Night Light Images Research Articles

Integration of LST and NDVI for adjusted night light index (ILNANLI): a novel index to reduce the effect of saturation and blooming of DMSP-OLS data

ABSTRACT DMSP-OLS night-light images have provided an effective way to monitor cities on a global scale. These images can separate urban areas and other human activities from the background without light with correct spatial measurement. However, due to saturation and blooming problems, the ability of these images to describe urban features is limited, especially in urban cores. Therefore, in this research, a new index, ILNANLI, has been developed and presented by integrating LST and NDVI for the adjusted night-light index. This index has two general goals: reducing the effects of saturation in the city centres, increasing the variety of brightness pixel values of night-light images, and reducing the blooming in the surrounding areas. The ILNANLI has been developed in such a way that it has put the indicators and relationships together, taking into account the characteristics of different regions of the city (such as buildings, vegetation, and soil) in such a way that in every urban environment (temperate and dry and semi-desert) to be successful. Another feature is its easy implementation and high success. In fact, the prominent feature of this index, which distinguishes it from the rest of the indices, is that it has been able to work successfully in arid and semi-desert areas in addition to temperate regions. The evaluation results showed that the average overall accuracy of ILNANLI in mapping and extracting built-up areas with automatic thresholds in cities with different climates has effectively increased by 8% compared to other indexes. Also, the examination of transects shows that the proposed index is more effective than the other four indexes in increasing the diversity of values and reducing blooming. Therefore, as a favourable option with easy implementation, the proposed index can be used to reduce the effect of saturation and blooming and study the urban structure at local scales.

VIIRS Night-Time Light Image in the Spatial Structure Identification of Banyuwangi Regency

A spatial structure is a hierarchical arrangement of settlement centers and a network system of infrastructure and facilities that support the community’s socio-economic activities. Spatial structure is important because the city’s rapid growth requires regional service center planning. In general, the method for determining the spatial structure uses the Centrality Index (IS) with a variable number of facilities, infrastructure, and people to obtain a hierarchical arrangement of regional service centers. However, this method is often constrained by data limitations, so in this study remote sensing technology was used through night light imagery in identifying potential centers. This study used the Visible and Infrared Imaging Radiometer Suite (VIIRS) sensor, one of the sensors carried by the Suomi-NPP (National Polar-orbiting Operational Environmental Satellite System Preparatory Project) satellite. Identification is done by overlaying the night light image with the existing spatial structure to produce potential centers. Based on the analysis results, eight classifications are based on the light emission interval class, where the higher the class, the higher the light emission value, which indicates the potential for a new center. The Pesanggaran sub-district is in class eight, the Tegaldimo sub-district is in class six, and the Banyuwangi sub-district is in class one.

Color Night Light Remote Sensing Images Generation Using Dual-Transformation.

Traditional night light images are black and white with a low resolution, which has largely limited their applications in areas such as high-accuracy urban electricity consumption estimation. For this reason, this study proposes a fusion algorithm based on a dual-transformation (wavelet transform and IHS (Intensity Hue Saturation) color space transform), is proposed to generate color night light remote sensing images (color-NLRSIs). In the dual-transformation, the red and green bands of Landsat multi-spectral images and "NPP-VIIRS-like" night light remote sensing images are merged. The three bands of the multi-band image are converted into independent components by the IHS modulated wavelet transformed algorithm, which represents the main effective information of the original image. With the color space transformation of the original image to the IHS color space, the components I, H, and S of Landsat multi-spectral images are obtained, and the histogram is optimally matched, and then it is combined with a two-dimensional discrete wavelet transform. Finally, it is inverted into RGB (red, green, and blue) color images. The experimental results demonstrate the following: (1) Compared with the traditional single-fusion algorithm, the dual-transformation has the best comprehensive performance effect on the spatial resolution, detail contrast, and color information before and after fusion, so the fusion image quality is the best; (2) The fused color-NLRSIs can visualize the information of the features covered by lights at night, and the resolution of the image has been improved from 500 m to 40 m, which can more accurately analyze the light of small-scale area and the ground features covered; (3) The fused color-NLRSIs are improved in terms of their MEAN (mean value), STD (standard deviation), EN (entropy), and AG (average gradient) so that the images have better advantages in terms of detail texture, spectral characteristics, and clarity of the images. In summary, the dual-transformation algorithm has the best overall performance and the highest quality of fused color-NLRSIs.

