Nighttime Light Images Research Articles

Covid-19 pandemic induced spatio-temporal changes in human activity in the United Arab Emirates using night-time light data

The COVID-19 pandemic outbreak in early 2020 impacted people’s lives and the working environment and led to a drastic change in human activities. This has opened a window to analyze the people’s response to the safety measures implemented to contain the virus, which can be reflected and monitored using Night-time light (NTL) images. This study aims to demonstrate the use of NTL images captured by satellites to detect changes in human activity in the UAE before and during the pandemic. The study period will include the pandemic year and the previous years (2017-2019) will be used as a control. Raw NTL data was pre-processed to obtain cloud-free radiance images through which the monthly average radiance was calculated. The monthly average radiance is categorized into three classes: residential, commercial, and roads. The radiance levels during the lockdown were compared against the months prior to the restriction imposed. The results revealed that the roads category showed the highest decrease in radiance levels due to the enforcement of the safety measures, followed by the commercial category, whereas the least reduction was observed for the residential category. The results show how NTL radiance could be used in monitoring the changes in human activities.

Investigating the spatiotemporal changes and driving factors of nighttime light patterns in RCEP Countries based on remote sensed satellite images

The Regional Comprehensive Economic Partnership (RCEP) agreement signed in November 2020 is the world's largest and greatest potential free trade area. It was officially enforced on January 1, 2022, which attracted great worldwide attention. The RCEP advocates for the development of the low-carbon economy but lacks effective monitoring methods for socioeconomic conditions and carbon emissions . Nighttime light (NTL) images can objectively reflect socioeconomic status and carbon emission potential. However, most studies focused only on consistency correction for multi-source NTL data without deeply analyzing NTL dynamics. An in-depth analysis of the NTL change in the RCEP region is necessary and will strongly facilitate the strategic deployment of a low-carbon economy in RCEP countries. Sen's slope estimator, the Mann-Kendall trend test, the Mann-Kendall mutation test, and Hurst analysis were adopted to analyze the spatiotemporal changes of NTL in the past and future, and gray relational analysis was applied to explore driving factors. The results showed that (1) RCEP's NTL became brighter from 2000 to 2018, and the total NTL amount in 2018 was 3.27 times that in 2000. NTL in all countries except Japan showed an increasing trend to varying degrees, and this trend was more pronounced in developing countries. The maximum increase amount and growth rate were in China and Vietnam, respectively. (2) Areas where NTL showed an increasing trend in the past accounted for 76.67% of the NTL zones, and the areas where predicted to be brighter in the future accounted for 63.87%. These regions were generally clustered in developed urban zones. However, most Japanese cities have observed darkening trends. (3) The relational grade between NTL and all socioeconomic factors in the RECP region was greater than 0.6, and construction land expansion was the most direct and profound driver. Compared with that in developed countries, NTL in developing countries was more closely related to socioeconomic factors. Spatiotemporal Changes and Driving Factors in Nighttime Light for the RCEP Region. • Nighttime light (NTL) in the RCEP region became brighter from 2000 to 2018. • NTL in developed cities increased significantly and is expected to be brighter. • China led the lighting trend, while Japan's NTL declined. • Construction land expansion was the dominant driver of NTL changes in RCEP region. • Socioeconomic factors in developing countries more strongly promoted NTL increases.

Monitoring Spatiotemporal Distribution of the GDP of Major Cities in China during the COVID-19 Pandemic.

