Nighttime Light Research Articles

Study of Small Area Estimation when Nighttime Lights as an Auxiliary Information is Measured with Error

The need for accelerated development requires rapid data collection. In today's increasingly advanced technological landscape, the utilization of big data emerges as a highly reliable solution for data collection. One exemplary form of big data is the daily capture of satellite imagery, particularly nighttime lights (NTL). NTL serves as a valuable product derived from satellite imagery and can be employed as an alternative dataset for analysis. This research utilizes Nighttime lights as an auxiliary variable to estimate the average household per capita expenditure in small areas, namely districts, employing the empirical best linear unbiased prediction Fay Herriot (EBLUP FH) method and small area estimation by incorporating measurement error effects on the covariate (SAE-ME). The study demonstrates that Nighttime lights can be employed as an alternative auxiliary variable for estimating the average per capita expenditure in districts, as evidenced by a lower RRMSE compared to direct estimation results. However, the measurement error effects on the NTL covariate should be considered by employing a model that takes into account measurement errors. The SAE-ME method provides estimated average expenditure values at the district level that closely align with BPS publications, with an average RRMSE per district of 7.5 percent.

Integrated satellite observations unravel the relationship between urbanization and anthropogenic non-methane volatile organic compound emissions globally

As urban areas expand globally, human activities are leading to a sustained increase in non-methane volatile organic compound (NMVOC) emissions, escalating both environmental and health-related concerns. Given their diverse origins, estimating anthropogenic NMVOC emissions levels from global urban areas remains challenging. Here, we integrate TROPOspheric Monitoring Instrument (TROPOMI) formaldehyde (HCHO) column data, Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light (NTL) radiance data, and the Emission Database for Global Atmospheric Research (EDGAR) to develop a method for estimating global anthropogenic NMVOC emissions. Furthermore, we construct a linear model to analyze the relationship between urbanization and anthropogenic NMVOC emissions. Our research reveals that meticulously filtered TROPOMI HCHO columns have a high Pearson correlation coefficient (r = 0.91) with anthropogenic NMVOC emissions, indicating its reliability as an indicator reflecting the levels of anthropogenic NMVOC emissions. We establish linear models at various scales, including global, continental, and national, linking HCHO columns (as indicators of anthropogenic NMVOC emissions) and NTL radiance (as an indicator of urbanization). The global-scale linear model exhibits an r of 0.81, with a slope of 0.42 × 1015 molec. cm−2 nanoWatts−1 cm2 sr and an intercept of 9.26 × 1015 molec. cm−2. This linear model reflects a positive correlation between urbanization and anthropogenic NMVOC emissions, also serving as a tool for estimating the levels of anthropogenic NMVOC emissions in urban areas. This study offers valuable insights for real-time monitoring of extensive anthropogenic NMVOC emissions.

Integrating “quality-risk-demand” framework and circuit theory to identify spatial range and priority area of ecological security pattern in a rapidly urbanizing landscape

Regional ecological security pattern (ESP) can provide the basis for regional territorial spatial planning and urban ecological protection. Taking the Wuhan metropolitan area as the study area, a binary logistic regression model was used to quantify the evolutionary driving forces and transformation risk of ecological land. An identification framework of ecological source based on “quality-risk-demand” was constructed to identify ecological source by calculating the ecological land comprehensive value from three aspects of ecological land quality, degradation risk, and human demand. The ecological resistance surface was calculated according to land-use type and nighttime light data, further using circuit theory to identify the spatial range and its key areas of ecological corridors, and to identify the specific spatial range of ESP and their priority area. The results showed ecological sources were 382.4 km2, accounting for 17.3% of the metropolitan area. The area of ecological corridors in the terrestrial ecosystem (TEC) was 14.38 km2, with an average width of 77.8 m, including 4.99 km2 of pinch points and 3.71 km2 of barriers, indicating poor actual connectivity of the TEC. Ecological corridors in the aquatic ecosystem (AEC) were 107.67 km long, including 57.86 km of pinch points and 14.24 km of barriers, indicating poor substitutability of the AEC. Overall, the ESP of “three horizontals, three verticals, and four clusters” were identified, where sources, pinch points and barriers were the priority areas for protection and restoration in constructing it. Existing protection spaces were within the ecological source range, and the identification results of the ecological corridor were highly consistent with the actual situation, verifying the ecological significance of the identification framework. This study focuses on the needs of urban residents, combining the intrinsic quality and extrinsic risks of urban ecological land, proposes an integrated framework for identifying urban ecological sources, identifies the spatial range of ecological corridors and their key areas based on circuit theory, and attempts to provide a quantitative framework and case reference for ESP construction in other urban areas.

