Combines kriging and spline interpolation to speed up the kriging with minimal loss in precision, whilst reducing generation of artifacts. Spline interpolation is implemented via the SAGA GIS function "Multilevel B-Spline Interpolation" (SAGA GIS needs to be installed separately).
formula that defines the dependent variable as a linear model of independent variables; usually in the form z~1
locations
object of class SpatialPoints; sampling locations
newdata
object of class SpatialPixels*; spatial domain of interest
newlocs
object of class SpatialPoints*; prediction locations produced using the resample.grid function (if missing it will be generated using the resample.grid function)
model
variogram model of dependent variable (or its residuals); see gstat::krige
te
numeric; a vector in the form c(xmin,ymin,xmax,ymax); sets bounding box of the kriging predictions
file.name
character; optional output file name pattern (without any file extension)
silent
logical; specifies whether to print out the progress
t_cellsize
numeric; target cell size (output grid)
optN
integer; optimal number of prediction locations per sampling location e.g. 1 sampling location is used to predict values for 20 new pixels
quant.nndist
numeric; threshold probability to determine the search radius (sigma)
nmax
integer; the number of nearest observations that
should be used for kriging
predictOnly
logical; specifies whether to generate only predictions (var1.pred column)
resample
logical; specifies whether to down or upscale SAGA GIS grids to match the grid system of newdata
saga.env
list; path to location of the SAGA binaries (extracted using rsaga.env())
saga.lib
character; names of the SAGA libraries used
saga.module
integer; corresponding module numbers
...
other optional arguments that can be passed to function gstat::krige
Value
Returns an object of class "SpatialGridDataFrame", or an output file name.
Note
This function adjusts grid density (prediction locations) in reference to the actual local sampling intensity. High resolution grids are created where sampling density is higher and vice versa (Hengl, 2006). Low resolution grids (due to sparse data) are then downscaled to the target resolution using spline interpolation. This allows for speeding up the kriging with minimal loss in precision, whilst reducing generation of artifacts.
Spline interpolation is implemented via the SAGA GIS v2.1 function "Multilevel B-Spline Interpolation" using the default settings. This function is especially suitable for producing predictions for large grids where the sampling locations show high spatial clustering. It is NOT intended for predicting using point samples collected using sampling designs with constant spatial sampling intensity e.g. point samples collected using simple random sampling or grid sampling.