@InProceedings{ServiánOliv:2017:AnEsRe,
               author = "Servi{\'a}n, Fernanda Carneiro Rola and Oliveira, Julio Cesar 
                         de",
                title = "An{\'a}lise espa{\c{c}}o\‐temporal para 
                         redu{\c{c}}{\~a}o de ru{\'{\i}}dos em s{\'e}ries temporais de 
                         NDVI",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "5119--5123",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "MODerate resolution Imaging Spectroradiometer (MODIS) data are 
                         largely used in multitemporal analysis of various Earth-related 
                         phenomena, such as mapping patterns of vegetation phenology and 
                         detecting land use/land cover change. NDVI time series are 
                         composite mosaics of the best quality pixels over a period of 
                         sixteen days. However, it is common to find low quality pixels in 
                         the composition that affect the time series analysis due to errors 
                         in the atmosphere conditions and in data acquisition. We present a 
                         filtering methodology that considers the pixel position (location 
                         in space) and time (position in the temporal data series) to 
                         define a new value for the low quality pixel. This methodology 
                         estimates the value of the point of interest, based first on a 
                         linear regression excluding pixels with low coefficient of 
                         determination R2 and second on excluding outliers according to a 
                         boxplot analysis. Thus, from the remaining group of pixels, a 
                         Smooth Spline is generated in order to reconstruct the time 
                         series.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59433",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSM4AQ",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSM4AQ",
           targetfile = "59433.pdf",
                 type = "An{\'a}lise de s{\'e}ries temporais de imagens de 
                         sat{\'e}lite",
        urlaccessdate = "27 abr. 2024"
}