TrendClassification: Classify a raster in greening and browning trends
In greenbrown: Land Surface Phenology and Trend Analysis

Description Usage Arguments Details Value Author(s) See Also

This function classifies a RasterBrick with trend estimates as computed with TrendRaster into positive, negative and no trend per each time series segment.

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TrendClassification(r, 
    min.length = 0, max.pval = 0.05, 
    ...)

`r`	multi-layer raster object of class `brick` as computed with `TrendRaster`
`min.length`	Minimum duration of a trend in time steps of the input raster (see Details).
`max.pval`	Maximum p-value to classify a trend as being significant.
`...`	additional arguments as for `writeRaster`

This function expects a RasterBrick as created with TrendRaster as input and classifies for each pixel and each time series segment if a trend is significant positive, significant negative or not significant (no trend). Per default a p-value of 0.05 is used to classify trends as significant. Additionally, the minimum duration of a trend can be specified with min.length: For example, if only time series segments longer than 10 years should be considered as trend, set min.length=11 in case of annual data. In case of monthly data set it to 132 (12 observations per year * 11 years). The function CompareClassification can be used to compare classified trends from different methods or data sets.

The function returns a RasterLayer in case of one time series segment or a RasterBrick in case of multiple time series segments. Pixels with a significant positive trend have the value 1; pixels with significant negative trends -1 and non-significant trends 0.

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

TrendRaster, CompareClassification

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