[A-CG33] Terrestrial monitoring using new-generation geostationary satellites

[ACG33-P04] Monitoring urban heat island of Jakarta using medium spatial resolution satellite data

*Fitria Nucifera¹, Widiyana Riasasi¹, Da Wang², Yuhei Yamamoto², Kazuhito Ichii² (1.Universitas AMIKOM Yogyakarta, 2.Center for Environmental Remote Sensing (CeRES), Chiba University)

Keywords:land surface temperature, urban heat island, urban expansion, MODIS, Jakarta

Rapid urbanization in developing countries has been triggering numerous environmental problems such as air pollution, ecological degradation, and urban heat island. Urban heat island (UHI) is the most common phenomenon that occurs in urban areas where the temperature of urban areas is higher than its surroundings. Satellite remote sensing data have been widely used to analyze UHI in many countries. So far, high spatial resolution data such as Landsat were commonly applied. However, only very few images are available for urban monitoring across humid tropical regions due to heavy and frequent cloud cover. In this study, we attempted to use multiple medium resolution satellite data which have better temporal observation frequency. MODIS satellite data have a medium spatial resolution and high temporal resolution which is capable of monitoring the development of urban heat island over time. Using multiple MODIS products such as land cover and land surface temperature from 2000 to 2019, we monitored urban expansion and UHI in Jakarta, the capital of Indonesia. MODIS land cover data (MCD12Q1) were applied to quantify urban expansion, while MODIS land surface temperature (MOD11A2) were used to analyze UHI. The Gaussian fitting method was performed to determine UHI magnitude and UHI pattern. The results show that the Jakarta urban area has expanded more than 20% larger from 2000 to 2019. Land surface temperature in Jakarta was likely to increase during that time. The expansion of the high LST area during the day and night had different characteristics. We also discuss the capability of hyper-temporal data by geostationary satellites such as Himawari-8.