Provided by: grass-doc_8.4.0-1_all 
NAME
r.proj - Re-projects a raster map from given project to the current project.
KEYWORDS
raster, projection, transformation, import
SYNOPSIS
r.proj
r.proj --help
r.proj [-lnpg] project=name [mapset=name] [input=name] [dbase=path] [output=name]
[method=string] [memory=memory in MB] [resolution=float] [pipeline=string] [--overwrite]
[--help] [--verbose] [--quiet] [--ui]
Flags:
-l
List raster maps in input mapset and exit
-n
Do not perform region cropping optimization. See Notes if working with a global latitude-longitude
projection
-p
Print input map’s bounds in the current projection and exit
-g
Print input map’s bounds in the current projection and exit (shell style)
--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:
project=name [required]
Project (location) containing input raster map
Project name (not path to project)
mapset=name
Mapset containing input raster map
Default: name of current mapset
input=name
Name of input raster map to re-project
dbase=path
Path to GRASS database of input project
Default: path to the current GRASS GIS database
output=name
Name for output raster map (default: same as ’input’)
method=string
Interpolation method to use
Options: nearest, bilinear, bicubic, lanczos, bilinear_f, bicubic_f, lanczos_f
Default: nearest
nearest: nearest neighbor
bilinear: bilinear interpolation
bicubic: bicubic interpolation
lanczos: lanczos filter
bilinear_f: bilinear interpolation with fallback
bicubic_f: bicubic interpolation with fallback
lanczos_f: lanczos filter with fallback
memory=memory in MB
Maximum memory to be used (in MB)
Cache size for raster rows
Default: 300
resolution=float
Resolution of output raster map
pipeline=string
PROJ pipeline for coordinate transformation
DESCRIPTION
r.proj is used to reproject a raster map from the coordinate reference system (CRS) of the input project
into a CRS of a specified project (previously called location). The CRS information is taken from the
current PROJ_INFO files, as set and viewed with g.proj.
Introduction
Map projections
Map projections are a method of representing information from a curved surface (usually a spheroid) in
two dimensions, typically to allow indexing through cartesian coordinates. There are a wide variety of
projections, with common ones divided into a number of classes, including cylindrical and
pseudo-cylindrical, conic and pseudo-conic, and azimuthal methods, each of which may be conformal,
equal-area, or neither.
The particular projection chosen depends on the purpose of the project, and the size, shape and location
of the area of interest. For example, normal cylindrical projections are good for maps which are of
greater extent east-west than north-south and in equatorial regions, while conic projections are better
in mid-latitudes; transverse cylindrical projections are used for maps which are of greater extent
north-south than east-west; azimuthal projections are used for polar regions. Oblique versions of any of
these may also be used. Conformal projections preserve angular relationships, and better preserve
arc-length, while equal-area projections are more appropriate for statistical studies and work in which
the amount of material is important.
Projections are defined by precise mathematical relations, so the method of projecting coordinates from a
geographic reference frame (latitude-longitude) into a projected cartesian reference frame (eg metres) is
governed by these equations. Inverse projections can also be achieved. The public-domain Unix software
package PROJ [1] has been designed to perform these transformations, and the user’s manual contains a
detailed description of over 100 useful projections. This also includes a programmers library of the
projection methods to support other software development.
Thus, converting a vector map - in which objects are located with arbitrary spatial precision - from one
projection into another is usually accomplished by a simple two-step process: first the location of all
the points in the map are converted from the source through an inverse projection into
latitude-longitude, and then through a forward projection into the target. (Of course the procedure will
be one-step if either the source or target is in geographic coordinates.)
Converting a raster map, or image, between different projections, however, involves additional
considerations. A raster may be considered to represent a sampling of a process at a regular, ordered
set of locations. The set of locations that lie at the intersections of a cartesian grid in one
projection will not, in general, coincide with the sample points in another projection. Thus, the
conversion of raster maps involves an interpolation step in which the values of points at intermediate
locations relative to the source grid are estimated.
Reprojecting vector maps within the GRASS GIS
GIS data capture, import and transfer often requires a reprojection step, since the source or client will
frequently be in a different CRS to the working CRS.
