A Spatiotemporal Analysis of Heat Island Effect in Guangzhou Based on Landsat

Chen Bing-jie; Chen Pei-min; Huang Ze-peng; Ng Alex Hay-Man; Wang Hua

doi:10.12052/gdutxb.200090

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A Spatiotemporal Analysis of Heat Island Effect in Guangzhou Based on Landsat

更新时间：2026-05-27

- A Spatiotemporal Analysis of Heat Island Effect in Guangzhou Based on Landsat
- Vol. 38, Issue 2, Pages: 53-59(2021)
- 作者机构：
  
  广东工业大学土木与交通工程学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.200090
  CLC： TP79
- Online First：10 March 2021，
  
  Published：2021
- 稿件说明：
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陈炳杰, 陈佩敏, 黄泽鹏, 吴希文, 王华. 基于Landsat的广州市热岛效应时空变化分析[J]. 广东工业大学学报, 2021, 38(2): 53-59.

Chen Bing-jie, Chen Pei-min, Huang Ze-peng, et al. A Spatiotemporal Analysis of Heat Island Effect in Guangzhou Based on Landsat[J]. 2021, 38(2): 53-59.
陈炳杰, 陈佩敏, 黄泽鹏, 吴希文, 王华. 基于Landsat的广州市热岛效应时空变化分析[J]. 广东工业大学学报, 2021, 38(2): 53-59. DOI： 10.12052/gdutxb.200090.

Chen Bing-jie, Chen Pei-min, Huang Ze-peng, et al. A Spatiotemporal Analysis of Heat Island Effect in Guangzhou Based on Landsat[J]. 2021, 38(2): 53-59. DOI： 10.12052/gdutxb.200090.

摘要

利用1995年~2019年间的15景的Landsat-8 OLI/TIRS及Landsat-5 TM影像的热红外波段

采用大气校正法和单窗算法反演广州市的地表温度。通过对研究结果进行时空分析可知

自1995年~2019年

广州市的热岛面积先处于稳定阶段

然后在2001年~2014年间整体呈快速增长趋势

于2014年后以缓慢速度减少。结果表明

热岛效应得到缓解的地区为旧城区

其变化原因可能与工业区外迁、绿化改善等原因有关。热岛空间分布格局由中心集聚型

逐渐向南北方向和东方向蔓延

演变成带状主导、多中心围绕的空间分布特征

其变化的趋势与城市发展模式基本一致。

Abstract

15 Landsat-8 OLI/TIRS and Landsat-5 TM images from 1995 to 2019 were used to retrieve the land surface temperature in Guangzhou by atmospheric correction and mono-window algorithm. According to the spatiotemporal analysis of the results

from 1995 to 2019

the area of Guangzhou's heat island began to be in a stable stage

then it showed a rapid growth trend from 2001 to 2014

and then decreased at a slow rate after 2014. The results show that the areas where the heat island effect has been alleviated are old urban areas

and the reasons for the change may be related to the relocation of industrial areas and the improvement of greening. The spatial distribution pattern of heat islands was agglomerated from the center

gradually spreading to the north-south direction and the east direction

and then evolved into a strip-shaped dominant

multi-center spatial distribution characteristic. Its changing trend was basically consistent with the urban development mode.

关键词

Keywords

references

MANLEY G. On the frequency of snowfall in metropolitan England [J]. Quarterly Journal of the Royal Meteorological Society, 1958, 84(359): 70-72.

HUANG Q, LU Y. Urban heat island research from 1991 to 2015: a bibliometric analysis [J]. Theoretical and Applied Climatology, 2018, 131(3-4): 1055-1067.

KING V J, DAVIS C. A case study of urban heat islands in the Carolinas [J]. Global Environmental Change Part B Environmental Hazards, 2007, 7(4): 353-359.

LI H D, ZHOU Y Y, WANG X, et al . Quantifying urban heat island intensity and its physical mechanism using WRF/UCM [J ] . The Science of the Total Environment, 2019, 650: 3110-3119.