Monitoring the fine spatiotemporal distribution of urban GDP is a critical research topic for assessing the impact of the COVID-19 outbreak on economic and social growth. Based on nighttime light (NTL) images and urban land use data, this study constructs a GDP machine learning and linear estimation model. Based on the linear model with better effect, the monthly GDP of 34 cities in China is estimated and the GDP spatialization is realized, and finally the GDP spatiotemporal correction is processed. This study analyzes the fine spatiotemporal distribution of GDP, reveals the spatiotemporal change trend of GDP in China’s major cities during the current COVID-19 pandemic, and explores the differences in the economic impact of the COVID-19 pandemic on China’s major cities. The result shows: (1) There is a significant linear association between the total value of NTL and the GDP of subindustries, with R2 models generated by the total value of NTL and the GDP of secondary and tertiary industries being 0.83 and 0.93. (2) The impact of the COVID-19 pandemic on the GDP of cities with varied degrees of development and industrial structures obviously varies across time and space. The GDP of economically developed cities such as Beijing and Shanghai are more affected by COVID-19, while the GDP of less developed cities such as Xining and Lanzhou are less affected by COVID-19. The GDP of China’s major cities fell significantly in February. As the COVID-19 outbreak was gradually brought under control in March, different cities achieved different levels of GDP recovery. This study establishes a fine spatial and temporal distribution estimation model of urban GDP by industry; it accurately monitors and assesses the spatial and temporal distribution characteristics of urban GDP during the COVID-19 pandemic, reveals the impact mechanism of the COVID-19 pandemic on the economic development of major Chinese cities. Moreover, economically developed cities should pay more attention to the spread of the COVID-19 pandemic. It should do well in pandemic prevention and control in airports and stations with large traffic flow. At the same time, after the COVID-19 pandemic is brought under control, they should speed up the resumption of work and production to achieve economic recovery. This study provides scientific references for COVID-19 pandemic prevention and control measures, as well as for the formulation of urban economic development policies.

Modeling the spatiotemporal dynamics of global electric power consumption (1992–2019) by utilizing consistent nighttime light data from DMSP-OLS and NPP-VIIRS

Adequate and up-to-date knowledge of the spatiotemporal dynamics of electricity power consumption (EPC) is important for the sustainable use of global electricity power resources. However, global EPC patterns were not clear after Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) in 2013 due to the significant differences between Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) and DMSP-OLS. In this paper, global EPC patterns in the recent decade are investigated and assessed for the first time by the proposed locally adaptive method with integrating two nighttime light (NTL) images to global pixel-level EPC from 1992 to 2019. The geospatial dataset of built-up area density (BUAD) is adopted with a higher spatial resolution and more direct relation to human activities. A two-step regression method is designed to simulate DMSP-like images after 2013, based on the inter-annual relationships of provincial-level VIIRS. With this consistent nighttime light dataset, pixel-level EPC over the 28 years are estimated for the first time, and then the spatiotemporal dynamics of EPC are investigated from global, continental, to national scales. The obtained EPC estimates are of satisfactory accuracy in 92.6% of the countries with a MARE (Mean of the Absolute Relative Error) of less than 20%. Over these 28 years, Japan, South Korea, and China experienced high proportion of EPC high-growth. These results provide reliable scientific basis for exploring the spatial pattern and temporal variations of global EPC, especially for the latest years.

UTILISING VIIRS DNB NIGHTTIME LIGHT DATA TOGETHER WITH LANDSAT DAYTIME DATA TO ASSESS CHANGES IN LIGHT POLLUTION IN ETHEKWINI METROPOLITAN MUNICIPALITY (EMM)

Abstract. Night-time light (NTL) data has become widely used in different studies, including urbanization and light pollution subjects. Some sources of NTL data have made the data freely available on online platforms, making it easy to study subjects that relate to the use of artificial light at night. African countries have not published much on the use of NTL data. Still, there has been a development in different countries in the continent, such as economic growth and the provision of new and improved infrastructure. These developments can be considered primary contributors to the increase in light pollution worldwide. This study aimed at using NTL data together with familiar daytime data to assess how light pollution has been changing in eThekwini Metropolitan Municipality and again assess the changes in the sources of light pollution through the assessment of Land-use Land-cover (LULC) change of the study area. NTL images were reclassified into five different classes of light pollution using the reclassification tool in ArcMap 10.7. Supervised classification was performed to assess the changes in LULC. The confusion/error matrix was done to verify the accuracy of these classifications. These showed that light pollution is increasing in eThekwini Metropolitan Municipality, with the increased built-up area also being witnessed. NTL data is becoming useful worldwide, and it is highly recommended for African countries also to do research using this data in their studies.