Extracting and Evaluating Urban Entities in China from 2000 to 2020 Based on SNPP-VIIRS-like Data

It is crucial to evaluate the expansion of urban entities to implement sustainable urban planning strategies in China. Thus, this study attempted to extract and evaluate the growth of urban entities 270 prefecture cities in mainland China (2000–2020) using a novel approach based on consistent night light images. After the urban entities were extracted, a rationality assessment was carried out to compare the derived urban entities with the LandScan population product, Landsat, and road network results. Additionally, the results were compared with other physical extent products, such as the Moderate Resolution Imaging Spectrometer (MODIS) and urban built-up area products (HE) products. According to the findings, the urban entities were basically consistent with the LandScan, road network, and HE and MODIS products. However, the urban entities more accurately reflected the concentration of human activities than did the impervious extents of the MODIS and HE products. At the prefecture levels, the area of urban entities increased from 8082 km2 to 74,417 km2 between 2000 and 2020, showing an average growth rate of 10.8% over those twenty years. As a reliable supplementary resource and guide for urban mapping, this research will inform new research on the K-means algorithm and on variations in NTL data brightness threshold dynamics at regional and global scales.

Study on the spatial distribution of urban carbon emissions at the micro level based on multisource data.

Global warming is currently an area of concern. Human activities are the leading cause of urban greenhouse gas intensification. Inversing the spatial distribution of carbon emissions at microscopic scales such as communities or controlling detailed planning plots can capture the critical emission areas of carbon emissions, thus providing scientific guidance for intracity low-carbon development planning. Using the Sino-Singapore Tianjin Eco-city as an example, this paper uses night-light images and statistical yearbooks to perform linear fitting within the Beijing-Tianjin-Hebei city-county region and then uses fine-scale data such as points of interest, road networks, and mobile signaling data to construct spatial characteristic indicators of carbon emissions distribution and assign weights to each indicator through the analytic hierarchy process. As a result, the spatial distribution of carbon emissions based on detailed control planning plots is calculated. The results show that among the selected indicators, the population distribution significantly influences carbon emissions, with a weight of 0.384. The spatial distribution of carbon emissions is relatively distinctive. The primary carbon emissions are from the Sino-Singapore Cooperation Zone due to its rapid urban construction and development. In contrast, carbon emissions from other areas are sparse, as there is mostly unused land under construction.

A first Chinese building height estimate at 10 m resolution (CNBH-10 m) using multi-source earth observations and machine learning

Building height is a crucial variable in the study of urban environments, regional climates, and human-environment interactions. However, high-resolution data on building height, especially at the national scale, are limited. Fortunately, high spatial-temporal resolution earth observations, harnessed using a cloud-based platform, offer an opportunity to fill this gap. We describe an approach to estimate 2020 building height for China at 10 m spatial resolution based on all-weather earth observations (radar, optical, and night light images) using the Random Forest (RF) model. Results show that our building height simulation has a strong correlation with real observations at the national scale (RMSE of 6.1 m, MAE = 5.2 m, R = 0.77). The Combinational Shadow Index (CSI) is the most important contributor (15.1%) to building height simulation. Analysis of the distribution of building morphology reveals significant differences in building volume and average building height at the city scale across China. Macau has the tallest buildings (22.3 m) among Chinese cities, while Shanghai has the largest building volume (298.4 108 m3). The strong correlation between modelled building volume and socio-economic parameters indicates the potential application of building height products. The building height map developed in this study with a resolution of 10 m is open access, provides insights into the 3D morphological characteristics of cities and serves as an important contribution to future urban studies in China.

