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

Authors

  • Eduardo da Silva Margalho Universidade Federal de Campina Grande, Unidade Acadêmica de Ciências Atmosféricas-Centro de Tecnologia e Recursos Naturais. Av. Aprígio Veloso 882, 58429-140, Bairro Universitário, Campina Grande, Paraíba, Brasil
  • Madson Tavares Silva Universidade Federal de Campina Grande, Unidade Acadêmica de Ciências Atmosféricas-Centro de Tecnologia e Recursos Naturais. Av. Aprígio Veloso 882, 58429-140, Bairro Universitário, Campina Grande, Paraíba, Brasil
  • Letícia Karyne da Silva Cardoso Universidade Federal de Campina Grande, Unidade Acadêmica de Ciências Atmosféricas-Centro de Tecnologia e Recursos Naturais. Av. Aprígio Veloso 882, 58429-140, Bairro Universitário, Campina Grande, Paraíba, Brasil
  • Ricardo Alves de Olinda Universidade Estadual da Paraíba, Departamento de Estatística-Centro de Ciência e Tecnologia, R. Baraúnas 351, 58429-500 – Bairro Universitário, Campina Grande, Paraíba, Brasil
  • José Felipe Gazel Menezes Universidade Federal do Pará, Instituto de Geociências, Programa de pós-Graduação de Ciências Ambientais. R. Augusto Corrêa 01, 66075-110, Bairro Guamá, Belém, Pará, Brasil

DOI:

https://doi.org/10.11137/2020_2_07_19

Keywords:

geoprocessing, urban climate, environment

Abstract

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.

References

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,

(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,

p.

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,

: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.

p.

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):

-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.

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,

: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,

:583-594.

Published

2020-08-21

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