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

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.

[1]  M. Bauer,et al.  Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery , 2007 .

[2]  J. Unger,et al.  A multiple linear statistical model for estimating the mean maximum urban heat island , 2003 .

[3]  José A. Sobrino,et al.  Land surface temperature retrieval from LANDSAT TM 5 , 2004 .

[4]  Ya Ma,et al.  Coupling urbanization analyses for studying urban thermal environment and its interplay with biophysical parameters based on TM/ETM+ imagery , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[5]  G. Asrar,et al.  Evaluating atmospheric correction models for retrieving surface temperatures from the AVHRR over a tallgrass prairie , 1989 .

[6]  T. Carlson,et al.  An assessment of satellite remotely-sensed land cover parameters in quantitatively describing the climatic effect of urbanization , 1998 .

[7]  Julia A. Barsi,et al.  The next Landsat satellite: The Landsat Data Continuity Mission , 2012 .

[8]  V. Caselles,et al.  Mapping land surface emissivity from NDVI: Application to European, African, and South American areas , 1996 .

[9]  Jorge Ricardo Ducati,et al.  Algorithm for Soybean Classification Using Medium Resolution Satellite Images , 2012, Remote. Sens..

[10]  Ivan Júlio Apolônio Callejas,et al.  Relationship between land use/cover and surface temperatures in the urban agglomeration of Cuiabá-Várzea Grande, Central Brazil , 2011 .

[11]  E. Pattey,et al.  Assessment of vegetation indices for regional crop green LAI estimation from Landsat images over multiple growing seasons , 2012 .

[12]  D. Lu,et al.  Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies , 2004 .

[13]  F. Creutzig,et al.  Developing a pragmatic approach to assess urban metobolism in Europe. A report to the European Environment Agency , 2011 .

[14]  J. A. Voogta,et al.  Thermal remote sensing of urban climates , 2003 .

[15]  Igor Ogashawara,et al.  A Quantitative Approach for Analyzing the Relationship between Urban Heat Islands and Land Cover , 2012, Remote. Sens..

[16]  Eyal Ben-Dor,et al.  Diurnal thermal behavior of selected urban objects using remote sensing measurements , 2004 .

[17]  A. Roth,et al.  The shuttle radar topography mission—a new class of digital elevation models acquired by spaceborne radar , 2003 .

[18]  Conghe Song,et al.  Impacts of landscape structure on surface urban heat islands: A case study of Shanghai, China , 2011 .

[19]  C. Kennedy,et al.  The study of urban metabolism and its applications to urban planning and design. , 2011, Environmental pollution.

[20]  J. R. Jensen.  Remote Sensing of the Environment: An Earth Resource Perspective , 2000 .

[21]  W. Oechel,et al.  A new model of gross primary productivity for North American ecosystems based solely on the enhanced vegetation index and land surface temperature from MODIS , 2008 .

[22]  Henrik Steen Andersen,et al.  Land surface temperature estimation based on NOAA-AVHRR data during the HAPEX-Sahel experiment , 1997 .

[23]  Brian L. Markham,et al.  Thematic Mapper bandpass solar exoatmospheric irradiances , 1987 .

[24]  A. Huete,et al.  Development of a two-band enhanced vegetation index without a blue band , 2008 .

[25]  M. Veronez,et al.  Evaluating the Thermal Spatial Distribution Signature for Environmental Management and Vegetation Health Monitoring , 2014 .

[26]  A. Karnieli,et al.  A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region , 2001 .

[27]  Conghe Song,et al.  Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in western Oregon , 2006 .

[28]  Wilhelm Koeppen,et al.  Climatología : con un estudio de los climas de la tierra , 1948 .

[29]  G. E. Wukelic,et al.  Radiometric calibration of Landsat Thematic Mapper Thermal Band , 1989 .

[30]  J. Campbell.  Introduction to remote sensing , 1987 .

[31]  Qihao Weng,et al.  A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis, United States , 2008, Int. J. Appl. Earth Obs. Geoinformation.

[32]  P. Chavez.  Image-Based Atmospheric Corrections - Revisited and Improved , 1996 .

[33]  Aníbal Gusso.  Integração de imagens NOAA/AVHRR: rede de cooperação para monitoramento nacional da safra de soja , 2013 .

[34]  J. Nichol,et al.  Spatial variability of frontal area index and its relationship with urban heat island intensity , 2013 .

[35]  Qihao Weng.  Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends , 2009 .

[36]  S. Running,et al.  Developing Satellite-derived Estimates of Surface Moisture Status , 1993 .

[37]  Xulin Guo,et al.  An Integrated Model Based on a Hierarchical Indices System for Monitoring and Evaluating Urban Sustainability , 2013 .

[38]  F. Kogan,et al.  World droughts in the new millennium from AVHRR‐based vegetation health indices , 2002 .

[39]  José A. Sobrino,et al.  Impact of spatial resolution and satellite overpass time on evaluation of the surface urban heat island effects , 2012 .

[40]  W. Schlenker,et al.  Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change , 2009, Proceedings of the National Academy of Sciences.

[41]  I. Sandholt,et al.  A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status , 2002 .

[42]  B. Markham,et al.  Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors , 2009 .

[43]  Aníbal Gusso,et al.  Mapeamento da temperatura da superfície terrestre com uso do sensor AVHRR/NOAA , 2007 .

[44]  A. French,et al.  Decreased CO2 availability and inactivation of Rubisco limit photosynthesis in cotton plants under heat and drought stress in the field , 2012 .

[45]  H. Landsberg.  Urban Climate , 2011, Urban Ecology for Citizens and Planners.

[46]  Pengyu Zhu,et al.  Demand for urban forests in United States cities , 2008 .

[47]  D. Quattrochi,et al.  Measurement and analysis of thermal energy responses from discrete urban surfaces using remote sensing data , 1994 .

[48]  G. R. Brundtland.  Our Common Future World Commission on Environment and Development , 1987 .

[49]  Eric F. Lambin,et al.  Combining vegetation indices and surface temperature for land-cover mapping at broad spatial scales , 1995 .

[50]  K. Shadan,et al.  Available online: , 2012 .