AOD Derivation from SDGSAT-1/GLI Dataset in Mega-City Area

Aerosol optical depth (AOD) is the key parameter for determining the aerosol radiative effects and air quality variation. It is important to quantify nighttime aerosols using satellite-based night light images to understand their diurnal variations. This study selected high-resolution low light images from the Glimmer Imager (GLI) aboard the SDGSAT-1 satellite to examine spatial–temporal changes in night light emitted from the urban surface of Beijing. The radiance observed by SDGSAT-1/GLI was used to discern the AOD changes using the radiance background method (RB) and standard deviation method (SD) based on the characterization of the radiance from artificial light sources. Cloud cleaning processes were conducted to reduce the influence of the cloud cover in the glimmer images of the derived AOD. The results showed that there are good consistencies between the derived AOD results from the remote sensing and nighttime site observations. The radiance background method is better than the standard deviation method for deriving AOD using SDGSAT-1/GLI with the RMSE of its RB (0.0984) being greater than that of the SD (0.7653). The influence of moonlight, atmospheric absorption, and positioning errors on the results is briefly discussed. This paper shows that SDGSAT-1 can obtain relatively reliable night AOD values based on our investigations using the available satellite images taken in winter and spring, and that it has the potential to provide the scientific products of nighttime AOD.

Fused Thermal and RGB Imagery for Robust Detection and Classification of Dynamic Objects in Mixed Datasets via Pre-Trained High-Level CNN

Smart vehicles with embedded Autonomous Vehicle (AV) technologies are currently equipped with different types of mounted sensors, aiming to ensure safe movement for both passengers and other road users. The sensors’ ability to capture and gather data to be synchronically interpreted by neural networks for a clear understanding of the surroundings is influenced by lighting conditions, such as natural lighting levels, artificial lighting effects, time of day, and various weather conditions, such as rain, fog, haze, and extreme temperatures. Such changing environmental conditions are also known as complex environments. In addition, the appearance of other road users is varied and relative to the vehicle’s perspective; thus, the identification of features in a complex background is still a challenge. This paper presents a pre-processing method using multi-sensorial RGB and thermal camera data. The aim is to handle issues arising from the combined inputs of multiple sensors, such as data registration and value unification. Foreground refinement, followed by a novel statistical anomaly-based feature extraction prior to image fusion, is presented. The results met the AV challenges in CNN’s classification. The reduction of the collected data and its variation level was achieved. The unified physical value contributed to the robustness of input data, providing a better perception of the surroundings under varied environmental conditions in mixed datasets for day and night images. The method presented uses fused images, robustly enriched with texture and feature depth and reduced dependency on lighting or environmental conditions, as an input for a CNN. The CNN was capable of extracting and classifying dynamic objects as vehicles and pedestrians from the complex background in both daylight and nightlight images.

Development of a GIS based hazard, exposure, and vulnerability analyzing method for monitoring drought risk at Karachi, Pakistan

Droughts have an adverse influence on agriculture, the environment, water supplies, and the global economy. The drought risk was computed using an integrated prospective approach: drought hazard, exposure, and vulnerability based on biophysical and socio-economic conditions over Karachi, Pakistan during 2000–2019. Drought hazard map (DHM) was created using annual Palmer drought severity Index (PDSI). Drought exposure map (DEM) was derived using population density and gross domestic product (GDP), as well as land surface temperature (LST), Normal difference vegetation index (NDVI), Night light images (NTL), land use land cover (LULC), and Distance to water were used for drought vulnerability map (DVM). An estimation of drought Risk (EDR) was derived by integrating layers of DHM, DEM, and DVM. Results showed that Central, South, and East regions of Karachi were at high risk, whereas the North East and North were less affected by the drought. The estimated average drought hazard (EDH) was 0.84, with minimum (maximum) value of 0.68 (1). Similarly, the average estimated drought exposure (estimated drought vulnerability) for EDE (EDV) was 0.27 (0.42), with the maximum value of 0.55 (0.84) and the minimum value of 0 (0). The drought risk assessment map (DRAM) shows that the average risk values is 0.18 while highest value is 0.36.

