Spatial Consistency of Economy and Population Based on NPP/VIIRS Nighttime Light Data and Population Imagery: A Case Study of the Yangtze River Delta

Population and economy are crucial factors contributing to regional disparities. Studying the patterns and relationships between these two elements is essential for promoting sustainable development in regions and cities. This study constructs multi-scale geographic concentration indices and inconsistency indices, utilizing NPP/VIIRS and LandScan data to quantitatively analyze the spatial pattern changes of population and economy in the Yangtze River Delta across various spatial scales, revealing the matching relationships between population and economic elements within cities. The results indicate that the economy in the Yangtze River Delta is spreading outward from the core areas, with the average population–nightlight inconsistency index decreasing from 1.57 to 1.33. This suggests that the imbalance between population and economy within the urban agglomeration is gradually improving, consistent with trends observed in statistical survey data. Within individual cities, there is a noticeable spatial mismatch between population and nightlight intensity, with the population primarily concentrated in urban core areas. As urban spaces expand, the areas where population concentration is significantly lower than nightlight concentration are gradually diminishing. By 2022, the land area where population and economic concentration are coordinated within the Yangtze River Delta urban areas increased from 9.13% to 16.24%. Population concentration in these coordinated regions rose from 11.33% to 16.33%, while nightlight concentration increased from 9.98% to 13.63%. The improved geographic concentration and inconsistency indices are effective indicators for multi-scale monitoring of population and economic spatial changes. The integration of NPP/VIIRS nighttime light data and LandScan data provides an effective method for uncovering different spatial patterns of population and socio-economic element aggregation in urban structures. This can offer insights for promoting sustainable regional and urban development.

Estimating GDP by Fusing Nighttime Light and Land Cover Data

Accurate information on gross domestic product (GDP) is essential for better understanding the dynamics of regional economies and urbanization processes. Satellite based nighttime light datasets can well track GDP in urban areas, however, they are difficult to be used in suburban, rural and sparsely populated areas. Thus, this study explored the potential of GlobeLand30 and relief degree for improving the ability of VIIRS Nighttime Light data of GDP estimation. Firstly, we calculated the Moran’s Index (Moran’s I) to measure spatial auto-correlation of GDP. At provincial level, Moran’s I Index of GDP is 0.14, Z value is 2.29. While at the prefecture city level, it is 0.11 and 14.54, respectively. Then, we compared the results derived from geographically weighted regression (GWR) and OLS models (i.e., R2, root mean square error, corrected Akaike information criteria and residuals). Both models suggest that land cover information can significantly improve GDP estimation performance, and total nighttime light (TNL) is the most important economic indicator for estimating GDP. The coefficients of the GWR model for TNL at the provincial and prefecture levels are 1.75 and 1.19, respectively, which are significantly larger than the coefficients for other factors such as land cover and terrain undulation. In addition, the GWR model performed better than OLS model in GDP estimation at both provincial and prefecture levels, and prefecture-level models can better depict the spatial variation in detail. In provincial-level models, GWR could account for 93% of economic development, while OLS could only reflect 82%. Likewise, in prefecture-level models, R2 of GWR model improved almost 50% compared with that of OLS model.

Global risk assessment of river pollution stress based on nighttime light remote sensing data

Rivers play a crucial role in the development of human civilization, and river pollution is a significant environmental issue that accompanies with intensified human activity. However, the evaluation of river pollution at a global scale is difficult because of the limitations of long-term pollution-related datasets. As human activities are the main factor causing river pollution, nighttime light (NTL) remote sensing data can be used as an alternative indicator of the risk of river pollution stress(RPS), which is closely related to human activities and refers to the amount of pollutants within the confluence range of river reaches. In this study, we propose a river pollution pressure index (PI) to indicate risk of RPS by considering the accumulation effect of water flow. Then we calculated PI of over 0.67 million reaches global with annual total flow >100 million m3/s from 2000 to 2022, which was validated using water quality data of >2000 river sections in China. The results show that, from 2000 to 2022, the spatial variations of the risk of RPS are uneven, with a migration trend from west to east. The risk of RPS continues to increase globally, especially rapidly after 2010. Central Asia, Southeast Asia, East Asia, and eastern China are the regions with the fastest growth rates. In most developed countries, developing countries, and underdeveloped countries, the risk of RPS is high and increasing slowly, moderate and increasing rapidly, and low and increasing slowly, respectively. However, in some special cases, such as Japan, the risk of RPS continues to decrease. These spatiotemporal variations of the risk of RPS correlate with global events, such as quantitative easing of global economy after 2008, China's “Belt and Road Initiative”, and COVID-19. This study demonstrates that NTL data can be applied to evaluate the global risk of RPS.

