A Geographically Weighted Regression Analysis of the Underlying Factors Related to the Surface Urban Heat Island Phenomenon

Chunhong Zhao,Russell Weaver,Jennifer Jensen,Qihao Weng

doi:10.3390/rs10091428

Abstract

This study investigated how underlying biophysical attributes affect the characterization of the Surface Urban Heat Island (SUHI) phenomenon using (and comparing) two statistical techniques: global regression and geographically weighted regression (GWR). Land surface temperature (LST) was calculated from Landsat 8 imagery for 20 July 2015 for the metropolitan areas of Austin and San Antonio, Texas. We sought to examine SUHI by relating LST to Lidar-derived terrain factors, land cover composition, and landscape pattern metrics developed using the National Land Cover Database (NLCD) 2011. The results indicate that (1) land cover composition is closely related to the SUHI effect for both metropolitan areas, as indicated by the global regression coefficients of building fraction and NDVI, with values of 0.29 and −0.74 for Austin, and 0.19 and −0.38 for San Antonio, respectively. The terrain morphology was also an indicator of the SUHI phenomenon, implied by the elevation (0.20 for Austin and 0.09 for San Antonio) and northness (0.20 for Austin and 0.09 for San Antonio); (2) the SUHI phenomenon of Austin on 20 July 2015 was affected by the spatial pattern of the land use and land cover (LULC), which was not detected for San Antonio; and (3) with a local determination coefficient higher than 0.8, GWR had higher explanatory power of the underlying factors compared to global regression. By accommodating spatial non-stationarity and allowing the model parameters to vary in space, GWR illustrated the spatial heterogeneity of the relationships between different land surface properties and the LST. The GWR analysis of SUHI phenomenon can provide unique information for site-specific land planning and policy implementation for SUHI mitigation.

Highlights

Urban land covers only 2–3% of the total area of the Earth [1,2], urban areas draw ample attention due to the rapid pace of urbanization across the world
Based on the Köppen climate classification scheme, the region is considered humid subtropical with long and hot summers, short and mild winters, and warm and rainy spring and fall seasons [34] (Table 1). Both the Austin–Round Rock and San Antonio–New Braunfels metropolitan areas are located in a Remote Sens. 2018, 10, 1428 unique and narrow transitional zone that ranges from semi-arid vegetation cover dominated by trees and shrubs in the west, to humid and more densely vegetated prairie/grassland to the east
This study found that the Surface Urban Heat Island (SUHI) of Austin on 20 July 2015 was affected by the spatial pattern of land use and land cover (LULC), measured by Shannon's Diversity Index (SHDI), which was not detected in San Antonio

Summary

Introduction

Urban land covers only 2–3% of the total area of the Earth [1,2], urban areas draw ample attention due to the rapid pace of urbanization across the world. Due to the air mixes and advection effect, the study of the atmosphere UHI is not capable of capturing the heterogeneous thermal pattern caused by land use and land cover (LULC) composition and configuration. Remote sensing data have been used to estimate the land surface temperature (LST), which is time-synchronized and grid-based for a considerable areal extent [14,15]. Different from the traditional UHI analysis, LST data contributes to a broader understanding of spatial thermal patterns and the influence of surface properties on SUHI formation [16]

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Conclusion