Decompose {greenbrown} R Documentation

## Simple decomposition of time series

### 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 <matthias.forkel@geo.tuwien.ac.at> [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.

`GetTsStatisticsRaster`

### Examples

```# load a time series of Normalized Difference Vegetation Index
data(ndvi)
plot(ndvi)

# decompose this time series
ndvi.dc <- Decompose(ndvi)
plot(ndvi.dc)

ndvi.dc2 <- Decompose(ndvi, breaks=2, mosum.pval=1)
plot(ndvi.dc2)

```

[Package greenbrown version 2.4.3 Index]