Please wait a minute...
REMOTE SENSING FOR LAND & RESOURCES    2017, Vol. 29 Issue (4) : 140-146     DOI: 10.6046/gtzyyg.2017.04.21
Analysis of temporal-spatial variation of heat island effect in Pearl River Delta using MODIS images and impermeable surface area
HE Liqin1, YANG Peng1, JING Xin1, YAN Lei1, SU Linlin1,2
1. Beijing Key Lab of Spatial Information Integration and 3S Application,Peking University, Beijing 100087, China;
2. Information Engineering College, Capital Normal University, Beijing 100037, China
Download: PDF(4134 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  The Pearl River Delta urban group is a region with rapid economic development; nevertheless, with the economic development, the heat island effect becomes increasingly obvious. Contrast with traditional point surveillance, the thermal infrared remote sensing method can make us understand the spatial distribution of urban heat island more intuitively. In this paper, the authors retrieved the land surface temperature(LST)of the four seasons of the Pearl River Delta region using the split window algorithm based on MODIS images, and divided the urban core and suburban region based on impermeable surface area(ISA), and finally obtained the surface urban heat island intensity. The results show that the Pearl River Delta region has a serious heat island phenomenon, with the most severe season being summer and the weakest season being winter. There is a tendency that the connection of cities has led to the formation of large urban heat island, especially in the two most serious cities, Foshan and Guangzhou. The heat island intensity is negatively correlated with NDVI value and positively correlated with the degree of the city’s economic development. The research results would provide some ecological instructions for urban development planning of the Pearl River Delta region.
Keywords geological survey with remote sensing method      siderite-hematite mineralization belt      West Kunlun metallogenic belt      Heiqia Pass     
:  TP79  
Issue Date: 04 December 2017
E-mail this article
E-mail Alert
Articles by authors
YANG Jinzhong
Cite this article:   
YANG Jinzhong,CHEN Wei,WANG Hui. Analysis of temporal-spatial variation of heat island effect in Pearl River Delta using MODIS images and impermeable surface area[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(4): 140-146.
URL:     OR
[1] 张晶晶,窦浩洋,张恩洁,等.珠江三角洲城市群热岛特征研究[C]//中国气象学会.中国气象学会2008年年会城市气象与城市可持续发展分会场论文集.北京:中国气象学会,2008.
Zhang J J,Dou H Y,Zhang E J,et al.The features study of the Pearl River Delta urban heat island[C]//Chinese Meteorological Society.The collected papers of urban meteorological and urban sustainable development of Chinese meteorological society annual conference 2008.Beijing:Chinese Meteorological Society,2008.
[2] 窦浩洋,张晶晶,赵昕奕.珠江三角洲城市热岛空间分布及热岛强度研究[J].地域研究与开发,2010,29(4):72-77.
Dou H Y,Zhao J J,Zhao X Y.Study on spatial distribution and intensity of urban heat island in Pearl River Delta[J].Areal Research and Development,2010,29(4):72-77.
[3] 曾 侠,钱光明,潘蔚娟.珠江三角洲都市群城市热岛效应初步研究[J].气象,2004,30(10):12-16.
Zeng X,Qian G M,Pan W J.Study on urban heat island effect in Pearl River Delta urban group[J].Meteorological Monthly,2004,30(10):12-16.
[4] 历 华,曾永年,贠培东,等.基于MODIS数据的长株潭地区城市热岛时空分析[J].测绘科学,2007,32(5):108-110,116.
Li H,Zeng Y N,Yun P D,et al.Temporal and spatial characteristics of urban heat island in Changsha-Zhuzhou-Xiangtan area based on MODIS data[J].Science of Surveying and Mapping,2007,32(5):108-110,116.
[5] 闫 峰,覃志豪,李茂松,等.基于MODIS数据的上海市热岛效应研究[J].武汉大学学报(信息科学版),2007,32(7):576-580.
Yan F,Qin Z H,Li M S,et al.On urban heat island of Shanghai City from MODIS data[J].Geomatics and Information Science of Wuhan University,2007,32(7):576-580.
[6] 杨 鹏,陈 静,高 祺,等.基于MODIS数据的石家庄城市热岛效应研究[J].河北遥感,2012(2):18-21.
Yang P,Chen J,Gao Q,et al.The research of Shijiazhuang urban heat island effect based on the MODIS data[J].Journal of Hebei Remote Sensing,2012(2):18-21.
[7] Elvidge C D,Tuttle B T,Sutton P C,et al.Global distribution and density of constructed impervious surfaces[J].Sensors,2007,7(9):1962-1979.
[8] Schueler T R.The importance of imperviousness[J].Watershed Protection Techniques,1994,1(3):100-111.
[9] Arnold Jr C L,Gibbons C J.Impervious surface coverage:The emergence of a key environmental indicator[J].Journal of the American Planning Association,1996,62(2):243-258.
[10] Qin Z H,Dall’Olmo G,Karnieli A,et al.Derivation of split window algorithm and its sensitivity analysis for retrieving land surface temperature from NOAA-advanced very high resolution radiometer data[J].Journal of Geophysical Research:Atmospheres(1984-2012),2001,106(D19):22655-22670.
[11] Zhang P,Imhoff M L,Wolfe R E,et al.Characterizing urban heat islands of global settlements using MODIS and nighttime lights products[J].Canadian Journal of Remote Sensing,2010,36(3):185-196.
[12] 何全军,曹 静,黄 江,等.基于多光谱综合的MODIS数据云检测研究[J].国土资源遥感,2006,18(3):19-22.doi:10.6046/gtzyyg.2006.03.05.
He Q J,Cao J,Huang J,et al.Cloud detection in MODIS data based on multi-spectrum synthesis[J].Remote Sensing for Land and Resources,2006,18(3):19-22.doi:10.6046/gtzyyg.2006.03.05.
[13] 杨铁利,何全军.MODIS数据的云检测处理[J].鞍山科技大学学报,2006,29(2):162-166.
Yang T L,He Q J.Cloud detection in MODIS data[J].Journal of Anshan University of Science and Technology,2006,29(2):162-166.
[14] Wan Z M,Dozier J.A generalized split-window algorithm for retrieving land-surface temperature from space[J].IEEE Transactions on Geoscience and Remote Sensing,1996,34(4):892-905.
[15] Becker F.The impact of spectral emissivity on the measurement of land surface temperature from a satellite[J].International Journal of Remote Sensing,1987,8(10):1509-1522.
[16] Mao K B,Qin Z,Shi J,et al.A practical split-window algorithm for retrieving land-surface temperature from MODIS data[J].International Journal of Remote Sensing,2005,26(15):3181-3204.
[1] YANG Jinzhong, CHEN Wei, WANG Hui. Delineation of iron formation in Wenquangou Group along Heiqia Pass in West Kunlun metallogenic belt[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 191-195.
Full text



Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech