Use of satellite images as a tool to aid urban planning
Keywords:
Urban planning, images processing, satellite imagesAbstract
The studied area, the São Bernardo do Campo Municipal District, is an important area located in the São Paulo State, Brazil, and where are located great industries and important fountainhead that it forms the dam Billings, that is part of the Henry Borden Hydroelectric. The concern with the disordered and irregular urban occupation in this area, took the municipal district city hall to look for alternative forms formonitoring and mapping of the area, because the traditional forms could not contain the illegal occupation. Thus, a land use monitoring methodology was developed, through Landsat 7 digital image processing, and the image classification with maximum likelihood algorithm. With this methodology the monitoring task is accomplished more quickness and with more precision, reducing the work and increasing the efficiency.
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References
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BARALDI, A. & PARMIGGIANI, F. Urban area classification by multispectral SPOT data. IEEE Transactions on Geoscience and Remote Sensing, 1990, Vol. 28, Nº 4, p.
674-680.
BERBEROGLU, S.; LLOYD, C.; ATKINSON, P. & CURRAN, P. The integration of spectral and textural information using neural networks for land cover mapping in the
Mediterranean. Computers & Geosciences, 2000, Vol. 26, Nº 4, p. 385-396.
CAMPOS, M. Incorporação de dados auxiliares no processo de classificação, 2002. Disponível em Internet: http://www.geoc.ufpr.br/~gfoto/pdi/dadosaux/dadosaux.html.
EASTMAN, J. IDRISI for Windows: Manual do usuário. Introdução e Exercícios tutoriais. Trad. de Heinrich Hasenack e Eliseu Webwe. Porto Alegre: UFRGS/ Centro de Recursos Idrisi, 1998. Disponível em Internet: http://www.ecologia.ufrgs.br/idrisi/download/tutorial.pdf.
FOODY,G. & ARORA, M. Incorporating mixed pixels in the training, allocation and testing stages of supervised classifications. Pattern Recognition Letters, 1996, Vol. 17, Nº 13, p. 1389-1398.
FRANKLIN, S.; WULDER, M. & LAVIGNE, M. Automated derivation of geographic window sizes for use in remote sensing digital image texture analysis. Computers & Geosciences , 1996, Vol. 22, Nº 6, p. 665-673.
GONZALEZ, R. & WOODS, R. Processamento de imagens digitais. São Paulo: Edgard Blucher, 2000.
HAYKIN, S. Neural Networks - A Comprehensive Foundation. New York: Macmillan, 1994.
IBGE (Instituto Brasileiro de Geografia e Estatística). Brasil em síntese. Dados geográficos. Disponível em Internet: http://www.ibge.gov.br.
JENSEN, J. Introductory digital image processing. New Jersey: Prentice-Hall, 1986.
MATTIKALLI, N. Soil color modeling for the visible and near-infrared bands of Landsat sensors using laboratory spectral measurements. Remote Sensing of Environment, 1997, Vol. 59, Nº 1, p. 14-28.
MOLLER-JENSEN, L. Knowledge-based classification of an urban area using texture and context information in Landsat TM imagery. Photogrammetric Enginering and Remote Sensing, 1990, Vol. 56, Nº 6, p. 899-904.
MOLLER-JENSEN, L. Classification of urban land cover based on expert systems, object models and texture. Computers, Environment and Urban Systems, 1997, Vol.
21, Nº 3-4, p. 291-302.
MUNICÍPIO DE SÃO BERNARDO DO CAMPO. Evolução da população. 2002. Disponível em Internet: h t t p : / /www. s a o b e r n a r d o . s p . g o v. b r /
sec_administracao/evolucaopopulacao.htm.
OLMO, M. & HERNANDEZ, F. Computing geostatistical image texture for remotely sensed data classification. Computers & Geosciences, 2000, Vol. 26, Nº 4, p.
373-383.
PREFEITURA DO MUNICÍPIO DE SÃO BERNARDO DO CAMPO. Compêndio Estatístico. São Bernardo do Campo: Prefeitura do município de São Bernardo do Campo,
1998.
QUINTANILHA, J.; IWAI, O. & HAMBURGUER, D. Technology transfer in digitalimage processing techniques. In: International IT Conference in Geo Spatial Education, Hong Kong, 2000. Hong Kong: Hong Kong Polytechnic University, 2000. Disponível em Internet: http://www.lsgi.polyu.edu.hk/publications/ proceedings/ITCGSE?Proceedings.pdf
RIDD, M. & LIU, J. A Comparison of Four Algorithms for Change Detection in an Urban Environment. Remote Sensing of Environment, 1998, Vol. 63, Nº 2, p. 95-100.
SECRETARIA DO MEIO AMBIENTE; CETESB; SECRETARIA DE RECURSOS HÍDRICOS, SANEAMENTO E OBRAS e SABESP, GOVERNO DO ESTADO DE SÃO PAULO. Termo de referência para o Programa de Recuperação Ambiental da Bacia da Billings. São Paulo: Technical Report, 1999.
SERPICO, S.; BRUZZONE, L. & ROLI, F. An experimental comparison of neural and statistical non-parametric algorithm for supervised classification of remote sensing images. Pattern Recognition Letters, 1996, Vol. 17, Nº 13, p.1331-1341.
SCHOWENGERDT, R. On the estimation of spatial-spectral mixing with classifier likelihood functions. Pattern Recognition Letters, 1996, Vol. 17, Nº 13, p. 1379-1387.
STUCKENS, J.; COPPIN, P. & BAUER, M. Integrating contextual information with per-pixel classification for improved land cover classification. Remote Sensing of Environment, 2000, Vol. 71, Nº 3, p. 282- 296.
TEIXEIRA, A. & CHRISTOFOLETTI, A. Sistemas de Informação Geográfica. Dicionário Ilustrado. São Paulo: Editora Hucitec, 1997.
ZHANG, Y. Optimisation of building detection in satellite images by combining multispectral classification and texture filtering. ISPRS Journal of Photogrammetry &
Remote Sensing, 1994, Vol. 54, Nº 1, p. 50-60.
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KAZUKO IWAI, O., & QUINTANILHA, J. A. (2021). Use of satellite images as a tool to aid urban planning. Revista De Geografía Norte Grande, (34), 65–82. Retrieved from https://ojs.uc.cl/index.php/RGNG/article/view/43765
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