Abstract
Using normalized index composition, this study estimated biophysical environment characteristics of urban heat island (UHI) hotspots and identified vulnerable residential district. For this study, we used a regression tree model to examine nonlinear relationships between Land Surface Temperature (LST) and three satellite-based indicators within the UHI clusters: normalized difference vegetation index (NDVI), normalized difference build-up index (NDBI), and normalized difference bareness index (NDBaI). The results show that both NDBI and NDBal were strongly correlated with the variations of LST whereas NDVI had a weaker correlation with LST in Daegu. In addition, when we analyzed the UHI hotspots by land use type, we found that industrial district showed the highest risk type and central commercial district (CBD) had the second high risk. The most vulnerable residential district was ‘Nowon dong, Buk gu’. This site mostly comprised of quasi-residential area type, and we found the high building density ratio is the main reason of urban heat island.
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