@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 = "15 jun. 2024"
}