TrendSeasonalAdjusted: Trend estimation based on seasonal-adjusted time series
In greenbrown: Land Surface Phenology and Trend Analysis

Description Usage Arguments Value Author(s) References See Also Examples

The function computes and substracts the seasonal cycle from a time series. Then a trend is estimated on the seasonal-adjusted time series. The function can be applied to gridded (raster) data using the function TrendRaster. A detailed description of this method can be found in Forkel et al. (2013).

TrendSeasonalAdjusted(Yt, 
    mosum.pval = 0.05, 
    h = 0.15, breaks = NULL, 
    funSeasonalCycle = MeanSeasonalCycle)

`Yt`	univariate time series of class `ts`
`mosum.pval`	Maximum p-value for the OLS-MOSUM test in order to search for breakpoints. If p = 0.05, breakpoints will be only searched in the time series trend component if the OLS-MOSUM test indicates a significant structural change in the time series. If p = 1 breakpoints will be always searched regardless if there is a significant structural change in the time series or not. See `sctest` for details.
`h`	minimal segment size either given as fraction relative to the sample size or as an integer giving the minimal number of observations in each segment. See `breakpoints` for details.
`breaks`	maximal number of `breaks` to be calculated (integer number). By default the maximal number allowed by `h` is used. See `breakpoints` for details.
`funSeasonalCycle`	a function to estimate the seasonal cycle of the time series. A own function can be defined to estimate the seasonal cycle which has to return the seasonal cycle as a time series of class "ts". Currently two approaches are part of this package: `MeanSeasonalCycle` is the default which computes the average seasonal cycle from all years. `LmSeasonalCycle` computes the seasonal cycle based on a linear model.

The function returns a list of class "Trend".

Matthias Forkel <matthias.forkel@tu-dresden.de> [aut, cre]

Forkel, M., N. Carvalhais, J. Verbesselt, M. Mahecha, C. Neigh and M. Reichstein (2013): Trend Change Detection in NDVI Time Series: Effects of Inter-Annual Variability and Methodology. - Remote Sensing 5.

Trend, TrendRaster, MeanSeasonalCycle, LmSeasonalCycle

# calculate trend on time series with removed mean seasonal cycle
MACtrend <- TrendSeasonalAdjusted(ndvi, funSeasonalCycle=MeanSeasonalCycle)
MACtrend
plot(MACtrend)

# plot the seasonal-adjusted time series
plot(ndvi)
lines(MACtrend$adjusted, col="orange")

# calculate trend on time series with removed mean seasonal cycle 
# but with limited number of breakpoints
MACtrend <- TrendSeasonalAdjusted(ndvi, breaks=1, funSeasonalCycle=MeanSeasonalCycle)
MACtrend
plot(MACtrend)