Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2010, Vol. 22 Issue (4) : 108-112     DOI: 10.6046/gtzyyg.2010.04.22
Technology Application |
A Quantitative Analysis of the Relationship Between Vegetation Indices and Land Surface Temperature Based on Remote Sensing:a Case Study of TM Data for Beijing
MA Wei 1, ZHAO Zhen-mei 1, LIU Xiang 2, YAN Dong-chuan 1
1.Institute of Mineral Resources Research, China Metallurgical Geology Bureau, Beijing 100025, China; 2.Beijing Oriental TITAN Technology Co, Ltd, Beijing 100083, China
Download: PDF(785 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

Through correcting the Landsat-5 TM image of atmospheric effects and employing the calculation method of mono-window algorithm,the LST in Beijing was calculated by inversion. On such a basis,five vegetation parameters were computed respectively, i.e., Normalized Difference Vegetation Index (NDVI),Ratio Vegetation Index (RVI),Greenness Vegetation Index (GVI),Modified Soil-Adjusted Vegetation Index (MSAVI) and vegetation fraction (fg). Combined with the spatial distribution of LST in Beijing,this paper compared and analyzed the relevance between LST and the five vegetation parameters. Quantitative analysis of vegetation effect on the Urban Heat Island (UHI) was also carried out. The results show that, of the five parameters of vegetation,fg has the strongest negative correlation with LST. The average urban LST is 1.6 K and 5.3 K higher than that of the suburban and outer suburban respectively.

Keywords Tibet plateau      Brahmaputra and Bangong-Nujiang suture belt      Infrared remote sensing      Thermal anomaly     
: 

 

 
  TP 79

 
Issue Date: 02 August 2011
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
BI Si-wen
YAN Hao
JING Dong-sheng
WANG Chang-yao
Cite this article:   
BI Si-wen,YAN Hao,JING Dong-sheng, et al. A Quantitative Analysis of the Relationship Between Vegetation Indices and Land Surface Temperature Based on Remote Sensing:a Case Study of TM Data for Beijing[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(4): 108-112.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2010.04.22     OR     https://www.gtzyyg.com/EN/Y2010/V22/I4/108

[1]Oke T R. The Energetic Basis of the Urban Heat Island [J]. Quarterly Journal of the Royal Meteorological Society,1982,108:1-24.

[2]Carson T N,Gillies R R,Perry E M. A Method to Make Use of Thermal Infrared Temperature and NDVI Measurements to Infer Surface Soil Water Content and Fractional Vegetation Cover [J]. Remote Sensing Reviews,1994,9:161-173.

[3]Goward S N,Xue Y,Czajkowski K P. Evaluating Land Surface Moisture Conditions from the Remotely Sensed Temperature/Vegetation Index Measurements:An Exploration with the Simplified Simple Biosphere Model [J]. Remote Sensing of Environment,2002,79:225-242.

[4]Weng Q H,Lu D S,Schubring J. Estimation of Land Surface Temperature-Vegetation Abundance Relationship for Urban Heat Island Studies [J]. Remote Sensing of Environment,2004,89:467-483.

[5]Boegh E,Soegaard H,Hanan N,et al. A Remote Sensing Study of the NDVI-Ts Relationship and the Transpiration from Sparse Vegetation in the Sahel Based on High Resolution Satellite Data [J]. Remote sensing of Environment,1998,69:224-240. 

[6]程承旗,吴宁,郭仕德,等. 城市热岛效应强度与植被覆盖关系研究的理论技术路线和北京案例分析[J]. 水土保持研究,2004,11(3):172-174.

[7]江樟焰,陈云浩,李京. 基于Landsat TM数据的北京城市热岛研究[J]. 武汉大学学报,2006,31(2):120-123.

[8]Small C. Estimation of Urban Vegetation Abundance by Spectral Mixture Analysis [J]. International Journal of Remote Sensing,2001,22,1305-1334.

[9]Chander G,Markham B. Revised Landsat-5 TM Radiometric Calibration Procedures and Postcalibration Dynamic Ranges [J]. IEEE Transactions on Geoscience and Remote Sensing,2003,41(11):2674-2677.

[10]Qin Z H,Karnieli A,Berliner P. A Mono-window Algorithm for Retrieving Land Surface Temperature from Landsat TM Data and Its Application to the Israel-Egypt Border Region [J]. International Journal of Remote Sensing,2001,22(18):3719-3746.

[11]Sobrino J A,Jime′nez-Mun~oz J C,Paolini L. Land Surface Temperature Retrieval from LANDSAT TM5 [J]. Remote Sensing of Environment. 2004,90:434-440.

[12]周淑贞. 气象学与气候学[M]. 北京:高等教育出版社,1996.

[13]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.

[14]覃志豪,Zhang M H,Karnieli A,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,2001,56(4):456-466.

