Statistical Correlation between Land Surface Temperature (LST) and Vegetation Index (NDVI) using Multi-Temporal Landsat TM Data

Anbazhagan S., Paramasivam C.R.


Remote sensing TIR as a part of the electromagnetic spectrum is one of the best observations of Land surface temperature (LST). Our earth contains heterogeneous land feature and it is composed of a variety of materials. Now-a-days there are many remote sensing satellites that provide thermal data. In the present study, the Landsat TM data with multi temporal periods such as 1992, 2001 and 2010 were utilized to study LST in the mining area. The aim of the study is to prepare LST mapping for estimating land surface temperature from multi temporal Landsat TM thermal bands and compare with the associated phenomenal condition. The Landsat 5 TM data of 11-03-1992, 09-02-2010 and Landsat 7 data of 15-05-2001 were used and the land surface emissivity’s for these particular periods were estimated. The Normalized Difference Vegetation Index (NDVI) was calculated in the same period. The image processing was adopted using the ENVI 4.7 software. The results of emissivity and vegetation index interpreted for each period indicated that when the emissivity increases vegetation index shows negative anomaly. The standardization of error coefficient was founded with the aid of statistical software SPSS, which strengthened the approach of the study. The statistical regression analysis of NDVI and LST were shown in Standardized regression coefficient (B) value as -0.209, -0.143 and -0.190 in the years of 1992, 2001 and 2010 respectively. Comparison of LST and its associated constraint will precisely indicate that these variables are mutually important. Remote sensing multi temporal satellite data when coupled with an image processing technique will support for estimate land surface temperature, normalized vegetation index and preferably utilized for an empirical data report from the Indian Meteorological Department (IMD) ground based observation data.


LST; NDVI; Remote Sensing; Image Processing; Regression

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