rasterintro(1grass)

📦
version 8.5.0 · 9 versions
, in alpine 3.24, ubuntu stonking

💫 Raster data processing in GRASS 💫

okay bestie, this is rasterintro(1grass), straight out of the GRASS User's Manual, and it's basically the ✨grand tour✨ of everything raster in GRASS. no cap, it's a lot, but we're gonna get through it together 🫶

🌈 Raster maps in general

okay so first things first: a "raster map" is a data layer that's a gridded array of cells. it has a certain number of rows and columns, and there's a data point (or a null value indicator, for when it's giving nothing 🙈) sitting in each cell. these can hang out as a 2D grid, or as a 3D cube made up of many smaller cubes — i.e. literally 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 that bound the map at its edges. and here's the tea 🍵: these lines do NOT pass through the center of the grid cells at the edge of the map — they run 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:

  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. Processing NULL (no data) values produces NULL values.
  4. Input raster maps are automatically masked if a raster mask is active. The mask is managed by the r.mask tool, and it is represented by a raster map called MASK by default. Unless specified otherwise, the raster mask is only applied when reading raster maps, which typically results in NULL values in the output for areas outside of the mask.

there are a few exceptions to this whole situation: 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 project'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. main character behaviour, honestly.

r.proj has to deal with two regions (source and destination) simultaneously; both will have an impact upon the final result. multitasking icon 💅

📦 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 project creation or extension of the default region to match the extent of the imported raster map. for special cases, other import modules are available too. the full map is always imported ✨

for importing scanned maps, the user will need to create a x,y-project, scan the map in the desired resolution, 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 rectified 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. order matters, bestie, don't skip steps 🙏

GRASS raster map exchange between different projects with the same CRS 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 managed with r.region.

so yeah — r.info to peek, r.support to edit the vibes, r.timestamp for time, r.region for space ⭐

🛠️ 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. no surprises, just region rules 💖

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.

⚠️ okay this part is important: 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 💀 so yeah, don't do that and expect a clean answer.

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.
  • 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 (r.surf.idw)
  • Interpolating from contour lines (r.contour)
  • Various vector modules for interpolation

for Lidar and similar data, r.in.pdal 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 it's used in a module as an input map. while the mask raster map can be managed directly, the r.mask tool is a more convenient way to create and manage masks 💅

alternatively, the GRASS_MASK environment variable can be used to specify the raster map which will be used as a mask.

the mask is read as an integer map. if the mask raster 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). since r.univar may be slow for extended statistics, these can be calculated using r.stats.quantile. without a zones input raster, the r.quantile module will be significantly more efficient for calculating percentiles with large maps. for calculating univariate statistics from a raster map based on a vector polygon map and uploading 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 floating-point cover maps, see the alternative r.stats.quantile. so many options, pick your fighter 🥊

🧮 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. math but make it slay 🔥

📈 Regression analysis

both linear (r.regression.line) and multiple regression (r.regression.multi) are supported. lines? slay. multiple lines? highkey slay 📐

🌊 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. water you waiting for 💧

🗂️ Raster format

in GRASS, raster data can be stored as 2D or 3D grids. that's it, that's the format ✨

✨ 2D raster maps ✨

ok so 2D rasters come in three data types bestie (if you want the full technical lore, go read the Wiki article GRASS raster semantics):

  • 32bit signed integer (CELL),
  • single-precision floating-point (FCELL), and
  • double-precision floating-point (DCELL).

And in pretty much every GRASS resource ever, 2D raster maps are just called "raster" maps. periodt. 💅

🌈 3D raster maps 🌈

The 3D raster map type is usually called "3D raster" but tbh it goes by so many names — RASTER3D, voxel, volume, GRID3D, 3d cell — all iconic, all valid, iykyk.

Here's the thing tho: 3D rasters only do single- and double-precision floating-point. No integers allowed, sorry. And 3D raster's single-precision data type is most often called "float", while the double-precision one is called "double". 🫶

🦋 No-data management and data portability 🦋

GRASS distinguishes NULL and zero — and this is NOT the same thing, bestie, do not conflate them. 😭

When you're working with NULL data, it's important to know that operations on NULL cells lead to NULL cells. It propagates. Like drama.

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

🎀 Raster compression 🎀

All GRASS raster map types are, by default, ZSTD compressed — if available — 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 7.2.0 or later. NULL file compression for a particular raster map can be managed with r.null -z. 💀 don't say I didn't warn you.

Integer (CELL type) raster maps can be compressed with RLE if the environment 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 uncompressed.

  • 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. (it's giving legacy code fr fr)
  • ZLIB — ZLIB's deflate is the default compression method for all raster maps, if ZSTD is not available. GRASS 8 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 GRASS_ZLIB_LEVEL.
  • LZ4 — 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 — 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.
  • 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 source code (history)

Accessed: Friday May 08 10:38:23 2026

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© 2003-2026 GRASS Development Team, GRASS 8.5.0 Reference Manual

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