scholarly journals Evaluating the Potential of LJ1-01 Nighttime Light Data for Modeling Socio-Economic Parameters

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1465 ◽  
Author(s):  
Guo Zhang ◽  
Xueyao Guo ◽  
Deren Li ◽  
Boyang Jiang
Keyword(s):  
Infrared Imaging ◽  
Electricity Consumption ◽  
The United States ◽  
Gross Regional Product ◽  
Linear Regression Models ◽  
Nighttime Light Data ◽  
Economic Parameters ◽  
Nighttime Light ◽  
Positive Correlations ◽  
Regional Product

The LJ1-01 satellite is the first dedicated nighttime light remote sensing satellite in the world and offers a higher spatial resolution than the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) satellites of the United States. This study compared the LJ1-01 nighttime light data with NPP/VIIRS data in the context of modeling socio-economic parameters. In the eastern and central regions of China, 10 parameters from the four aspects of gross regional product (annual average population, electricity consumption, and area of land in use) were selected to build linear regression models. The results showed that the LJ1-01 nighttime light data offered better potential for modeling socio-economic parameters than the equivalent NPP/VIIRS data; the former can be an effective tool for establishing models for socio-economic parameters. There were significant positive correlations between the two types of nighttime light data and the 10 socio-economic parameters; that for the gross regional product was the highest.

scholarly journals Global Nighttime Light Change from 1992 to 2017: Brighter and More Uniform

2020 ◽  
Vol 12 (12) ◽  
pp. 4905
Author(s):  
Yunfeng Hu ◽  
Yunzhi Zhang
Keyword(s):  
United States ◽  
Infrared Imaging ◽  
Electricity Consumption ◽  
The United States ◽  
Added Value ◽  
Light Change ◽  
Nighttime Lights ◽  
Long Time ◽  
Nighttime Light

Nighttime light images record the brightness of the Earth surface, indicating the scope and intensity of human activities. However, there are few studies on the long-term changes in global nighttime lights. In this paper, the authors constructed a long time series (1992~2017) nighttime light dataset combining the Defense Meteorological Satellites Program/Operational Linescan System (DMSP-OLS) and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) data sources and observed the following: (1) Global nighttime lights have become brighter. The global nighttime brightness in 2017 was 2.2 times that of 1992. Approximately 40.3% of the lighted area was significantly brightened, and an area of 1.3 × 107 km2 transitioned from an unlighted area to a lighted area. (2) Approximately 85.7% of the nighttime light increase occurred in the low-brightness zone (LBZ). Therefore, global brightness has become more uniform than before. (3) China, India, and the United States have led the global lighting trend. The increase in Chinese nighttime lights is the largest, with an average annual growth of 6.48%, followed by the light growth in India, while the United States has the largest brightened area. (4) The changes in nighttime lights in developing countries (e.g., China and India) are closely and positively related to their electricity consumption, industrial added value and gross domestic product (GDP). The shift of the LBZ center from Asia to Africa indicates the intercontinental transition of poverty.

scholarly journals Identification of Shrinkage and Growth Patterns of a Shrinking City in China Based on Nighttime Light Data: A Case Study of Yichun

2019 ◽  
Vol 11 (24) ◽  
pp. 6906 ◽  
Author(s):  
Ying Zhou ◽  
Chenggu Li ◽  
Zuopeng Ma ◽  
Shuju Hu ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Satellite Images ◽  
Infrared Imaging ◽  
Demographic Data ◽  
Growth Patterns ◽  
Nighttime Light Data ◽  
Nighttime Light ◽  
Urban Science ◽  
New Perspective ◽  
Shrinking City

Urban shrinkage has become a topic of major concern to scholars of geography and urban science. However, the methods of identifying urban shrinkage and growth have mostly focused on traditional statistical methods, and studies based on nighttime light (NTL) data are rare. Here, we use the NTL data for 56 months from 2012 to 2019 obtained by the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar Orbiting Partnership (NPP) to identify the shrinkage and growth patterns of Yichun in China, by calculating the slope of the NTL radiance value after denoising. At the same time, by combining high-resolution Google satellite images and traditional demographic data, we analyzed the shrinkage characteristics of Yichun. The results of the study confirmed the characteristics of partial shrinkage in China’s shrinking cities. In addition, the use of NPP-VIIRS NTL data was able to more accurately identify the urban shrinkage and growth patterns, and may also be seen to present a more objective picture of reality, thus providing a new perspective for studies of urban shrinkage.

scholarly journals Does China’s Urban Development Satisfy Zipf’s Law? A Multiscale Perspective from the NPP-VIIRS Nighttime Light Data