The electricity scene from above: Exploring power grid inconsistencies using satellite data in Accra, Ghana

Complicated systems are complicated to monitor. The electric grid is one of the most complicated systems, and subsequently goes under-monitored in many regions around the world that cannot easily afford expensive meters. However, the electric grid is also critical for sustaining a high quality of life, and requires better monitoring than is often available to ensure consistent service is provided. Past work has shown that images taken by satellites during the night, capturing nighttime illumination (“nightlights”), could provide a proxy measurement of grid performance for minimal cost. We build upon earlier work by identifying the pixel z-score – a statistical measurement of a pixel’s illumination relative to its history – as a key method for detecting electricity outages from the often-noisy nightlights dataset. We then train and validate our approach against observations from a network of on-the-ground power outage sensors in our observation area of Accra, Ghana, a dataset representing the largest collection of utility-independent electricity reliability measurements on the African continent. Using multiple machine learning techniques for estimating potential outages from nightlight images, we obtain high performance for predicting outages in Accra at scales as small as a single pixel (0.2 km2) and with training datasets as small as three months of illumination/sensor data. We further validate our methodology beyond the spatio-temporal coverage of the on-the-ground sensor deployment against a human-labeled dataset of outages by neighborhood throughout Accra. Delving deeper into the applications and limitations of available datasets and our work, we conclude by highlighting questions about the generality of our method vital to understanding its potential for low-cost worldwide measurements of grid reliability.

Luojia-1 Nightlight Image Registration Based on Sparse Lights

When mosaicking adjacent nightlight images of a large area that lacks human activities, traditional registration methods have difficulty realizing the tie point registrations due to the lack of structural information. In order to address this issue, this study devises an easy-to-implement engineering solution that allows for the registration of sparse light areas with high efficiency while guaranteeing accuracy in non-sparse light areas. The proposed method first extracts the sparsely distributed light point positions through use of roundness detection and the centroid method. Then, geometric positioning forward and backward algorithms and the random consistency sampling detection algorithm (RANSAC) are used in order to achieve a rough registration of the nightlight images and the remaining tie points are expanded through the affine model. Through experimentation it was found that, compared with traditional registration methods, the proposed method is more reliable and has a wider distribution in sparse light areas. Finally, through the registration test of 275 scenes of nightlight images of China from Luojia-1, the coverage ratio of the tie points was increased from 59.3% from the traditional method to 95.3% in this study and the accuracy of the block adjustment was 0.63 pixels, which verifies the effectiveness of the method. The proposed method provides a basis for the registration, block adjustment, and mosaicking of nightlight images.

Modelling Electricity Consumption in Cambodia Based on Remote Sensing Night-Light Images

The accurate estimation of electricity consumption and its spatial distribution are important in electricity infrastructural planning and the achievement of the United Nations Sustainable Development Goal 7 (SDG7). Electricity consumption can be estimated based on its correlation with nighttime lights observed using remote sensing imagery. Since night-light images are easily affected by cloud cover, few previous studies have estimated electricity consumption in cloudy areas. Taking Cambodia as an example, the present study proposes a method for denoising night-light images in cloudy areas and estimating electricity consumption. The results show that an exponential model is superior to linear and power function models for modelling the relationship between total night-light data and electricity consumption in Cambodia. The month-specific substitution method is best for annual night-light image synthesis in cloudy areas. Cambodia’s greatest electricity consumption occurs in its four most economically developed cities. Electricity consumption spreads outwards from these cities along the main transport routes to a large number of unelectrified areas.

A Global Assessment of Night Lights as an Indicator for Shipping Activity in Anchorage Areas

Accurate information on port shipping activities is critical for monitoring global and local traffic flows and assessing the state of development of the maritime industry. Such information is necessary for managers and analysts to make strategic decisions and monitor the maritime industry in achieving management goals. In this study, we used monthly night light (NTL) images of the Suomi National Polar-Orbiting Partnership (Suomi NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band, between 2012 and 2020, to study the night lights emitted by ships in ports’ anchorage areas, as an indicator for shipping activity in anchorage areas and ports. Using a dataset covering 601 anchorage areas from 97 countries, we found a strong correspondence between NTL data and shipping metrics at the country level (n = 97), such as container port throughput (Rs = 0.84, p < 0.01) and maximum cargo carried by ships (Rs = 0.66, p < 0.01), as well as a strong correlation between the number of anchorage points and the NTL values in anchorage areas across the world (Rs = 0.69, p < 0.01; n = 601). The high correspondence levels of the VIIRS NTL data with various shipping indicators show the potential of using NTL data to analyze the spatio-temporal dynamic changes of the shipping activity in anchorage areas, providing convenient open access and a normalized assessment method for shipping industry parameters that are often lacking.