Georeferencing Urban Nighttime Lights Imagery Using Street Network Maps

Astronaut photography acquired from the International Space Station presently is the only available option for free global high-resolution nighttime light (NTL) imagery. Unfortunately, these data are not georeferenced, meaning they cannot easily be used for many remote sensing applications such as change detection or fusion. Georeferencing such NTL data manually, for example, by finding tie points, is difficult due to the strongly differing appearance of any potential references. Therefore, realizing an automatic method for georeferencing NTL imagery is preferable. In this article, such an automatic processing chain for the georeferencing of NTL imagery is presented. The novel approach works by simulating reference NTL images from vector-based street network maps and finding tie points between these references and the NTL imagery. To test this approach, here, publicly available open street maps are used. The tie points identified in the reference and NTL imagery are then used for rectification and thereby for georeferencing. The presented processing chain is tested using nine different astronaut photographs of urban areas, illustrating the strengths and weaknesses of the algorithm. To evaluate the geometric accuracy, the photography is finally matched manually against an independent reference. The results of this evaluation depict that all nine astronaut photographs are georeferenced with accuracies between 2.03 px and 6.70 px. This analysis demonstrates that an automatic georeferencing of high-resolution urban NTL imagery is feasible even with limited attitude and orbit determination (AOD). Furthermore, especially for future spaceborne NTL missions with precise AOD, the algorithm’s performance will increase and could also be used for quality-control purposes.

Global gridded GDP data set consistent with the shared socioeconomic pathways

The vulnerability, exposure and resilience of socioeconomic activities to future climate extremes call for high-resolution gridded GDP in climate change adaptation and mitigation research. While global socioeconomic projections are provided mainly at the national level, and downscaling approaches using nighttime light (NTL) images or gridded population data can increase the uncertainty due to limitations. Therefore, we adopt an NTL-population-based approach, which exhibits higher accuracy in socioeconomic disaggregation. Gross regional product of over 800 provinces, which covering over 60% of the global land surface and accounted for more than 80% of GDP in 2005, were used as input. We present a first set of comparable spatially explicit global gridded GDP projections with fine spatial resolutions of 30 arc-seconds and 0.25 arc-degrees for the historical period of 2005 and for 2030–2100 at 10-year intervals under the five SSPs, accounting for the two-child policy in China. This gridded GDP projection dataset can broaden the applicability of GDP data, the availability of which is necessary for socioeconomic and climate change research.

Satellite monitoring of shrinking cities on the globe and containment solutions

SummaryShrinking cities are often neglected in the context of global urbanization, the tip of the iceberg that was driven by underlying complex sets of causes. Therefore, it is urgent and crucial to investigate the invisible aspects of global urbanization propelling specific challenges to attain Sustainable Development Goal 11 (SDG 11) related to sustainable cities and communities. Here, we identify shrinking cities in 1992–2000, 2000–2012, and 2013–2018 and predict them in 2018–2050, using nighttime light images and redefined natural city boundaries. The proportion of shrinking cities increased from 9% to 16 and 25%. Looking ahead, there will be 7,166 predicted shrinking cities in 2050, accounting for 37% of all cities. In this context, synergistic efforts like regreening vacant lands and constructing compact cities would help achieve SDG 11 in consideration of the new urban shrinking landscape with multisource data like CO2 emissions and points of interests (POIs).

Mapping Building-Based Spatiotemporal Distributions of Carbon Dioxide Emission: A Case Study in England.