Does hospital competition improve the quality of outpatient care? - empirical evidence from a quasi-experiment in a Chinese city

BackgroundAlthough countries worldwide have launched a series of pro-competition reforms, the literature on the impacts of hospital competition has produced a complex and contradictory picture. This study examined whether hospital competition contributed to an increase in the quality of outpatient care.MethodsThe dataset comprises encounter data on 406,664 outpatients with influenza between 2015 and 2019 in China. Competition was measured using the Herfindahl-Hirschman index (HHI). Whether patients had 14-day follow-up encounter for influenza at any healthcare facility, outpatient facility, and hospital outpatient department were the three quality outcomes assessed. Binary regression models with crossed random intercepts were constructed to estimate the impacts of the HHI on the quality of outpatient care. The intensity of nighttime lights was employed as an instrumental variable to address the endogenous relationship between the HHI and the quality of outpatient care.ResultsWe demonstrated that an increase in the degree of hospital competition was associated with improved quality of outpatient care. For each 1% increase in the degree of hospital competition, an individual’s risk of having a 14-day follow-up encounter for influenza at any healthcare facility, outpatient facility, and hospital outpatient department fell by 34.9%, 18.3%, and 20.8%, respectively. The impacts of hospital competition on improving the quality of outpatient care were more substantial among females, individuals who used the Urban and Rural Residents Basic Medical Insurance to pay for their medical costs, individuals who visited accredited hospitals, and adults aged 25 to 64 years when compared with their counterparts.ConclusionThis study demonstrated that hospital competition contributed to better quality of outpatient care under a regime with a regulated ceiling price. Competition is suggested to be promoted in the outpatient care market where hospitals have control over quality and government sets a limit on the prices that hospitals may charge.

Constructing ecological security pattern based on spatio-temporal evaluation of ecosystem services and ecological health in Chengdu, Southwest China

IntroductionWith the acceleration of urbanization, human population and built surface in urban areas have increased rapidly, triggering numerous environmental problems. Identification of ecological security pattern (ESP) can be helpful to optimize the interaction and relationship between ecological conservation and socioeconomic development in a given region. In this study, taking Chengdu City as an example, a methodology was used to analyze the city’s ESP.MethodologyEcological sources were identified based on their ecological security values, which were derived from the spatio-temporal evaluation of ecosystem services and ecological health. The ecological resistance surface was revised with nighttime light index. Linkage Mapper combined with circuit theory was used to extracted ecological corridors and key ecological nodes.Results:The results showed that the mean values of ecological security in Chengdu City presented a downward and then upward trend from 2000 to 2018. In 2018, the ESP in Chengdu City was formed by 140 ecological sources with the total area of 8,819.78 km2, 302 ecological corridors with the area of 456.91 km2, as well as 61 pinch-points and 17 barrier points. The ecological sources in Chengdu City were mainly distributed in or nearby Longmen Mountain and Longquan Mountain, two flanks of the Chengdu Plain, and connected each other with hundreds of ecological corridors. Most of ecological sources and corridors were composited of forests in mountainous areas and linpan units (wooded lots) in rural plain areas. On the contrary, pinch-points and barrier points were mainly distributed in the districts nearby the metropolitan urban center.ConclusionFinally, this study proposed that constructing ESP must adopt a dynamic and holistic approach in considering the change of restoring ecosystems, shift of urban demands for ecosystem services and transformation of urban-rural land use/cover. Restoring and managing corridors sustainably are important for improving the ecological connectivity of all over ecological sources in a city, all of which together, if functioning well, could further support the realization of sustainable urbanization.

Night-time light loss during the 2022 Kyiv offensive revealed through VIIRS DNB

ABSTRACT The satellite low-light imagers can detect changes in nighttime lights caused by ground military conflict. The Kyiv Offensive in Kyiv, Ukraine, which lasted less than 40 days during the Russia – Ukraine conflict, began on 24 February 2022. In addition to the monthly light product, we included a daily light product to analyse the number and average intensity of light pixels in the area around Kyiv over time. The changing trends of two parameters are similar. From February to March, the number of light pixels dropped by 65.2%, and average intensity by 24.5%. To account for daily fluctuations, the relative light ratio was introduced, comparing Kyiv to Moscow, Gomel, and Warsaw. This parameter showed a significant decrease in both the number and intensity of light pixels between days 411 and 423 since January 1, 2021, aligning with the conflict’s start. The relative light ratio revealed a 70.6%, 63.5%, and 57.3% drop in light pixel numbers, and a 9.5%, 24.2%, and 48.4% drop in their average intensity for the respective city pairs. However, unusual fluctuations in the reference cities, such as in Gomel during Christmas and in Moscow at the conflict’s onset, present some problems in using the relative light ratio.