Measuring the urban land use efficiency of three urban agglomerations in China under carbon emissions.

On the basis of DMSP/OLS and NPP-VIIRS night light images, this study realized carbon emission estimations based on the municipal level from 1999 to 2017, compensating for the characteristics of incomplete statistical data and different statistical calibers. On this basis, the epsilon-based measure (EBM) super-efficiency model and the global Malmquist-Luenberger (GML) index are used to measure the urban land use efficiency (ULUE) and urban land total factor productivity (ULTFP) of the three urban agglomerations under the carbon emission constraints from 1999 to 2017. The following conclusions are drawn through research. (1) The correlation coefficient between the total value of night light pixels and energy consumption carbon emissions was relatively high in the three major urban agglomerations during 1999-2017, and they all passed the significance test of 1%. (2) The ULUE of the three major urban agglomerations generally shows a downward trend and then an upward trend, and spatial heterogeneity is obvious. The spatial distribution of the average level of ULUE is Pearl River Delta Urban Agglomeration (PRDUA) > Yangtze River Delta Urban agglomeration (YRDUA) > Beijing-Tianjin-Hebei Urban agglomeration (BTHUA). (3) The ULTFP of the three major urban agglomerations are all showing an increasing trend, but the geometric mean of URTFP in the PRDUA, BTHUA, and YRDUA decreases successively. Technological progress is the main driving force to promote the progress of ULTFP in each urban agglomeration. (4) The kernel density estimation shows a significant gap in ULUE between the three major urban agglomerations in China, and a phenomenon of polarization or multipolarization is observed. The main reason is the hysteresis of technology diffusion.

Estimating the Spatial Heterogeneity and Seasonal Differences of the Contribution of Tourism Industry Activities to Night Light Index by POI

The spatial distribution of tourism has a profound impact on its operational efficiency and geographical relevance. Point of interest (POI), as a kind of spatial data shared by subject and object, can reflect the spatial distribution form and function of tourism geographical objects under the all-for-one tourism policy. Continuous satellite observation and in-depth study of night lights pave the way to clarify human activities and socio-economic dynamics. The purpose of this paper is to investigate the seasonal changes of night light images and their correlation with tourism in 122 counties (cities, districts) of Hunan Province. We obtained night earth observation data (seasonality) and POI in 2019 and processed them by Geographic Information System and statistical analysis (ordinary least squares (OLS) and geographically weighted regression (GWR)). The results show that the luminous radiation intensity is highly correlated with the POI of tourism activities. The POI of different tourism activities in different regions shows obvious spatial heterogeneity and seasonal differences, which is the result of the comprehensive effect of tourism resource distribution and social environment in Hunan Province. GWR has proved to be a more effective tool. It provides a new method and perspective for tourism research and especially reveals the geographical spatial differences of tourism activities, which is helpful to study the spatial distribution and seasonality of tourism at the county level. In addition, the spatial evaluation of the contribution of tourism and luminous radiation can provide reference and suggestions for relevant departments to formulate tourism night protection measures.

Viewing the COVID-19 Pandemic from Space: The Effect of COVID-related Mobility Declines on Night Light Brightness in Canada

High-frequency economic data for small areas is often difficult to obtain in Canada and other countries. This paper overcomes this limitation by using monthly data derived from satellite night light images as a proxy for economic activity in Canadian Census Divisions. This proxy is used in conjunction with Facebook mobility data to estimate the effects of mobility declines due to COVID-19 on economic activity. I find robust evidence that reductions in movements are strongly negatively associated with declines in luminosity. Further analyses suggest that this effect is weaker in more densely populated areas, but stronger in Census Divisions with a higher concentration of retail businesses. My findings suggest that policies which reduce the need for in-person activities can mitigate the negative effects of COVID-related mobility reductions on economic activity. This paper also further highlights the value of using monthly satellite night lights data in economic analysis.

