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Remote Sens. 2016, 8(9), 760; doi:10.3390/rs8090760

Scale Effects of the Relationships between Urban Heat Islands and Impact Factors Based on a Geographically-Weighted Regression Model

1
Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, 2 Chongwen Road, Nan’an District, Chongqing 400065, China
2
Chongqing Institute of Meteorological Science, Chongqing 401147, China
*
Author to whom correspondence should be addressed.
Academic Editors: Yuei-An Liou, Chyi-Tyi Lee, Yuriy Kuleshov, Jean-Pierre Barriot, Chung-Ru Ho, Dale A. Quattrochi, Zhaoliang Li and Prasad S. Thenkabail
Received: 8 July 2016 / Revised: 14 August 2016 / Accepted: 9 September 2016 / Published: 15 September 2016
(This article belongs to the Special Issue Earth Observations for a Better Future Earth)
View Full-Text   |   Download PDF [13314 KB, uploaded 15 September 2016]   |  

Abstract

Urban heat island (UHI) effect, the side effect of rapid urbanization, has become an obstacle to the further healthy development of the city. Understanding its relationships with impact factors is important to provide useful information for climate adaptation urban planning strategies. For this purpose, the geographically-weighted regression (GWR) approach is used to explore the scale effects in a mountainous city, namely the change laws and characteristics of the relationships between land surface temperature and impact factors at different spatial resolutions (30–960 m). The impact factors include the Soil-adjusted Vegetation Index (SAVI), the Index-based Built-up Index (IBI), and the Soil Brightness Index (NDSI), which indicate the coverage of the vegetation, built-up, and bare land, respectively. For reference, the ordinary least squares (OLS) model, a global regression technique, is also employed by using the same dependent variable and explanatory variables as in the GWR model. Results from the experiment exemplified by Chongqing showed that the GWR approach had a better prediction accuracy and a better ability to describe spatial non-stationarity than the OLS approach judged by the analysis of the local coefficient of determination (R2), Corrected Akaike Information Criterion (AICc), and F-test at small spatial resolution (< 240 m); however, when the spatial scale was increased to 480 m, this advantage has become relatively weak. This indicates that the GWR model becomes increasingly global, revealing the relationships with more generalized geographical patterns, and then spatial non-stationarity in the relationship will tend to be neglected with the increase of spatial resolution. View Full-Text
Keywords: urban heat island; SAVI; IBI; NDSI; geographically weighted regression; scale effect urban heat island; SAVI; IBI; NDSI; geographically weighted regression; scale effect
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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).

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Luo, X.; Peng, Y. Scale Effects of the Relationships between Urban Heat Islands and Impact Factors Based on a Geographically-Weighted Regression Model. Remote Sens. 2016, 8, 760.

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