Influence of Land Use and Coverage Change on Continental Surface Temperature in the Urban Area of Belem-PA

Detalhes bibliográficos
Autor(a) principal: Margalho, Eduardo da Silva
Data de Publicação: 2020
Outros Autores: Silva, Madson Tavares, Cardoso, Letícia Karyne da Silva, Olinda, Ricardo Alves de, Menezes, José Felipe Gazel
Tipo de documento: Artigo
Idioma: por
Título da fonte: Anuário do Instituto de Geociências (Online)
Texto Completo: https://revistas.ufrj.br/index.php/aigeo/article/view/35780
Resumo: The objective of this work is to examine the spatial distribution of Continental Surface Temperature (CST) of the urban area of Belem / PA and the influence of the change of use and soil cover from remote sensing techniques. Products from Thematic Mapper (TM) and Thermal Infrared Sensor (TIRS) sensors coupled, respectively, to Landsat 5 and 8 satellites were used. The images acquired from the years 1994, 2008 and 2017 were processed, resampled (spatial resolution of 120 meters) and, finally, centroids were extracted with a total of 1252 points, using the Quantum GIS software. Subsequently, spectral indices, NDVI, NDBI and albedo were calculated, which represent, respectively, the presence of vegetation, exposed soil or built area and reflectivity rate. The results showed that CST showed an increase in all sectors of the study area, mainly between the years 2008 and 2017. The sector with the highest elevation of the CST was the urban center, as it presented values below 25.0 ºC in the image of 1994 and above 35.0 ºC in the 2017 image. In contrast, the ecological park sector showed the lowest increase in CST, from 20.0 ºC (1994) to 25.0 ºC (2017). According to the analysis of the spectral indices, the intensification of CST is directly associated with the strong territorial expansion, since from the NDVI values the degradation of the vegetation cover was noted. This degradation is observed in the comparisons of the images, in which it is possible to verify the decrease in the NDVI values in the entire study area, whose values represent the decrease in the vegetation cover. The sector with the greatest withdrawal of green areas was the northern zone, as it showed a drop in NDVI values, from 0.7 in 1994 to 0.3 in the 2017 image. It was also observed that the density of the constructed area was intensified, presenting increasing values of NDBI. Added to these NDVI and NDBI values, higher reflectivity rate values were noted, whose values in the urban center of Belem in 1994 were 0.1% and which exceeded 0.5% in the image for the year 2017, ratifying the impact of changes in land cover and the direct association between changes in the environment and CST. In general, the results indicate that the uncontrolled expansion of the urban process and the change in land cover cause the intensification of CST.
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spelling Influence of Land Use and Coverage Change on Continental Surface Temperature in the Urban Area of Belem-PAgeoprocessing; urban climate; environmentThe objective of this work is to examine the spatial distribution of Continental Surface Temperature (CST) of the urban area of Belem / PA and the influence of the change of use and soil cover from remote sensing techniques. Products from Thematic Mapper (TM) and Thermal Infrared Sensor (TIRS) sensors coupled, respectively, to Landsat 5 and 8 satellites were used. The images acquired from the years 1994, 2008 and 2017 were processed, resampled (spatial resolution of 120 meters) and, finally, centroids were extracted with a total of 1252 points, using the Quantum GIS software. Subsequently, spectral indices, NDVI, NDBI and albedo were calculated, which represent, respectively, the presence of vegetation, exposed soil or built area and reflectivity rate. The results showed that CST showed an increase in all sectors of the study area, mainly between the years 2008 and 2017. The sector with the highest elevation of the CST was the urban center, as it presented values below 25.0 ºC in the image of 1994 and above 35.0 ºC in the 2017 image. In contrast, the ecological park sector showed the lowest increase in CST, from 20.0 ºC (1994) to 25.0 ºC (2017). According to the analysis of the spectral indices, the intensification