NAME
r.resamp.tps - Performs thin plate spline interpolation with regularization and covariables.
KEYWORDS
raster,
surface,
interpolation,
TPS
SYNOPSIS
r.resamp.tps
r.resamp.tps --help
r.resamp.tps [-c] input=name [smooth=float] [overlap=float] [min=float] [max=float] [radius=integer] [icovars=name[,name,...]] [ocovars=name[,name,...]] [lmfilter=float] [epfilter=float] output=name [mask=name] [memory=integer] [--overwrite] [--help] [--verbose] [--quiet] [--ui]
Flags:
- -c
- Input points are dense clusters separated by empty areas
- --overwrite
- Allow output files to overwrite existing files
- --help
- Print usage summary
- --verbose
- Verbose module output
- --quiet
- Quiet module output
- --ui
- Force launching GUI dialog
Parameters:
- input=name [required]
- Name of input raster map
- smooth=float
- Smoothing factor
- Default: 0
- overlap=float
- Overlap factor <= 1
- A larger value increase the tile overlap
- Default: 0.2
- min=float
- Minimum number of points to use for TPS interpolation
- Default: 100
- max=float
- Maximum number of points to use for TPS interpolation
- radius=integer
- Radius for moving window interpolation
- The unit for radius is cells. If radius is > 0, moving window interpolation will be used instead of nearest neighbor search
- Default: 0
- icovars=name[,name,...]
- Name of input raster map(s) to use as covariables matching the input raster
- Name of input raster map(s)
- ocovars=name[,name,...]
- Name of input raster map(s) to use as covariables matching the current region
- Name of input raster map(s)
- lmfilter=float
- Threshold to avoid interpolation outliers when using covariables
- Disabled when set to zero, must be within [0, 1], larger values will cause more outliers
- Default: 0
- epfilter=float
- Threshold to avoid extrapolation when using covariables
- Disabled when set to zero, must be > 0, smaller values will cause more outliers
- Default: 0
- output=name [required]
- Name for output raster map
- mask=name
- Raster map to use for masking
- Only cells where the mask map is not NULL and not zero are interpolated
- memory=integer
- Memory in MB
- Default: 300
r.resamp.tps performs multivariate thin plate spline
interpolation with regularization. The
input is a raster
map to be resampled to a higher resolution or where NULL cells need to
be interpolated. Output is a raster map. Optionally, several
raster maps can be specified to be used as covariables which will
improve results in areas with few points. Raster maps to be used as
covariables need to be provided separately matching the grid geometry
of the
input raster map with the
icovars option and
matching the grid geometry of the
output raster map with the
ocovars option. The module can be regarded as a combination of
a multiple regression and spline interpolation.
The min options specifies the minimum number of points to be
used for interpolation. r.resamp.tps always performs tiled
local TPS interpolation. Tile sizes are variable and dependent on the
extents of the min nearest neighbors when a new tile is generated.
The smooth option can be used to reduce the influence of the
splines and increase the influence of the covariables. Without
covariables, the resulting surface will be smoother. With covariables
and a large smooting value, the resulting surface will be mainly
determined by the multiple regression component.
The overlap option controls how much tiles are overlapping when
the min option is smaller than the numer of input points.
Tiling artefacts occur with low values for the min option and the
overlap option. Increasing both options will reduce tiling
artefacts but processing will take more time.
The module works best with evenly spaced points. In case of
highly unevenly spaced points, e.g. remote sensing data with gaps due
to cloud cover, the module will take a long time to finish. For data
with large gaps, it is recommended to use first a different
interpolation method and then optionally use r.resamp.tps with
the smooth option to identify outliers (difference between the
output of r.resamp.tps and the data interpolated with a
different method).
When using covariables, outliers might be created if the values of the
covariables of the current output cell are far outside the observed
range of covariables, or if the linear regression component of the TPS
interpolation for the covariables does not provide a good solution. Two
methods are provided to avoid outliers caused by covariables. The first
method (lmfilter) will discard covariables if R squared is
larger than the value provided with the lmfilter option. The
second method (epfilter) will discard covariables if the
current value of a covariable is outside the observed range of
covariables by a factor of (epfilter). The epfilter
option typically results in more interpolations using the supplied
covariables than the lmfilter option when both are adjusted to
reject the same outliers.
The memory option controls only how much memory should be used
for the covariables and the intermediate output. The data needed for
TPS interpolation are always completely loaded to memory.
- Hutchinson MF, 1995, Interpolating mean rainfall using thin plate
smoothing splines. International Journal of Geographical Information
Systems, 9(4), pp. 385-403
- Wahba G, 1990, Spline models for observational data. In CBMS-NSF
Regional Conference Series in Applied Mathematics. Philadelpia:
Society for Industrial and Applied Mathematics
v.surf.tps,
v.surf.rst,
v.surf.bspline,
v.surf.idw
Markus Metz
SOURCE CODE
Available at:
r.resamp.tps source code
(history)
Latest change: Monday Jun 24 13:07:24 2024 in commit: 71dbc88614ccda943c73db28f3531855610f6146
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