Mapping deciduous forests by using time series of filtered modis nfvi and neural networks

Detalhes bibliográficos
Autor(a) principal: Oliveira, Thomaz C. de A.
Data de Publicação: 2010
Outros Autores: Carvalho, Luis Marcelo Tavares de, Oliveira, Luciano T. de, Martinhago, Adriana Z., Júnior, Fausto W. A., Lima, Mariana P. de
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFLA
Texto Completo: http://repositorio.ufla.br/jspui/handle/1/874
Resumo: 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.
id UFLA_1d46be06aa904b3ebef80136cde4426b
oai_identifier_str oai:localhost:1/874
network_acronym_str UFLA
network_name_str Repositório Institucional da UFLA
repository_id_str
spelling Mapping deciduous forests by using time series of filtered modis nfvi and neural networksMapeamento de florestas decíduas através de redes neurais artificiais e séries temporais de ndvi modisRemote sensingSignal processingTime seriesWavelets analysisFourierSensoriamento remotoProcessamento de sinaisAnálise waveletsMulti-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.Imagens multitemporais são de pronominal uso no Sensoriamento Remoto, para o monitoramento e classificação da vegetação. As decorrentes assinaturas temporais da vegetação possuem muitos desafios na sua utilização em razão da elevada relação sinal/ruído. Este estudo investigou dois métodos para gerar assinaturas temporais suavizadas de vegetação do índice de vegetação de diferença normalizada (NDVI), sendo estas originadas do sensor MODIS. As técnicas de filtragem utilizadas foram o algoritmo baseado em Fourier HANTS e algoritmo Wavelet Temporal que utiliza análise Wavelet. O estudo foi conduzido em 4 diferentes conjuntos de dados, correspondente a áreas separadas geograficamente no estado de Minas Gerais. Para realizar a comparação entre as séries temporais filtradas pelos diferentes algoritmos, as séries filtradas foram utilizadas como entradas de dados para classificação da vegetação em diferentes fitofisionomias. A Classificação foi feita por meio das redes neurais artificiais. O resultado dessa classificação mostrou similaridade entre os métodos de filtragem de séries temporais NDVI comparados neste trabalho.Universidade Federal de Lavras2013-08-07T13:51:59Z2013-08-07T13:51:59Z2010info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfOLIVEIRA, T. C. de A. et al. Mapping deciduous forests by using time series of filtered MODIS NDVI and neural networks. Cerne, Lavras, MG, v. 16, n. 2, p. 123-130, abr./jun. 2010.http://repositorio.ufla.br/jspui/handle/1/874Cernereponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAOliveira, Thomaz C. de A.Carvalho, Luis Marcelo Tavares deOliveira, Luciano T. deMartinhago, Adriana Z.Júnior, Fausto W. A.Lima, Mariana P. deinfo:eu-repo/semantics/openAccesseng2013-09-04T17:32:39Zoai:localhost:1/874Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2013-09-04T17:32:39Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false
dc.title.none.fl_str_mv Mapping deciduous forests by using time series of filtered modis nfvi and neural networks
Mapeamento de florestas decíduas através de redes neurais artificiais e séries temporais de ndvi modis
title Mapping deciduous forests by using time series of filtered modis nfvi and neural networks
spellingShingle Mapping deciduous forests by using time series of filtered modis nfvi and neural networks
Oliveira, Thomaz C. de A.
Remote sensing
Signal processing
Time series
Wavelets analysis
Fourier
Sensoriamento remoto
Processamento de sinais
Análise wavelets
title_short Mapping deciduous forests by using time series of filtered modis nfvi and neural networks
title_full Mapping deciduous forests by using time series of filtered modis nfvi and neural networks
title_fullStr Mapping deciduous forests by using time series of filtered modis nfvi and neural networks
title_full_unstemmed Mapping deciduous forests by using time series of filtered modis nfvi and neural networks
title_sort Mapping deciduous forests by using time series of filtered modis nfvi and neural networks
author Oliveira, Thomaz C. de A.
author_facet Oliveira, Thomaz C. de A.
Carvalho, Luis Marcelo Tavares de
Oliveira, Luciano T. de
Martinhago, Adriana Z.
Júnior, Fausto W. A.
Lima, Mariana P. de
author_role author
author2 Carvalho, Luis Marcelo Tavares de
Oliveira, Luciano T. de
Martinhago, Adriana Z.
Júnior, Fausto W. A.
Lima, Mariana P. de
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Oliveira, Thomaz C. de A.
Carvalho, Luis Marcelo Tavares de
Oliveira, Luciano T. de
Martinhago, Adriana Z.
Júnior, Fausto W. A.
Lima, Mariana P. de
dc.subject.por.fl_str_mv Remote sensing
Signal processing
Time series
Wavelets analysis
Fourier
Sensoriamento remoto
Processamento de sinais
Análise wavelets
topic Remote sensing
Signal processing
Time series
Wavelets analysis
Fourier
Sensoriamento remoto
Processamento de sinais
Análise wavelets
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.
publishDate 2010
dc.date.none.fl_str_mv 2010
2013-08-07T13:51:59Z
2013-08-07T13:51:59Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv OLIVEIRA, T. C. de A. et al. Mapping deciduous forests by using time series of filtered MODIS NDVI and neural networks. Cerne, Lavras, MG, v. 16, n. 2, p. 123-130, abr./jun. 2010.
http://repositorio.ufla.br/jspui/handle/1/874
identifier_str_mv OLIVEIRA, T. C. de A. et al. Mapping deciduous forests by using time series of filtered MODIS NDVI and neural networks. Cerne, Lavras, MG, v. 16, n. 2, p. 123-130, abr./jun. 2010.
url http://repositorio.ufla.br/jspui/handle/1/874
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal de Lavras
publisher.none.fl_str_mv Universidade Federal de Lavras
dc.source.none.fl_str_mv Cerne
reponame:Repositório Institucional da UFLA
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str Repositório Institucional da UFLA
collection Repositório Institucional da UFLA
repository.name.fl_str_mv Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv nivaldo@ufla.br || repositorio.biblioteca@ufla.br
_version_ 1807835206441238528

Registros relacionados