Description Usage Arguments Value Author(s) References See Also Examples
This function fills gaps and smoothes a time series by fitting for each year a double logisitic function. Two definitions for the shape of the double logistic function are available: 'Elmore' fits a function according to Elmore et al. (2012) and 'Beck' fits a according to Beck et al. (2006). If the time series has no Seasonality, double logistic fitting will not be performed but smoothing and interpolation will be done according to the selected backup algorithm.
1 2 3 4 5 | TSGFdoublelog(Yt, interpolate = FALSE,
method = c("Elmore",
"Beck"), backup = NULL,
check.seasonality = 1:3,
...)
|
Yt |
univariate time series of class |
interpolate |
Should the smoothed and gap filled time series be interpolated to daily values by using the logistic fit function? |
method |
Which kind of double logistic curve should be used? 'Elmore' (Elmore et al. 2012) or 'Beck' (Beck et al. 2006). |
backup |
Which |
check.seasonality |
Which methods in |
... |
further arguments (currently not used) |
The function returns a gap-filled and smoothed version of the time series.
Matthias Forkel <matthias.forkel@tu-dresden.de> [aut, cre]
Beck, P.S.A., C. Atzberger, K.A. Hodga, B. Johansen, A. Skidmore (2006): Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI. - Remote Sensing of Environment 100:321-334.
Elmore, A.J., S.M. Guinn, B.J. Minsley and A.D. Richardson (2012): Landscape controls on the timing of spring, autumn, and growing season length in mid-Atlantic forests. - Global Change Biology 18, 656-674.
FitDoubleLogBeck, FitDoubleLogElmore, TsPP, Phenology
1 2 3 4 5 6 7 8 9 | # # introduce random gaps
# gaps <- ndvi
# gaps[runif(100, 1, length(ndvi))] <- NA
# plot(gaps)
#
# # do smoothing and gap filling
# tsgf <- TSGFdoublelog(gaps, method="Elmore")
# plot(gaps)
# lines(tsgf, col="red")
|
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