Note: A new GRASS GIS stable version has been released: GRASS GIS 7.4, available here.
Updated manual page: here
Raster data processing in GRASS GIS
A "raster map" is a data layer consisting of a gridded array of cells.
It has a certain number of rows and columns, with a data point (or null
value indicator) in each cell. These may exist as a 2D grid or as a 3D
cube made up of many smaller cubes, i.e. a stack of 2D grids.
The geographic boundaries of the raster map are described by the north,
south, east, and west fields. These values describe the lines which bound
the map at its edges. These lines do NOT pass through the center of the
grid cells at the edge of the map, but along the edge of the map itself.
i.e. the geographic extent of the map is described by the outer bounds of
all cells within the map.
As a general rule in GRASS GIS:
- Raster output maps have their bounds and resolution equal to those
of the current computational region.
- Raster input maps are automatically cropped/padded and rescaled
(using nearest-neighbour resampling) to match the current region.
- Raster input maps are automatically masked if a raster map named
MASK exists. The MASK is only applied when reading maps
from the disk.
There are a few exceptions to this:
programs read the data cell-for-cell, with no resampling. When
reading non-georeferenced data, the imported map will usually have its
lower-left corner at (0,0) in the location's coordinate system; the user
needs to use r.region
to "place" the imported map.
Some programs which need to perform specific types of resampling (e.g.
r.resamp.rst) read the input maps at
their original resolution then do the resampling themselves.
r.proj has to deal with two regions (source
and destination) simultaneously; both will have an impact upon the
The module r.in.gdal
offers a common
interface for many different raster formats. Additionally, it also
offers options such as on-the-fly location creation or extension of
the default region to match the extent of the imported raster map.
For special cases, other import modules are available. The full map
is always imported.
For importing scanned maps, the user will need to create a
x,y-location, scan the map in the desired resolution and save it into
an appropriate raster format (e.g. tiff, jpeg, png, pbm) and then use
r.in.gdal to import it. Based on
reference points the scanned map can be recified to obtain geocoded
Raster maps are exported with r.out.gdal
into common formats. Also r.out.bin,
and other export modules are available. They export the data according
to the current region settings. If those differ from the original map,
the map is resampled on the fly (nearest neighbor algorithm). In other
words, the output will have as many rows and columns as the current region.
To export maps with various grid spacings (e.g, 500x500 or 200x500), you
can just change the region resolution with g.region
and then export the map. The resampling is done with nearest neighbor
algorithm in this case. If you want some other form of resampling,
first change the region, then explicitly resample the map with e.g.
r.resamp.stats, then export the
GRASS GIS raster map exchange between different locations (same projection)
can be done in a lossless way using the r.pack
and r.unpack modules.
module displays general information
about a map such as region extent, data range, data type, creation history,
and other metadata.
Metadata such as map title, units, vertical datum etc. can be updated
. Timestamps are managed
. Region extent and
resolution are mangaged with r.region
Resampling methods and interpolation methods
GRASS raster map processing is always performed in the current region
settings (see g.region
), i.e. the current
region extent and current raster resolution is used. If the resolution
differs from that of the input raster map(s), on-the-fly resampling is
performed (nearest neighbor resampling). If this is not desired, the
input map(s) has/have to be resampled beforehand with one of the dedicated
The built-in nearest-neighbour resampling of raster data calculates
the centre of each region cell, and takes the value of the raster cell
in which that point falls.
If the point falls exactly upon a grid line, the exact result will be
determined by the direction of any rounding error. One consequence of
this is that downsampling by a factor which is an even integer will
always sample exactly on the boundary between cells, meaning that the
result is ill-defined.
The following modules are available for reinterpolation of "filled"
raster maps (continuous data) to a different resolution:
- r.resample uses the built-in resampling,
so it should produce identical results as the on-the-fly resampling done
via the raster import modules.
- r.resamp.interp Resampling with
nearest neighbor, bilinear, and bicubic method: method=nearest uses the
same algorithm as r.resample, but not the same
code, so it may not produce identical results in cases which are decided
by the rounding of floating-point numbers.
For r.resamp.interp method=bilinear
and method=bicubic, the raster values are treated as samples at each
raster cell's centre, defining a piecewise-continuous surface. The resulting
raster values are obtained by sampling the surface at each region cell's centre.
As the algorithm only interpolates, and doesn't extrapolate, a margin of 0.5
(for bilinear) or 1.5 (for bicubic) cells is lost from the extent of the original
raster. Any samples taken within this margin will be null.
- r.resamp.rst Regularized Spline with Tension
(RST) interpolation 2D: Behaves similarly, i.e. it computes a surface assuming
that the values are samples at each raster cell's centre, and samples the surface
at each region cell's centre.
- r.resamp.bspline Bicubic or bilinear
spline interpolation with Tykhonov regularization.
- For r.resamp.stats without -w, the value of
each region cell is the chosen aggregate of the values from all of the raster
cells whose centres fall within the bounds of the region cell.
