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

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dc.creatorThomaz Chaves de Andrade Oliveira
dc.creatorLuis Marcelo Tavares de Carvalho
dc.creatorLuciano Teixeira de Oliveira
dc.creatorAdriana Zanella Martinhago
dc.creatorFausto Weimar Acerbi Júnior
dc.creatorMariana Peres de Lima
dc.date2010
dc.date.accessioned2026-07-07T02:40:33Z
dc.descriptionMulti-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.
dc.formatapplication/pdf
dc.identifier0104-7760
dc.identifierhttps://www.redalyc.org/articulo.oa?id=74421665002
dc.identifier.urihttp://hdl.handle.net/123456789/421168
dc.languageen
dc.publisherUniversidade Federal de Lavras
dc.relationhttp://www.redalyc.org/revista.oa?id=744
dc.rightsCERNE
dc.sourceCERNE (Brasil) Num.2 Vol.16
dc.subjectAgrociencias
dc.titleMapping deciduous forests by using time series of filtered MODIS NDVI and neural networks
dc.typeartículo científico

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