The spatiotemporal inventory of carbon dioxide (CO2) emissions from the building sector is significant for formulating regional and global warming mitigation policies. Previous studies have attempted to use energy consumption models associated with field investigations to estimate CO2 emissions from buildings at local scales, or they used spatial proxies to downscale emission sources from large geographic units to grid cells for larger scales. However, mapping the spatiotemporal distributions of CO2 emissions on a large scale based on buildings remains challenging. Hence, we conducted a case study in England in 2015, wherein we developed linear regression models to analyze monthly CO2 emissions at the building scale by integrating the Emissions Database for Global Atmospheric Research, building data, and Visible Infrared Imaging Radiometer Suite night-time lights images. The results showed that the proposed model that considered building data and night-time light imagery achieved the best fit. Fine-scale spatial heterogeneity was observed in the distributions of building-based CO2 emissions compared to grid-based emission maps. In addition, we observed seasonal differences in CO2 emissions. Specifically, buildings emitted significantly more CO2 in winter than in summer in England. We believe our results have great potential for use in carbon neutrality policy making and climate monitoring.

Estimation and Analysis of PM2.5 Concentrations with NPP-VIIRS Nighttime Light Images: A Case Study in the Chang-Zhu-Tan Urban Agglomeration of China.

Rapid economic and social development has caused serious atmospheric environmental problems. The temporal and spatial distribution characteristics of PM2.5 concentrations have become an important research topic for sustainable social development monitoring. Based on NPP-VIIRS nighttime light images, meteorological data, and SRTM DEM data, this article builds a PM2.5 concentration estimation model for the Chang-Zhu-Tan urban agglomeration. First, the partial least squares method is used to calculate the nighttime light radiance, meteorological elements (temperature, relative humidity, and wind speed), and topographic elements (elevation, slope, and topographic undulation) for correlation analysis. Second, we construct seasonal and annual PM2.5 concentration estimation models, including multiple linear regression, support random forest, vector regression, Gaussian process regression, etc., with different factor sets. Finally, the accuracy of the PM2.5 concentration estimation model that results in the Chang-Zhu-Tan urban agglomeration is analyzed, and the spatial distribution of the PM2.5 concentration is inverted. The results show that the PM2.5 concentration correlation of meteorological elements is the strongest, and the topographic elements are the weakest. In terms of seasonal estimation, the spring estimation results of multiple linear regression and machine learning estimation models are the worst, the winter estimation results of multiple linear regression estimation models are the best, and the annual estimation results of machine learning estimation models are the best. At the same time, the study found that there is a significant difference in the temporal and spatial distribution of PM2.5 concentrations. The methods in this article overcome the high cost and spatial resolution limitations of traditional large-scale PM2.5 concentration monitoring, to a certain extent, and can provide a reference for the study of PM2.5 concentration estimation and prediction based on satellite remote sensing technology.

Impact of temporal compositing on nighttime light data and its applications

In recent decades, nighttime light (NTL) images have been widely explored to portray human footprints. Most of the studies used monthly or yearly temporal composite NTL products as a solution for invalid observations due to cloud coverage and outlier signals. However, the impact of temporal compositing on NTL data and its applications remains largely unclear. Here, we utilized over 180,000 daily NTL tiles from NASA's Black Marble VIIRS product (VNP46A2, 2012–2020), covering 230 cities from China and the United States, to delve into the influence of temporal compositing on valid pixel coverage and spatiotemporal pattern of NTL data and the performance of three representative types of NTL-based applications. Our analysis showed temporal compositing was an imperative and efficient solution to the prevailing invalid observations. On average a 16-day composite was required to ensure at least 95% of valid pixel coverage for a city, where a longer composite period was needed for cities in a pluvial temperate climate zone. Compositing daily NTL data into a 3-day to 31-day period markedly reduced its spatiotemporal variation and incurred a 3–9 nWatts/cm2/sr, or 22%–37%, absolute difference in NTL magnitude, which was particularly high in developed cities and intra-city areas. We attributed such effect to the number of valid observations available for generating the composite data and the extremely high variation in daily NTL stemmed from human activities, as well as the uncertainties in VNP46 product and VIIRS instrument. The impact of temporal compositing on NTL-based applications varied greatly, from insignificant to very sensitive, across application types and spaces. Our analysis provides a comprehensive understanding of the capability and uncertainties in NTL data processing and applications, facilitating end-users to make the best use of NTL observations in high temporal frequency.