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

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

Gentrification outcomes of greening in different urbanization stages: A longitudinal analysis of Chinese cities, 2012–2020

Green-space-triggered gentrification, wherein original residents are displaced by wealthier individuals owing to the creation of new green spaces, has been criticized for exacerbating environmental injustice. While previous studies have explored green-space-triggered gentrification in individual cities, few have examined heterogeneities across multiple cities, especially in developing countries where cities are at different stages of expansion. The roles of green space in different stages of urbanization can produce varying outcomes of gentrification. This study investigated the relationship between green space and gentrification in Chinese cities from 2012 to 2020. The normalized difference vegetation index (NDVI), distance to adjacent parks, and area of adjacent parks were used as indicators of green space, while nighttime lights and residential land prices were used as proxies for gentrification. Nationwide analyses indicated that both increasing NDVI and building new parks nearby could lead to gentrification, but the park area had a marginal effect. Stratified analyses further showed that the effect of green space on gentrification was related to the different roles of green space associated with the stage of urbanization. Cities with higher urbanization rates were more affected by NDVI but less affected by park distance. Our findings provide insights for urban planners and decision-makers on developing localized strategies that mitigate the varying outcomes of gentrification at different stages of urban development.

Multilevel Change of Urban Green Space and Spatiotemporal Heterogeneity Analysis of Driving Factors

Analyzing the change trend of urban green space (UGS) and exploring related driving forces can provide scientific reference for sustainable development in rapidly urbanizing areas. However, the spatial and temporal driving mechanisms of the drivers on UGS patterns at different scales are still not deeply understood. Based on the GlobeLand30 land cover data, nighttime lighting data and spatial statistics from 2000 to 2020, this study analyzed the size, shape and diversity of UGS in Guangzhou at the urban level, gradient level and township level with multiple landscape indices. Diversity means the richness of UGS patch types. The selected indices include percent of landscape (PLAND), largest path index (LPI), landscape shape index (LSI), aggregation index (AI) and Shannon’s diversity index (SHDI). The spatiotemporal heterogeneity of the drivers was then explored using the spatiotemporal weighted regression (GTWR) method. Results showed the following: (1) During 2000−2020, the total amount of UGS in Guangzhou increased slightly and then decreased gradually. UGS was mainly transferred into artificial surfaces (lands modified by human activities). (2) The UGS landscape showed a non-linear trend along the urban–rural gradient and fluctuated more in the interval of 20–60% urbanization level. PLAND, LPI and AI decreased significantly in areas with higher levels of urbanization. LSI increased and SHDI decreased significantly in areas with lower levels of urbanization. At township level, the landscape indices showed significant spatial autocorrelation. They transformed from discrete changes at the edge and at the junction of the administrative district to large-scale aggregated change, especially in northern areas. (3) The size of UGSs was mainly influenced by natural factors and population density, but their shape and diversity were mainly influenced by socio-economic factors. More regular shapes of green patches were expected in higher urbanization areas. Population agglomeration positively influenced green space patterns in the northeastern and southern regions (Zengcheng, Conghua and Nansha). Meanwhile the negative influence of urban expansion on the green space pattern in the central and southern regions decreased over time. This study contributes to an in-depth understanding of how the key factors affect the different changes of UGS with time and space and provides methodological support for the long-term zoning planning and management of UGS.

Spatiotemporal Dynamics of COVID‐19 Pandemic City Lockdown: Insights From Nighttime Light Remote Sensing

AbstractThe global COVID‐19 outbreak severely hampered the growth of the global economy, prompting the implementation of the strictest prevention policies in China. Establishing a significant relationship between changes in nighttime light and COVID‐19 lockdowns from a geospatial perspective is essential. In light of nighttime light remote sensing, we evaluated the spatiotemporal dynamic effects of COVID‐19 city lockdowns on human activity intensity in the Zhengzhou region. Prior to the COVID‐19 outbreak, nighttime light in the Zhengzhou region maintained a significant growth trend, even under regular control measures. However, following the October 2022 COVID‐19 lockdown, nighttime light experienced a substantial decrease. In the central area of Zhengzhou, nighttime light decreased by at least 18% compared to pre‐lockdown levels, while in the sub‐center, the decrease was around 14%. The areas where nighttime light decreased the most in the central region were primarily within a 15 km radius, while in the sub‐center, the decrease was concentrated within a 5 km radius. These changes in both statistical data and nighttime light underscored the significant impact of the COVID‐19 lockdown on economic activities in the Zhengzhou region.

