Nighttime Light Satellite Imagery Research Articles

야간 위성영상 기반 도시단위 탄소배출량 추정과 영향 요인 분석

야간 위성영상 기반 도시단위 탄소배출량 추정과 영향 요인 분석

Using Histogram Extracted from Satelite Imagery and Convolutional Network to Predict GRDP in Java Region

Inequality is one of the problems faced by all countries in the world, including Indonesia. The data used to measure development inequality between regions mostly use GRDP data. However, the GRDP data issued by BPS has a deficiency, it was released after the current year, and this figure is provisional. Therefore, a new data source is needed that can be used to estimate the value of economic activity so that it can be used to measure the level of inequality in development in a region. Nighttime Light (NTL) satellite imagery data can be an alternative to see socioeconomic activity in an area and have been shown to have a strong correlation with socioeconomic activity. In this study, we used VIIRS NTL satellite imagery data and Dynamic World land cover data to estimate GRDP. Rather than using statistical features for each area of interest, we use features in the form of histograms extracted from NTL images and land cover images for each area of interest. Using a histogram, we do not lose spatial information from satellite imagery. Then we proposed a deep learning method in the form of a one-dimensional convolutional neural network using the Huber loss function. This model obtained good precision with an R-square value of 0.8549, beating the baseline method with two-dimensional convolutional networks. The use of the Huber loss function can improve the performance of the model, which has a smaller total loss and has a smoother gradient.

Influence of COVID-19 on night-time lights in Czechia

ABSTRACT Night-Time Light (NTL) satellite imagery is widely recognized and valued as a powerful tool for spatial analysis and research. Despite its global popularity, there is absence of similar studies dealing with NTL intensity in Czechia. Using VIIRS/DNB data, our study focused on analyzing the changes in night-time light intensity before and during the coronavirus restrictions. These restrictions had a substantial impact on social and economic life, particularly during the peak of pandemic, affecting not only Czechia but also other regions worldwide. A mosaic of the study area was prepared, followed by the aggregation of the original raster data to municipalities with extended power (MEPs) and calculation of the average light index (ALI). The resulting typology made it possible to identify regional differences in NTL intensity. The main hypothesis was confirmed, it was observed that the ALIexhibited a decrease of 18% in 2020, whereas in 2021, the decline averaged at 9%.

Fractal Dimensions Analysis of Urban Agglomeration at Road Intersections in Metropolitan Malang Raya

The agglomeration review is mostly carried out by identifying the physical and economic dispersal processes of the two growth poles. Many researchers conclude on the result of agglomeration rather than the process nor the forecasts. This study tries to examine the process of urban agglomeration in the metropolitan city of Malang. This is done by combining city fractal theory and urban percolation. The DBSCAN clustering approach at the intersection is used to identify the agglomeration process between the growth poles. While Minkowski’s Dimensional theory is used to identify the fractals that occur. The calculation results show that the maximum Minkowski dimension distribution figure is 200m. It indicates the fractals that occur in the road network data. The DBSCAN clustering modeling at 200m has a strong correlation with the number of light intensity values in the 2021 nighttime light satellite imagery. On the other hand, modeling at 250m to 350m provides an overview of the future agglomeration. This research can be the basis for urban planners to analyze the agglomeration process and predict the future.

Relationship Between Urbanization And Road Networks In The Lower Northeastern Region Of Thailand Using Nighttime Light Satellite Imagery

The objective of this research on the relationship between urbanization and road networks in the lower Northeastern region of Thailand was to compare the urban area in 2006, 2013 and 2016 using nighttime light satellite images from the National Oceanic and Atmospheric Administration (NOAA), acquired by the Defense Meteorological Satellite Program (DMSP/OLS) and the Suomi National Polar-orbiting Partnership (Suomi NPP). After that the relationship between urbanization and road network was identified using nighttime light satellite images from these satellites. The nighttime light data was used to determine the urbanization levels, which were then compared with Landsat 8 Satellite images taken in 2016 in order to find the Pearson correlation coefficient. The results indicated that areas with high urbanization identified from the nighttime light satellite images taken by the Suomi NPP Satellite had a day/night band reflectance of 172-255 indicated and were located primarily along the roads. The analysis of these data suggested that urbanization has a significantly positive relationship with the road network at 0.01 level, with R2 values of 0.800 for urbanization and 0.985 for the road network.