Multi-frame image restoration method for Luojia 1-01 night-light remote sensing images

ABSTRACT To address blur and noise issues caused by airlight and atmospheric scattering in nighttime imaging environment, we proposed a multi-frame image restoration method. First, the Luojia 1–01 night-light image degradation model was derived. Thereafter, the APSF (Atmospheric Point Spread Function) for night-light images was estimated. Improved dark channel prior and sparse constraint models were used to eliminate effects ofairlight and atmospheric scattering. Finally, a multi-frame sparse constraint model was established to eliminate image noise. The results show the proposed method is effective as it can reduce the image blur phenomenon, suppress image noise, and improve image quality.

A Fused Variable by Night Light Images and MODIS Products for Improving Urban Built-Up Area Extraction

The boundary of urban built-up areas is the baseline data of a city. Rapid and accurate monitoring of urban built-up areas is the prerequisite for the boundary control and the layout of urban spaces. In recent years, the night light satellite sensors have been employed in urban built-up area extraction. However, the existing extraction methods have not fully considered the properties that directly reflect the urban built-up areas, like the land surface temperature. This research first converted multi-source data into a uniform projection, geographic coordinate system and resampling size. Then, a fused variable that integrated the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) night light images, the Moderate-resolution Imaging Spectroradiometer (MODIS) surface temperature product and the normalized difference vegetation index (NDVI) product was designed to extract the built-up areas. The fusion results showed that the values of the proposed index presented a sharper gradient within a smaller spatial range, compared with the only night light images. The extraction results were tested in both the area sizes and the spatial locations. The proposed index performed better in both accuracies (average error rate 1.10%) and visual perspective. We further discussed the regularity of the optimal thresholds in the final boundary determination. The optimal thresholds of the proposed index were more stable in different cases on the premise of higher accuracies.

The Extraction of Built-up Areas in Chinese Mainland Cities Based on the Local Optimal Threshold Method Using NPP-VIIRS Images

Accurate extraction of urban built-up areas is an essential prerequisite of urbanization research. This paper proposes a local optimal threshold method based on NPP-VIIRS nighttime light images to extract urban built-up areas in mainland China. First, according to the regional economic differences, the image is divided into eight regions. Second, the maximum variance ratio threshold method and particle swarm optimization algorithm are combined to extract the optimal threshold for segmenting the night light images in each region. The optimal threshold for each region is used to extract the urban built-up areas from the NPP-VIIRS images of each region. Finally, eight urban built-up areas are merged. In order to verify the extraction accuracy, one city-level city and one county-level city are extracted in each region, and the boundaries of urban built-up areas are compared with a Google Earth historical image from June 15, 2015. The experimental results show that the kappa for the eight selected city-level cities (Shanghai, Chengdu, Lanzhou, Changsha, Changzhi, Tianjin, Shenzhen, Harbin) is approximately 0.80. The kappa for the eight selected county-level cities (Baoying, Luoping, Fuhai, Dongxiang, Dongsheng, Miyun, Longchuan, Shuangyang) is approximately 0.75. The method exhibits high precision for large-scale urban built-up area extraction without the aid of auxiliary data. The experimental results indicate that light intensity can reflect the development level of a city.

Linking Luojia 1-01 nightlight imagery to urban crime

Various environmental criminology theories and empirical studies have linked the urban environment to crime. The crime pattern theory, in particular, argues that edges, either social or physical, affect crime. A recent study has combined both social and physical edges to derive composite edges. A composite edge index measured by NPP-VIIRS satellite nightlights at the census tract level is found to be related to street robbery and burglary. Nightlight images of Luojia 1-01, launched in June 2018, have a much higher spatial resolution than that of NPP-VIIRS. This study applies Luojia 1-01 nightlight data to measure composite edges by nightlight gradients at the smaller census block group level. The effects of the composite edges on street robbery and burglary are explored by negative binomial models. Results show that composite edges measured by Luojia 1-01 nightlight data improve the fitness of models noticeably on street robbery but not on burglary. Nightlight gradients make a statistically significant and positive impact on the street robbery rate, but an insignificant and negative impact on the burglary rate. Furthermore, the composite edge effect on street robbery is more substantial than that on burglary. In sum, this study provides evidence that Luojia 1-01 nightlight imagery can help explain crime at the aggregated block group level, but its impact on crime varies by crime type.