Investigating the relationship between air temperature and the intensity of urban development using on-site measurement, satellite imagery and machine learning

Tsz Kin Lau, Tzu Ping Lin

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

Given the seriousness of the urban heat problem, the relationship between urbanization and air temperature has become a critical concern worldwide. In this study, common urban planning indicators, including the building coverage ratio (BCR), floor area ratio (FAR), and fractional vegetation cover (FVC), were extracted from satellite images to determine the intensity of urban development. On-site measurements and machine learning (ML) were used to observe and analyze the relationship between the intensity of urban development and air temperature. From the on-site measurement results, the air temperature in downtown Taipei decreased by an average of approximately 0.32 °C with every 10 % increase in the FVC. However, it increased by an average of approximately 0.28 °C and 0.03 °C with every 10 % increase in the BCR and FAR, respectively. The results obtained from the ML models demonstrated the same trend, with minor differences from the on-site measurement results, which were regarded as reasonable and acceptable. In this study, a more convenient method was proposed to extract urban planning indicators, describe the intensity of urban development within an area, and help estimate air temperature in areas without measuring instruments. The relationship determined herein may aid in the decision-making process of the balance of urbanization and vegetation.

原文English
文章編號104982
期刊Sustainable Cities and Society
100
DOIs
出版狀態Published - 2024 1月

All Science Journal Classification (ASJC) codes

  • 地理、規劃與發展
  • 土木與結構工程
  • 可再生能源、永續發展與環境
  • 運輸

指紋

深入研究「Investigating the relationship between air temperature and the intensity of urban development using on-site measurement, satellite imagery and machine learning」主題。共同形成了獨特的指紋。

引用此