Big Data Review of East Java Community Compliance Index Against the Recommendation of Stay At Home During the Covid-19 Pandemic

The Covid-19 pandemic period provides a change in the framework of discovering community mobility patterns as the basis for determining policies to control the spread of the virus. Big Data then becomes one of the indicators in finding mobility patterns because, while doing their activities, the internet and social media users continuously carry out even when staying at home. The Indonesian government controls the spread by issuing the Large-Scale Social Restrictions (PSBB) policy in 2020 and the Enforcement of Restrictions on Community Activities (PPKM) in 2021 and 2022. East Java Province is confirmed to have the highest level of COVID-19 spread in Indonesia, so it requires a pattern of proper handling to control its spread. This study provides information on the compliance index to the stay-at-home recommendations during the PSBB and PPKM periods. Wherefrom the Big Data analysis and Nighttime Light satellite imagery, the highest level of compliance occurred during PPKM in February 2022. Also, in general, the compliance index of the people of East Java is increased.

Sensing global changes in local patterns of energy consumption in cities during the early stages of the COVID-19 pandemic

COVID-19, and the wider social and economic impacts that a global pandemic entails, led to unprecedented reductions in energy consumption globally. Whilst estimates of changes in energy consumption have emerged at the national scale, detailed sub-regional estimates to allow for global comparisons are less developed. Using night-time light satellite imagery from December 2019–June 2020 across 50 of the world's largest urban conurbations, we provide high resolution estimates (450 m2) of spatio-temporal changes in urban energy consumption in response to COVID-19. Contextualising this imagery with modelling based on indicators of mobility, stringency of government response, and COVID-19 rates, we provide novel insights into the potential drivers of changes in urban energy consumption during a global pandemic. Our results highlight the diversity of changes in energy consumption between and within cities in response to COVID-19, moderating dominant narratives of a shift in energy demand away from dense urban areas. Further modelling highlights how the stringency of the government's response to COVID-19 is likely a defining factor in shaping resultant reductions in urban energy consumption.

Summer suburbanization in Moscow Region: Investigation with nighttime lights satellite imagery

In the summertime, many Moscow residents move to second homes in the countryside, which causes strong seasonal fluxes in the population size of the Russian capital, its surroundings, and more remote rural areas. To better understand the spatial scale and patterns of seasonal suburbanization, we tracked the changes in average monthly nighttime light radiance between August and October 2018 at 1 km resolution. Our results revealed a significant, 1.5 times increase of the summer nighttime lighting in the surroundings of Moscow and a more than 2 times increase in the rural areas to the West and North of the capital. This study illustrates the seasonal character of suburbanization in the Moscow agglomeration and raises the question of its effects on both urban and rural development of the area.

Economic activity and distribution of an invasive species: Evidence from night-time lights satellite imagery data

This study addresses economic and ecological aspects related to aquatic invasive species occurrence and spread. The relative importance of these two factors driving the probability of occurrence of Elodea canadensis species in lakes of Sweden is quantified. Night-time lights data, a proxy for economic activity, is generated from satellite imagery and mapped to key environmental variables known to constrain the species' distribution. Economic and environmental data are then matched with the species' observational records at the lake scale. Evidence from a spatial probit model shows a robust positive relationship between economic activity and invasion of uninvaded lakes.

Modeling and spatio-temporal analysis of city-level carbon emissions based on nighttime light satellite imagery

Climate warming due to carbon emissions has been recognized as a threat to food security, human health and natural ecosystem, and carbon emission reduction is a challenging job for each country in the world. In this study, an ensemble structure-based neural network model (NNEnsemble) is proposed to analyze the nonlinear relationship between the Defense Meteorological Satellite Program Operational Line-Scan System (DMSP-OLS) nighttime stable light (NSL) data, and province-scale statistical data on carbon emissions. Given the challenge of obtaining urban-scale carbon emission data, a weighted coefficient strategy by using the NSL data were employed to analyze the carbon emissions at the urban scale. The performance of the proposed method was found to be superior to that of comparable methods with respect to various evaluation indices. Under these circumstances, a hot spot and Standard deviational ellipse analysis of three northeast provinces in China was conducted from 1998 to 2013. The results can promote a better understanding of the spatio-temporal characteristics of carbon emissions in three northeastern Chinese provinces. Moreover, the developed application software based on NNEnsemble can serve as a basis for the development of carbon emission mitigation policies for other provinces.