2020 ◽  
Vol 17 (4) ◽  
pp. 1460 ◽  
Author(s):  
Yizhen Wu ◽  
Mingyue Jiang ◽  
Zhijian Chang ◽  
Yuanqing Li ◽  
Kaifang Shi
Keyword(s):  
Urban Development ◽  
Infrared Imaging ◽  
Multiple Scales ◽  
Decision Makers ◽  
Zipf’S Law ◽  
Zipf's Law ◽  
Nighttime Light Data ◽  
Nighttime Light ◽  
Zipf Law ◽  
The Ideal

Currently, whether the urban development in China satisfies Zipf’s law across different scales is still unclear. Thus, this study attempted to explore whether China’s urban development satisfies Zipf’s law across different scales from the National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data. First, the NPP-VIIRS data were corrected. Then, based on the Zipf law model, the corrected NPP-VIIRS data were used to evaluate China’s urban development at multiple scales. The results showed that the corrected NPP-VIIRS data could effectively reflect the state of urban development in China. Additionally, the Zipf index (q) values, which could express the degree of urban development, decreased from 2012 to 2018 overall in all provinces, prefectures, and counties. Since the value of q was relatively close to 1 with an R2 value > 0.70, the development of the provinces and prefectures was close to the ideal Zipf’s law state. In all counties, q > 1 with an R2 value > 0.70, which showed that the primate county had a relatively stronger monopoly capacity. When the value of q < 1 with a continuous declination in the top 2000 counties, the top 250 prefectures, and the top 20 provinces in equilibrium, there was little difference in the scale of development at the multiscale level with an R2 > 0.90. The results enriched our understanding of urban development in terms of Zipf’s law and had valuable implications for relevant decision-makers and stakeholders.

scholarly journals Spatiotemporal Variations of City-Level Carbon Emissions in China during 2000–2017 Using Nighttime Light Data

2020 ◽  
Vol 12 (18) ◽  
pp. 2916
Author(s):  
Yu Sun ◽  
Sheng Zheng ◽  
Yuzhe Wu ◽  
Uwe Schlink ◽  
Ramesh P. Singh
Keyword(s):  
Carbon Emissions ◽  
Infrared Imaging ◽  
Yangtze River Delta ◽  
Chinese Government ◽  
Low Carbon ◽  
Spatiotemporal Variations ◽  
Nighttime Light Data ◽  
Low Emission ◽  
High Emission ◽  
Nighttime Light

China is one of the largest carbon emitting countries in the world. Numerous strategies have been considered by the Chinese government to mitigate carbon emissions in recent years. Accurate and timely estimation of spatiotemporal variations of city-level carbon emissions is of vital importance for planning of low-carbon strategies. For an assessment of the spatiotemporal variations of city-level carbon emissions in China during the periods 2000–2017, we used nighttime light data as a proxy from two sources: Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) data and the Suomi National Polar-orbiting Partnership satellite’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). The results show that cities with low carbon emissions are located in the western and central parts of China. In contrast, cities with high carbon emissions are mainly located in the Beijing-Tianjin-Hebei region (BTH) and Yangtze River Delta (YRD). Half of the cities of China have been making efforts to reduce carbon emissions since 2012, and regional disparities among cities are steadily decreasing. Two clusters of high-emission cities located in the BTH and YRD followed two different paths of carbon emissions owing to the diverse political status and pillar industries. We conclude that carbon emissions in China have undergone a transformation to decline, but a very slow balancing between the spatial pattern of high-emission versus low-emission regions in China can be presumed.

scholarly journals China’s city-level carbon emissions during 1992–2017 based on the inter-calibration of nighttime light data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jiandong Chen ◽  
Ming Gao ◽  
Shulei Cheng ◽  
Xin Liu ◽  
Wenxuan Hou ◽  
...  
Keyword(s):  
Carbon Emissions ◽  
Infrared Imaging ◽  
Carbon Intensity ◽  
Nighttime Light Data ◽  
Reduction Strategies ◽  
Nighttime Light ◽  
Novel Method ◽  
Data Source ◽  
Emission Reduction Strategies

