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http://dx.doi.org/10.7780/kjrs.2017.33.6.2.6

Thermal Characteristics of Daegu using Land Cover Data and Satellite-derived Surface Temperature Downscaled Based on Machine Learning  

Yoo, Cheolhee (Department of Urban and Environment Engineering, Ulsan National Institute of Science and Technology)
Im, Jungho (Department of Urban and Environment Engineering, Ulsan National Institute of Science and Technology)
Park, Seonyoung (Department of Urban and Environment Engineering, Ulsan National Institute of Science and Technology)
Cho, Dongjin (Department of Urban and Environment Engineering, Ulsan National Institute of Science and Technology)
Publication Information
Korean Journal of Remote Sensing / v.33, no.6_2, 2017 , pp. 1101-1118 More about this Journal
Abstract
Temperatures in urban areas are steadily rising due to rapid urbanization and on-going climate change. Since the spatial distribution of heat in a city varies by region, it is crucial to investigate detailed thermal characteristics of urban areas. Recently, many studies have been conducted to identify thermal characteristics of urban areas using satellite data. However,satellite data are not sufficient for precise analysis due to the trade-off of temporal and spatial resolutions.In this study, in order to examine the thermal characteristics of Daegu Metropolitan City during the summers between 2012 and 2016, Moderate Resolution Imaging Spectroradiometer (MODIS) daytime and nighttime land surface temperature (LST) data at 1 km spatial resolution were downscaled to a spatial resolution of 250 m using a machine learning method called random forest. Compared to the original 1 km LST, the downscaled 250 m LST showed a higher correlation between the proportion of impervious areas and mean land surface temperatures in Daegu by the administrative neighborhood unit. Hot spot analysis was then conducted using downscaled daytime and nighttime 250 m LST. The clustered hot spot areas for daytime and nighttime were compared and examined based on the land cover data provided by the Ministry of Environment. The high-value hot spots were relatively more clustered in industrial and commercial areas during the daytime and in residential areas at night. The thermal characterization of urban areas using the method proposed in this study is expected to contribute to the establishment of city and national security policies.
Keywords
land surface temperature; downscaling; Hot spot analysis; random forest; urban climate; land cover;
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