Next Article in Journal
Supervised Classification of Multisensor Remotely Sensed Images Using a Deep Learning Framework
Previous Article in Journal
Remote Sensing Approach to Detect Burn Severity Risk Zones in Palo Verde National Park, Costa Rica
Previous Article in Special Issue
Correction for the Impact of the Surface Characteristics on the Estimation of the Effective Emissivity at Fine Resolution in Urban Areas
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2018, 10(9), 1428; https://doi.org/10.3390/rs10091428

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

1
Department of Geography, Texas State University, San Marcos, TX 78666, USA
2
Swiss Federal Research Institute WSL, Land Change Science Research Unit, Z├╝rcherstrasse 111, CH-8903 Birmensdorf, Switzerland
3
Center for Urban and Environmental Change, Department of Earth and Environmental Systems, Indiana State University, Terre Haute, IN 47809, USA
*
Author to whom correspondence should be addressed.
Received: 24 July 2018 / Revised: 19 August 2018 / Accepted: 28 August 2018 / Published: 7 September 2018
(This article belongs to the Special Issue Urban Heat Island Remote Sensing)
Full-Text   |   PDF [8279 KB, uploaded 7 September 2018]   |  

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. View Full-Text
Keywords: Surface Urban Heat Island (SUHI); landscape; geographically weighted regression; spatial pattern; Austin; San Antonio Surface Urban Heat Island (SUHI); landscape; geographically weighted regression; spatial pattern; Austin; San Antonio
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Zhao, C.; Jensen, J.; Weng, Q.; Weaver, R. A Geographically Weighted Regression Analysis of the Underlying Factors Related to the Surface Urban Heat Island Phenomenon. Remote Sens. 2018, 10, 1428.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top