Provided by: grass-doc_8.3.0-1_all 
NAME
r.proj - Re-projects a raster map from given location to the current location.
KEYWORDS
raster, projection, transformation, import
SYNOPSIS
r.proj
r.proj --help
r.proj [-lnpg] location=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
-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:
location=name [required]
Location containing input raster map
Location name (not location path)
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 location
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 projects a raster map in a specified mapset of a specified location from the
projection of the input location to a raster map in the current location. The projection
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.
Projecting vector maps within the GRASS GIS
GIS data capture, import and transfer often requires a projection step, since the source
or client will frequently be in a different projection to the working projection.
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 projection definition and parameters which are stored in the
PROJ_INFO and PROJ_UNITS files in the PERMANENT mapset directory for every GRASS location.
Design of r.proj
As discussed briefly above, the fundamental step in re-projecting a raster is resampling
the source grid at locations corresponding to the intersections of a grid in the target
projection. The basic procedure for accomplishing this, therefore, is as follows:
r.proj converts a map to a new geographic projection. It reads a map from a different
location, projects it and write it out to the current location. The projected 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 location is stored in another separate GRASS database.
To avoid excessive time consumption when reprojecting a map the region and resolution of
the target location should be set appropriately beforehand.
A simple way to do this is to check the projected bounds of the input map in the current
location’s projection 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 location using v.in.region -d. This "box" map is then
reprojected into the target location with v.proj. Next the region in the target location
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 locations 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 projections other than
latitude-longitude so results may be odd with trimming.
EXAMPLES
Inline method
With GRASS running in the destination location use the -g flag to show the input map’s
bounds once projected into the current working projection, then use that to set the region
bounds before performing the reprojection:
# calculate where output map will be
r.proj input=elevation location=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 location=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 location=ll_wgs84 mapset=user1 memory=800
v.in.region method
# In the source location, 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 location and import the vector bounding box
# (you can run v.proj -l to get a list of vector maps in the source location):
v.proj input=bounds location=source_location_name output=bounds_reprojected
# Set the region in the target location 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 location
r.proj input=elevation.dem output=elevation.dem.reproj \
location=source_location_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: Tuesday Jun 27 11:13:04 2023
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