| TrendSeasonalAdjusted {greenbrown} | R Documentation |
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 |
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 |
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 |
breaks |
maximal number of breaks to be calculated (integer number). By default the maximal number allowed by h is used. See |
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:
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The function returns a list of class "Trend".
Matthias Forkel <matthias.forkel@geo.tuwien.ac.at> [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, SSASeasonalCycle
# load a time series of NDVI (normalized difference vegetation index) data(ndvi) plot(ndvi) # 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$adjusted, col="orange") # calculate trend on time series with removed mean seasonal cycle # but with limited number of breakpoints MACtrend <- TrendSeasonalAdjusted(ndvi, breaks=1, funSeasonalCycle=MeanSeasonalCycle) MACtrend plot(MACtrend) ## calculate trend on time series with removed seasonal cycle but seasonal cycle computed based ## on singular spectrum analysis #SSAtrend <- TrendSeasonalAdjusted(ndvi, funSeasonalCycle=SSASeasonalCycle) #SSAtrend #plot(SSAtrend) #lines(SSAtrend$adjusted, col="orange")