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

CERNE

Thomaz Chaves De Andrade Oliveira²  Luis Marcelo Tavares De Carvalho¹  Luciano Teixeira De Oliveira¹  Adriana Zanella Martinhago¹  Fausto Weimar Acerbi Júnior¹  Mariana Peres De Lima¹ 

[1] ,Universidade Estadual de Campinas/UNICAMP Faculdade de Engenharia Elétrica e de Computação Campinas SP ,Brasil

关键词: Remote sensing;    signal processing;    time series;    wavelets analysis;    Fourier;    Sensoriamento remoto;    processamento de sinais;    análise wavelets;    Fourier;   

DOI  :  10.1590/S0104-77602010000200002

来源: SciELO

【 摘 要 】

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.

【 授权许可】

CC BY   
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