MAPPING DECIDUOUS FORESTS BY USING SERIES OF FILTERED MODIS NDVI AND NEURAL NETWORKS

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Thomaz Chaves de Andrade Oliveira Luis Marcelo Tavares de Carvalho Luciano Teixeira de Oliveira Adriana Zanella Martinhago Fausto Weimar Acerbi Júnior Mariana Peres de Lima

Abstract

Multi-temporal images are now of standard use in remote sensing of vegetation during monitoring and classification. Temporal vegetation signatures (i. e., vegetation indices as functions of time) generated, poses many challenges, primarily due to signal to noise-related issues. This study investigates which methods generate the most appropriate smoothed curves of vegetation signatures on MODIS NDVI time series. The filtering techniques compared were the HANTS algorithm which is based on Fourier analyses and Wavelet temporal algorithm which uses the wavelet analysis to generate the smoothed curves. The study was conducted in four different regions of the Minas Gerais State. The smoothed data were used as input data vectors for vegetation classification by means of artificial neural networks for comparison purpose. A comparison of the results was ultimately discussed in this work showing encouraging results and similarity between the two filtering techniques used.

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How to Cite
OLIVEIRA, Thomaz Chaves de Andrade et al. MAPPING DECIDUOUS FORESTS BY USING SERIES OF FILTERED MODIS NDVI AND NEURAL NETWORKS. CERNE, [S.l.], v. 16, n. 2, p. 123–130, oct. 2015. ISSN 2317-6342. Available at: <http://cerne.ufla.br/site/index.php/CERNE/article/view/673>. Date accessed: 16 sep. 2019.
Keywords
Remote sensing, signal processing, time series, wavelets analysis, Fourier
Section
Article