The power of spectrally enhanced artificial night-time lights data: Assessing NTL risks along the urban-natural interface

Artificial night-time lights (NTL) have long been known for their adverse effects on humans and the environment. Recent studies report that the severity of NTL impact on organisms is associated not only with its intensity but also a spectrum. The spectral resolution of freely available satellite NTL data is restricted to red, green, and blue sub-spectra, which are significantly wider than the ranges of vulnerability, reported by laboratory studies for various species. The present study is the first attempt to overlap spectrum-specific NTL data, describing the intensities of light emitted by different lamp types with relatively narrow emission peaks, with the sites where species vulnerable to specific NTL sub-spectra were detected. We overlap those light intensity maps with increasingly detailed maps of natural areas located along the urban-natural interface of the Haifa region. We analyze light pollution in the ecological corridors, which host numerous species with different, but unknown, spectrum-specific effects of NTL (a coarse-level analysis), and in the sites of several species, with either known or unknown spectrum-specific effects of NTL (a fine-level analysis). We show that a considerable part of the ecological corridors is polluted by metal halide and high-pressure sodium lamps which may negatively influence plants, bees, sea turtles, birds, and mammals. One habitat site of the Near Eastern fire salamander (Salamandra infraimmaculata) is polluted by lamps with green-light emission peaks which may explain the low reproductive success of this population. Despite the study limitations, related to the region-specific NTL data of spectrum-specific resolution and scarcity of evidence about the spectrum-specific NTL harmful effects on organisms, we believe that the obtained results would contribute to the elaboration of more informed fine-tuned artificial lighting policies which would diminish the burden of urban built-up zones on their neighboring natural areas.

Linkages between digital economy and ecosystem services: Revealing the spatiotemporal relationship based on U-shaped effect

This study employs geographically and temporally weighted regression and spatial Durbin models to investigate the impact of digital economy index (DEI) on ecosystem service value (ESV). Estimates of DEI are obtained from night-time light data, and ESV is assessed by the equivalent factor method. The findings indicate significant spatial agglomeration of both DEI and ESV in China. Overall, the impact of DEI on ESV exhibits a U-shaped effect: initially restrained, then promoted. Moreover, the analysis detects spatiotemporal heterogeneity in the DEI-ESV interaction. Spatially, higher DEI positively impacts lower ESV in eastern China, while lower DEI negatively impacts higher ESV in western China. Temporally, most cities in eastern China, unlike those in western China, surpassed the U-shaped curve's inflection point during the study period. Furthermore, the number of cities exceeding the inflection point has increased, amplifying the dual positive and negative impacts of DEI on ESV over time. By revealing the multi-staged and geographically diverse impact of DEI on ESV, this study helps to address the environmental sustainability challenges posed by the rapid advancement of digital technologies.