With -w, the samples are weighted according to the proportion of the
raster cell which falls within the bounds of the region cell, so the
result is normally unaffected by rounding error (a minuscule difference
in the position of the boundary results in the addition or subtraction of
a sample weighted by a minuscule factor; also, The min and max aggregates
can't use weights, so -w has no effect for those).
- r.fillnulls for Regularized Spline with Tension (RST)
interpolation 2D for hole filling (e.g., SRTM DEM)
Furthermore, there are modules available for reinterpolation of "sparse"
(scattered points or lines) maps:
- Inverse distance weighted average (IDW) interpolation
- Interpolating from contour lines (r.contour)
- Various vector modules for interpolation
For Lidar and similar data, r.in.lidar
support loading and binning of ungridded x,y,z ASCII data into a new raster map.
The user may choose from a variety of statistical methods in creating the new raster map.
Otherwise, for interpolation of scattered data, use the v.surf.* set of
If a raster map named "MASK" exists, most GRASS raster modules will operate
only on data falling inside the masked area, and treat any data falling
outside of the mask as if its value were NULL. The mask is only applied
an existing GRASS raster map, for example when used
in a module as an input map.
The mask is read as an integer map. If MASK is actually a
floating-point map, the values will be converted to integers using the
map's quantisation rules (this defaults to round-to-nearest, but can
be changed with r.quant).
A couple of commands are available to calculate local statistics
), and global statistics
Profiles and transects can be generated
) as well as histograms
) and polar diagrams
Univariate statistics (r.univar
reports are also available (r.report
command provides raster map
command resamples raster
map layers using various aggregation methods, the r.statistics
command aggregates one map based on a second map.
resamples raster map
layers using interpolation.
Both linear (r.regression.line
multiple regression (r.regression.multi
Watershed modeling related modules are
Water flow related modules are
Flooding can be simulated with r.lake
Hydrologic simulation model are available as
In GRASS GIS, raster data can be stored as 2D or 3D grids.
2D raster maps
2D rasters support three data types (for technical details, please refer
to the Wiki article
GRASS raster semantics
- 32bit signed integer (CELL),
- single-precision floating-point (FCELL), and
- double-precision floating-point (DCELL).
In most GRASS GIS resources, 2D raster maps are usually called "raster" maps.
3D raster maps
The 3D raster map type is usually called "3D raster" but other names like
"RASTER3D", "voxel", "volume", "GRID3D" or "3d cell" are yet common.
3D rasters support only single- and double-precision floating-point.
3D raster's single-precision data type is most often called "float",
and the double-precision one "double".
No-data management and data portability
GRASS GIS distinguishes NULL and zero. When working with NULL data, it
is important to know that operations on NULL cells lead to NULL cells.
The GRASS GIS raster format is architecture independent and portable between
32bit and 64bit machines.
All GRASS GIS raster map types are by default ZLIB compressed, i.e. using
ZLIB's deflate algorithm. Through the environment variable
the compression method can be set to RLE, ZLIB,
LZ4, or BZIP2.
Important: the NULL file compression must be explicitly turned on with
export GRASS_COMPRESS_NULLS=1 - such raster maps can then only
be opened with GRASS GIS 7.2.0 or later. NULL file compression can be
managed with r.null -z.
Integer (CELL type) raster maps can be compressed with RLE if
the environment variable GRASS_INT_ZLIB exists and is set to value
0. However, this is not recommended.
Floating point (FCELL, DCELL) raster maps never use RLE compression;
they are either compressed with ZLIB, LZ4, BZIP2 or are uncompressed.
- DEPRECATED Run-Length Encoding, poor compression ratio but
fast. It is kept for backwards compatibility to read raster maps
created with GRASS 6. It is only used for raster maps of type CELL.
FCELL and DCELL maps are never and have never been compressed with RLE.
- ZLIB's deflate is the default compression method for all raster
maps. GRASS GIS 7 uses by default 1 as ZLIB compression level which is the
best compromise between speed and compression ratio, also when
compared to other available compression methods. Valid levels are in
the range [1, 9] and can be set with the environment variable
- LZ4 is a very fast compression method, about as fast as no
compression. Decompression is also very fast. The compression ratio is
generally higher than for RLE but worse than for ZLIB. LZ4 is
recommended if disk space is not a limiting factor.
- BZIP2 can provide compression ratios much higher than the other
methods, but only for large raster maps (> 10000 columns). For large
raster maps, disk space consumption can be reduced by 30 - 50% when
using BZIP2 instead of ZLIB's deflate. BZIP2 is the slowest compression
and decompression method. However, if reading from / writing to a
storage device is the limiting factor, BZIP2 compression can speed up
raster map processing. Be aware that for smaller raster maps, BZIP2
compression ratio can be worse than other compression methods.
In the internal cellhd file, the value for "compressed" is 1 for RLE, 2
for ZLIB, 3 for LZ4, and 4 for BZIP2.
Obviously, decompression is controlled by the raster map's compression,
not the environment variable.
Available at: Raster data processing in GRASS GIS source code (history)
Note: A new GRASS GIS stable version has been released: GRASS GIS 7.4, available here.
Updated manual page: here
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GRASS Development Team,
GRASS GIS 7.2.4svn Reference Manual