Spatial Pattern of the Vitality of Chinese Characteristic Towns: A Perspective from Nighttime Lights

Nighttime light images are valuable indicators of regional economic development, and nighttime light data are now widely used in town monitoring and evaluation studies. Using the nighttime light data acquired through Luojia1-01 and the geographic information system spatial analysis method, this study analyzed the spatial vitality pattern of 402 characteristic towns in six geographic divisions of China. The average DN (Digital Number) value of Guzhen, having the highest vitality level, was 0.05665221, whereas that of Xin’an, having the lowest vitality level, was 0.00000186. A total of 89.5% of towns have a low level of vitality. The regional differences were significant; high vitality towns are concentrated in economically developed coastal areas, mainly in two large regions of east China and south central. The average lighting densities of the towns in east China and south central were 0.004838 and 0.003190, respectively. The lighting density of the towns in west central was low, and the vitality intensity was generally low. A spatially significant positive correlation of small-town vitality was observed, and “high–high” agglomeration was primarily distributed in the Yangtze River Delta, Pearl River Delta, and Fujian coastal areas in east and south China. The towns with high vitality intensity had similarities in their geographical location, convenient transportation conditions, and profound historical heritage or cultural accumulation along with many industrial enterprises. This research empirically demonstrates the feasibility of using the 130-m-high resolution of the nighttime lighting data of Luojia1-01 to evaluate the vitality at the town scale, and the vitality evaluation focuses on the spatial attributes of the town, which is meaningful to guide the development of the town in each region given the vast area of China and the large differences in the development of different regions.

High-Spatial-Resolution Nighttime Light Dataset Acquisition Based on Volunteered Passenger Aircraft Remote Sensing

Remotely sensed nighttime light (NTL) data provides an opportunity to observe human economic activities at night. However, existing NTL remote sensing data does not sufficiently reflect human activities at a fine resolution. In this study, we obtain a set of NTL data comprising volunteered passenger aircraft nighttime remote sensing (VPAN-RS) imagery by adopting a passenger plane as the remote sensing platform and using low-cost portable photography equipment as sensors. This method demonstrates the advantages of VPAN-RS data in high spatial resolution, frequent revisits, and low cost, which can supplement existing NTL data. The dataset acquired in this study covers 16 cities in China and one city in Japan, with a spatial resolution up to meter level. The data acquisition method and processing workflow are introduced in this article. Quality assessment shows that the reprojection error of the dataset falls within the range from 5–10 pixels, and the geometric error can be within 15 m. Via a comparison to reference data derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor of the Suomi-National Polar-Orbiting Partnership (NPP) polar-orbiting satellite, we found that the VPAN-RS data provides more details of city lighting. Additionally, the radiance of the VPAN-RS data suitably fits that of monthly composite NPP/VIIRS data. We further outline an application example of VPAN-RS nighttime imagery, examine the unique challenges associated with VPAN-RS data with a focus on the influence of the image acquisition method on the data quality, and provide an outlook for the future of VPAN-RS data. We conclude that VPAN-RS NTL images are an effective data source for nighttime earth observations and have the potential for various applications.

Cross-Sensor Nighttime Lights Image Calibration for DMSP/OLS and SNPP/VIIRS with Residual U-Net

Remote sensing of nighttime lights (NTL) is widely used in socio-economic studies of economic growth, urbanization, stability of power grid, environmental light pollution, pandemics and military conflicts. Currently, NTL data are collected with two sensors: (1) Operational Line-scan System (OLS) onboard the satellites from the Defense Meteorology Satellite Program (DMSP) and (2) Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi NPP (SNPP) and NOAA-20 satellites from the Joint Polar Satellite System (JPSS). However, the nighttime images acquired by these two sensors are incompatible in spatial resolution and dynamic range. To address this problem, we propose a method for the cross-sensor calibration with residual U-net convolutional neural network (CNN). The CNN produces DMSP-like NTL composites from the VIIRS annual NTL composites. The pixel radiances predicted from VIIRS are highly correlated with NTL observed with OLS (0.96 < R2 < 0.99). The method can be used to extend long-term series of annual NTL after the end of DMSP mission or to cross-calibrate same year NTL from different satellites to study diurnal variations.