Exploring the dynamics and trends of carbon emission spatiotemporal patterns in the Chengdu–Chongqing Economic Zone, China, from 2000 to 2020

Analysis of the spatial–temporal pattern and trend of carbon emissions provides an important scientific basis for the development of a low-carbon economy. Based on the corrected NPP-VIIRS and DMSP/OLS nighttime light data, a carbon emission model for the Chengdu–Chongqing Economic Zone (CCEZ) in China is constructed. Furthermore, the article establishes an integrated qualitative and quantitative research system. The qualitative results show that at the city and county scales, the high carbon emission areas and counties are mainly distributed in Chengdu and Chongqing, while the low carbon emission areas are concentrated in the marginal cities of the CCEZ and the counties with low levels of industrialization around the Sichuan Basin. The high-carbon emission zone tended to expand to the north, and the low-carbon emission zone tended to expand to the south. At the grid scale, the carbon emissions of the CCEZ fluctuated and increased from 2000 to 2020, forming a trend connected with those of the central city, with high carbon emissions at the core and radiating outward expansion. Quantitative analysis revealed that carbon emissions at the county and grid scales exhibited a significant positive global spatial correlation, and the overall correlation degree exhibited an increasing trend.

Using Nighttime Light Data to Explore the Extent of Power Outages in the Florida Panhandle after 2018 Hurricane Michael

The destructive forces of tropical cyclones can have significant impacts on the land, contributing to degradation through various mechanisms such as erosion, debris, loss of vegetation, and widespread damage to infrastructure. Storm surge and flooding can wash away buildings and other structures, deposit debris and sediments, and contaminate freshwater resources, making them unsuitable for both human use and agriculture. High winds and flooding often damage electrical disubstations and transformers, leading to disruptions in electricity supply. Restoration can take days or even weeks, depending on the extent of the damage and the resources available. In the meantime, communities affected by power outages may experience difficulties accessing essential services and maintaining communication. In this study, we used a weighted maximum likelihood classification algorithm to reclassify NOAA’s National Geodetic Survey Emergency Response Imagery scenes into debris, sand, water, trees, and roofs to assess the extent of the damage around Mexico Beach, Florida, following the 2018 Hurricane Michael. NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) was processed to estimate power outage duration and rate of restoration in the Florida Panhandle based on the 7-day moving averages. Percent loss of electrical service at a neighborhood level was estimated using the 2013–2017 American Community Survey block group data. Spatial lag models were employed to examine the association between restoration rates and socioeconomic factors. The analysis revealed notable differences in power-restoration rates between urbanized and rural areas and between disadvantaged and more affluent communities. The findings indicated that block groups with higher proportions of minorities, multi-family housing units, rural locations, and households receiving public assistance experienced slower restoration of power compared to urban and more affluent neighborhoods. These results underscore the importance of integrating socioeconomic factors into disaster preparedness and recovery-planning efforts, emphasizing the need for targeted interventions to mitigate disparities in recovery times following natural disasters.

Spatiotemporal evolution of carbon emissions and influencing factors in county-level based on nighttime lighting data: a case study in Huaihai economic zone core city

ABSTRACT Driven by China’s carbon peaking and carbon neutrality goals, this study constructs a carbon emission inversion model for 57 county-level in the core city cluster of the Huaihai Economic Zone. It analyzes the spatial-temporal characteristics of carbon emissions in these counties between 2001 and 2021. It also explores the impacts of different socio-economic factors on carbon emissions by adopting geographically and temporally weighted regression methods. The results showed that: (1) From 2001 to 2021, the total carbon emissions of counties increased from 63.42 Mt to 279.89 Mt, and the distribution pattern of the region is “high in the middle and low in the east and west.” (2) Carbon emissions have significant positive spatial correlations, with the global Moran’s I index showing a wave-like downward trend from 0.233 to 0.0954, indicating that the spatial concentration of carbon emissions in different counties is gradually decreasing, and the differences show a trend of narrowing. (3) Significant spatial and temporal heterogeneity is among the influencing factors. It is mainly manifested in population size and industrial structure, while economic scale and technological progress are the dominant factors in carbon emissions. The research results will inform county-level low-carbon development.

