v.surf.rst.cv - GRASS GIS manual

Note: This document is for an older version of GRASS GIS that has been discontinued. You should upgrade, and read the current manual page.

NAME

v.surf.rst.cv - Performs cross-validation proceedure to optimize the parameterization of v.surf.rst tension and smoothing paramters.

KEYWORDS

raster, surface, interpolation, cross-validation, rst, json

SYNOPSIS

v.surf.rst.cv

v.surf.rst.cv --help

v.surf.rst.cv point_cloud=name [mask=name] [tension=integer[,integer,...]] [smooth=float[,float,...]] [layer=string] [zcolumn=name] [where=sql_query] [segmax=integer] [dmin=float] [dmax=float] [zscale=float] [theta=float] [scalex=float] [cv_prefix=string] [output_file=name] [format=string] [nprocs=integer] [--overwrite] [--help] [--verbose] [--quiet] [--ui]

Flags:

--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:

point_cloud=name [required]: Name of the input point cloud vector map; Name of the input point cloud vector map
mask=name: Mask raster map; Name of the mask raster map
tension=integer[,integer,...]: Tension parameter for cross-validation; Default: 10, 20, 40, 60, 80, 100
smooth=float[,float,...]: Smoothing parameter for cross-validation; Default: 0.01, 0.1, 0.5, 1.0, 5.0, 10.0
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
zcolumn=name: Name of the attribute column with values to be used for approximation; If not given and input is 2D vector map then category values are used. If input is 3D vector map then z-coordinates are used.
where=sql_query: WHERE conditions of SQL statement without 'where' keyword; Example: elevation < 500 and elevation >= 1
segmax=integer: Maximum number of points in segment; Default: 40
dmin=float: Minimum distance between points; Default: 0.0
dmax=float: Maximum distance between points on isoline (to insert additional points); Default: 0.0
zscale=float: Conversion factor for values used for approximation; Default: 1.0
theta=float: Anisotropy angle (in degrees counterclockwise from East)
scalex=float: Anisotropy scaling factor
cv_prefix=string: Prefix to use for cross-validation output maps; Prefix to use for cross-validation output cross-validation errors vector point map. Value must be set to save the cross-validation errors to a vector maps.
output_file=name: Output file; Output file for the results (default: None) json or csv
format=string: Output format; Output format for the results; Options: json, text
nprocs=integer: Number of threads for parallel computing; Default: 1

Description
EXAMPLES
REFERENCES
SEE ALSO
AUTHORS

Description

v.surf.rst.cv - Cross-validation of regularized spline with tension (RST) surface model. Predictive error of surface approximation for given parameters are computed and a cross-validation procedure is then performed using the data given in the vector map input. The estimated predictive errors are stored in the optionally saved vector point map cvdev. For larger data sets, CV should be applied to a representative subset of the data. The cross-validation procedure works well only for well-sampled phenomena and when minimizing the predictive error is the goal. The parameters found by minimizing the predictive (CV) error may not not be the best for for poorly sampled phenomena (result could be strongly smoothed with lost details and fluctuations) or when significant noise is present that needs to be smoothed out.

EXAMPLES

        v.surf.rst.cv point_cloud=lidar

REFERENCES

Mitasova, H., Mitas, L. and Harmon, R.S., 2005, Simultaneous spline approximation and topographic analysis for lidar elevation data in open source GIS, IEEE GRSL 2 (4), 375- 379.
Hofierka, J., 2005, Interpolation of Radioactivity Data Using Regularized Spline with Tension. Applied GIS, Vol. 1, No. 2, pp. 16-01 to 16-13. DOI: 10.2104/ag050016
Hofierka J., Parajka J., Mitasova H., Mitas L., 2002, Multivariate Interpolation of Precipitation Using Regularized Spline with Tension. Transactions in GIS 6(2), pp. 135-150.
H. Mitasova, L. Mitas, B.M. Brown, D.P. Gerdes, I. Kosinovsky, 1995, Modeling spatially and temporally distributed phenomena: New methods and tools for GRASS GIS. International Journal of GIS, 9 (4), special issue on Integrating GIS and Environmental modeling, 433-446.
Mitasova, H. and Mitas, L., 1993: Interpolation by Regularized Spline with Tension: I. Theory and Implementation, Mathematical Geology ,25, 641-655.
Mitasova, H. and Hofierka, J., 1993: Interpolation by Regularized Spline with Tension: II. Application to Terrain Modeling and Surface Geometry Analysis, Mathematical Geology 25, 657-667.
Mitas, L., and Mitasova H., 1988, General variational approach to the approximation problem, Computers and Mathematics with Applications, v.16, p. 983-992.
Neteler, M. and Mitasova, H., 2008, Open Source GIS: A GRASS GIS Approach, 3rd Edition, Springer, New York, 406 pages.
Talmi, A. and Gilat, G., 1977 : Method for Smooth Approximation of Data, Journal of Computational Physics, 23, p.93-123.
Wahba, G., 1990, : Spline Models for Observational Data, CNMS-NSF Regional Conference series in applied mathematics, 59, SIAM, Philadelphia, Pennsylvania.

AUTHORS

Corey T. White NCSU GeoForAll Lab

SOURCE CODE

Available at: v.surf.rst.cv source code (history)

Latest change: Saturday Apr 05 13:02:33 2025 in commit: cafc53f49233dd09433ffc7ae8597416b1f0a920