of CST is directly associated with the strong territorial expansion, since from the NDVI values the degradation of the vegetation cover was noted. This degradation is observed in the comparisons of the images, in which it is possible to verify the decrease in the NDVI values in the entire study area, whose values represent the decrease in the vegetation cover. The sector with the greatest withdrawal of green areas was the northern zone, as it showed a drop in NDVI values, from 0.7 in 1994 to 0.3 in the 2017 image. It was also observed that the density of the constructed area was intensified, presenting increasing values of NDBI. Added to these NDVI and NDBI values, higher reflectivity rate values were noted, whose values in the urban center of Belem in 1994 were 0.1% and which exceeded 0.5% in the image for the year 2017, ratifying the impact of changes in land cover and the direct association between changes in the environment and CST. In general, the results indicate that the uncontrolled expansion of the urban process and the change in land cover cause the intensification of CST.Universidade Federal do Rio de JaneiroUnidade Acadêmica de Ciências Atmosféricas da Universidade Federal de Campina Grande, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Programa de Pós-Graduação em Meteorologia, Grupo de Pesquisa em Geotecnologias e Modelagem de Risco AmbientaMargalho, Eduardo da SilvaSilva, Madson TavaresCardoso, Letícia Karyne da SilvaOlinda, Ricardo Alves deMenezes, José Felipe Gazel2020-08-21info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistas.ufrj.br/index.php/aigeo/article/view/3578010.11137/2020_2_07_19Anuário do Instituto de Geociências; Vol 43, No 2 (2020); 07_19Anuário do Instituto de Geociências; Vol 43, No 2 (2020); 07_191982-39080101-9759reponame:Anuário do Instituto de Geociências (Online)instname:Universidade Federal do Rio de Janeiro (UFRJ)instacron:UFRJporhttps://revistas.ufrj.br/index.php/aigeo/article/view/35780/pdf/*ref*/Allen R.G.; Tasumi, M.; Trezza, R. & Bastiaanssen, W. 2002. SEBAL (Surface Energy Balance Algorithms for Land), Idaho Implementation: Advanced Training and User’s Manual. NASA EOSDIS/Raytheon Company/Idaho Department of Water Resources, 97p. Almeida, D.N.O.; Oliveira, L.M.M.; Candeias, A.L.B.; Bezerra, U.A. & Souza Leite, A.C. 2018. Uso e cobertura do solo utilizando geoprocessamento em municípios do Agreste de Pernambuco. Revista Brasileira de Meio Ambiente, 4(1): 58-68. Amorim, M. C. C. T.; Dubreuil, V. 2017. A interferência da precipitação na intensidade e na distribuição espacial das ilhas de calor de superfície nas estações do ano em ambiente tropical. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, XVIII, 1314-1320. Bastiaanssen, W.G.M. 2000. SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin. Turkey. Journal of Hidrology, 229:87-100. Chen, X.L.; Zhao, H.M.; Li, P.X. & Yin, Z.Y. 2006. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment, 104 (2):133-146. Cordeiro, M. C. 2016. Ilhas de calor urbanas no nordeste brasileiro: Uma avaliação com base em imagens de satélite. Programa de Pós-graduação em Meteorologia, Universidade Federal de Campina Grande, Dissertação de Mestrado, 70p. Costa, E. C. P.; Augusto, R. C.; Seabra, V. S. 2017. Análise da eficiência dos índices Built-up e NDBI para classificação de áreas urbanas em imagens Landsat 8 OLI. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, XVIII, 6632-6639. Costa, A.C.L.; Cunha, A.C.; Uchoa, P.W.; Silva Junior, J.A. & Feitosa, J.R.P. 2013. Variações termo-higrométricas e influências de processo de expansão urbana em cidade equatorial de médio porte. Brazilian Geographical Journal: geosciences and humanities research medium, 4:615-632. Espinoza, N.S. 2017. Avaliação da ilha de calor urbana em Manaus com dados observados IN SITU e sensoriamento remoto. Programa de Pós-graduação em Meteorologia, Universidade Federal de Campina Grande, Dissertação de Mestrado, 54p. Gartland, L. 2010. Ilhas de calor: como mitigar zonas de calor em áreas urbanas. São Paulo: Oficina de textos. 