stef_global_spatial_normaliser: Global spatial normalisation

In hamun001/stFeatures: Space-time change analysis of earth observation data for forest cover change monitoring and detection

Description

Performs global spatial normalisation of satellite data to reduce seasonal variations and intersensor dfferences. For local spatial normalisation, see stef_local_spatial_normaliser

Usage

stef_global_spatial_normaliser(inraster, isStack = T, xpercentile = 0.95,
  output_filename = NULL)

Arguments

inraster	Input raster stack or single raster.
isStack	Logical. Set to TRUE if the input raster is a raster stack.
xpercentile	The upper percentile to use for normalisation. Default is 0.95, which represent 95th percentile.
output_filename	The name of the output normalised raster or raster stack. The name must contain the file format (e.g. raster_normalised.tif)

Details

To be completed.

Author(s)

Eliakim Hamunyela

References

1. Hamunyela, E., Verbesselt, J., Herold, M. (2016) Using spatial context to improve early detection of deforestationfrom Landsat time series. Remote Sensing of Environment,172, 126<c3><a2><e2><82><ac><e2><80><9c>138. http://dx.doi.org/10.1016/j.rse.2015.11.006

2. Hamunyela, E., Verbesselt, J.,de Bruin, S., Herold, M. (2016). Monitoring Deforestation at Sub-Annual Scales as Extreme Events in Landsat Data Cubes. Remote Sensing, 8(8), 651. http://dx.doi.org/10.3390/rs8080651

3. Hamunyela, E., Reiche, J., Verbesselt, J., & Herold, M. (2017). Using space-time features to improve detection of forest disturbances from Landsat time series. Remote Sensing, 9(6), 515. http://dx.doi:10.3390/rs9060515

Examples

## Not run: 

#create a raster stack
ra <- raster(ncols=360, nrows=180)
ra[] <- rnorm(ncell(ra))
for (i in 1: 86){
ro <- raster(ncols=360, nrows=180)
ro[] <- rnorm(ncell(ro))
ra <- stack(ra, ro)
}

# example:
stef_global_spatial_normaliser(ra, isStack = T, xpercentile = 0.95,output_filename =""ra_global_normalised.tif)

## End(Not run)

hamun001/stFeatures documentation built on May 21, 2019, 2:22 p.m.