@InProceedings{VictoriaOlivGreg:2009:AnHaSé,
               author = "Victoria, Daniel de Castro and Oliveira, Aryeverton Fortes de and 
                         Grego, C{\'e}lia Regina",
          affiliation = "{Embrapa Monitoramento por Sat{\'e}lite / SP} and {Embrapa 
                         Monitoramento por Sat{\'e}lite / SP} and {Embrapa Monitoramento 
                         por Sat{\'e}lite / SP}",
                title = "An{\'a}lise harm{\^o}nica de s{\'e}ries temporais de imagens 
                         NDVI/MODIS para discrimina{\c{c}}{\~a}o de coberturas vegetais",
            booktitle = "Anais...",
                 year = "2009",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "1589--1596",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 14. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "MODIS, NDVI, time series, Fourier, series temporais.",
             abstract = "The high temporal resolution information obtained with the 
                         Moderate Resolution Imaging Spectroradiometer (MODIS) is of great 
                         value when it comes to monitoring changes in Earth surface. Since 
                         several land cover types presents a distinguished temporal pattern 
                         in its spectral response, MODIS high temporal resolution can be 
                         used to identify such covers. This is specially true when 
                         observing the Normalized Difference Vegetation Index (NDVI) of 
                         agricultural land covers. Fourier transformations decomposes any 
                         signal represented in time to a frequency domain. Applying this 
                         transformation in a NDVI time-series results in parameters that 
                         describe how this signal behaves along several time frequencies 
                         (annual, semestral, etc). A strong annual signal indicates a land 
                         cover with a long growth cycle, such as sugar-cane (1 to 1.5 
                         years) while stronger semestral signals are typical of other 
                         agricultural crops (soy, corn, beans). Also, observing the annual 
                         and semestral signals, its possible to distinguish agricultural 
                         areas with one or two crop cycles per year. A computational 
                         routine, independent of any commercial remote sensing package, has 
                         been developed in order to calculate Fourier amplitude and phase 
                         images of a NDVI time series. Applying such analysis over a 
                         diverse agricultural region in S{\~a}o Paulo state (Ribeir{\~a}o 
                         Preto) indicates that long and short growth period crops are 
                         easily distinguished (sugar-cane and annual crops such as soy, 
                         corn, beans). Silvicultural areas are also easily distinguished 
                         due to their long growth period (5 years) however, these are 
                         confused with natural forests. A longer time series analysis could 
                         easily solve this.",
  conference-location = "Natal",
      conference-year = "25-30 abr. 2009",
                 isbn = "978-85-17-00044-7",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "dpi.inpe.br/sbsr@80/2008/11.13.16.38",
                  url = "http://urlib.net/ibi/dpi.inpe.br/sbsr@80/2008/11.13.16.38",
           targetfile = "1589-1596.pdf",
                 type = "An{\'a}lise e Aplica{\c{c}}{\~a}o de Imagens Multitemporais",
        urlaccessdate = "15 jun. 2024"
}