The objectives of this study including 1) to do a comparative study of vegetation indices to analyze land use patterns 2) to analyze relationship between vegetation indices and land surface temperature (LST). There were 5 different types of vegetation indices in the analysis including Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Building Index (NDBI), Soil Adjustment Vegetation Index (SAVI), and Transform Vegetation Index (TVI) from Landsat 8 OLI. The classification method used maximum likelihood classifier and the accuracy assessment was done by the confusion matrix. Split –window technique was used to analyze LST from Landsat 8 TIRS. Finally, Pearson product moment correlation coefficient and linear regression were used. The results found that NDVI provided the highest overall accuracy and kappa statistic followed by TVI, NDBI, RVI and SAVI, respectively. Analysis of LST presented urban and built-up areas had the highest LST. The relationship analysis between the LST and the vegetation indices found that LST and NDBI had positive correlation. However, LST and RVI, NDVI, SAVI and TVI had negative correlation, respectively. From the result, high accuracy of vegetation indices could be applied to image classification. The analysis of LST, which was received from each of land use, would be useful for urban planning.
Keywords: Vegetation indices, Land surface temperature, Split – window technique, Land use classification, Spectral index
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