Utilization of remote sensing data for thermal comfort estimation in the coastal urban of Jakarta

Abstract

Remote sensing has been completing limitation of meteorological data observed on the station. Meteorological data of temperature and humidity is necessary to estimate thermal comfort. As a coastal urban area, Jakarta has been experiencing rapid urbanization that leads to densely populated and built-up area. The objective of this study is to measure thermal comfort by estimating Discomfort Index. MODIS land surface temperature (LST) used to obtain air temperature. The temperature dew point data was derived from ERA5 climate data. The data was validated by meteorological station data, afterward were computed and classified based on the Thom's discomfort index. Seasonal analysis for thermal comfort was represented by 4 seasonal period included DJF (December, January, February), MAM (March, April, May), JJA (June, July, August), and SON (September, October, November). The results show that discomfort index values were varied by season. Generally, the dry season (JJA) tends to be less discomfort than the rainy season (SON and MAM). JJA is the peak of dry season which is characterized by lower temperature and lower humidity. MAM and SON are the transition season which is characterized by higher temperature and higher humidity.