248p. Huete, A.R.A. 1988. Soil-Adjusted Vegetation Index (SAVI). Remote Sensing of Environment, 25(3):205-309. Llopart, M.; Reboita, M.; Coppola, E.; Giorgi, F.; Rocha, R. & Souza, D. 2018. Land use change over the Amazon Forest and its impact on the local climate. Water, 10 (2):149p. Luchiari, A. 2011. Identificação da cobertura vegetal em áreas urbanas por meio de produtos de sensoriamento remoto e de um sistema de informação geográfica. Revista do Departamento de Geografia (USP), 14:47-58. Markham, B.L. & Barker, J.L. 1987. Thematic mapper band pass solar exoatmospherical irradiances. International Journal of Remote Sensing, 8 (3):517-523. Melos, N.D. 2018. Índice de qualidade urbana do município de Uruguaiana–RS por análises de geoprocessamento. Especialização em Geomática, Universidade de Santa Maria. Trabalho de conclusão de curso, 50p. Nichol, J. 2009. An emissivity modulation method for spatial enhancement of thermal satellite images in urban heat island analysis. Photogrammetric Engineering & Remote Sensing, 75(5):547-556. Oke, T.R. 1987. Boundary layer climates. 2. ed. [s.l.] Routledge. 464p. Oliveira, M.; Alves, W. S. 2013. A influência da vegetação no clima urbano de cidades pequenas: um estudo sobre as praças públicas de Iporá-GO. Revista Territorial, 2(2): 61-77. Polydoros, A.; Mavrakou, T.; Cartalis, C. 2018. Quantifying the trends in land surface temperature and surface urban heat island intensity in mediterranean cities in view of smart urbanization. Urban Science, 2(1): 16p. Pontes, A.K.S.; Silva, P.V.C.; Santos, J.T.S. & Sousa, A.M.L. 2017. Temperatura em superfície urbanas usando sensor TIRS-Landsat 5 e 8: estudo de caso em Belém-PA. Revista Brasileira de Iniciação Científica, 4:118-132. Purevdorj, T.S.; Tateishi, R.; Ishiyama, T. & Honda, Y. 1998. Relationships between percent vegetation cover and vegetation indices. International Journal of Remote Sensing, 19:3519-3535. Rouse, J.W.; Haas, R.H.; Schell, J.A. & Deering, D.W. 1973. Monitoring vegetation systems in the Great Plains with ERTS, In: 3RD EARTH RESOURCES TECHNOLOGY SATELLITE SYMPOSIUM, 1:309-317. Tasumi, M.; Allen, R.G.; Trezza, R. & Wright, J.L. 2008. Satellite- Based Energy Balance to Assess Within-population Variance of Crop Coefficient Curve. Journal of Irrigation and Drainage Engineering, 131(1):95-108. United States Geological Survey – USGS, 2019. Landsat 8 (L8) Data Users Handbook. Disponível em: https://landsat. usgs.gov/. Acesso 20 maio de 2019. Zha, Y. & Gao, J.N.I.S. 2003. Use of normalized difference built- up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24:583-594.Copyright (c) 2020 Anuário do Instituto de Geociênciashttp://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccess2020-09-21T10:59:44Zoai:www.revistas.ufrj.br:article/35780Revistahttps://revistas.ufrj.br/index.php/aigeo/indexPUBhttps://revistas.ufrj.br/index.php/aigeo/oaianuario@igeo.ufrj.br||1982-39080101-9759opendoar:2020-09-21T10:59:44Anuário do Instituto de Geociências (Online) - Universidade Federal do Rio de Janeiro (UFRJ)false
dc.title.none.fl_str_mv Influence of Land Use and Coverage Change on Continental Surface Temperature in the Urban Area of Belem-PA
title Influence of Land Use and Coverage Change on Continental Surface Temperature in the Urban Area of Belem-PA
spellingShingle Influence of Land Use and Coverage Change on Continental Surface Temperature in the Urban Area of Belem-PA
Margalho, Eduardo da Silva
geoprocessing; urban climate; environment
title_short Influence of Land Use and Coverage Change on Continental Surface Temperature in the Urban Area of Belem-PA
title_full Influence of Land Use and Coverage Change on Continental Surface Temperature in the Urban Area of Belem-PA
title_fullStr Influence of Land Use and Coverage Change on Continental Surface Temperature in the Urban Area of Belem-PA
title_full_unstemmed Influence of Land Use and Coverage Change on Continental Surface Temperature in the Urban Area of Belem-PA
title_sort Influence of Land Use and Coverage Change on Continental Surface Temperature in the Urban Area of Belem-PA
author Margalho, Eduardo da Silva
author_facet Margalho, Eduardo da Silva
Silva, Madson Tavares
Cardoso, Letícia Karyne da Silva
Olinda, Ricardo Alves de
Menezes, José Felipe Gazel
author_role author
author2 Silva, Madson Tavares
Cardoso, Letícia Karyne da Silva
Olinda, Ricardo Alves de
Menezes, José Felipe Gazel
