@InProceedings{MoreiraFontKupl:2017:OnCoAp,
author = "Moreira, Andreise and Fontana, Denise Cybis and Kuplich, Tatiana
Mora",
affiliation = "{} and {} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Ondaleta Coer{\^e}ncia aplicada em s{\'e}rie temporal NDVI/MODIS
para avalia{\c{c}}{\~a}o de fitofisionomias campestres",
booktitle = "Anais...",
year = "2017",
editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz
Eduardo Oliveira e Cruz de",
pages = "1878--1885",
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 = "In this paper, the technique of Wavelet Coherence was used to
identify patterns in time series of NDVI/MODIS for different
grassland typologies and the relationship with meteorological
variables, such as rainfall and air temperature. The life cycle of
plant species is sensitive to variations in weather conditions
and, for a climate change scenario, is important for studying the
variability of climate factors in local, regional and global
scales. The study covered 10 grassland typologies in Rio Grande do
Sul (RS) state, Southern Brazil and a NDVI/MODIS time series with
a spatial resolution of 250 m, from 2000 to 2014. The Wavelet
Coherence was applied in the NDVI/MODIS data and rainfall and air
temperature to check for consistency. Two well-marked cycles were
seen: an annual cycle, ranging between 1 and 23 observations and
an interannual cycle that varies between 92 and 184 observations.
These cycles were defined and represented by the y axis in the
power spectrum and were distributed throughout the series
(2000-2014) on the x-axis. In the humid subtropical climate
conditions prevalent in the RS, the application of Wavelet
Coherence was effective in showing where and when changes in the
phenological pattern of grassland vegetation occurred, as well as
to point out the intensity (correlation) between NDVI data and
variability of weather conditions. In the annual cycle the
association between the NDVI and air temperature (high frequency)
was dominant. For the interannual cycles, events such as El Niņo
and La Niņa, which impact strongly the precipitation in RS, caused
the high correlation observed between NDVI data and rainfall.",
conference-location = "Santos",
conference-year = "28-31 maio 2017",
isbn = "978-85-17-00088-1",
label = "60155",
language = "pt",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP6W34M/3PSLPGF",
url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSLPGF",
targetfile = "60155.pdf",
type = "Paisagens naturais",
urlaccessdate = "27 abr. 2024"
}