AbstractAccurate, long-term, full-coverage carbon dioxide (CO2) data in units of prefecture-level cities are necessary for evaluations of CO2 emission reductions in China, which has become one of the world’s largest carbon-emitting countries. This study develops a novel method to match satellite-based Defense Meteorological Satellite Program’s Operational Landscan System (DMSP/OLS) and Suomi National Polar-orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) nighttime light data, and estimates the CO2 emissions of 334 prefecture-level cities in China from 1992 to 2017. Results indicated that the eastern and coastal regions had higher carbon emissions, but their carbon intensity decreased more rapidly than other regions. Compared to previous studies, we provide the most extensive and long-term CO2 dataset to date, and these data will be of great value for further socioeconomic research. Specifically, this dataset provides a foundational data source for China’s future CO2 research and emission reduction strategies. Additionally, the methodology can be applied to other regions around the world.

scholarly journals A novel classification regression method for gridded electric power consumption estimation in China

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mulin Chen ◽  
Hongyan Cai ◽  
Xiaohuan Yang ◽  
Cui Jin
Keyword(s):  
Power Consumption ◽  
Electric Power ◽  
Infrared Imaging ◽  
Electricity Consumption ◽  
Ordinary Least Squares ◽  
Regression Method ◽  
Electric Power Consumption ◽  
Percentage Error ◽  
Spatially Explicit ◽  
Nighttime Light

Abstract Spatially explicit information on electric power consumption (EPC) is crucial for effective electricity allocation and utilization. Many studies have estimated fine-scale spatial EPC based on remotely sensed nighttime light (NTL). However, the spatial non-stationary relationship between EPC and NTL at prefectural level tends to be overlooked in existing literature. In this study, a classification regression method to estimate the gridded EPC in China based on imaging NTL via a Visible Infrared Imaging Radiometer Suite (VIIRS) was described. In addition, owing to some inherent omissions in the VIIRS NTL data, the study has employed the cubic Hermite interpolation to produce a more appropriate NTL dataset for estimation. The proposed method was compared with ordinary least squares (OLS) and geographically weighted regression (GWR) approaches. The results showed that our proposed method outperformed OLS and GWR in relative error (RE) and mean absolute percentage error (MAPE). The desirable results benefited mainly from a reasonable classification scheme that fully considered the spatial non-stationary relationship between EPC and NTL. Thus, the analysis suggested that the proposed classification regression method would enhance the accuracy of the gridded EPC estimation and provide a valuable reference predictive model for electricity consumption.

scholarly journals Identification of Polycentric Cities in China Based on NPP-VIIRS Nighttime Light Data

2020 ◽  
Vol 12 (19) ◽  
pp. 3248
Author(s):  
Mingguo Ma ◽  
Qin Lang ◽  
Hong Yang ◽  
Kaifang Shi ◽  
Wei Ge
Keyword(s):  
Spatial Scale ◽  
City Planning ◽  
Infrared Imaging ◽  
Terrain Analysis ◽  
Urban Centers ◽  
Large Spatial Scale ◽  
Nighttime Light Data ◽  
Digital Elevation ◽  
Nighttime Light ◽  
Elevation Model

Nighttime light data play an important role in the research on cities, while the urban centers over a large spatial scale are still far from clearly understood. Aiming at the current challenges in monitoring the spatial structure of cities using nighttime light data, this paper proposes a new method for identifying urban centers for massive cities at the large spatial scale based on the brightness information captured by the Suomi National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) sensor. Based on the method for extracting the peak point based on digital elevation model (DEM) data in terrain analysis, the maximum neighborhood and difference algorithms were applied to the NPP-VIIRS data to extract the pixels with the peak nighttime light intensity to identify the potential locations of urban centers. The results show 7239 urban centers in 2200 cities in China in 2017, with an average of 3.3 urban centers per city. Approximately 68% of the cities had significant polycentric structures. The developed method in this paper is useful for identifying the urban centers and can provide the reference to the city planning and construction.

scholarly journals Energy Consumption in Regions on the Way to Sustainable Innovative Development under Crisis

2021 ◽  
Vol 11 (2) ◽  
pp. 1302-1311
Author(s):  
Anna Vladimirovna Vinogradova
Keyword(s):  
Energy Consumption ◽  
Statistical Data ◽  
Electricity Consumption ◽  
Innovative Development ◽  
Gross Regional Product ◽  
Factors Affecting ◽  
Balanced Approach ◽  
The Russian Federation ◽  
Main Factors ◽  
Regional Product

The article analyses the volume of electricity consumption in the Russian Federation and in several Russian regions. The study was held taking into consideration the statistical data from 1998 to 2019. The authors developed an economic and mathematical model showing the influence of various factors on the electricity consumption. Among the main factors, they identified the gross regional product, the gross regional product per capita, electricity prices, and the exchange rate. They also draw conclusions about the significance of the factors included into the model that influence on the amount of energy consumed. An overview of approaches to the analysis of factors affecting the volume of energy consumption is made. The influence on the process of sustainable innovative development is determined, and a balanced approach to improving the energy efficiency of the domestic economy is proposed.

