Login / Signup

Seasonal SUHI Analysis Using Local Climate Zone Classification: A Case Study of Wuhan, China.

Lingfei ShiFeng LingGiles M FoodyZhen YangXixi LiuYun Du
Published in: International journal of environmental research and public health (2021)
The surface urban heat island (SUHI) effect poses a significant threat to the urban environment and public health. This paper utilized the Local Climate Zone (LCZ) classification and land surface temperature (LST) data to analyze the seasonal dynamics of SUHI in Wuhan based on the Google Earth Engine platform. In addition, the SUHI intensity derived from the traditional urban-rural dichotomy was also calculated for comparison. Seasonal SUHI analysis showed that (1) both LCZ classification and the urban-rural dichotomy confirmed that Wuhan's SHUI effect was the strongest in summer, followed by spring, autumn and winter; (2) the maximum SUHI intensity derived from LCZ classification reached 6.53 °C, which indicated that the SUHI effect was very significant in Wuhan; (3) LCZ 8 (i.e., large low-rise) had the maximum LST value and LCZ G (i.e., water) had the minimum LST value in all seasons; (4) the LST values of compact high-rise/midrise/low-rise (i.e., LCZ 1-3) were higher than those of open high-rise/midrise/low-rise (i.e., LCZ 4-6) in all seasons, which indicated that building density had a positive correlation with LST; (5) the LST values of dense trees (i.e., LCZ A) were less than those of scattered trees (i.e., LCZ B) in all seasons, which indicated that vegetation density had a negative correlation with LST. This paper provides some useful information for urban planning and contributes to the healthy and sustainable development of Wuhan.
Keyphrases
  • coronavirus disease
  • machine learning
  • climate change
  • deep learning
  • public health
  • south africa
  • high throughput
  • high intensity
  • artificial intelligence
  • heat stress
  • single cell
  • big data