Inversion Study of Urban Surface Temperature in Luoyang City Based on Landsat Images

WANG Jiangli; ZHANG Minggang; YANG Xiaohan; ZHU Maochao

doi:10.3724/j.gyjzG22112007

Volume 54 Issue 4

Apr. 2024

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WANG Jiangli, ZHANG Minggang, YANG Xiaohan, ZHU Maochao. Inversion Study of Urban Surface Temperature in Luoyang City Based on Landsat Images[J]. INDUSTRIAL CONSTRUCTION, 2024, 54(4): 30-38. doi: 10.3724/j.gyjzG22112007

Citation:

WANG Jiangli, ZHANG Minggang, YANG Xiaohan, ZHU Maochao. Inversion Study of Urban Surface Temperature in Luoyang City Based on Landsat Images[J]. INDUSTRIAL CONSTRUCTION, 2024, 54(4): 30-38. doi: 10.3724/j.gyjzG22112007

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Inversion Study of Urban Surface Temperature in Luoyang City Based on Landsat Images

doi: 10.3724/j.gyjzG22112007

1. School of Civil Engineering and Architecture, Henan University of Science and Technology, Luoyang 471003, China;
2. State key Laboratory of Beijing safety and Environment, Beijing 100013, China;
3. Institute of Building Energy and Thermal Science, Henan University of Science and Technology, Luoyang 471003, China;
4. Henan Provincial Engineering Research Center of Building Environmental Control and Safety, Luoyang 471003, China;
5. School of Architecture, Southwest Jiaotong University, Chengdu 610000, China

Received Date: 2022-11-20
Available Online: 2024-05-29

Abstract

Abstract

In order to study the relations between the urban development of Luoyang and the urban heat island, the surface temperatures of the urban area in the past two decades were taken as the research object. Landsat satellite remote sensing data was used as the data source, and the temperature inversion method was used to study the surface temperatures at four different periods. The analysis was conducted by using ArcGIS, ENVI 5.3, and Origin 2018 software. The spatial distribution and spatiotemporal variation characteristics of the heat island effect, as well as the relations between surface temperature and land use type, were obtained. The results indicated that from 2001 to 2019, the surface temperature of the old city center (Xigong District) increased by 15, and the surface ℃ temperature of the new city center (Luolong District) increased by 11 ℃. Among different land use classifications, ℃ the surface temperature of water bodies was the lowest, and with the increase of the heat island, the cold island effect of water bodies had slightly increased. The surface temperature of the Luo River was 26 in 2001, with a ℃ cold island intensity of 12, and in 2019, it was 34 ℃ with a cold island intensity of 14 ℃.
- Landsat satellite data,
- temperature inversion,
- surface temperature,
- heat island effect

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References(19)

References

[1]	张景哲,刘继韩,周一星,等.北京城市热岛的几种类型[J].地理学报, 1984(4):428-435.
[2]	郭家林,王永波.近40年哈尔滨的气温变化与城市化影响[J].气象, 2005(8):74-76.
[3]	苑睿洋,黄凤荣,唐硕.城市热岛效应研究综述[J].国土与自然资源研究, 2019(1):11-12.
[4]	ZHANG X M, ESTOQUR C, MURAYAMA Y. An urban heat island study in Nanchang City, China based on land surface temperature and social-ecological variables[J]. Sustainable Cities and Society, 2017, 32:557-568.
[5]	MNALEY G. On the frequency of snowfall in metropolitan England[J]. Quarterly Journal of the Royal Meteorological Society, 1958, 84:70-72.
[6]	SOBRINO J A, JIMENEZ-MUNOZ J C, LEONARDO P. Land surface temperature retrieval from LANDSAT TM 5[J]. Remote Sensing of Environment:An Interdisciplinary Journal, 2004, 90(4):434-440.
[7]	RAO P K. Remote sensing of urban heat islands from an environmental satellite[J]. Bulletin of the American Meteorological Society, 1972(53):647-648.
[8]	JR R C B, BRAZEL S W. High-resolution surface temperature patterns in a complex urban terrain[J]. Photogrammetric Engineering and Remote Sensing, 1988, 54(9):1289-1293.
[9]	GALLO K P, MCNAB A L, KARL T R, et al. The use of a vegetation index for assessment of the urban heat island effect[J]. International Journal of Remote Sensing, 1993, 14(11):2223-2230.
[10]	刘恩勤,杨武年,陈宁.基于遥感技术的城市热岛效应研究:以成都地区为例[J].四川地质学报, 2009, 29(4):484-487.
[11]	马伟,赵珍梅,刘翔,等.植被指数与地表温度定量关系遥感分析:以北京市TM数据为例[J].国土资源遥感, 2010(4):108-112.
[12]	王刚,张秋平,肖荣波,等.土地利用、人口密度及海拔对城市热岛的影响[J].云南大学学报(自然科学版), 2019, 41(1):82-90.
[13]	朱贞榕,程朋根,桂新,等.地表温度反演的算法综述[J].测绘与空间地理信息, 2016, 39(5):70-75.
[14]	宫思羽.哈尔滨市主城区夏季热岛效应分析与绿地冷岛效应的研究及实践[D].哈尔滨:东北农业大学, 2021.
[15]	张志杰,张浩,常玉光,等. Landsat系列卫星光学遥感器辐射定标方法综述[J].遥感学报, 2015, 19(5):719-732.
[16]	姚薇,李志军,姚珙,等. Landsat卫星遥感影像的大气校正方法研究[J].大气科学学报, 2011, 34(2):251-256.
[17]	MYINT S W, BRAZEL A, OKIN G, et al. Combined effects of impervious surface and vegetation cover on air temperature variations in a rapidly expanding desert city[J]. GIScience&Remote Sensing, 2010, 47(3):301-320.
[18]	孙同贺,闫国庆.基于遥感技术的土地利用分类方法[J].测绘与空间地理信息, 2013, 36(1):5-8.
[19]	ESTOQUE R C,MURAYAMA Y. Monitoring surface urban heat island formation in a tropical mountain city using Landsat data (1987-2015)[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 133:18-29.