scholarly journals An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration

2021 ◽  
Vol 13 (3) ◽  
pp. 889-906
Author(s):  
Zuoqi Chen ◽  
Bailang Yu ◽  
Chengshu Yang ◽  
Yuyu Zhou ◽  
Shenjun Yao ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Infrared Imaging ◽  
Great Accuracy ◽  
Sensor Calibration ◽  
Temporal Consistency ◽  
Extended Time ◽  
Urbanization Process ◽  
Nighttime Light Data ◽  
Nighttime Light

Abstract. The nighttime light (NTL) satellite data have been widely used to investigate the urbanization process. The Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) stable nighttime light data and Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data are two widely used NTL datasets. However, the difference in their spatial resolutions and sensor design requires a cross-sensor calibration of these two datasets for analyzing a long-term urbanization process. Different from the traditional cross-sensor calibration of NTL data by converting NPP-VIIRS to DMSP-OLS-like NTL data, this study built an extended time series (2000–2018) of NPP-VIIRS-like NTL data through a new cross-sensor calibration from DMSP-OLS NTL data (2000–2012) and a composition of monthly NPP-VIIRS NTL data (2013–2018). The proposed cross-sensor calibration is unique due to the image enhancement by using a vegetation index and an auto-encoder model. Compared with the annual composited NPP-VIIRS NTL data in 2012, our product of extended NPP-VIIRS-like NTL data shows a good consistency at the pixel and city levels with R2 of 0.87 and 0.95, respectively. We also found that our product has great accuracy by comparing it with DMSP-OLS radiance-calibrated NTL (RNTL) data in 2000, 2004, 2006, and 2010. Generally, our extended NPP-VIIRS-like NTL data (2000–2018) have an excellent spatial pattern and temporal consistency which are similar to the composited NPP-VIIRS NTL data. In addition, the resulting product could be easily updated and provide a useful proxy to monitor the dynamics of demographic and socioeconomic activities for a longer time period compared to existing products. The extended time series (2000–2018) of nighttime light data is freely accessible at https://doi.org/10.7910/DVN/YGIVCD (Chen et al., 2020).

scholarly journals Filtering the NPP-VIIRS Nighttime Light Data for Improved Detection of Settlements in Africa

2019 ◽  
Vol 11 (24) ◽  
pp. 3002
Author(s):  
Xiaotian Yuan ◽  
Li Jia ◽  
Massimo Menenti ◽  
Jie Zhou ◽  
Qiting Chen
Keyword(s):  
Biomass Burning ◽  
Human Activities ◽  
Infrared Imaging ◽  
Recognition Rate ◽  
European Space Agency ◽  
Environmental Research ◽  
Equatorial Africa ◽  
Nighttime Lights ◽  
Nighttime Light Data ◽  
Nighttime Light

Observing and understanding changes in Africa is a hotspot in global ecological environmental research since the early 1970s. As possible causes of environmental degradation, frequent droughts and human activities attracted wide attention. Remote sensing of nighttime light provides an effective way to map human activities and assess their intensity. To identify settlements more effectively, this study focused on nighttime light in the northern Equatorial Africa and Sahel settlements to propose a new method, namely, the patches filtering method (PFM) to identify nighttime lights related to settlements from the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) monthly nighttime light data by separating signal components induced by biomass burning, thereby generating a new annual image in 2016. The results show that PFM is useful for improving the quality of NPP-VIIRS monthly nighttime light data. Settlement lights were effectively separated from biomass burning lights, in addition to capturing the seasonality of biomass burning. We show that the new 2016 nighttime light image can very effectively identify even small settlements, notwithstanding their fragmentation and unstable power supply. We compared the image with earlier NPP-VIIRS annual nighttime light data from the National Oceanic and Atmospheric Administration (NOAA) National Center for Environmental Information (NCEI) for 2016 and the Sentinel-2 prototype Land Cover 20 m 2016 map of Africa released by the European Space Agency (ESA-S2-AFRICA-LC20). We found that the new annual nighttime light data performed best among the three datasets in capturing settlements, with a high recognition rate of 61.8%, and absolute superiority for settlements of 2.5 square kilometers or less. This shows that the method separates biomass burning signals very effectively, while retaining the relatively stable, although dim, lights of small settlements. The new 2016 annual image demonstrates good performance in identifying human settlements in sparsely populated areas toward a better understanding of human activities.

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