Ubuntu Manpage: Raster maps in general

Provided by: grass-doc_7.0.3-1build1_all

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 region.

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) simultaneously; 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 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 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 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.interp or r.resamp.stats, then export the resampled map.

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.

Metadata
The r.info 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 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 neighbor 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 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 miniscule difference in the position of the
boundary results in the addition or subtraction of a sample weighted by a
miniscule 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 (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 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 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 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).

(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). Profiles and transects can be generated
(d.profile, r.profile, r.transect) as well as histograms (d.histogram) and polar diagrams
(d.polar). Univariate statistics (r.univar) and reports are also available
(r.report,r.stats, r.volume).

Raster map algebra and aggregation
The r.mapcalc command provides raster map algebra methods. The r.resamp.stats command
resamples raster map layers using various aggregation methods, the r.statistics command
aggregates one map based on a second map. r.resamp.interp resamples raster map layers
using interpolation.

Regression analysis
Both linear (r.regression.line) and multiple regression (r.regression.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
Raster data can be stored in GRASS as 2D or 3D grids. 2D rasters support 3 data types:
32bit signed integer, single- and double-precision floating-point. 3D rasters support only
single- and double-precision floating-point. In most GRASS resources, 2D raster maps are
usually called "raster", their integer data type "CELL", single-precision floating-point
data type "FCELL" and double-precision floating-point "DCELL". The 3D raster map type is
usually called "3D raster" but other names like "RASTER3D", "voxel", "volume", "GRID3D" or
"3d cell" are common. 3D raster’s single-precision data type is most often called
"float", and the double-precision one "double".

The GRASS GIS raster format is architecture independent and portable between 32bit and
64bit machines.

GRASS distinguishes NULL and zero. When working with NULL data, it is important to know
that operations on NULL cells lead to NULL cells.

See also
• Introduction into 3D raster data (voxel) processing

• Introduction into vector data processing

• Introduction into image processing

• Database management

• Projections and spatial transformations

Main index | raster index | Topics index | Keywords index | Full index