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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