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Unidade Acadêmica de Ciências Atmosféricas da Universidade Federal de Campina Grande, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Programa de Pós-Graduação em Meteorologia, Grupo de Pesquisa em Geotecnologias e Modelagem de Risco Ambienta
dc.contributor.author.fl_str_mv Margalho, Eduardo da Silva
Silva, Madson Tavares
Cardoso, Letícia Karyne da Silva
Olinda, Ricardo Alves de
Menezes, José Felipe Gazel
dc.subject.por.fl_str_mv geoprocessing; urban climate; environment
topic geoprocessing; urban climate; environment
description The objective of this work is to examine the spatial distribution of Continental Surface Temperature (CST) of the urban area of Belem / PA and the influence of the change of use and soil cover from remote sensing techniques. Products from Thematic Mapper (TM) and Thermal Infrared Sensor (TIRS) sensors coupled, respectively, to Landsat 5 and 8 satellites were used. The images acquired from the years 1994, 2008 and 2017 were processed, resampled (spatial resolution of 120 meters) and, finally, centroids were extracted with a total of 1252 points, using the Quantum GIS software. Subsequently, spectral indices, NDVI, NDBI and albedo were calculated, which represent, respectively, the presence of vegetation, exposed soil or built area and reflectivity rate. The results showed that CST showed an increase in all sectors of the study area, mainly between the years 2008 and 2017. The sector with the highest elevation of the CST was the urban center, as it presented values below 25.0 ºC in the image of 1994 and above 35.0 ºC in the 2017 image. In contrast, the ecological park sector showed the lowest increase in CST, from 20.0 ºC (1994) to 25.0 ºC (2017). According to the analysis of the spectral indices, the intensification of CST is directly associated with the strong territorial expansion, since from the NDVI values the degradation of the vegetation cover was noted. This degradation is observed in the comparisons of the images, in which it is possible to verify the decrease in the NDVI values in the entire study area, whose values represent the decrease in the vegetation cover. The sector with the greatest withdrawal of green areas was the northern zone, as it showed a drop in NDVI values, from 0.7 in 1994 to 0.3 in the 2017 image. It was also observed that the density of the constructed area was intensified, presenting increasing values of NDBI. Added to these NDVI and NDBI values, higher reflectivity rate values were noted, whose values in the urban center of Belem in 1994 were 0.1% and which exceeded 0.5% in the image for the year 2017, ratifying the impact of changes in land cover and the direct association between changes in the environment and CST. In general, the results indicate that the uncontrolled expansion of the urban process and the change in land cover cause the intensification of CST.
publishDate 2020
dc.date.none.fl_str_mv 2020-08-21
dc.type.none.fl_str_mv

dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://revistas.ufrj.br/index.php/aigeo/article/view/35780
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url https://revistas.ufrj.br/index.php/aigeo/article/view/35780
identifier_str_mv 10.11137/2020_2_07_19
dc.language.iso.fl_str_mv por
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dc.relation.none.fl_str_mv https://revistas.ufrj.br/index.php/aigeo/article/view/35780/pdf
/*ref*/Allen R.G.; Tasumi, M.; Trezza, R. & Bastiaanssen, W. 2002. SEBAL (Surface Energy Balance Algorithms for Land), Idaho Implementation: Advanced Training and User’s Manual. NASA EOSDIS/Raytheon Company/Idaho Department of Water Resources, 97p. Almeida, D.N.O.; Oliveira, L.M.M.; Candeias, A.L.B.; Bezerra, U.A. & Souza Leite, A.C. 2018. Uso e cobertura do solo utilizando geoprocessamento em municípios do Agreste de Pernambuco. Revista Brasileira de Meio Ambiente, 4(1): 58-68. Amorim, M. C. C. T.; Dubreuil, V. 2017. A interferência da precipitação na intensidade e na distribuição espacial das ilhas de calor de superfície nas estações do ano em ambiente tropical. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, XVIII, 1314-1320. Bastiaanssen, W.G.M. 2000. SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin. Turkey. Journal of Hidrology, 229:87-100. Chen, X.L.; Zhao, H.M.; Li, P.X. & Yin, Z.Y. 2006. