NAME
v.surf.tps - Performs thin plate spline interpolation with regularization and covariables.
KEYWORDS
vector,
surface,
interpolation,
TPS
SYNOPSIS
v.surf.tps
v.surf.tps --help
v.surf.tps [-c] input=name [layer=string] [column=name] [smooth=float] [overlap=float] [min=float] [covars=name[,name,...]] [thin=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 vector point map
- Or data source for direct OGR access
- layer=string
- Layer number or name
- Vector features can have category values in different layers. This number determines which layer to use. When used with direct OGR access this is the layer name.
- Default: 1
- column=name
- Name of the attribute column with values to be used for interpolation
- If not given, z-coordinates are used.
- smooth=float
- Smoothing factor
- Default: 0
- overlap=float
- Overlap factor <= 1
- A larger value increase the tile overlap
- Default: 0.1
- min=float
- Minimum number of points to use for TPS interpolation
- Default: 20
- covars=name[,name,...]
- Name of input raster map(s) to use as covariables
- Name of input raster map(s)
- thin=float
- Point cloud thinning factor in number of cells of the current region
- Minimum distance between neighboring points for local TPS interpolation
- Default: 1.5
- output=name [required]
- Name for output raster map
- mask=name
- Raster map to use for masking
- Only cells that are not NULL and not zero are interpolated
- memory=integer
- Memory in MB
- Default: 300
v.surf.tps performs multivariate thin plate spline
interpolation with regularization. The
input is a 2D
or 3D vector
points map. Values to interpolate can be the z
values of 3D points or the values in a user-specified attribute column
in a 2D or 3D vector map. 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. 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. If the number of input points is smaller than
or equal to the minimum number of points, global TPS interpolation is
used. If the number of input points is larger than the minimum number
of points, tiled local TPS interpolation is used. 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. Values for the
overlap option must be between 0 and 1.
The module works best with evenly spaced sparse points. In case of
highly unevenly spaced points, e.g. remote sensing data with gaps due
to cloud cover, the thin option should be used in order to avoid
tiling artefacts, otherwise a high number of minimum points and a large
overlap value are required, slowing down the module.
The memory option controls only how much memory should be used
for the covariables and the intermediate output. The input points are
always completely loaded to memory.
The computational region setting for the following examples:
g.region -p rast=elev_state_500m
Interpolation of 30 year precipitation normals in the North Carlolina
sample dataset:
v.surf.tps input=precip_30ynormals_3d output=precip_30ynormals_3d \
column=annual min=140
v.surf.tps input=precip_30ynormals_3d output=precip_30ynormals_3d \
column=annual min=140 covars=elev_state_500m
v.surf.tps input=precip_30ynormals_3d output=precip_30ynormals_3d \
column=annual min=140 covars=elev_state_500m smooth=0.1
v.surf.tps input=precip_30ynormals_3d output=precip_30ynormals_3d \
column=annual min=20 covars=elev_state_500m smooth=0.1 \
overlap=0.1
Precipitation computed based on annual normals and
elevation as a covariable
- 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.rst,
v.surf.rst,
v.surf.idw
Markus Metz
SOURCE CODE
Available at:
v.surf.tps source code
(history)
Latest change: Monday Jun 24 13:07:24 2024 in commit: 71dbc88614ccda943c73db28f3531855610f6146
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GRASS Development Team,
GRASS GIS 8.4.1dev Reference Manual