Urban sprawl and its stress on the risk of extreme hydrological events (EHEs) in the Kelani River basin, Sri Lanka

Land modification and urban sprawl cause incremental changes in hydrological processes due to the results of novel or hybrid ecosystems. This study mainly investigated the unforeseen urban expansion in the lower Kelani River basin, Sri Lanka, and its impact on the increment of hydrological extremes. Remote sensing data, including night-time light images (NOAA/AVHRR) and Landsat (TM/ETM+/OLI) data of different wavelengths, were analysed in this study. Land use and land cover data of the river basin were obtained from 1995. Shannon's entropy was used to demarcate urban sprawl in the river basin over nearly two decades. A spatial regression model was built to identify the correlation between increments of hydrological extremes and urban sprawl. This study revealed that the Kelani River basin has experienced a high urban sprawl rate over the past 23 years and that the total urban land area has increased by 130%. The flood risk analysis revealed that the flood frequency has also dramatically increased due to urban sprawl, and nearly 20 minor flood events have been recorded over the last two decades. Most of the urban areas situated in the lower river basin have invaded into the area with a higher flood risk, and the total flood damage has increased over the study period. The built-up land extent increased from 24.07 km2 to 56.39 km2. In 2018, the flood plain and the lower basin were mostly occupied by human settlements. Therefore, it is essential to improve current policies and mitigation plans to minimize the negative impact of rapid urban sprawl in the study area.

Evaluation and Analysis of Poverty-Stricken Counties under the Framework of the UN Sustainable Development Goals: A Case Study of Hunan Province, China

Eliminating all forms of poverty in the world is the first United Nations Sustainable Development Goal (SDG). Developing a scientific and feasible method for monitoring and evaluating local poverty is important for the implementation of the SDG agenda. Based on the 2030 United Nations SDGs, in this paper, a quantitative evaluation model is built and applied to all poverty-stricken counties in Hunan Province. First, based on the SDG global index framework and local index system of China, a local SDG index system for poverty-related goals is designed, and the weights of the indexes are derived using an entropy method. The scores obtained for counties and districts with data available are then taken as the true value for the poverty assessment. Second, using National Polar-orbiting Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light images and land use and digital elevation model data, six factors, including socioeconomic, land cover, terrain and traffic factors, are extracted. Third, we then construct multiple linear evaluation models of poverty targets defined by the SDGs and machine learning evaluation models, including regression trees, support vector machines, Gaussian process regressions and ensemble trees. Last, combined with statistical data of poverty-stricken counties in Hunan Province, model validation and accuracy evaluation are carried out. The results show that the R2 and relative error of the localized, multiple linear evaluation model, including all six factors, are 0.76 and 19.12%, respectively. The poverty-stricken counties in Hunan Province were spatially aggregated and distributed mainly in the southeastern and northwestern regions. The proposed method for regional poverty assessment based on multisource geographic data provides an effective poverty monitoring reference scheme for the implementation of the poverty eradication goals in the 2030 agenda.

Estimating District-Level Electricity Consumption Using Remotely Sensed Data in Eastern Economic Corridor, Thailand

The intensive industrial development in special economic zones, such as Thailand’s Eastern Economic Corridor, increases energy consumption, leading to an imbalance of energy supply and a challenge for energy management. Electricity consumption at a local level is crucial for utility planners to manage and invest in the electrical grid. With this study, we propose an electricity consumption estimation model at the district level using machine learning with publicly available statistical data and built-up area (BU), area of lit (AL), and sum of light intensity (SL) data extracted from Landsat 8 and Suomi NPP satellite nighttime light images. The models created from three machine learning algorithms, which included Multiple Linear Regression (MR), Decision Tree (DT), and Support Vector Regression (SVR), were compared. The results show that (1) electricity consumption is highly correlated with SL, AL, and BU; and (2) the DT model demonstrated a better performance in predicting local electricity consumption when compared to MR and SVR with the lowest error rate and highest R2. The local government in developing countries with limited data and financial resources can adopt the proposed approach to benefit from utilizing commonly available remote sensing and statistical data with simple machine learning models such as DT (regression method) for sustainable electricity management.