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment, 104 (2):133-146. Cordeiro, M. C. 2016. Ilhas de calor urbanas no nordeste brasileiro: Uma avaliação com base em imagens de satélite. Programa de Pós-graduação em Meteorologia, Universidade Federal de Campina Grande, Dissertação de Mestrado, 70p. Costa, E. C. P.; Augusto, R. C.; Seabra, V. S. 2017. Análise da eficiência dos índices Built-up e NDBI para classificação de áreas urbanas em imagens Landsat 8 OLI. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, XVIII, 6632-6639. Costa, A.C.L.; Cunha, A.C.; Uchoa, P.W.; Silva Junior, J.A. & Feitosa, J.R.P. 2013. Variações termo-higrométricas e influências de processo de expansão urbana em cidade equatorial de médio porte. Brazilian Geographical Journal: geosciences and humanities research medium, 4:615-632. Espinoza, N.S. 2017. Avaliação da ilha de calor urbana em Manaus com dados observados IN SITU e sensoriamento remoto. Programa de Pós-graduação em Meteorologia, Universidade Federal de Campina Grande, Dissertação de Mestrado, 54p. Gartland, L. 2010. Ilhas de calor: como mitigar zonas de calor em áreas urbanas. São Paulo: Oficina de textos. 248p. Huete, A.R.A. 1988. Soil-Adjusted Vegetation Index (SAVI). Remote Sensing of Environment, 25(3):205-309. Llopart, M.; Reboita, M.; Coppola, E.; Giorgi, F.; Rocha, R. & Souza, D. 2018. Land use change over the Amazon Forest and its impact on the local climate. Water, 10 (2):149p. Luchiari, A. 2011. Identificação da cobertura vegetal em áreas urbanas por meio de produtos de sensoriamento remoto e de um sistema de informação geográfica. Revista do Departamento de Geografia (USP), 14:47-58. Markham, B.L. & Barker, J.L. 1987. Thematic mapper band pass solar exoatmospherical irradiances. International Journal of Remote Sensing, 8 (3):517-523. Melos, N.D. 2018. Índice de qualidade urbana do município de Uruguaiana–RS por análises de geoprocessamento. Especialização em Geomática, Universidade de Santa Maria. Trabalho de conclusão de curso, 50p. Nichol, J. 2009. An emissivity modulation method for spatial enhancement of thermal satellite images in urban heat island analysis. Photogrammetric Engineering & Remote Sensing, 75(5):547-556. Oke, T.R. 1987. Boundary layer climates. 2. ed. [s.l.] Routledge. 464p. Oliveira, M.; Alves, W. S. 2013. A influência da vegetação no clima urbano de cidades pequenas: um estudo sobre as praças públicas de Iporá-GO. Revista Territorial, 2(2): 61-77. Polydoros, A.; Mavrakou, T.; Cartalis, C. 2018. Quantifying the trends in land surface temperature and surface urban heat island intensity in mediterranean cities in view of smart urbanization. Urban Science, 2(1): 16p. Pontes, A.K.S.; Silva, P.V.C.; Santos, J.T.S. & Sousa, A.M.L. 2017. Temperatura em superfície urbanas usando sensor TIRS-Landsat 5 e 8: estudo de caso em Belém-PA. Revista Brasileira de Iniciação Científica, 4:118-132. Purevdorj, T.S.; Tateishi, R.; Ishiyama, T. & Honda, Y. 1998. Relationships between percent vegetation cover and vegetation indices. International Journal of Remote Sensing, 19:3519-3535. Rouse, J.W.; Haas, R.H.; Schell, J.A. & Deering, D.W. 1973. Monitoring vegetation systems in the Great Plains with ERTS, In: 3RD EARTH RESOURCES TECHNOLOGY SATELLITE SYMPOSIUM, 1:309-317. Tasumi, M.; Allen, R.G.; Trezza, R. & Wright, J.L. 2008. Satellite- Based Energy Balance to Assess Within-population Variance of Crop Coefficient Curve. Journal of Irrigation and Drainage Engineering, 131(1):95-108. United States Geological Survey – USGS, 2019. Landsat 8 (L8) Data Users Handbook. Disponível em: https://landsat. usgs.gov/. Acesso 20 maio de 2019. Zha, Y. & Gao, J.N.I.S. 2003. Use of normalized difference built- up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24:583-594.
dc.rights.driver.fl_str_mv Copyright (c) 2020 Anuário do Instituto de Geociências
http://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2020 Anuário do Instituto de Geociências
http://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal do Rio de Janeiro
publisher.none.fl_str_mv Universidade Federal do Rio de Janeiro
dc.source.none.fl_str_mv Anuário do Instituto de Geociências; Vol 43, No 2 (2020); 07_19
Anuário do Instituto de Geociências; Vol 43, No 2 (2020); 07_19
1982-3908
0101-9759
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