Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/14002
metadata.revistascielo.dc.title: MAPPING DECIDUOUS FORESTS BY USING SERIES OF FILTERED MODIS NDVI AND NEURAL NETWORKS
metadata.revistascielo.dc.creator: Oliveira, Thomaz Chaves de Andrade
Carvalho, Luis Marcelo Tavares de
Oliveira, Luciano Teixeira de
Martinhago, Adriana Zanella
Júnior, Fausto Weimar Acerbi
Lima, Mariana Peres de
metadata.revistascielo.dc.subject: Remote sensing, signal processing, time series, wavelets analysis, Fourier
metadata.revistascielo.dc.publisher: CERNE
CERNE
metadata.revistascielo.dc.date: 29-Oct-2015
metadata.revistascielo.dc.identifier: http://www.cerne.ufla.br/site/index.php/CERNE/article/view/673
metadata.revistascielo.dc.description: 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.
metadata.revistascielo.dc.language: eng
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