广东工业大学学报 ›› 2021, Vol. 38 ›› Issue (02): 53-59.doi: 10.12052/gdutxb.200090

• 综合研究 • 上一篇    下一篇

基于Landsat的广州市热岛效应时空变化分析

陈炳杰, 陈佩敏, 黄泽鹏, 吴希文, 王华   

  1. 广东工业大学 土木与交通工程学院, 广东 广州 510006
  • 收稿日期:2020-07-15 出版日期:2021-03-10 发布日期:2021-01-13
  • 通信作者: 吴希文(1983-),男,教授,博士生导师,主要研究方向为InSAR,E-mail:hayman.ng@foxmail.com E-mail:hayman.ng@foxmail.com
  • 作者简介:陈炳杰(1995-)，男，硕士研究生，主要研究方向为遥感影像处理与地表形变监测
  • 基金资助:
    广东省自然科学基金资助项目(2018A030310538)；广州市科技计划项目(201904010254)；广东工业大学大学生创新训练资助项目(xj202011845274)

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   

  1. School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2020-07-15 Online:2021-03-10 Published:2021-01-13

HTML

PDF (PC)

2568

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。

关键词: 热岛, 温度反演, 广州, Landsat

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.

Key words: heat island, temperature retrieval, Guangzhou, Landsat

中图分类号: 

  • TP79
[1] MANLEY G. On the frequency of snowfall in metropolitan England [J]. Quarterly Journal of the Royal Meteorological Society, 1958, 84(359): 70-72.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] 陈康林, 龚建周, 陈晓越. 广州市热岛强度的空间格局及其分异特征[J]. 生态学杂志, 2017, 36(3): 792-799.
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.
[10] 广州市统计局. 广州统计年鉴[M/OL]. 北京: 中国统计出版社, 2000-2019. http://112.94.72.17/portal/queryInfo/statisticsYearbook/index.
[11] U. S. Geological Survey. Landsat—earth observation satellites[R]. Reston, VA: U. S. Geological Survey, 2016.
[12] THORNE K, MARKHARN B, SLATER P, et al. Radiometric calibration of Landsat [J]. Photogrammetric Engineering and Remote Sensing, 1997, 63(7): 853-858.
[13] 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.
[14] 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.
[15] 覃志豪, ZHANG M H, ARNON K, 等. 用陆地卫星TM6数据演算地表温度的单窗算法[J]. 地理学报, 2001, 68(4): 456-466.
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.
[16] 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.
[17] 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.
[18] 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.
[19] 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.
[20] 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.
[21] 覃志豪, LI W J, ZHANG M H, 等. 单窗算法的大气参数估计方法[J]. 国土资源遥感, 2003(2): 37-43.
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.
[22] 杨景梅, 邱金桓. 用地面湿度参量计算我国整层大气可降水量及有效水汽含量方法的研究[J]. 大气科学, 2002, 26(1): 9-22.
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.
[23] 覃志豪, 李文娟, 徐斌, 等. 陆地卫星TM6波段范围内地表比辐射率的估计[J]. 国土资源遥感, 2004, 3: 28-32.
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.
[24] 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.
[1] 梁仕华, 周锦程, 罗祺, 林焕生. 有机质对水泥固化淤泥土的力学特性影响试验研究[J]. 广东工业大学学报, 2019, 36(06): 86-91.
[2] 李逢春, 周为吉, 黄诗琪. 基于改进TOPSIS的城市土地综合效益评价及障碍因子诊断[J]. 广东工业大学学报, 2017, 34(05): 45-51.
[3] 梁仕华, 周世宗, 张朗, 王蒙. 广州东部地区花岗岩残积土物理力学指标统计分析[J]. 广东工业大学学报, 2015, 32(1): 29-33.
[4] 吕凤琴, 袁奇峰, 陈世栋. 广州农村集资房空间分布特征及其影响因素研究——基于GIS空间分析和SPSS数理分析[J]. 广东工业大学学报, 2015, 32(04): 8-16.
[5] 朱火保； 周祥； 王立山； 王瑜； 张俊杰； . 广州小城镇建设“有机聚集”理论探索[J]. 广东工业大学学报, 2005, 22(4): 83-88.
[6] 熊文辉； 孙水裕； . 广州市居民生活垃圾分类收集的探讨[J]. 广东工业大学学报, 2004, 21(3): 15-20.
[7] 姜波涌； 章云； . 柔性制造系统（FMS）报文通讯的研究[J]. 广东工业大学学报, 1994, 11(1): 23-23.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2017 《广东工业大学学报》编辑部
地址：广州市东风东路729号广东工业大学 邮编：510090
电话：020-37626653,020-37626139,020-37626396
邮箱：xbzrb@gdut.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发