Surface Urban Heat Island Effects on Bio-Productive Rural Land Covers Surrounding a Low Density Urban Center

Authors: Michael Burnett*, Queens University, Dongmei Chen, Queens University
Topics: Urban Geography, Land Use, Remote Sensing
Keywords: land surface temperature, urban heat island, vegetation phenology, bioproductive land covers, Gaussian surface model, single exponential decay
Session Type: Virtual Guided Poster
Day: 4/8/2021
Start / End Time: 1:30 PM / 2:45 PM
Room: Virtual 53
Presentation File: Download



The urban heat island (UHI), a common effect caused by urbanization, has been studied to evaluate the thermal condition in cities worldwide from different aspects; however, most previous UHI analyses are performed in major metropolitan cities. This study conducts a spatiotemporal analysis of UHI in a rapidly expanding low-density suburban center and determines how bio-productive land covers react and the extent of the disturbance to each land cover based on time series land surface temperatures extracted from Landsat 7 images. Two methods applied and compared are the single exponential decay method, which measures UHI footprint on vegetation phenology, and the two dimensional Gaussian surface, which quantifies the influence based on distance from the local urban perimeter. The results show that the UHI footprint (UHIFP) of the studied low-density suburban center is 3.6 times larger than the size of the urban center. All vegetated land covers experienced their maximum cooling effects well before reaching the UHIFP perimeter while urban surfaces only begin to diverge from the Gaussian model outside of the UHIFP. The similar results from both methods indicate a strong urban cover influence overpowering the dominantly distributed agricultural surfaces at the beginning and end of the growing season.

Three spectral indices (NDVI, NDMI, and EVI) were extracted and the residuals were compared to better understand the thermal variations of each land cover within a UHI. Urban residuals showed moderate correlations with NDVI and NDMI while other land covers maintained strong correlations with each index throughout the growing season.

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