Inferring Nighttime Satellite Imagery from Human Mobility

Nighttime lights satellite imagery has been used for decades as a uniform, global source of data for studying a wide range of socioeconomic factors. Recently, another more terrestrial source is producing data with similarly uniform global coverage: anonymous and aggregated smart phone location. This data, which measures the movement patterns of people and populations rather than the light they produce, could prove just as valuable in decades to come. In fact, since human mobility is far more directly related to the socioeconomic variables being predicted, it has an even greater potential. Additionally, since cell phone locations can be aggregated in real time while preserving individual user privacy, it will be possible to conduct studies that would previously have been impossible because they require data from the present. Of course, it will take quite some time to establish the new techniques necessary to apply human mobility data to problems traditionally studied with satellite imagery and to conceptualize and develop new real time applications. In this study we demonstrate that it is possible to accelerate this process by inferring artificial nighttime satellite imagery from human mobility data, while maintaining a strong differential privacy guarantee. We also show that these artificial maps can be used to infer socioeconomic variables, often with greater accuracy than using actual satellite imagery. Along the way, we find that the relationship between mobility and light emissions is both nonlinear and varies considerably around the globe. Finally, we show that models based on human mobility can significantly improve our understanding of society at a global scale.

An Approach of Identifying and Extracting Urban Commercial Areas Using the Nighttime Lights Satellite Imagery

Urban commercial areas can reflect the spatial distribution of business activities. However, the scope of urban commercial areas cannot be easily detected by traditional methods because of difficulties in data collection. Considering the positive correlation between business scale and nighttime lighting, this paper proposes a method of urban commercial areas detection based on nighttime lights satellite imagery. First, an imagery preprocess model is proposed to correct imageries and improve efficiency of cluster analysis. Then, an exploratory spatial data analysis and hotspots clustering method is employed to detect commercial areas by geographic distribution metric with urban commercial hotspots. Furthermore, four imageries of Wuhan City and Shenyang City are selected as an example for urban commercial areas detection experiments. Finally, a comparison is made to find out the time and space factors that affect the detection results of the commercial areas. By comparing the results with the existing map data, we are convinced that the nighttime lights satellite imagery can effectively detect the urban commercial areas. The time of image acquisition and the vegetation coverage in the area are two important factors affecting the detection effect. Harsh weather conditions and high vegetation coverage are conducive to the effective implementation of this method. This approach can be integrated with traditional methods to form a fast commercial areas detection model, which can then play a role in large-scale socio-economic surveys and dynamic detection of commercial areas evolution. Hence, a conclusion can be reached that this study provides a new method for the perception of urban socio-economic activities.

MAPPING POVERTY IN THE PHILIPPINES USING MACHINE LEARNING, SATELLITE IMAGERY, AND CROWD-SOURCED GEOSPATIAL INFORMATION

Abstract. Mapping the distribution of poverty in developing countries is essential for humanitarian organizations and policymakers to formulate targeted programs and aid. However, traditional methods for obtaining socioeconomic data can be time-consuming, expensive, and labor-intensive. Recent studies have demonstrated the effectiveness of combining machine learning and satellite images to estimate wealth in sub-Saharan African countries (Xie et al., 2016, Jean et al., 2016). In this study, we investigate the extent to which this method can be applied in the context of the Philippine archipelago to predict four different socioeconomic indicators: wealth level, years of education, access to electricity, and access to water. We also propose an alternative, cost-effective approach that leverages a combination of volunteered geographic information from OpenStreetMap and nighttime lights satellite imagery for estimating socioeconomic indicators. The best models, which incorporate regional indicators as predictors, explain approximately 63% of the variation in asset-based wealth. Our findings also indicate that models trained on publicly available, volunteer-curated geographic data achieve the same predictive performance as that of models trained using proprietary satellite images.

