Urban Design Factors Influencing Surface Urban Heat Island in the High-Density City of Guangzhou Based on the Local Climate Zone

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 16; p. 3459
• Main Authors: Shi, Yurong, Xiang, Yirui, Zhang, Yufeng
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 08.08.2019; MDPI
• Subjects: Cities; Classification; Climate; Daytime; Design factors; Geographic information systems; Heat; Land surface temperature; Morphology; Remote sensing; retrieval algorithms; Sensors; social climate zone; Summer; surface urban heat island; Temperature; Thermal environments; Urban areas; Urban heat islands; Urban planning; Vegetation; China; surface urban heat island; social climate zone; land surface temperature; retrieval algorithms
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19163459

Surface urban heat island (SUHI) depicts the deteriorating thermal environment in high-density cities and local climate zone (LCZ) classification provides a universal protocol for SUHI identification. In this study, taking the central urbanized area of Guangzhou in the humid subtropical region of China as the study area, the maps or images of LCZ, land surface temperature, SUHI, and urban design factors were achieved using Landsat satellite data, GIS database, and a series of retrieval and classification algorithms, and the urban design factors influencing SUHI were investigated based on 625 samples of LCZs. The results show that on the 18 September 2016 at the local time of 10:51 a.m., the land surface temperature (LST) varied greatly from 26 °C to 40 °C and the SUHI changed with a wide range of -6 °C to 8 °C in the LCZs of the study area. Seven and five urban design factors influencing the summer daytime SUHI were identified for the two dominant LCZs of LCZs 1-5 (LCZ 1 to LCZ 5) and the mixed LCZ (containing at least three types of LCZs), respectively, in which vegetation cover ratio, floor area ratio, ground emissivity, and complete surface area ratio showed negative correlations and building density showed positive correlations. The summer daytime SUHI prediction models were obtained by using the step-wise multiple linear regression, with the performance of R of 0.774, RMSE of 0.95 °C, and the d value of 0.91 for the model of LCZs 1-5, and the values of 0.819, 0.81 °C, and 0.94 for the model of the mixed LCZ, indicating that the models can effectively predict the changes of SUHI with LCZs. This study presents a methodology to efficiently achieve a large sample of SUHI and urban design factors of LCZs, and provides information beneficial to the urban designs and regenerations in high-density cities.