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

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

Efficacy and Optimal Timing of Warm-white or Red + Far-red LED Lamps in Regulation of Flowering in Long-day Ornamentals

When natural days are short, photoperiodic lighting at the end or beginning of the day (day extension) or in the middle of the night (night break) promotes flowering of long-day plants. The objective of this study was to compare broad-spectrum warm-white light-emitting diodes (LEDs) and red (R) + far-red (FR) LEDs at flowering regulation when delivered at different timings in the night period. We performed a greenhouse experiment on four long-day ornamentals [coreopsis (Coreopsis grandiflora) ‘Early Sunrise’, snapdragon (Antirrhinum majus) ‘Liberty Classic Yellow’, petunia (Petunia ×hybrida) ‘Easy Wave Burgundy Star’, and petunia ‘Wave Purple Improved’]. We grew plants under a truncated 8-hour photoperiod with or without low-intensity (∼2 μmol·m−2·s−1) nighttime lighting from warm-white or R+FR LEDs. For each light quality, we delivered four timings: 1) 8 hours after dusk; 2) 8 hours before dawn; 3) 4 hours after dusk + 4 hours before dawn; and 4) 4-hour night break. The effectiveness of floral promotion was determined by time from the treatment onset to the first open flower. Coreopsis flowered similarly under all lighting treatments, irrespective of light quality and timing, but did not flower under the short-day treatment by the end of the experiment. At flowering, coreopsis was 18% to 19% shorter under white than R+FR LEDs. In contrast, snapdragon flowered 9 to 20 days later under white than R+FR LEDs, when delivered for 8 hours at night, but flowered similarly under these two lamp types as a 4-hour night break. Compared with the short-day treatment, white and R+FR LEDs promoted flowering of both petunia cultivars, although flowering generally occurred later under white than R+FR LEDs. Snapdragon and petunia ‘Easy Wave Burgundy Star’ developed 30% to 122% more lateral branches under white than R+FR LEDs, when delivered for 8 hours at night. The effectiveness of warm-white LEDs was generally unaffected by timing, although it was most promotive of flowering in snapdragon when delivered for 8 hours before dawn. For R+FR LEDs, 8-hour day-extension lighting was generally more effective than 4-hour night-break lighting, irrespective of timing. We conclude when delivered for 8 hours at night, warm-white LEDs are generally less effective than R+FR LEDs at promoting flowering of long-day ornamentals but similarly effective as 4-hour night-break lighting. The effectiveness of day-extension lighting is generally independent of timing, although for R+FR LEDs, 8 hours after-dusk and/or before-dawn lighting was generally more effective than 4-hour night-break lighting.

P‐61: Dynamic Edge Enhancement Method

The Visteon True Color Image Enhancement system currently allows either edge detection to be activated or disabled. This study proposes a third option of implementing a dynamic edge detection method which dynamically enhances symbology edges as a function of the ambient lighting condition. Under nighttime lighting conditions, edge enhancement is not desirable since image artifacts are introduced that are not required for image visibility. As the reflected ambient lighting conditions increase, it is desirable to increase the visibility of black borders around symbology.

Estimating built-up area carbon emissions through addressing regional development disparities with population and nighttime light data

Carbon emission inventories in urban built-up areas are critical for formulating effective emission reduction policies. Nighttime light data, reflecting socioeconomic activities, is routinely used as a proxy for estimating emissions. However, the reliability of this proxy is limited in areas with minimal or absent lighting and demonstrates variability according to the urban development. To address these limitations, a novel approach was developed. Firstly, it enhances the representation of human activities under different light values through integrating light values and population distributions into multidimensional proxy features. Secondly, cities with different socio-economic development were clustered to minimize the development disparities within the same cluster. Four clusters were detected including ‘Agriculture-dependent & Low-density’, ‘Agriculture-dependent & High-density’, ‘Industry-oriented’, and ‘Tertiary-oriented’. Then A multilayer perceptron regressor, refined with a moving average method, was employed to model the nonlinear relationships between emissions and these proxy features. The results show a significant increase in modeling accuracy when regional development disparities are incorporated through this clustering strategy. Additionally, the Shapley Additive Explanatory method was utilized to analyze the influence of regional development heterogeneity on the estimation model. It demonstrated that areas with high light intensity and population significantly influence emission estimates in all clusters, except for the ‘Industry-oriented cluster’. This finding underscores the varying effectiveness of proxy features across different regions, emphasizing the importance of considering regional development disparities. Finally, based on the estimated inventory obtained, spatio-temporal hot spot detection pinpointed eastern and central China as significant emission hot spots. This research not only enhances the data resources for managing carbon emissions but also underscores the need for inter-city cooperation strategies to achieve China's carbon reduction targets.

