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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=integer]        [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=integer
           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.4  [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.4’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
       iterpolation 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=lanzcos 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.4 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 4: Projection/datum support library.

       Further reading

           ·   ASPRS Grids and Datum

           ·   Projections Transform List (PROJ.4)

           ·   MapRef - The Collection of Map Projections and Reference Systems for Europe

           ·   Information and Service System for European Coordinate Reference Systems - CRS

           ·   Cartographical Map Projections by Carlos A. Furuti

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

       Last changed: $Date: 2016-01-29 10:29:57 +0100 (Fri, 29 Jan 2016) $

SOURCE CODE

       Available at: r.proj source code (history)

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       © 2003-2019 GRASS Development Team, GRASS GIS 7.6.1 Reference Manual