Article
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Preserved in Portico This version is not peer-reviewed
Economic Driven Patterns of the Urban Environment
Version 1
: Received: 15 December 2016 / Approved: 16 December 2016 / Online: 16 December 2016 (08:00:59 CET)
A peer-reviewed article of this Preprint also exists.
Gusso, A.; Silva, A.; Boland, J.; Lenz, L.; Philipp, C. Income Driven Patterns of the Urban Environment. Sustainability 2017, 9, 275. Gusso, A.; Silva, A.; Boland, J.; Lenz, L.; Philipp, C. Income Driven Patterns of the Urban Environment. Sustainability 2017, 9, 275.
Abstract
This study investigates the land surface temperature (LST) distribution from thermal infrared data for analyzing the characteristics of surface coverage using the Vegetation-Impervious-Soil (VIS) approach. A set of ten images, obtained from Landsat-5 Thematic Mapper, between 2001 and 2010, were used to study the urban environmental conditions of 47 neighborhoods of Porto Alegre city, Brazil. Porto Alegre has had the smallest population growth rate of all 27 state capitals in the last two decades in Brazil, with an increase of 11.55% in inhabitants from 1,263 million in 1991 to 1,409 million in 2010. We applied the environmental Kuznets curve (EKC) theory in order to test the influence of the economically-related scenario on the spatial nature of social-environmental arrangement of the city at neighborhood scale. Our results suggest that the economically-related scenario exerts a non-negligible influence on the physically driven characteristics of the urban environmental conditions as predicted by EKC theory. The linear inverse correlation R2 between household income (HI) and LST is 0.36 and has shown to be comparable to all other studied variables. Future research may investigate the relation between other economically-related indicators to specific land surface characteristics.
Keywords
thermal remote sensing; EKC theory; urban development
Subject
Environmental and Earth Sciences, Environmental Science
Copyright: 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.
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