scholarly journals An Analysis of the Work Resumption in China under the COVID-19 Epidemic Based on Night Time Lights Data

2021 ◽  
Vol 10 (9) ◽  
pp. 614
Author(s):  
Suzheng Tian ◽  
Ruyi Feng ◽  
Ji Zhao ◽  
Lizhe Wang
Keyword(s):  
Remote Sensing ◽  
Chinese Spring ◽  
Quantitative Measurement ◽  
Remote Sensing Data ◽  
Specific Situation ◽  
Night Time ◽  
Migration Data ◽  
Spring Festival ◽  
Production And Operation ◽  
Work Resumption

Public emergencies often have an impact on the production and operation of enterprises. Timely and effective quantitative measurement of enterprises’ offline resumption of work after public emergencies is conducive to the formulation and implementation of relevant policies. In this study, we analyze the level of work resumption after the coronavirus disease 2019 (COVID-19)-influenced Chinese Spring Festival in 2020 with night time lights remote sensing data and Baidu Migration data. The results are verified by official statistics and facts, which demonstrates that COVID-19 has seriously affected the resumption of work after the Spring Festival holiday. Since 10 February, work has been resuming in localities. By the end of March, the work resumption index of most cities exceeded 70% and even Shanghai, Nanjing and Suzhou had achieved complete resumption of work. Wuhan only started to resume work in the last week of March due to the more severe outbreak. Although the level of work resumption is gradually increasing in every area, the specific situation of resumption of work varies in different regions. The process of work resumption in coastal areas is faster, while the process is relatively slow in inland cities.

scholarly journals Can More Accurate Night-Time Remote Sensing Data Simulate a More Detailed Population Distribution?

2019 ◽  
Vol 11 (16) ◽  
pp. 4488 ◽  
Author(s):  
Nannan Gao ◽  
Fen Li ◽  
Hui Zeng ◽  
Daniël van Bilsen ◽  
Martin De Jong
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Large Scale ◽  
Population Distribution ◽  
Remote Sensing Data ◽  
Urban Land Use ◽  
Residential Areas ◽  
Night Time ◽  
Sensing Data ◽  
Population Distributions

Aging, shrinking cities, urban agglomerations and other new key terms continue to emerge when describing the large-scale population changes in various cities in mainland China. It is important to simulate the distribution of residential populations at a coarse scale to manage cities as a whole, and at a fine scale for policy making in infrastructure development. This paper analyzes the relationship between the DN (Digital number, value assigned to a pixel in a digital image) value of NPP-VIIRS (the Suomi National Polar-orbiting Partnership satellite’s Visible Infrared Imaging Radiometer Suite) and LuoJia1-01 and the residential populations of urban areas at a district, sub-district, community and court level, to compare the influence of resolution of remote sensing data by taking urban land use to map out auxiliary data in which first-class (R1), second-class (R2) and third-class residential areas (R3) are distinguished by house price. The results show that LuoJia1-01 more accurately analyzes population distributions at a court level for second- and third-class residential areas, which account for over 85% of the total population. The accuracy of the LuoJia1-01 simulation data is higher than that of Landscan and GHS (European Commission Global Human Settlement) population. This can be used as an important tool for refining the simulation of residential population distributions. In the future, higher-resolution night-time light data could be used for research on accurate simulation analysis that scales down large-scale populations.

scholarly journals Population Spatialization in Beijing City Based on Machine Learning and Multisource Remote Sensing Data

2020 ◽  
Vol 12 (12) ◽  
pp. 1910 ◽  
Author(s):  
Miao He ◽  
Yongming Xu ◽  
Ning Li
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Census Data ◽  
Remote Sensing Data ◽  
Remotely Sensed ◽  
Google Earth ◽  
Population Census ◽  
Beijing City ◽  
Night Time ◽  
Sensing Data