Landscape of human fear in Neotropical rainforest mammals

The landscape of fear has profound effects on the species behavior, with most organisms engaging in risk avoidance behaviors in areas perceived as riskier. Most risk avoidance behaviors, such as temporal avoidance, have severe trade-offs between foraging efficiency and risk reduction. Human activities are able to affect the species landscape of fear, by increasing mortality of individuals (i.e. hunting, roadkill) and by disruption of the clues used by the species to estimate predation risk (e.g. light pollution). In this study, we used an extensive camera-trapping and night-time light satellite imagery to evaluate whether human activities affect the diel activity patterns of 17 species of rainforest dwelling mammals. We found evidence of diel activity shifts in eight of 17 analyzed species, in which five species become 21.6 % more nocturnal and three species become 11.7% more diurnal in high disturbed areas. This activity shifts were observed for both diurnal and nocturnal species. Persecuted species (game and predators) were more susceptible to present activity shifts. Since changes in foraging activity may affect species fitness, the behavior of humans’ avoidance may be another driver of the Anthropocene defaunation.

Evaluating the Use of DMSP/OLS Nighttime Light Imagery in Predicting PM2.5 Concentrations in the Northeastern United States

Degraded air quality by PM2.5 can cause various health problems. Satellite observations provide abundant data for monitoring PM2.5 pollution. While satellite-derived products, such as aerosol optical depth (AOD) and normalized difference vegetation index (NDVI), have been widely used in estimating PM2.5 concentration, little research was focused on the use of remotely sensed nighttime light (NTL) imagery. This study evaluated the merits of using NTL satellite images in predicting ground-level PM2.5 at a regional scale. Geographically weighted regression (GWR) was employed to estimate the PM2.5 concentration and analyze its relationships with AOD, meteorological variables, and NTL data across the New England region. Observed data in 2013 were used to test the constructed GWR models for PM2.5 prediction. The Vegetation Adjusted NTL Urban Index (VANUI), which incorporates Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI into NTL to overcome the defects of NTL data, was used as a predictor variable for final PM2.5 prediction. Results showed that Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) NTL imagery could be an important dataset for more accurately estimating PM2.5 exposure, especially in urbanized and densely populated areas. VANUI data could obviously improve the performance of GWR for the warm season (GWR model with VANUI performed 17% better than GWR model without NDVI and NTL data and 7.26% better than GWR model without NTL data in terms of RMSE), while its improvements were less obvious for the cold season (GWR model with VANUI performed 3.6% better than the GWR model without NDVI and NTL data and 1.83% better than the GWR model without NTL data in terms of RMSE). Moreover, the spatial distribution of the estimated PM2.5 levels clearly revealed patterns consistent with those densely populated areas and high traffic areas, implying a close and positive correlation between VANUI and PM2.5 concentration. In general, the DMSP/OLS NTL satellite imagery is promising for providing additional information for PM2.5 monitoring and prediction.

Estimating Location-Adjustment Factors for Conceptual Cost Estimating Based on Nighttime Light Satellite Imagery

AbstractA fundamental process in construction cost estimation is the appropriate adjustment of costs to reflect project location. Unfortunately, location adjustment factors are not available for all locations. To overcome this lack of data, cost estimators in the United States often use adjustment factors from adjacent locations, referred to as the nearest neighbor (NN) method. However, these adjacent locations may not have similar economic conditions, which limit the accuracy of the NN method. This research proposes a new method of using nighttime light satellite imagery (NLSI) to estimate location adjustment factors where they do not exist. The NLSI method for estimating location adjustment factors was evaluated against an established cost index database, and results show that NLSI can be used to effectively estimate location adjustment factors. When compared with NN and other alternative proximity-based location adjustment methods, the proposed NLSI method leads to a 25–40% reduction of the median abso...

Ecological approaches in veterinary epidemiology: mapping the risk of bat-borne rabies using vegetation indices and night-time light satellite imagery.

