Mapping deciduous forests by using time series of filtered MODIS NDVI and neural networks

Mapping deciduous forests by using time series of filtered MODIS NDVI and neural networks

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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Main Authors: Oliveira,Thomaz Chaves de Andrade, Carvalho,Luis Marcelo Tavares de, Oliveira,Luciano Teixeira de, Martinhago,Adriana Zanella, Acerbi Júnior,Fausto Weimar, Lima,Mariana Peres de
Format: Digital revista
Language:English
Published: UFLA - Universidade Federal de Lavras 2010
Online Access:http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-77602010000200002
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spelling oai:scielo:S0104-776020100002000022014-09-23Mapping deciduous forests by using time series of filtered MODIS NDVI and neural networksOliveira,Thomaz Chaves de AndradeCarvalho,Luis Marcelo Tavares deOliveira,Luciano Teixeira deMartinhago,Adriana ZanellaAcerbi Júnior,Fausto WeimarLima,Mariana Peres de Remote sensing signal processing time series wavelets analysis Fourier 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.info:eu-repo/semantics/openAccessUFLA - Universidade Federal de LavrasCERNE v.16 n.2 20102010-06-01info:eu-repo/semantics/articletext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-77602010000200002en10.1590/S0104-77602010000200002
institution SCIELO
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country Brasil
countrycode BR
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databasecode rev-scielo-br
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region America del Sur
libraryname SciELO
language English
format Digital
author Oliveira,Thomaz Chaves de Andrade
Carvalho,Luis Marcelo Tavares de
Oliveira,Luciano Teixeira de
Martinhago,Adriana Zanella
Acerbi Júnior,Fausto Weimar
Lima,Mariana Peres de
spellingShingle Oliveira,Thomaz Chaves de Andrade
Carvalho,Luis Marcelo Tavares de
Oliveira,Luciano Teixeira de
Martinhago,Adriana Zanella
Acerbi Júnior,Fausto Weimar
Lima,Mariana Peres de
Mapping deciduous forests by using time series of filtered MODIS NDVI and neural networks
author_facet Oliveira,Thomaz Chaves de Andrade
Carvalho,Luis Marcelo Tavares de
Oliveira,Luciano Teixeira de
Martinhago,Adriana Zanella
Acerbi Júnior,Fausto Weimar
Lima,Mariana Peres de
author_sort Oliveira,Thomaz Chaves de Andrade
title Mapping deciduous forests by using time series of filtered MODIS NDVI and neural networks
title_short Mapping deciduous forests by using time series of filtered MODIS NDVI and neural networks
title_full Mapping deciduous forests by using time series of filtered MODIS NDVI and neural networks
title_fullStr Mapping deciduous forests by using time series of filtered MODIS NDVI and neural networks
title_full_unstemmed Mapping deciduous forests by using time series of filtered MODIS NDVI and neural networks
title_sort mapping deciduous forests by using time series of filtered modis ndvi and neural networks
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.
publisher UFLA - Universidade Federal de Lavras
publishDate 2010
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-77602010000200002
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AT oliveiralucianoteixeirade mappingdeciduousforestsbyusingtimeseriesoffilteredmodisndviandneuralnetworks
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