| FillPermanentGaps {greenbrown} | R Documentation |
Satellite time series are often affected by permanent gaps like missing observations during winter periods. Often time series methods can not deal with missing observations and require gap-free data. This function fills winter gaps with a constant fill value or according to the approach described in Beck et al. (2006).
FillPermanentGaps(Yt, min.gapfrac = 0.2, lower = TRUE, fillval = NA,
fun = min, ...)
Yt |
univariate time series of class |
min.gapfrac |
How often has an observation to be NA to be considered as a permanent gap? (fraction of time series length) Example: If the month January is 5 times NA in a 10 year time series (= 0.5), then the month January is considered as permanent gap if min.gapfrac = 0.4. |
lower |
fill lower gaps (TRUE), upper gaps (FALSE) or lower and upper gaps (NULL) |
fillval |
constant fill values for gaps. If NA the fill value will be estimated from the data using fun. |
fun |
function to be used to compute fill values. By default, minimum. |
... |
further arguments (currently not used) |
The function returns a time series with filled permanent gaps.
Matthias Forkel <matthias.forkel@geo.tuwien.ac.at> [aut, cre]
# load NDVI data data(ndvi) plot(ndvi) # sample some winter months to be set as gaps winter <- (1:length(ndvi))[cycle(ndvi) == 1 | cycle(ndvi) == 2 | cycle(ndvi) == 12] gaps <- sample(winter, length(winter)*0.3) # introduce systematic winter gaps in time series ndvi2 <- ndvi ndvi2[gaps] <- NA plot(ndvi2) IsPermanentGap(ndvi2) # fill winter with observed minimum fill <- FillPermanentGaps(ndvi2) plot(fill, col="red"); lines(ndvi) # fill winter with mean fill <- FillPermanentGaps(ndvi2, fun=mean) plot(fill, col="red"); lines(ndvi) # fill winter with 0 fill <- FillPermanentGaps(ndvi2, fillval=0) plot(fill, col="red"); lines(ndvi)