In some cases it is convenient to do the conversion outside the package, prior to import or after export,
using software such as PROJ’s cs2cs [1]. This is an easy method for converting an ASCII file containing a
list of coordinate points, since there is no topology to be preserved and cs2cs can be used to process
simple lists using a one-line command. The m.proj module provides a handy front end to cs2cs.
Vector maps is generally more complex, as parts of the data stored in the files will describe topology,
and not just coordinates. In GRASS GIS the v.proj module is provided to reproject vector maps,
transferring topology and attributes as well as node coordinates. This program uses the CRS definition
and parameters which are stored in the PROJ_INFO and PROJ_UNITS files in the PERMANENT mapset directory
for every GRASS project.
Design of r.proj
As discussed briefly above, the fundamental step in reprojecting a raster is resampling the source grid
at locations corresponding to the intersections of a grid in the target CRS. The basic procedure for
accomplishing this, therefore, is as follows:
r.proj converts a map to a new CRS. It reads a map from a different project, reprojects it and writes it
out to the current project. The reprojected data is resampled with one of four different methods: nearest
neighbor, bilinear, bicubic interpolation or lanczos.
The method=nearest method, which performs a nearest neighbor assignment, is the fastest of the three
resampling methods. It is primarily used for categorical data such as a land use classification, since it
will not change the values of the data cells. The method=bilinear method determines the new value of the
cell based on a weighted distance average of the 4 surrounding cells in the input map. The method=bicubic
method determines the new value of the cell based on a weighted distance average of the 16 surrounding
cells in the input map. The method=lanczos method determines the new value of the cell based on a
weighted distance average of the 25 surrounding cells in the input map. Compared to bicubic, lanczos puts
a higher weight on cells close to the center and a lower weight on cells away from the center, resulting
in slightly better contrast.
The bilinear, bicubic and lanczos interpolation methods are most appropriate for continuous data and
cause some smoothing. The amount of smoothing decreases from bilinear to bicubic to lanczos. These
options should not be used with categorical data, since the cell values will be altered.
In the bilinear, bicubic and lanczos methods, if any of the surrounding cells used to interpolate the new
cell value are NULL, the resulting cell will be NULL, even if the nearest cell is not NULL. This will
cause some thinning along NULL borders, such as the coasts of land areas in a DEM. The bilinear_f,
bicubic_f and lanczos_f interpolation methods can be used if thinning along NULL edges is not desired.
These methods "fall back" to simpler interpolation methods along NULL borders. That is, from lanczos to
bicubic to bilinear to nearest.
If nearest neighbor assignment is used, the output map has the same raster format as the input map. If
any of the interpolations is used, the output map is written as floating point.
Note that, following normal GRASS conventions, the coverage and resolution of the resulting grid is set
by the current region settings, which may be adjusted using g.region. The target raster will be
relatively unbiased for all cases if its grid has a similar resolution to the source, so that the
resampling/interpolation step is only a local operation. If the resolution is changed significantly,
then the behaviour of the generalisation or refinement will depend on the model of the process being
represented. This will be very different for categorical versus numerical data. Note that three methods
for the local interpolation step are provided.
r.proj supports general datum transformations, making use of the PROJ co-ordinate system translation
library.
NOTES
If output is not specified it is set to be the same as input map name.
If mapset is not specified, its name is assumed to be the same as the current mapset’s name.
If dbase is not specified it is assumed to be the current database. The user only has to specify dbase if
the source project is stored in another separate GRASS database.
To avoid excessive time consumption when reprojecting a map the region and resolution of the target
project should be set appropriately beforehand.
A simple way to do this is to check the projected bounds of the input map in the current project’s CRS
using the -p flag. The -g flag reports the same thing, but in a form which can be directly cut and pasted
into a g.region command. After setting the region in that way you might check the cell resolution with
"g.region -p" then snap it to a regular grid with g.region’s -a flag. E.g. g.region -a res=5 -p. Note
that this is just a rough guide.
A more involved, but more accurate, way to do this is to generate a vector "box" map of the region in the
source project using v.in.region -d. This "box" map is then reprojected into the target project with
v.proj. Next the region in the target project is set to the extent of the new vector map with g.region
along with the desired raster resolution (g.region -m can be used in Latitude/Longitude projects to
measure the geodetic length of a pixel). r.proj is then run for the raster map the user wants to
reproject. In this case a little preparation goes a long way.
