rasterintro(1grass) GRASS GIS User's Manual rasterintro(1grass)
Raster data processing in GRASS GIS
Raster maps in general
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:
1 Raster output maps have their bounds and resolution equal to
those of the current computational region.
2 Raster input maps are automatically cropped/padded and rescaled
(using nearest-neighbour resampling) to match the current re-
gion.
3 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: r.in.* 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) simultane-
ously; both will have an impact upon the final result.
Raster import and export
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 ex-
tent of the imported raster map. For special cases, other import mod-
ules are available. The full map is always imported.
For importing scanned maps, the user will need to create a x,y-loca-
tion, scan the map in the desired resolution and save it into an appro-
priate 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 data.
Raster maps are exported with r.out.gdal into common formats. Also
r.out.bin, r.out.vtk, r.out.ascii and other export modules are avail-
able. 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 vari-
ous 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 re-
sample the map with e.g. r.resamp.interp or r.resamp.stats, then ex-
port the resampled map.
GRASS GIS raster map exchange between different locations (same projec-
tion) can be done in a lossless way using the r.pack and r.unpack mod-
ules.
Metadata
The r.info module displays general information about a map such as re-
gion extent, data range, data type, creation history, and other meta-
data. Metadata such as map title, units, vertical datum etc. can be
updated with r.support. Timestamps are managed with r.timestamp. Region
extent and resolution are mangaged with r.region.
Raster map operations
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 neigh-
bor resampling). If this is not desired, the input map(s) has/have to
be resampled beforehand with one of the dedicated modules.
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 al-
ways sample exactly on the boundary between cells, meaning that the re-
sult 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.re-
sample, but not the same code, so it may not produce identical
results in cases which are decided by the rounding of float-
ing-point numbers.
For r.resamp.interp method=bilinear and method=bicubic, the
raster values are treated as samples at each raster cell’s cen-
tre, defining a piecewise-continuous surface. The resulting
raster values are obtained by sampling the surface at each re-
gion 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) interpola-
tion 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) interpo-
lation 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
(r.surf.idw)
• Interpolating from contour lines (r.contour)
• Various vector modules for interpolation
For Lidar and similar data, r.in.lidar and r.in.xyz 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
modules.
Raster MASKs
If a raster map named "MASK" exists, most GRASS raster modules will op-
erate 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 when reading 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 float-
ing-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).
(see r.mask)
Raster map statistics
A couple of commands are available to calculate local statistics
(r.neighbors), and global statistics (r.statistics, r.surf.area). Pro-
files and transects can be generated (d.profile, r.profile, r.transect)
as well as histograms (d.histogram) and polar diagrams (d.polar). Uni-
variate statistics (r.univar) and reports are also available (r.re-
port,r.stats, r.volume). Since r.univar may be slow for extended sta-
tistics these can be calculated using r.stats.quantile. Without a zones
input raster, the r.quantile module will be significantly more effi-
cient for calculating percentiles with large maps. For calculating uni-
variate statistics from a raster map based on vector polygon map and
upload statistics to new attribute columns, see v.rast.stats. Category
or object oriented statistics can be computed with r.statistics. For
floating-point cover map support for this, see the alternative
r.stats.zonal. For quantile calculations with support for float-
ing-point cover maps, see the alternative r.stats.quantile.
Raster map algebra and aggregation
The r.mapcalc command provides raster map algebra methods. The r.re-
samp.stats command resamples raster map layers using various aggrega-
tion methods, the r.statistics command aggregates one map based on a
second map. r.resamp.interp resamples raster map layers using interpo-
lation.
Regression analysis
Both linear (r.regression.line) and multiple regression (r.regres-
sion.multi) are supported.
Hydrologic modeling toolbox
Watershed modeling related modules are r.basins.fill, r.water.outlet,
r.watershed, and r.terraflow. Water flow related modules are r.carve,
r.drain, r.fill.dir, r.fillnulls, r.flow, and r.topidx. Flooding can
be simulated with r.lake. Hydrologic simulation model are available as
r.sim.sediment, r.sim.water, and r.topmodel.
Raster format
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 re-
fer 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 com-
mon. 3D rasters support only single- and double-precision float-
ing-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.
Raster compression
All GRASS GIS raster map types are by default ZSTD compressed if avail-
able, otherwise ZLIB compressed. Through the environment variable
GRASS_COMPRESSOR the compression method can be set to RLE, ZLIB, LZ4,
BZIP2, or ZSTD.
Important: the NULL file compression can be turned off with export
GRASS_COMPRESS_NULLS=0. Raster maps with NULL file compression can only
be opened with GRASS GIS 7.2.0 or later. NULL file compression for a
particular raster map can be managed with r.null -z.
Integer (CELL type) raster maps can be compressed with RLE if the envi-
ronment variable GRASS_COMPRESSOR exists and is set to RLE. However,
this is not recommended.
Floating point (FCELL, DCELL) raster maps never use RLE compression;
they are either compressed with ZLIB, LZ4, BZIP2, ZSTD or are uncom-
pressed.
RLE
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
ZLIB’s deflate is the default compression method for all raster
maps, if ZSTD is not available. 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 com-
pression methods. Valid levels are in the range [1, 9] and can be
set with the environment variable GRASS_ZLIB_LEVEL.
LZ4
LZ4 is a very fast compression method, about as fast as no compres-
sion. Decompression is also very fast. The compression ratio is
generally higher than for RLE but worse than for ZLIB. LZ4 is rec-
ommended if disk space is not a limiting factor.
BZIP2
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 slow-
est compression and decompression method. However, if reading from
/ writing to a storage device is the limiting factor, BZIP2 com-
pression can speed up raster map processing. Be aware that for
smaller raster maps, BZIP2 compression ratio can be worse than
other compression methods.
ZSTD
ZSTD (Zstandard) provides compression ratios higher than ZLIB but
lower than BZIP2 (for large data). ZSTD compresses up to 4x faster
than ZLIB, and usually decompresses 6x faster than ZLIB. ZSTD is
the default compression method if available.
In the internal cellhd file, the value for "compressed" is 1 for RLE, 2
for ZLIB, 3 for LZ4,4 for BZIP2, and 5 for ZSTD.
Obviously, decompression is controlled by the raster map’s compression,
not the environment variable.
See also
• Introduction into 3D raster data (voxel) processing
• Introduction into vector data processing
• Introduction into image processing
• Introduction into temporal data processing
• Database management
• Projections and spatial transformations
SOURCE CODE
Available at: Raster data processing in GRASS GIS source code (history)
Accessed: unknown
Main index | Raster index | Topics index | Keywords index | Graphical
index | Full index
© 2003-2022 GRASS Development Team, GRASS GIS 7.8.7 Reference Manual
GRASS 7.8.7 rasterintro(1grass)
Generated by dwww version 1.14 on Mon Feb 3 08:28:29 CET 2025.