The survey of Landsat satellite image is effective in the continuous monitoring of a vast area during long periods of time.
It is increasingly being used to derive and analyze spatial distribution data of both the Normalized Difference Vegetation
Index (NDVI) and Land Surface Temperature (LST) that are major indicators for an analysis of vegetation-environment.
Likewise, NDVI and LST are essential in order to detect, as well as to monitor, the environmental changes in arable land.
Therefore, the relationship between NDVI and LST should be quantified for the accuracy improvement of agricultural
statistical data based on Remote Sensing. This study has intended to analyze the characteristics of NDVI and LST using
Landsat imagery of arable land in Cheongju City, to quantify the relationship between NDVI and LST. The results
indicated that time seasonal change of raster data for four times of the highest group of LST and the lowest group of
vegetation located in the Cheongju city, Chungcheongbuk-do, Korea, are observed and analyzed their correlations for the
change detection of land cover. This experiment, based on proposed algorithms, detected a strong and proportional
correlation relationship between the highest group of LST and the lowest group of vegetation index which exceeded
R=(+)0.9. Therefore, the proposed Correlation Analysis Model between the highest group of LST and the lowest group
of vegetation index will be able to give proof of an effective suitability to the land cover change detection and