Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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A Statistic Correlation Analysis Algorithm Between Land Surface Temperature and Vegetation Index

  • Kim, Hyung-Moo (Department of Computer Engineering, Chonbuk National University) ;
  • Kim, Beob-Kyun (Department of Computer Engineering, Chonbuk National University) ;
  • You, Kang-Soo (School of liberal arts Jeonju University)
  • Published : 2005.12.01
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Abstract

As long as the effective contributions of satellite images in the continuous monitoring of the wide area and long range of time period, Landsat TM and Landsat ETM+ satellite images are surveyed. After quantization and classification of the deviations between TM and ETM+ images based on approved thresholds such as gains and biases or offsets, a correlation analysis method for the compared calibration is suggested in this paper. Four time points of raster data for 15 years of the highest group of land surface temperature and the lowest group of vegetation of the Kunsan city Chollabuk_do Korea located beneath the Yellow sea coast, are observed and analyzed their correlations for the change detection of urban land cover. This experiment based on proposed algorithm detected strong and proportional correlation relationship between the highest group of land surface temperature and the lowest group of vegetation index which exceeded R=(+)0.9478, so the proposed Correlation Analysis Model between the highest group of land surface temperature and the lowest group of vegetation index will be able to give proof an effective suitability to the land cover change detection and monitoring.

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References

  1. Chander, Gyanesh and Brian L. Markham. 2003. Revised Landsat-5 TM Radiometric Calibration Procedures and Post-calibration Dynamic Ranges, IEEE Transactions on geoscience and remote sensing, 41(11):2674-2677 https://doi.org/10.1109/TGRS.2003.818464
  2. Fisher, Jeremy I., John F., Mustard. 2004. High spatial resolution sea surface climatology from Landsat thermal infrared data, Remote Sensing of Environment 90:293-307 https://doi.org/10.1016/j.rse.2004.01.008
  3. Markham, Brian L., and John L. Baker. 1986. Landsat MSS and TM Post-Calibration Dynamic Ranges, Atmospheric Reflectance and At-Satellite Temperatures, Laboratory for Terrestrial Physics- NASA/Goddard Space Flight Center, Greenbelt, MD. 20771, pp.3-7
  4. Melesse, Assefa M. 2004. Spatiotemporal dynamics of land surface parameters in the Red river of the north basin, Physics and Chemistry of the Earth 29:795-810 https://doi.org/10.1016/j.pce.2004.05.007
  5. NASA. 2004. Landsat Project Science Office. Landsat 7 Science Data Users Handbook. Chapt.11- Data Products, http://ltpwww.gsfc.nasa.gov/IAS/ handbook/handbook_htmls/chapter11/chapter11.ht ml:11.1-11.4
  6. Rouse, J. W., R. H. Haas, J. A. Schell. 1974. Monitoring vegetation systems in the Great Plains with ERTS. In: Proc., Third Earth Resources Technology Satellite-1 Symposium, Greenbelt, NASA SP-351, pp.3010-3017
  7. Suga, Y., Ohno, H. Ogawa, K. Ohno, K. Yamada. 2003. Detection of surface temperature from Landsat-7/ETM+. Adv. Space Res. 32(11):2235- 2240 https://doi.org/10.1016/S0273-1177(03)90548-5

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