Classification of Land Use and Occupancy with Emphasis on Urban Areas

Urbanization in Brazil occurred in a disorganized way and without observing criteria that could ensure the sustainability of this process, resulting in high population concentrations in large urban centers. In this way, it is recognized the need for an adequate planning that contemplates the economi...

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Bibliographic Details
Published in:	Anuário do Instituto de Geociências Vol. 42; no. 3; pp. 377 - 386
Main Authors:	RIEGEL, R. P., ALVES, D. D., BIRLEM, L. E., ROQUE, D. C., OLIVEIRA, G. G., HAETINGER, C., OSÓRIO, D. M. M., RODRIGUES, M. A. S., QUEVEDO, D. M.
Format:	Journal Article
Language:	English
Published:	Universidade Federal do Rio de Janeiro 30-09-2019
Subjects:	classification of land use and occupancy photointerpretation urban planning validation
Online Access:	Get full text
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Summary:	Urbanization in Brazil occurred in a disorganized way and without observing criteria that could ensure the sustainability of this process, resulting in high population concentrations in large urban centers. In this way, it is recognized the need for an adequate planning that contemplates the economic and social interests and respects the environmental demands. Thus, the present work aims to map the land use and occupancy, with an emphasis in an urban area, having as a case study the municipality of Novo Hamburgo, RS. The methodology is established in two levels of detailing of classes, in which supervised classification and manual vectorization were used, seeking a more accurate specification of the urban areas. Validation was established through two databases: Google Earth and Remotely Piloted Aircraft System (RPAS), using the confusion matrix and the Kappa index. The results show the efficiency of the hybrid method for high-resolution images, besides highlighting the existing anthropogenic differences in urban areas. In relation to the use of different databases for validation, close values are also perceived between the two processes, with a Kappa index of 0.928 for data obtained from Google Earth and 0.943 for the RPAS.
ISSN:	0101-9759 1982-3908 1982-3908 0101-9759
DOI:	10.11137/2019_3_377_386