Changes in human settlement environments and their drivers in valley cities located in arid and semi-arid regions: A case study of Lanzhou in Western China

Development of urban human settlement environments (HSEs) is an integral part of promoting high-quality and sustainable regional development and constructing a beautiful China. The city of Lanzhou, located at the geometric center of China, is the only provincial capital traversed by the Yellow River. Given the constraints posed by the valley topography and the need for economic development, the development of this HSE, which is located within an arid region, poses considerable challenges. Evidently, an understanding of the evolution of HSEs and drivers of changes in them contributes to high-quality, sustainable urban development in arid and semi-arid regions. An analytical model was developed using the parameters of relief degree of land surface, human comfort days, the land cover index, nighttime light index, and precipitation. This model was used in combination with population density and the gross domestic product to analyze the spatial distribution of Lanzhou's HSE and its drivers. The results showed that landscapes in Lanzhou underwent significant changes between 2000 and 2022, with an increase in building-up land (+0.946%), cultivated land (+0.134%), and forest land (+0.018%) and a decrease in grassland (−1.10%). There was significant outward expansion of the main urban zone of Lanzhou and of various county towns, with the increase in building-up land being most prominent. During this period, there were significant changes in the periphery of the core urban area and county towns in Lanzhou, with decreases moving from the urban center (the highest value) to the surrounding areas (Yongdeng County had the lowest value). The correlation between the HSE and population density grew stronger in Anning and Chengguan Districts but became weaker in Xigu and Qilihe Districts. Spatiotemporal variations in the HSE were primarily caused by climate change, followed by human activities, and were also influenced by the valley topography. Overall, the spatial distribution of population density and the HSE in Lanzhou demonstrated good consistency under the influence of economic development and urbanization.

Spatialization of electricity consumption by combining high-resolution nighttime light remote sensing and urban functional zoning information

ABSTRACT An accurate and timely spatialization of electricity consumption is fundamental for energy management and sustainable urban development. While previous research has relied heavily on statistical data or moderate-resolution nighttime light data, this study has presented a new spatialization method by combining high-resolution Luojia 1–01 nighttime light and urban functional zoning information. The total electricity consumption volume can be allocated to each land use pixel based on its strong linear relationship with nighttime light. Specifically, urban functional zoning data were connected with the corresponding economic sectors to differentiate the complex relationships between electricity consumption and nighttime light within different zones. The digital number value of every pixel can be multiplied by its corresponding electricity consumption coefficient. A number of comparisons have indicated that the proposed method can more accurately characterize the spatial distribution of electricity consumption. Our results can exhibit much clearer outlines and more detailed internal characteristics. More importantly, urban functional zoning information can be used to distinguish the electricity consumption of various economic sectors at fine scales. The proposed method is expected to capture the spatial characteristics of electricity consumption in a timely and accurate manner. The findings can help local authorities formulate sustainable energy utilization and emission reduction strategies.

