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Luis Marcelo Tavares de Carvalho ; Luciano Teixeira de Oliveira ; Adriana Zanella Martinhago ; Fausto Weimar Acerbi Júnior ; Mariana Peres de Lima ;CERNE 2010, 16 2

Autor: Thomaz Chaves de Andrade Oliveira

Fuente: http://www.redalyc.org/


Introducción



CERNE ISSN: 0104-7760 cerne@dcf.ufla.br Universidade Federal de Lavras Brasil Chaves de Andrade Oliveira, Thomaz; Tavares de Carvalho, Luis Marcelo; Teixeira de Oliveira, Luciano; Zanella Martinhago, Adriana; Weimar Acerbi Júnior, Fausto; Peres de Lima, Mariana Mapping deciduous forests by using time series of filtered MODIS NDVI and neural networks CERNE, vol.
16, núm.
2, abril-junio, 2010, pp.
123-130 Universidade Federal de Lavras Lavras, Brasil Available in: http:--www.redalyc.org-articulo.oa?id=74421665002 How to cite Complete issue More information about this article Journals homepage in redalyc.org Scientific Information System Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Non-profit academic project, developed under the open access initiative MAPPING Mapping deciduous forests by usingDECIDUOUS time series .FORESTS BY USING TIME SERIES OF FILTERED MODIS NDVI AND NEURAL NETWORKS 123 Thomaz Chaves de Andrade Oliveira1, Luis Marcelo Tavares de Carvalho2, Luciano Teixeira de Oliveira3, Adriana Zanella Martinhago4, Fausto Weimar Acerbi Júnior5, Mariana Peres de Lima6 (received: March 30, 2009; accepted: February 26, 2010) 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 net...





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