Análise temporal de índices de vegetação no apoio à classificação de imagens: cobertura do solo na bacia hidrográfica do rio Sucuru.

Abstract

The Caatinga is a unique biome that only occurs in the semi-arid region of Brazil. This biome is distinguished, mainly, by the capacity to develop mechanisms of adaptation to the low rainfall rates of the region. Plus, it also has a high resilience level upon high anthropic pressure. Considering that land cover plays a very important role in environmental balance, remote sensing techniques have been widely used to extract biophysical information from vegetation. The objective of this work is to establish a methodology that incorporates a time series vegetation index characterization to the image classification technique for improving the land cover classification in the Sucuru River basin in Cariri, Paraíba. In this sense, a Land Cover classification is acquired from SR images, for a specific data. For this data, there is a ground truth validation. In addition, an evaluation of a vegetation index time series is performed to improve the classification. In this research was adopted part of the methodology proposed by Chaves et al. (2008), to classify land cover patterns and a time series of EVI, processed with 88 images selected from the ETM + and OLI / TIRS sensors of the Landsat series for the period between October 2014 to September 2016. The results evidence that analyzing the land cover for a single time stamp could not reliably portray the land cover patterns since we often have fast changes before and after a rainfall event in this semi-arid environment. In this sense, the results show that a spatiotemporal analysis, using a vegetation index, can establish a better distinction of the categories assigned to a classification of land cover patterns, allowing a better perception of vegetation behavior for a period of 24 Months observed.