CNN-Based Spectral Super-Resolution of Panchromatic Night-Time Light Imagery: City-Size-Associated Neighborhood Effects.

Data on artificial night-time light (NTL), emitted from the areas, and captured by satellites, are available at a global scale in panchromatic format. In the meantime, data on spectral properties of NTL give more information for further analysis. Such data, however, are available locally or on a commercial basis only. In our recent work, we examined several machine learning techniques, such as linear regression, kernel regression, random forest, and elastic map models, to convert the panchromatic NTL images into colored ones. We compared red, green, and blue light levels for eight geographical areas all over the world with panchromatic light intensities and characteristics of built-up extent from spatially corresponding pixels and their nearest neighbors. In the meantime, information from more distant neighboring pixels might improve the predictive power of models. In the present study, we explore this neighborhood effect using convolutional neural networks (CNN). The main outcome of our analysis is that the neighborhood effect goes in line with the geographical extent of metropolitan areas under analysis: For smaller areas, optimal input image size is smaller than for bigger ones. At that, for relatively large cities, the optimal input image size tends to differ for different colors, being on average higher for red and lower for blue lights. Compared to other machine learning techniques, CNN models emerged comparable in terms of Pearson’s correlation but showed performed better in terms of WMSE, especially for testing datasets.

Monitoring and analysis of green sustainable development in impoverished counties based on nighttime light and RSEI

Sustainable assessment of the quality of green sustainable development in poor counties is essential for urban environmental management, planning as well as achieving sustainable development goals. Currently, there are shortage of related researches on poverty alleviation and ecological environment monitoring in poverty-stricken counties, this study which is based on the nighttime light images and Landsat8 OLI images, constructing a light index, remote sensing ecological index (RSEI) and coupling coordination model of the two. Analyze the situation from 2013 to 2019 poverty alleviation in poor counties in Fujian Province and the ecological environment and explore its coupling and coordination relationship. The results show that from 2013 to 2019, the economy of poor counties has doubled and the most obvious accomplishments of poverty alleviation were in the year 2015 to 2017; the RSEI value of poor counties has all increased, the overall ecological level is at a high-quality level, and the coverage rate is high. The coupling degree C and coordination degree D of the light index and RSEI show a fluctuating upward trend, and both of them are at a good coupling and coordination level, achieving a win-win situation for economic and ecological benefits.

Spatiotemporal dynamics evaluation of pixel-level gross domestic product, electric power consumption, and carbon emissions in countries along the belt and road

The ambiguous relationship between the international economy, energy, and carbon emissions has become a significant factor restricting sustainable development. This study attempts to reveal the spatiotemporal dynamics and interrelationships of gross domestic product (GDP), electric power consumption (EPC), and carbon emissions (CE) in the Belt and Road (B&R) regions using Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) remote sensing data. Annual nighttime light (ANL) images were synthesized, and the accuracy of the ANL data was improved using the multiperiod mask denoising method. Then, the GDP, EPC, and CE at 0.5 km resolution were modelled. Finally, the spatiotemporal dynamics of regional development were comprehensively analysed at multiple scales. The results reveal that the development in the eastern and western parts of the B&R regions has significant differences. China, India, and some countries in Southeast Asia have developed rapidly and in a relatively balanced manner, whereas the development of Central and Eastern Europe and western Russia has been relatively slow and uncoordinated. The geographical centre of the overall development of the B&R continued to migrate to the southeast. This study provides more detailed and comprehensive insights into GDP, EPC, and CE in the B&R regions.