Flags:

-e

Estimate resolution only

-n

Do not perform region cropping optimization

-l

Force Lat/Lon maps to fit into geographic coordinates (90N,S; 180E,W)

-o

Override projection check (use current project’s CRS)
Assume that the dataset has the same coordinate reference system (CRS) as the current project

--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:

input=name [required]

Name of GDAL dataset to be imported

band=integer[,integer,...]

Input band(s) to select (default is all bands)

memory=memory in MB

Maximum memory to be used (in MB)
Cache size for raster rows
Default: 300

output=name

Name for output raster map

resample=string

Resampling method to use for reprojection
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

extent=string

Output raster map extent
Options: input, region
Default: input
input: extent of input map
region: extent of current region

resolution=string

Resolution of output raster map (default: estimated)
Options: estimated, value, region
Default: estimated
estimated: estimated resolution
value: user-specified resolution
region: current region resolution

resolution_value=float

Resolution of output raster map (use with option resolution=value)

title=phrase

Title for resultant raster map

Resolution

r.import reports the estimated target resolution for each input band. The estimated resolution will usually be some floating point number, e.g. 271.301. In case option resolution is set to estimated (default), this floating point number will be used as target resolution. Since the target resolution should be typically the rounded estimated resolution, e.g. 250 or 300 instead of 271.301, flag -e can be used first to obtain the estimate without importing the raster bands. Then the desired resolution is set with option resolution_value and option resolution=value. For latlong projects, the resolution might be set to arc seconds, e.g. 1, 3, 7.5, 15, and 30 arc seconds are commonly used resolutions.

Resampling methods

When reprojecting a map to a new spatial reference system, the projected data is resampled with one of four different methods: nearest neighbor, bilinear, bicubic interpolation or lanczos.

In the following, common use cases are:

nearest is the simplest method and the only possible method for categorical data.

bilinear does linear interpolation and provides smoother output than nearest. bilinear is recommended when reprojecting a DEM for hydrological analysis or for surfaces where overshoots must be avoided, e.g. precipitation should not become negative.

bicubic produces smoother output than bilinear, at the cost of overshoots. Here, valid pixels that are adjacent to NULL pixels or edge pixels are set to NULL.

lanczos produces the smoothest output of all methods and preserves contrast best. lanczos is recommended for imagery. Both bicubic and lanczos preserve linear features. With nearest or bilinear, linear features can become zigzag features after reprojection.

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.

For explanation of the -l flag, please refer to the r.in.gdal manual.

When importing whole-world maps the user should disable map-trimming with the -n flag. For further explanations of -n flag, please refer the to r.proj manual.

Import of SRTM V3 global data at 1 arc-seconds resolution

The SRTM V3 1 arc-second global data (~30 meters resolution) are available from EarthExplorer (https://earthexplorer.usgs.gov/). The SRTM collections are located under the "Digital Elevation" category.

Example for North Carolina sample dataset (the tile name is "n35_w079_1arc_v3.tif"):

# set computational region to e.g. 10m elevation model:
g.region raster=elevation -p
# Import with reprojection on the fly. Recommended parameters:
# resample   Resampling method to use for reprojection - bilinear
# extent     Output raster map extent - region: extent of current region
# resolution Resolution of output raster map
#  - region: current region resolution - limit to g.region setting from above
r.import input=n35_w079_1arc_v3.tif output=srtmv3_resamp10m resample=bilinear \


  extent=region resolution=region title="SRTM V3 resampled to 10m resolution"
# beautify colors:
r.colors srtmv3_resamp10m color=elevation

Import of WorldClim data

Import of a subset from WorldClim Bioclim data set, to be reprojected to current project CRS (North Carolina sample dataset). Different resolutions are available, in this example we use the 2.5 arc-minutes resolution data. During import, we spatially subset the world data to the North Carolina region using the extent parameter:

# download selected Bioclim data (2.5 arc-minutes resolution)
# optionally tiles are available for the 30 arc-sec resolution
wget https://geodata.ucdavis.edu/climate/worldclim/1_4/grid/cur/bio_2-5m_bil.zip
# extract BIO1 from package (BIO1 = Annual Mean Temperature):
unzip bio_2-5m_bil.zip bio1.bil bio1.hdr
# prior to import, fix broken WorldClim extent using GDAL tool
gdal_translate -a_ullr -180 90 180 -60 bio1.bil bio1_fixed.tif
# set computational region to North Carolina, 4000 m target pixel resolution
g.region -d res=4000 -ap
# subset to current region and reproject on the fly to current project CRS,
# using -n since whole-world map is imported:
r.import input=bio1_fixed.tif output=bioclim01 resample=bilinear \


         extent=region resolution=region -n
# temperature data are in °C * 10
r.info bioclim01
r.univar -e bioclim01