imageryintro(1grass) GRASS GIS User's Manual imageryintro(1grass)
Image processing in GRASS GIS
Image processing in general
Digital numbers and physical values (reflection/radiance-at-sensor):
Satellite imagery is commonly stored in Digital Numbers (DN) for mini-
mizing the storage volume, i.e. the originally sampled analog physical
value (color, temperature, etc) is stored a discrete representation in
8-16 bits. For example, Landsat data are stored in 8bit values (i.e.,
ranging from 0 to 255); other satellite data may be stored in 10 or 16
bits. Having data stored in DN, it implies that these data are not yet
the observed ground reality. Such data are called "at-satellite", for
example the amount of energy sensed by the sensor of the satellite
platform is encoded in 8 or more bits. This energy is called radi-
ance-at-sensor. To obtain physical values from DNs, satellite image
providers use a linear transform equation (y = a * x + b) to encode the
radiance-at-sensor in 8 to 16 bits. DNs can be turned back into physi-
cal values by applying the reverse formula (x = (y - b) / a).
The GRASS GIS module i.landsat.toar easily transforms Landsat DN to ra-
diance-at-sensor (top of atmosphere, TOA). The equivalent module for
ASTER data is i.aster.toar. For other satellites, r.mapcalc can be em-
ployed.
Reflection/radiance-at-sensor and surface reflectance
When radiance-at-sensor has been obtained, still the atmosphere influ-
ences the signal as recorded at the sensor. This atmospheric interac-
tion with the sun energy reflected back into space by ground/vegeta-
tion/soil needs to be corrected. The need of removing atmospheric arti-
facts stems from the fact that the atmosphericic conditions are chang-
ing over time. Hence, to gain comparability between Earth surface im-
ages taken at different times, atmospheric need to be removed convert-
ing at-sensor values which are top of atmosphere to surface reflectance
values.
In GRASS GIS, there are two ways to apply atmospheric correction for
satellite imagery. A simple, less accurate way for Landsat is with
i.landsat.toar, using the DOS correction method. The more accurate way
is using i.atcorr (which supports many satellite sensors). The atmo-
spherically corrected sensor data represent surface reflectance, which
ranges theoretically from 0% to 100%. Note that this level of data cor-
rection is the proper level of correction to calculate vegetation in-
dices.
In GRASS GIS, image data are identical to raster data. However, a cou-
ple of commands are explicitly dedicated to image processing. The geo-
graphic boundaries of the raster/imagery file 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.
As a general rule in GRASS:
1 Raster/imagery output maps have their bounds and resolution
equal to those of the current region.
2 Raster/imagery input maps are automatically cropped/padded and
rescaled (using nearest-neighbor resampling) to match the cur-
rent region.
Imagery import
The module r.in.gdal offers a common interface for many different
raster and satellite image formats. Additionally, it also offers op-
tions 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. Always the full map is im-
ported. Imagery data can be group (e.g. channel-wise) with i.group.
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 recti-
fied to obtain geocoded data.
Image processing operations
GRASS raster/imagery map processing is always performed in the current
region settings (see g.region), i.e. the current region extent and cur-
rent 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.
Geocoding of imagery data
GRASS is able to geocode raster and image data of various types:
• unreferenced scanned maps by defining four corner points
(i.group, i.target, g.gui.gcp, i.rectify)
• unreferenced satellite data from optical and Radar sensors by
defining a certain number of ground control points (i.group,
i.target, g.gui.gcp, i.rectify)
• interactive graphical Ground Control Point (GCP) manager
• orthophoto generation based on DEM: i.ortho.photo
• digital handheld camera geocoding: modified procedure for i.or-
tho.photo
Visualizing (true) color composites
To quickly combine the first three channels to a near natural color im-
age, the GRASS command d.rgb can be used or the graphical GIS manager
(wxGUI). It assigns each channel to a color which is then mixed while
displayed. With a bit more work of tuning the grey scales of the chan-
nels, nearly perfect colors can be achieved. Channel histograms can be
shown with d.histogram.
Calculation of vegetation indices
An example for indices derived from multispectral data is the NDVI
(normalized difference vegetation index). To study the vegetation sta-
tus with NDVI, the Red and the Near Infrared channels (NIR) are taken
as used as input for simple map algebra in the GRASS command r.mapcalc
(ndvi = 1.0 * (nir - red)/(nir + red)). With r.colors an optimized
"ndvi" color table can be assigned afterward. Also other vegetation in-
dices can be generated likewise.
Calibration of thermal channel
The encoded digital numbers of a thermal infrared channel can be trans-
formed to degree Celsius (or other temperature units) which represent
the temperature of the observed land surface. This requires a few alge-
braic steps with r.mapcalc which are outlined in the literature to ap-
ply gain and bias values from the image metadata.
Image classification
Single and multispectral data can be classified to user defined land
use/land cover classes. In case of a single channel, segmentation will
be used. GRASS supports the following methods:
• Radiometric classification:
• Unsupervised classification (i.cluster, i.maxlik) using the
Maximum Likelihood classification method
• Supervised classification (i.gensig or g.gui.iclass, i.max-
lik) using the Maximum Likelihood classification method
• Combined radiometric/geometric (segmentation based) classifica-
tion:
• Supervised classification (i.gensigset, i.smap)
• Object-oriented classification:
• Unsupervised classification (segmentation based: i.segment)
Kappa statistic can be calculated to validate the results (r.kappa).
Covariance/correlation matrices can be calculated with r.covar.
Image fusion
In case of using multispectral data, improvements of the resolution can
be gained by merging the panchromatic channel with color channels.
GRASS provides the HIS (i.rgb.his, i.his.rgb) and the Brovey and PCA
transform (i.pansharpen) methods.
Radiometric corrections
Atmospheric effects can be removed with i.atcorr. Correction for topo-
graphic/terrain effects is offered in i.topo.corr. Clouds in LANDSAT
data can be identified and removed with i.landsat.acca. Calibrated
digital numbers of LANDSAT and ASTER imagery may be converted to
top-of-atmosphere radiance or reflectance and temperature
(i.aster.toar, i.landsat.toar).
Time series processing
GRASS also offers support for time series processing (r.series). Sta-
tistics can be derived from a set of coregistered input maps such as
multitemporal satellite data. The common univariate statistics and also
linear regression can be calculated.
Evapotranspiration modeling
In GRASS, several types of evapotranspiration (ET) modeling methods are
available:
• Reference ET: Hargreaves (i.evapo.mh), Penman-Monteith
(i.evapo.pm);
• Potential ET: Priestley-Taylor (i.evapo.pt);
• