CLASSIFICAÇÃO SUPERVISIONADA POR MÁXIMA VEROSSIMILHANÇA GAUSSIANA COMO FERRAMENTA PARA MONITORAMENTO DE ÁREAS DE VEGETAÇÃO EM REGIÕES METROPOLITANAS

Abstract

Remote sensing has proven to be a great tool for mapping urban growth in regions with increasing urbanization. The most used methodology is the digital classification of multispectral data, performed supervised or unsupervised. This study aims to highlight the areas of vegetation that suffered reduction due to the implementation of projects, consequently the urban expansion, based on the classification supervised by Maximum Gaussian Likelihood (MAXVER), using digital images from the Landsat 5 satellite, having as its area metropolitan region of Porto Alegre, Rio Grande do Sul. The Landsat 5 orbital images dated in 2005 and 2011 were collected in the image bank of the National Institute for Space Research (INPE) and classified in the MultiSpec software. using the urban classes, vegetation, water, soil and sand. With an overall accuracy of over 90% for all classes, a reduction of 19.8% of the vegetation area and a 16.1% increase in the area of urbanization were observed, showing the impact of urbanization on the reduction of areas. of vegetation.