Decompose: Simple decomposition of time series

In greenbrown: Land Surface Phenology and Trend Analysis

Description

This function decomposes time series in different components using a simple step-wise approach.

Usage

Decompose(Yt, breaks = 0, 
    mosum.pval = 0.05)

Arguments

Yt	univariate time series of class ts
breaks	maximal number of breaks in the trend component to be calculated (integer number).
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.

Details

The decomposition of the time series is based on a simple step-wise approach:

• The mean of the NDVI time series is calculated.

• In the second step, monthly values are aggregated per year by using the average value and the trend is calculated based on annual aggregated values using TrendAAT.

• The mean of the time series and the derived trend component from step (2) are subtracted from the annual values to derive the trend-removed and mean-centred annual values (annual anomalies). If the trend slope is not significant (p > 0.05), only the mean is subtracted.

• In the next step, the mean, the trend component and the annual anomalies are subtracted from the original time series to calculate a detrended, mean-centered and for annual anomalies adjusted time series. From this time series the seasonal cycle is estimated as the mean seasonal cycle.

• In the last step, the short-term anomalies are computed. For this, the mean, the trend component, the annual anomalies and the mean seasonal cycle are subtracted from the original time series.

Value

The function returns a multi-variate object of class ts including the time series components.

Author(s)

Matthias Forkel <[e-mail]> [aut, cre]

References

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.

See Also

GetTsStatisticsRaster

Examples

# decompose a time series
ndvi.dc <- Decompose(ndvi)
plot(ndvi.dc)
	
ndvi.dc2 <- Decompose(ndvi, breaks=2, mosum.pval=1)
plot(ndvi.dc2)

greenbrown documentation built on Dec. 18, 2020, 3:02 p.m.