Remote sensing data have been widely used in research on population spatialization. Previous studies have generally divided study areas into several sub-areas with similar features by artificial or clustering algorithms and then developed models for these sub-areas separately using statistical methods. These approaches have drawbacks due to their subjectivity and uncertainty. In this paper, we present a study of population spatialization in Beijing City, China based on multisource remote sensing data and town-level population census data. Six predictive algorithms were compared for estimating population using the spatial variables derived from The National Polar-Orbiting Partnership/ Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) night-time light and other remote sensing data. Random forest achieved the highest accuracy and therefore was employed for population spatialization. Feature selection was performed to determine the optimal variable combinations for population modeling by random forest. Cross-validation results indicated that the developed model achieved a mean absolute error (MAE) of 2129.52 people/km2 and a R2 of 0.63. The gridded population density in Beijing at a spatial resolution of 500 m produced by the random forest model was also adjusted to be consistent with the census population at the town scale. By comparison with Google Earth high-resolution images, the remotely-sensed population was qualitatively validated at the intra-town scale. Validation results indicated that remotely sensed results can effectively depict the spatial distribution of population within town-level districts. This study provides a valuable reference for urban planning, public health and disaster prevention in Beijing, and a reference for population mapping in other cities.

scholarly journals Business Circle Identification and Spatiotemporal Characteristics in the Main Urban Area of Yiwu City Based on POI and Night-Time Light Data

2021 ◽  
Vol 13 (24) ◽  
pp. 5153
Author(s):  
Liangliang Zhou ◽  
Yishao Shi ◽  
Jianwen Zheng
Keyword(s):  
Remote Sensing ◽  
Urban Area ◽  
Remote Sensing Data ◽  
Residential Areas ◽  
Economic Activities ◽  
Night Time ◽  
Sensing Data ◽  
Dbscan Clustering ◽  
The Difference ◽  
The Impact

The activity of the urban night-time economy is one of the most important indicators reflecting the prosperity of an urban economy. The business circle is an important carrier of urban commercial activities and the core area of urban nightlife. This paper takes the main urban area of Yiwu city as the research object. Based on POI data and night-time light remote sensing data, two-factor mapping, kernel density analysis, DBSCAN clustering, and local contour tree methods are adopted to identify the business circle structure of the main urban area of Yiwu city and analyse the relationship between business circle characteristics and the night-time economy. The following conclusions can be drawn. (1) The spatial superimposition relationship between the night-time remote sensing data and points of interest (POI) data in the main urban area of Yiwu city is good, and the overall coupling results show obvious circle structure characteristics. (2) The spatial distribution of different business combinations has obvious regularity: comprehensive shopping business shows a multicentre distribution pattern and has a hierarchical feature. In contrast, professional food and beverage and leisure and entertainment businesses are close to urban residential areas, and different groups of people live in different places with their own characteristics. (3) From 2015 to 2019, the brightness value of each business circle showed a continuously increasing trend. In 2020, due to the impact of COVID-19, most of them declined. (4) Overall, the difference in business circle tiers reflects the difference in the level of night-time economic activities.

A surface network based method for studying urban hierarchies by night time light remote sensing data

2019 ◽  
Vol 33 (7) ◽  
pp. 1377-1398 ◽  
Author(s):  
Bin Wu ◽  
Bailang Yu ◽  
Shenjun Yao ◽  
Qiusheng Wu ◽  
Zuoqi Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Night Time ◽  
Sensing Data ◽  
Surface Network

scholarly journals RECOGNITION OF URBAN FUNCTIONAL REGIONS AT STREET SCALE BASED ON LJ1-01 NIGHT-TIME LIGHT REMOTE SENSING AND MOBILE BIG DATA

2019 ◽  
Vol IV-4/W9 ◽  
pp. 119-124
Author(s):  
Q. Zhou ◽  
Y. Zhang ◽  
D. Gao ◽  
B. Sun
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Big Data ◽  
Remote Sensing Data ◽  
Pollution Monitoring ◽  
Light Pollution ◽  
Night Time ◽  
Sensing Data ◽  
Functional Regions ◽  
Mobile Big Data

