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http://dx.doi.org/10.7780/kjrs.2021.37.5.3.9

Analysis of Surface Urban Heat Island and Land Surface Temperature Using Deep Learning Based Local Climate Zone Classification: A Case Study of Suwon and Daegu, Korea  

Lee, Yeonsu (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Lee, Siwoo (School of Convergence & Fusion System Engineering, Kyungpook National University)
Im, Jungho (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Yoo, Cheolhee (Department of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Publication Information
Korean Journal of Remote Sensing / v.37, no.5_3, 2021 , pp. 1447-1460 More about this Journal
Abstract
Urbanization increases the amount of impervious surface and artificial heat emission, resulting in urban heat island (UHI) effect. Local climate zones (LCZ) are a classification scheme for urban areas considering urban land cover characteristics and the geometry and structure of buildings, which can be used for analyzing urban heat island effect in detail. This study aimed to examine the UHI effect by urban structure in Suwon and Daegu using the LCZ scheme. First, the LCZ maps were generated using Landsat 8 images and convolutional neural network (CNN) deep learning over the two cities. Then, Surface UHI (SUHI), which indicates the land surface temperature (LST) difference between urban and rural areas, was analyzed by LCZ class. The results showed that the overall accuracies of the CNN models for LCZ classification were relatively high 87.9% and 81.7% for Suwon and Daegu, respectively. In general, Daegu had higher LST for all LCZ classes than Suwon. For both cities, LST tended to increase with increasing building density with relatively low building height. For both cities, the intensity of SUHI was very high in summer regardless of LCZ classes and was also relatively high except for a few classes in spring and fall. In winter the SUHI intensity was low, resulting in negative values for many LCZ classes. This implies that UHI is very strong in summer, and some urban areas often are colder than rural areas in winter. The research findings demonstrated the applicability of the LCZ data for SUHI analysis and can provide a basis for establishing timely strategies to respond urban on-going climate change over urban areas.
Keywords
Local Climate Zone; Deep Learning; Convolutional Neural Network; Urban Heat Island; Urban Climate;
Citations & Related Records
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  • Reference
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