Mapeamento do uso e ocupação da terra utilizando os satélites Landsat 8, Sentinel 2B e Cbers 4A em área de agricultura no município de Limoeiro do Norte - CE

Abstract

With the advancement of technology in the 21st century, the area of agriculture had to adapt to new demands and thus sought to modernize. An example of this is the emergence of precision agriculture, which together with remote sensing, seeks to optimize production in a way that does not have a major impact on the environment. As a way to have a better knowledge about a certain area, in order to have a control over what makes up a given region, the supervised classification method is used and with the wide variety of satellites, it is often difficult for the user (be it academic or not) to know which satellite is most suitable for this application. With this, the present work was done in an agricultural area, in the central sertão region of the state of Ceará, in the municipality of Limoeiro do Norte. Where three different satellites were compared: Landsat 8, Sentinel 2B and CBERS 4A and different supervised classification methods: Minimum Distance, Maximum Likelihood and Spectral Angle Mapper. In order to find the best classification method associated with satellite images, the accuracy of each classification method was measured, in order to prove how reliable these methods are, based on the Kappa and Global Accuracy indices. The present work concluded that the best supervised classification method was the Mindist, presenting considerable accuracy. Among the three satellites mentioned, the one that showed the best results was the Sentinel 2B. For future studies, a temporal analysis can be made, using the supervised classification method that showed the best performance.