[15]Rouse J W,Haas R H,Schell J A,et al. Monitoring Vegetation Systems in the Great Plain with ERTS [C]∥Proceedings of the Third Earth Resource Technology Satellite-1 Symposium. Greenbelt: NASA SP-351,1974: 3010-317.

[16]Jordan C F. Derivation of Leaf Area Index from Quality of Light on the Forest Floor [J]. Ecology,1969,50:663-666.

[17]Crist E P,Cicone R C. Application of the Tasseled Cap Concept to Simulated Thematic Mapper Data [J]. Photogrammetric Engineering and Remote Sensing,1984,50:343-352.

[18]Qi J,Chehbouni A,Huete A R,et al. A Modified Soil Adjusted Vegetation Index (MSAVI) [J]. Remote Sensing of Environment,1994,48:119-126.

[19]Gutman G,Ignatov A. The Derivation of the Green Vegetation Fraction from NOAA/AVHRR Data for Use in Numerical Weather Prediction Models [J]. International Journal of Remote sensing,1998,19:1533-1543.

[20]陈云浩,李晓兵,史培军,等. 北京海淀区植被覆盖的遥感动态研究[J]. 植物生态学报,2001,25 (5):588-593.

[1] LI Teya, SONG Yan, YU Xinli, ZHOU Yuanxiu. Monthly production estimation model for steel companies based on inversion of satellite thermal infrared temperature[J]. Remote Sensing for Natural Resources, 2021, 33(4): 121-129.
[2] GU Yanchun, MENG Qingyan, HU Die, ZHOU Xiaocheng. Analysis of environmental effects of industrial thermal anomalies[J]. Remote Sensing for Land & Resources, 2020, 32(4): 190-198.
[3] Jing LI, Qiangqiang SUN, Ping ZHANG, Danfeng SUN, Li WEN, Xianwen LI. A study of auxiliary monitoring in iron and steel plant based on multi-temporal thermal infrared remote sensing[J]. Remote Sensing for Land & Resources, 2019, 31(1): 220-228.
[4] Jian YU, Yunjun YAO, Shaohua ZHAO, Kun JIA, Xiaotong ZHANG, Xiang ZHAO, Liang SUN. Estimating latent heat flux over farmland from Landsat images using the improved METRIC model[J]. Remote Sensing for Land & Resources, 2018, 30(3): 83-88.
[5] Hanyue CHEN, Li ZHU, Jiaguo LI, Xieyu FAN. A comparison of two mono-window algorithms for retrieving sea surface temperature from Landsat8 data in coastal water of Hongyan River nuclear power station[J]. Remote Sensing for Land & Resources, 2018, 30(1): 45-53.
[6] XING Yu. Spatial responses of wetland change to climate in 32 years in Qinghai-Tibet Plateau[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(3): 99-107.
[7] GUAN Zhen, WU Hong, CAO Cui, HUANG Xiaojuan, GUO Lin, LIU Yan, HAO Min. Uranium ore prediction based on inversion of ETM+6-γ mineral information in Huashan granite area[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(3): 92-98.
[8] WEN Shaoyan, QU Chunyan, SHAN Xinjian, YAN Lili, SONG Dongmei. Satellite thermal infrared background field variation characteristics of the Qilian Mountains and the Capital Zone[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(3): 138-144.
[9] ZHAO Fu-yue, ZHANG Rui-jiang, CHEN Hua, SUN Yan-gui. Study of Qinghai-Tibet Plateau Uplift Response to Eco-geological Environment Based on Remote Sensing[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(3): 116-121.
[10] MA Hong-Zhang, LIU Qin-Huo, WEN Jian-Guang, SHI Jian. The Numerical Simulation and Difference Analysis of Soil Temperature on Thermal Infrared and L Bands[J]. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(2): 26-32.
[11] XU Yong-Meng, QIN Zhi-Hao, WAN Hong-Xiu.
Advances in the Study of Near Surface Air Temperature Retrieval from Thermal Infrared Remote Sensing
[J]. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(1): 9-14.
[12] YAN Yun-Peng, HE Zheng-Min. The Design and Implementation of Remote Sensing Monitoring Database System for Eco-geological Environment of Qinghai-Tibet Plateau Based on Arc Engine Techniques[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(s1): 35-38.
[13] ZHAO Fu-Yue. A Tentative Discussion on the Continental Glacial Sheet in East Qinghai-Tibet Plateau[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(s1): 39-44.
[14] ZHANG Rui-Jiang, FANG Hong-Bin, ZHAO Fu-Yue, ZENG Fu-Nian. Remote Sensing Survey of Existing Glaciers in Qinghai-Tibet Plateau[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(s1): 45-48.
[15] ZHANG Rui-Jiang, FANG Hong-Bin, ZHAO Fu-Yue. The Evolution of Existing Glaciers in the Past 30 Years in Qinghai-Tibet Plateau[J]. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(s1): 49-53.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech