Multi-Temporal Patterns of Urban Heat Island as Response to Economic Growth Management

Anibal Gusso, Cristina Cafruni, Fabiane Bordin, Mauricio Roberto Veronez, Leticia Lenz and Sabrina Crija
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Anibal Gusso: Environmental Engineering, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo-RS CP275, Brazil
Cristina Cafruni: Graduate Program in Collective Health, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo-RS CP275, Brazil
Fabiane Bordin: Advanced Visualization Laboratory, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo-RS CP275, Brazil
Mauricio Roberto Veronez: Advanced Visualization Laboratory, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo-RS CP275, Brazil
Leticia Lenz: Advanced Visualization Laboratory, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo-RS CP275, Brazil
Sabrina Crija: Advanced Visualization Laboratory, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo-RS CP275, Brazil

Sustainability, 2015, vol. 7, issue 3, 1-17

Abstract: For a reliable assessment of sustainability in big cities, it is imperative to evaluate urban ecosystem conditions and the environment of the cities undergoing economic growth. Urban green spaces are valuable sources of evapotranspiration, which is generated by trees and vegetation; these spaces mitigate urban heat islands in cities. Land surface temperature (LST) is closely related to the distribution of land-use and land-cover characteristics and can be used as an indicator of urban environment conditions and development. This study evaluates the patterns of LST distribution through time by employing the thermal spatial distribution signature procedure using thermal infrared data obtained from Landsat-5 Thematic Mapper. A set of 18 images, between 1985 and 2010, was used to study the urban environment during summer in 47 neighborhoods of Porto Alegre, Brazil. On a neighborhood scale, results show a non-linear inverse correlation (R² = 0.55) between vegetation index and LST. The overall average of the LST is 300.23 K (27.8 °C) with a standard deviation of 1.25 K and the maximum average difference of 2.83 K between neighborhoods. Results show that the Thermal Spatial Distribution Signature (TSDS) analysis can help multi-temporal studies for the evaluation of UHI through time.

Keywords: thermal remote sensing; urban environment; economic growth; UHI (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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