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 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.

scholarly journals Estimation of the temperature distribution in urban areas by using remote sensing data.

1994 ◽  
Vol 22 ◽  
pp. 267-273 ◽  
Author(s):  
Shinji KANEKO ◽  
Toshiie MAEDA ◽  
Takahito UENO ◽  
Hidefumi IMURA
Keyword(s):  
Remote Sensing ◽  
Temperature Distribution ◽  
Urban Areas ◽  
Remote Sensing Data ◽  
Sensing Data

LESS: LargE-Scale remote sensing data and image simulation framework over heterogeneous 3D scenes

2019 ◽  
Vol 221 ◽  
pp. 695-706 ◽  
Author(s):  
Jianbo Qi ◽  
Donghui Xie ◽  
Tiangang Yin ◽  
Guangjian Yan ◽  
Jean-Philippe Gastellu-Etchegorry ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Image Simulation ◽  
Simulation Framework ◽  
Sensing Data

Have vehicle emissions of primary NO2 peaked?

2016 ◽  
Vol 189 ◽  
pp. 439-454 ◽  
Author(s):  
David C. Carslaw ◽  
Tim P. Murrells ◽  
Jon Andersson ◽  
Matthew Keenan
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Vehicle Emissions ◽  
Remote Sensing Data ◽  
Vehicle Emission ◽  
Oxides Of Nitrogen ◽  
Vehicle Exhaust ◽  
Road Vehicles ◽  
Limit Values ◽  
Sensing Data

Reducing ambient concentrations of nitrogen dioxide (NO2) remains a key challenge across many European urban areas, particularly close to roads. This challenge mostly relates to the lack of reduction in emissions of oxides of nitrogen (NOx) from diesel road vehicles relative to the reductions expected through increasingly stringent vehicle emissions legislation. However, a key component of near-road concentrations of NO2 derives from directly emitted (primary) NO2 from diesel vehicles. It is well-established that the proportion of NO2 (i.e. the NO2/NOx ratio) in vehicle exhaust has increased over the past decade as a result of vehicle after-treatment technologies that oxidise carbon monoxide and hydrocarbons and generate NO2 to aid the emissions control of diesel particulate. In this work we bring together an analysis of ambient NOx and NO2 measurements with comprehensive vehicle emission remote sensing data obtained in London to better understand recent trends in the NO2/NOx ratio from road vehicles. We show that there is evidence that NO2 concentrations have decreased since around 2010 despite less evidence of a reduction in total NOx. The decrease is shown to be driven by relatively large reductions in the amount of NO2 directly emitted by vehicles; from around 25 vol% in 2010 to 15 vol% in 2014 in inner London, for example. The analysis of NOx and NO2 vehicle emission remote sensing data shows that these reductions have been mostly driven by reduced NO2/NOx emission ratios from heavy duty vehicles and buses rather than light duty vehicles. However, there is also evidence from the analysis of Euro 4 and 5 diesel passenger cars that as vehicles age the NO2/NOx ratio decreases. For example the NO2/NOx ratio decreased from 29.5 ± 2.0% in Euro 5 diesel cars up to one year old to 22.7 ± 2.5% for four-year old vehicles. At some roadside locations the reductions in primary NO2 have had a large effect on reducing both the annual mean and number of hourly exceedances of the European Limit Values of NO2.

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