Rabies remains a disease of significant public health concern. In the Americas, bats are an important source of rabies for pets, livestock, and humans. For effective rabies control and prevention, identifying potential areas for disease occurrence is critical to guide future research, inform public health policies, and design interventions. To anticipate zoonotic infectious diseases distribution at coarse scale, veterinary epidemiology needs to advance via exploring current geographic ecology tools and data using a biological approach. We analyzed bat-borne rabies reports in Chile from 2002 to 2012 to establish associations between rabies occurrence and environmental factors to generate an ecological niche model (ENM). The main rabies reservoir in Chile is the bat species Tadarida brasiliensis; we mapped 726 occurrences of rabies virus variant AgV4 in this bat species and integrated them with contemporary Normalized Difference Vegetation Index (NDVI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The correct prediction of areas with rabies in bats and the reliable anticipation of human rabies in our study illustrate the usefulness of ENM for mapping rabies and other zoonotic pathogens. Additionally, we highlight critical issues with selection of environmental variables, methods for model validation, and consideration of sampling bias. Indeed, models with weak or incorrect validation approaches should be interpreted with caution. In conclusion, ecological niche modeling applications for mapping disease risk at coarse geographic scales have a promising future, especially with refinement and enrichment of models with additional information, such as night-time light data, which increased substantially the model’s ability to anticipate human rabies.Electronic supplementary materialThe online version of this article (doi:10.1186/s13567-015-0235-7) contains supplementary material, which is available to authorized users.

Anthropogenic disturbance and habitat loss for the red-listed Asiatic black bear (Ursus thibetanus): Using ecological niche modeling and nighttime light satellite imagery

Habitat loss is a critical factor driving extinction of biodiversity worldwide, with models of future land use anticipating increases in rates of destruction of native habitats worldwide. The Asian black bear (Ursus thibetanus) is a red-listed species with a broad geographic range that has been fragmented dramatically by land use change. Remaining populations of U. thibetanus occupy diverse habitats, ranging from highlands to coastal regions. We integrated ecological niche models (ENMs) with nighttime satellite imagery to identify areas suitable for U. thibetanus after anthropogenic alteration. We found that at least 10% of the potential distributional area for the species is not suitable owing to urban or suburban encroachment. U. thibetanus seems to persist in highland areas, characterized by low temperature and high precipitation, whereas humans concentrate in lowlands and less-extreme climatic conditions. ENMs based solely on climate frequently overestimate suitable areas available for species; nighttime light imagery offers a robust alternative to refining estimates of species' ranges, designing protected areas and corridors, prioritizing threatened species, and determining areas of human–wildlife conflict across broad areas. Our approach is transferable to other taxa and contexts, and should be considered in conservation planning and policy implementation.

Remotely measuring populations during a crisis by overlaying two data sources.

BackgroundSocietal instability and crises can cause rapid, large-scale movements. These movements are poorly understood and difficult to measure but strongly impact health. Data on these movements are important for planning response efforts. We retrospectively analyzed movement patterns surrounding a 2010 humanitarian crisis caused by internal political conflict in Côte d'Ivoire using two different methods.MethodsWe used two remote measures, nighttime lights satellite imagery and anonymized mobile phone call detail records, to assess average population sizes as well as dynamic population changes. These data sources detect movements across different spatial and temporal scales.ResultsThe two data sources showed strong agreement in average measures of population sizes. Because the spatiotemporal resolution of the data sources differed, we were able to obtain measurements on long- and short-term dynamic elements of populations at different points throughout the crisis.ConclusionsUsing complementary, remote data sources to measure movement shows promise for future use in humanitarian crises. We conclude with challenges of remotely measuring movement and provide suggestions for future research and methodological developments.

Evaluating the Compliance of Sea Turtle Light Ordinances in Florida Using Remote Sensing

AbstractSatellite‐based remote sensing has essentially been absent from processes evaluating compliance with environmental policy. Through a case study of the observance of sea turtle light ordinances in Florida, this study seeks to demonstrate that remotely sensed data can be beneficial in evaluation of environmental policy compliance. As a means to increase the successful sea turtle nesting rate, 21 counties and several other municipalities adapted policies prohibiting the lighting of beaches during sea turtle nesting season from May to October. Using visible/infrared imager radiometer suite (VIIRS) nighttime lights satellite imagery to determine mean radiance values and diamond plots of difference images of nighttime lights radiance values, this study confirms compliance with these light ordinances while emphasizing the benefits of remote sensing techniques in the process.