Large greenhouse gases emissions from lakes in Inner Mongolia, China

Lakes are important component of the regional carbon cycle and a significant sources of greenhouse gases (GHGs) to the atmosphere. However, the regional variations in carbon dioxide (CO2) and methane (CH4) emissions, as well as the factors regulating these emissions are poorly constrained. To investigate the regional GHG emission from lakes and their potential influencing factors, we conducted field measurements in 13 lakes in Inner Mongolia from July to August 2020. We found that the studied lakes were predominantly sources of atmosphere CO2 and CH4. The CO2 partial pressure (pCO2) exhibited supersaturation in 80 % of samplings, but all samplings of CH4 partial pressure (pCH4) showed supersaturation. Moreover, the average pCH4 is 40 times greater than that in the atmosphere. The lakes emitted substantial amounts CO2 and CH4 with average values of 25.8 ± 4.9 mmol m−2 d−1 and 2.1 ± 0.3 mmol m−2 d−1, respectively. At the regional scale, the dynamics of pCO2 and pCH4 are regulated by combination of various environmental factors. CO2 emissions from studied lakes decreased with the catchment net primary productivity (NPP), normalized difference vegetation index (NDVI), and nighttime lights which is indicative of urbanization. Conversely, CH4 fluxes increased with NPP, NDVI, and nighttime lights. We conclude that terrestrial carbon-cycling related variables and human activities significantly affect CO2 and CH4 emissions from lakes by transporting different nutrients, but they show opposite trends. Our results also exhibited that the freshwater lakes are the main contributors of CH4, while saline lakes are the main contributors of CO2. We suggest that increased human activity leads to changes in terrestrial carbon-cycle related processes which affects lake carbon emissions by altering the amount of nutrients input to the lakes.

Assessment of post-pandemic economic recovery in Beijing, China, using night-time light data

ABSTRACT The coronavirus disease 2019 (COVID-19) pandemic posed a significant impact on human society, resulting in substantial changes in night-time light (NTL). This study developed a novel NTL index to estimate the quarterly Gross Domestic Product (GDP) at the county level in Beijing. This study aimed to investigate the economic recovery status in Beijing by establishing regression models of economic development before and after the pandemic. The estimated quarterly GDP at the county level was found to have a strong correlation with actual quarterly GDP statistics, with an average coefficient of determination (R 2) of 0.790. The regression models of the economic development of Beijing county-level districts before and after the pandemic were established after seasonal decomposition of the quarterly GDP. Based on the economic recovery framework for the pandemic in Beijing, most districts in the city had exhibited low economic resilience in terms of resistance and recoverability before and after the pandemic. Although the economy of Beijing is recovering gradually, it has not yet restored to pre-pandemic normal levels. These findings imply that NTL data can help assess the impact of emergency events on the disturbance of economic activity.

The Effects of Different Durations of Night-Time Supplementary Lighting on the Growth, Yield, Quality and Economic Returns of Tomato.

To achieve higher economic returns, we employ inexpensive valley electricity for night-time supplementary lighting (NSL) of tomato plants, investigating the effects of various durations of NSL on the growth, yield, and quality of tomato. Tomato plants were treated with supplementary light for a period of 0 h, 3 h, 4 h, and 5 h during the autumn-winter season. The findings revealed superior growth and yield of tomato plants exposed to 3 h, 4 h, and 5 h of NSL compared to their untreated counterparts. Notably, providing lighting for 3 h demonstrated greater yields per plant and per trough than 5 h exposure. To investigate if a reduced duration of NSL would display similar effects on the growth and yield of tomato plants, tomato plants received supplementary light for 0 h, 1 h, 2 h, and 3 h at night during the early spring season. Compared to the control group, the stem diameter, chlorophyll content, photosynthesis rate, and yield of tomatoes significantly increased upon supplementation with lighting. Furthermore, the input-output ratios of 1 h, 2 h, and 3 h NSL were calculated as 1:10.11, 1:4.38, and 1:3.92, respectively. Nonetheless, there was no detectable difference in yield between the 1 h, 2 h, and 3 h NSL groups. These findings imply that supplemental LED lighting at night affects tomato growth in the form of light signals. Night-time supplemental lighting duration of 1 h is beneficial to plant growth and yield, and its input-output ratio is the lowest, which is an appropriate NSL mode for tomato cultivation.

Illuminating the Nile: estimating subnational GDP in Egypt using nighttime lights and machine learning

Egypt has been reporting several subnational socioeconomic indicators for more than three decades. However, utilizing these valuable datasets for monitoring long temporal trends in local development and inequalities has been hindered by the lack of a key indicator, the Gross Domestic Product (GDP), which was only reported subnationally starting 2013. This paper aims to address this data gap, by employing satellite-generated nighttime lights (NTL) and machine learning, to estimate subnational GDP in Egypt from 1992 to 2012. The paper relies on the harmonized global nighttime lights dataset that extends from 1992 to 2021, to carry out a twofold process. First, it validates NTL as a useful proxy for subnational economic activity in Egypt using econometric methods; then it estimates missing GDP using machine learning algorithms. Results show that the concentration of nearly the entire Egyptian population densely around the Nile River is challenging to nighttime lights accuracy; however, upon accounting for population density and agricultural activity, NTL could serve as a valuable proxy for subnational GDP in Egypt, and consequently a coherent GDP dataset is constructed since 1992.