A multiscale study of thermal environment pattern in Lanzhou-Xining agglomeration

doi:10.6046/gtzyyg.2018.02.19

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Abstract

The study of the changing pattern of urban thermal environment from different spatial scales can provide a scientific reference for the construction of urban human settlements. On the basis of MODIS and Landsat TM/OLI/TIRS data, land surface temperature is retrieval. Spatial pattern of thermal field in Lanzhou-Xining agglomeration (LXA) was analyzed from macro scale and micro scale. Diurnal variation, seasonal variation, and annual variation of urban heat island effect of LXA were explored. The heat island ratio index was introduced to describe the variation characteristics of thermal field pattern in LXA from 1992 to 2015. The results show that, on the large scale, there is no obvious urban heat island effect in LXA, whereas the spatial pattern of urban heat island effect in internal areas of central urban region of Lanzhou and Xining-Haidong changed greatly from 1992 to 2015. The urban sprawl had a spatial consistency with the urban heat island extension. Specifically, the heat island ratio index first increased and then decreased in central urban area of Lanzhou, whereas the heat island ratio index continuously increased significantly in central urban area of Xining-Haidong. As a typical valley agglomeration, the temperature of central cities was lower than that of surrounding loess hilly regions. The main influence factors were vegetation and the duration and amount of solar radiation. It seems that the land surface temperature is negatively correlated with normalized difference vegetation index(NDVI) and positively correlated with normalized difference building index(NDBI).

Keywords urban heat island heat island effect valley city Lanzhou-Xining agglomeration.

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Issue Date: 30 May 2018

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	Jinghu PAN
	Leilei DONG
	Nayun WANG
	Zijin YANG

Cite this article:

Jinghu PAN,Leilei DONG,Nayun WANG, et al. A multiscale study of thermal environment pattern in Lanzhou-Xining agglomeration[J]. Remote Sensing for Land & Resources, 2018, 30(2): 138-146.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.02.19 OR https://www.gtzyyg.com/EN/Y2018/V30/I2/138

Fig.1 Location of Lanzhou-Xining agglomeration

LST等级	温度范围
低温	T_i<T_mean-2.5 $T s ①$
较低温	T_mean-2.5T_s≤T_i<T_mean-1.5T_s
次中温	T_mean-1.5T_s≤ T_i<T_mean-0.5T_s
中温	T_mean-0.5T_s≤T_i< T_mean+0.5T_s
次高温	T_mean+0.5T_s≤T_i<T_mean+1.5T_s
高温	T_mean+1.5T_s≤T_i<T_mean+2.5T_s
特高温	T_i≥T_mean+2.5T_s

Tab.1 Rank partition of LST

Fig.2-1 Spatial distribution of average daytime LST

Fig.3 Spatial distribution of average nighttime LST

Fig.4 Spatial distribution of average daytime LST in four seasons

Fig.5 Spatial distribution of average nighttime LST in four seasons

Fig.6 Spatial distribution of LST in central urban areas of Lanzhou

Fig.7 Spatial distribution of LST in central urban areas of Xining-Haidong

Fig.8 Relationships between LST and NDVI, NDBI in central urban areas of Lanzhou on Aug.14, 2015

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