Refining Long-Time Series of Urban Built-Up-Area Extraction Based on Night-Time Light—A Case Study of the Dongting Lake Area in China

By studying the development law of urbanization, the problems of disorderly expansion and resource wastage in urban built-up areas can be effectively avoided, which is crucial for the long-term sustainable development of cities. This study proposes a high-precision urban built-up-area extraction method for county-level cities for small and medium-sized towns in county-level regions. Our process is based on the Defense Meteorological Satellite/Operational Linescan System (DMSP/OLS) and the NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS), which develops long-term series of coordinated night-time light (NTL) datasets. We then combined this with the Normalized Vegetation Index (NDVI) to calculate the Vegetation-Adjusted NTL Urban Index (VANUI). We combine land use data and a support vector machine (SVM) for semi-supervised classification learning to propose a high-precision urban built-up-area extraction method for county-level cities. We achieved the following results: (1) we fit binary polynomials to the DMSP/OLS and VIIRS NTL datasets based on the correspondence of the mean values to construct a consistent time series of NTL data. (2) Our method effectively improves the accuracy of urban built-up-area extraction, especially for county-level cities, with an overall accuracy of 91.84% and a Kappa coefficient of 0.83. (3) Our method can perform a long-time series of urban built-up-area extraction, and, by studying the spatial and temporal changes in urban built-up areas, it can provide valuable information for sustainable urban development and urban planning.

Quantifying heterogeneous impacts of 2D/3D built environment on carbon emissions across urban functional zones: A case study in Beijing, China

Rapid urbanization has led to a significant increase in carbon emissions (CES), adversely affecting the urban ecological environment. As fundamental units in urban planning and management, urban functional zones (UFZs) exhibit similar energy consumption patterns. Previous studies on urban CES have often focused on large-scale regions, lacking research on CES differentials at the scale of UFZs. This study, using Beijing as a case study, employed multi-source geospatial data to estimate CES and analyze influencing factors at the UFZ scale. First, a multi-scale semantic segmentation network was constructed to produce UFZ map using high-resolution remote sensing images and OpenStreetMap road data. Second, a top-down approach was employed, incorporating nighttime light data to calculate the UFZ-scale CES to analyze differences in CES among various UFZs. Finally, factors influencing urban CES, including socioeconomic factors and two-dimensional (2D) and three-dimensional (3D) urban built environment, were analyzed using Pearson analysis and GeoDetector analysis. Results indicate that: 1) Among the 2813 UFZs in Beijing, the majority belong to residential zones (34.23 %), followed by institutional zones (20.33 %); 2) The total CES in study area amount to 4.07 × 107 tons. Commercial zones contribute significantly higher CES than other UFZs, followed by residential zones. 3) For different types of UFZs, the impact of various factors on CES differs significantly. Population density, the highest building height, base area, and average building volume are the main correlation factors. Spatial analysis of CES at the UFZ scale contributes to the management of low-carbon cities, energy allocation, and optimization of urban planning implementation.

Resilience analysis of urban commercial area under the COVID-19 epidemic using night-time light remote sensing data

ABSTRACT The global outbreak of the novel coronavirus has had a great impact on commercial areas, particularly in Wuhan, the first city in China affected by the epidemic. Exploring the changing pattern of these areas during the epidemic period is crucial for effectively restoring the urban economic level and promoting economic development on the basis of epidemic prevention and control in the post-epidemic era. The paper proposes a resilience analysis of urban commercial areas during the COVID-19 epidemic using time-series night-time light remote sensing data. Based on the constructed time-series night-time light remote sensing dataset and considering the urban impervious surface, the urban commercial area was extracted under multi-scale segmentation. Based on night-time light data before and after the epidemic, the resilience of representative commercial areas in Wuhan City, Hubei Province during the COVID-19 epidemic was analysed using a modified improved resilience assessment framework. The results showed that the lighting values of commercial areas and impervious areas decreased by up to 30% following the implementation of city lockdown measures. The change of light brightness in the commercial areas was more prominent, and the range of change is larger than that in the non-commercial areas. The lighting brightness of the commercial areas exhibited a significant downward trend throughout the entire lockdown period, and the downward trend will still remain for a period of time after unlocking. Among all the commercial areas, Wuguang commercial area demonstrated the highest recovery rate during the lockdown period and instantaneous recovery rate after the lockdown, with relatively low closure loss and the highest level of resilience.