<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This article is a quantitative study of the urban expansion of Beijing in the past two decades and its impact on the city’s heat island effect. The overall idea of this paper is a ‘basic fact description to phenomenon and law reveal-basic cause analysis-mechanism analysis-model simulation’. In this paper, the effects of urbanisation on warming in Beijing are deduced by nonlinear fitting method. Based on Google Earth Engine remote sensing image data within Beijing Inner Sixth Ring Road and using ArcGIS to retrieve Beijing’s surface temperature, the heat island effect intensity index is calculated. This paper quantitatively analyses the temporal and spatial distribution characteristics and development trends of Beijing’s urban heat island effect, combined with land use cover change (LUCC), Land Surface Temperature (LST), Normalised difference Vegetation Index (NDVI), Normalised difference Building Index (NDBI), which can explore the impact of surface vegetation distribution and building density on the urban heat island effect.</p>
</abstract>

This article is a quantitative study of the urban expansion of Beijing in the past two decades and its impact on the city’s heat island effect. The overall idea of this paper is a ‘basic fact description to phenomenon and law reveal-basic cause analysis-mechanism analysis-model simulation’. In this paper, the effects of urbanisation on warming in Beijing are deduced by nonlinear fitting method. Based on Google Earth Engine remote sensing image data within Beijing Inner Sixth Ring Road and using ArcGIS to retrieve Beijing’s surface temperature, the heat island effect intensity index is calculated. This paper quantitatively analyses the temporal and spatial distribution characteristics and development trends of Beijing’s urban heat island effect, combined with land use cover change (LUCC), Land Surface Temperature (LST), Normalised difference Vegetation Index (NDVI), Normalised difference Building Index (NDBI), which can explore the impact of surface vegetation distribution and building density on the urban heat island effect.

The impact of urban expansion in Beijing and Metropolitan Area urban heat Island from 1999 to 2019

Department of Information Technology, Faculty of Computing and Information Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This article is a quantitative study of the urban expansion of Beijing in the past two decades and its impact on the city’s heat island effect. The overall...

Time	Tool
999.08.10	Landsat 5
2005.07.25	Landsat 5
2010.08.08	Landsat 5
2014.08.19	Landsat 8
2019.08.17	Landsat 8

Thermal grade	Range
High temperature	T> u+std
Sub high-temperature	u+0.5std < T < u+std
Mid temperature range	u-0.5std < T < u+0.5std
Sub low temperature	u-std < T < u-0.5std
Low temperature	< T < u-std

Land use	Year 1999	Year 2010	Year 2019	Year 1999	Year 2010	Year 2019
Land use	Percentage			km²
Cropland	47.44%	31.13%	12.97%	1075.81	705.97	294.04
Woodland	7.40%	8.84%	7.55%	167.71	200.50	171.27
Grassland	6.07%	2.81%	5.67%	137.56	63.61	128.64
Water body	1.07%	1.12%	1.67%	24.20	25.40	37.87
Built-up land	38.03%	56.10%	72.14%	862.31	1272.11	1635.79

The impact of urban expansion in Beijing and Metropolitan Area urban heat Island from 1999 to 2019

Online veröffentlicht: 22. Nov. 2021

Seitenbereich: 581 - 600

Eingereicht: 06. Juni 2021

Akzeptiert: 24. Sept. 2021

DOI: https://doi.org/10.2478/amns.2021.1.00104

Schlüsselwörter
simulation model, remote sensing image, NDVI, NDBI

© 2021 Jinpei Yang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 1

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Fig. 4

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Database

Classification of heat island intensity

Land Use Classification

The impact of urban expansion in Beijing and Metropolitan Area urban heat Island from 1999 to 2019

Online veröffentlicht: 22. Nov. 2021

Seitenbereich: 581 - 600

Eingereicht: 06. Juni 2021

Akzeptiert: 24. Sept. 2021

DOI: https://doi.org/10.2478/amns.2021.1.00104

Schlüsselwörtersimulation model, remote sensing image, NDVI, NDBI

© 2021 Jinpei Yang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Database

Classification of heat island intensity

Land Use Classification

Schlüsselwörter
simulation model, remote sensing image, NDVI, NDBI