Abstract. Night-time light (NTL) remote sensing data has been widely used in the analysis of human activities at night for a large extent, such as light pollution monitoring and recognition of urban functional regions. In most previous studies, the spatial resolutions of NTL remote sensing data are rather low (e.g., 500 m or coarser). Besides, remote sensing classification of land use rather than land cover types is often a hard task. Due to the reasons, it is difficult to meet the demand of urban refined management. In this study, mobile big data and Luojia1-01 (LJ1-01) NTL remote sensing satellite data with higher spatial resolution are adopted for extracting urban functional regions at the street-level scale. Taking Shenzhen city as a case, mobile big data (i.e., the volume of mobile devices) with the accuracy of approximate 150 m (i.e., 7-bit GeoHash encoding format) is integrated with NTL remote sensing data. After a hot spot analysis, the correlation between the two types of data are analysed. The typical supervised classification algorithms including KNN, SVM and random forest are used to distinguish urban functional regions, which are represented by five types, namely residential, business and commercial, industrial, transportation and other areas. The results show the effectiveness in extracting land use types in cities by combination of Luojia1-01 night-time light remote sensing and mobile big data. This study provides a basis for monitoring night light pollution of residential area, urban planning and the construction of smart cities.

scholarly journals The Potential of Moonlight Remote Sensing: A Systematic Assessment with Multi-Source Nightlight Remote Sensing Data

2021 ◽  
Vol 13 (22) ◽  
pp. 4639
Author(s):  
Di Liu ◽  
Qingling Zhang ◽  
Jiao Wang ◽  
Yifang Wang ◽  
Yanyun Shen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Infrared Imaging ◽  
Space Station ◽  
Remote Sensing Data ◽  
Optical Remote Sensing ◽  
Night Time ◽  
Systematic Assessment ◽  
Sensing Applications ◽  
Satellite Sensors

One recent trend in optical remote sensing is to increase observation frequencies. However, there are still challenges on the night side when sunlight is not available. Due to their powerful capabilities in low-light sensing, nightlight satellite sensors have been deployed to capture nightscapes of Earth from space, observing anthropomorphic and natural activities at night. To date, the mainstream of nightlight remote sensing applications has mainly focused on artificial lights, especially within cities or self-luminous bodies, such as fisheries, oil, offshore rigs, etc. Observations taken under moonlight are often discarded or corrected to reduce lunar effects. Some researchers have discussed the possibility of using moonlight as a useful illuminating source at night for the detection of nocturnal features on Earth, but no quantitative analysis has been reported so far. This study aims to systematically evaluate the potential of moonlight remote sensing with mono-spectral Visible Infrared Imaging Radiometer Suite/Day-Night-Band (VIIRS/DNB) imagery and multi-spectral photos taken by astronauts from the International Space Station (ISS), as well as unmanned aerial vehicle (UAV) night-time imagery. Using the VIIRS/DNB, ISS and UAV moonlight images, the possibilities of the moonlight remote sensing were first discussed. Then, the VIIRS/DNB, ISS, UAV images were classified over different non-self-lighting land surfaces to explore the potential of moonlight remote sensing. The overall accuracies (OA) and kappa coefficients are 79.80% and 0.45, 87.16% and 0.77, 91.49% and 0.85, respectively, indicating a capability to characterize land surface that is very similar to daytime remote sensing. Finally, the characteristics of current moonlight remote sensing are discussed in terms of bands, spatial resolutions, and sensors. The results confirm that moonlight remote sensing has huge potential for Earth observation, which will be of great importance to significantly increase the temporal coverage of optical remote sensing during the whole diurnal cycle. Based on these discussions, we further examined requirements for next-generation nightlight remote sensing satellite sensors.

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