When reprojecting whole-world maps the user should disable map-trimming with the -n flag. Trimming is not
useful here because the module has the whole map in memory anyway. Besides that, world "edges" are hard
(or impossible) to find in CRSs other than latitude-longitude so results may be odd with trimming.
EXAMPLES
Inline method
With GRASS running in the destination project use the -g flag to show the input map’s bounds once
reprojected into the current working CRS, then use that to set the region bounds before performing the
reprojection:
# calculate where output map will be
r.proj input=elevation project=ll_wgs84 mapset=user1 -p
Source cols: 8162
Source rows: 12277
Local north: -4265502.30382993
Local south: -4473453.15255565
Local west: 14271663.19157564
Local east: 14409956.2693866
# same calculation, but in a form which can be cut and pasted into a g.region call
r.proj input=elevation project=ll_wgs84 mapset=user1 -g
n=-4265502.30382993 s=-4473453.15255565 w=14271663.19157564 e=14409956.2693866 rows=12277 cols=8162
g.region n=-4265502.30382993 s=-4473453.15255565 \
w=14271663.19157564 e=14409956.2693866 rows=12277 cols=8162 -p
projection: 99 (Mercator)
zone: 0
datum: wgs84
ellipsoid: wgs84
north: -4265502.30382993
south: -4473453.15255565
west: 14271663.19157564
east: 14409956.2693866
nsres: 16.93824621
ewres: 16.94352828
rows: 12277
cols: 8162
cells: 100204874
# round resolution to something cleaner
g.region res=17 -a -p
projection: 99 (Mercator)
zone: 0
datum: wgs84
ellipsoid: wgs84
north: -4265487
south: -4473465
west: 14271653
east: 14409965
nsres: 17
ewres: 17
rows: 12234
cols: 8136
cells: 99535824
# finally, perform the reprojection
r.proj input=elevation project=ll_wgs84 mapset=user1 memory=800
v.in.region method
# In the source project, use v.in.region to generate a bounding box around the
# region of interest:
v.in.region -d output=bounds type=area
# Now switch to the target project and import the vector bounding box
# (you can run v.proj -l to get a list of vector maps in the source project):
v.proj input=bounds project=source_project_name output=bounds_reprojected
# Set the region in the target project with that of the newly-imported vector
# bounds map, and align the resolution to the desired cell resolution of the
# final, reprojected raster map:
g.region vector=bounds_reprojected res=5 -a
# Now reproject the raster into the target project
r.proj input=elevation.dem output=elevation.dem.reproj \
project=source_project_name mapset=PERMANENT res=5 method=bicubic
REFERENCES
1 Evenden, G.I. (1990) Cartographic projection procedures for the UNIX environment - a user’s
manual. USGS Open-File Report 90-284 (OF90-284.pdf) See also there: Interim Report and 2nd
Interim Report on Release 4, Evenden 1994).
2 Richards, John A. (1993), Remote Sensing Digital Image Analysis, Springer-Verlag, Berlin, 2nd
edition.
PROJ: Projection/datum support library
Further reading
• ASPRS Grids and Datum
• Projections Transform List (PROJ)
• Coordinate operations by PROJ (projections, conversions, transformations, pipeline operator)
• MapRef - The Collection of Map Projections and Reference Systems for Europe
• Information and Service System for European Coordinate Reference Systems - CRS
SEE ALSO
g.region, g.proj, i.rectify, m.proj, r.support, r.stats, v.proj, v.in.region
The ’gdalwarp’ and ’gdal_translate’ utilities are available from the GDAL project.
AUTHORS
Martin Schroeder, University of Heidelberg, Germany
Man page text from S.J.D. Cox, AGCRC, CSIRO Exploration & Mining, Nedlands, WA
Updated by Morten Hulden
Datum transformation support and cleanup by Paul Kelly
Support of PROJ5+ by Markus Metz, mundialis
SOURCE CODE
Available at: r.proj source code (history)
Accessed: Thursday Aug 01 11:30:13 2024
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© 2003-2024 GRASS Development Team, GRASS GIS 8.4.0 Reference Manual