SULTANA S, SATYANARAYANA A N V. Assessment of urbanisation and urban heat island intensities using landsat imageries during 2000-2018 over a sub-tropical Indian City [J]. Sustainable Cities and Society, 2020, 52: 101846.

GONG J, HU Z, CHEN W, et al . Urban expansion dynamics and modes in metropolitan Guangzhou, China [J ] . Land Use Policy, 2018, 72: 100-109.

LIU F, ZHANG X, MURAYAMA Y, et al . Impacts of land cover/use on the urban thermal environment: a comparative study of 10 megacities in China [J ] . Remote Sensing, 2020, 12(2): 307.

SUN Q, WU Z, TAN J. The relationship between land surface temperature and land use/land cover in Guangzhou, China [J]. Environmental Earth Sciences, 2012, 65(6): 1687-1694.

CHEN K L, GONG J Z, CHEN X Y. Spatial pattern and differentiation characteristics of urban heat island intensity in Guangzhou city [J]. Chinese Journal of Ecology, 2017, 36(3): 792-799.

U. S. Geological Survey. Landsat—earth observation satellites[R]. Reston, VA: U. S. Geological Survey, 2016.

THORNE K, MARKHARN B, SLATER P, et al . Radiometric calibration of Landsat [J ] . Photogrammetric Engineering and Remote Sensing, 1997, 63(7): 853-858.

BERNSTEIN L S, JIN X, GREGOR B, et al . Quick atmospheric correction code: algorithm description and recent upgrades [J ] . Optical Engineering, 2012, 51(11): 111719.

CHANG C C, LIN C J. LIBSVM: a library for support vector machines [J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2011, 2(3): 1-27.

QIN Z H, ZHANG M H, ARNON K, et al . Mono-window algorithm for retrieving land surface temperature from Landsat TM6 data [J ] . Acta Geographica Sinica, 2001, 68(4): 456-466.

RONGALI G, KESHARI A K, GOSAIN A K, et al . Split-window algorithm for retrieval of land surface temperature using Landsat 8 thermal infrared data [J ] . Journal of Geovisualization and Spatial Analysis, 2018, 2(2): 14.

JORDI C, JUAN J M O, ANUPMA P, et al . An improved single-channel method to retrieve land surface temperature from the Landsat-8 thermal band [J ] . Remote Sensing, 2018, 10(3): 431.

YU X, GUO X, WU Z. Land surface temperature retrieval from Landsat 8 tirs—comparison between radiative transfer equation-based method, split window algorithm and single channel method [J]. Remote Sensing, 2014, 10(6): 9829-9852.

GILABERT M A, CONESE C, MASELLI F. An atmospheric correction method for the automatic retrieval of surface reflectances from TM images [J]. International Journal of Remote Sensing, 1994, 15(10): 2065-2086.

SEKERTEKIN A, BONAFONI S. Land surface temperature retrieval from Landsat 5, 7, and 8 over rural areas: assessment of different retrieval algorithms and emissivity models and toolbox implementation [J]. Remote Sensing, 2020, 12(2): 294.

QIN Z H, LI W J, ZHANG M H, et al . Estimating of the essential atmospheric parameters of mono-window algorithm for land surface temperature retrieval from Landsat TM6 [J ] . Remote Sensing for Land Resources, 2003(2): 37-43.

YANG J M, QIU J H. A method for estimating precipitable water and effective water vapor content from ground humidity parameters [J]. Chinese Journal of Atmospheric Sciences, 2002, 26(1): 9-22.

QIN Z H, LI W J, XU B, et al . The estimation of land surface emissivity for Landsat TM6 [J ] . Remote Sensing for Land Resources, 2004, 3: 28-32.

马鑫鑫. 广州产业园区建设对城市形态的影响(1978~2017年)[D]. 广州: 华南理工大学, 2018.

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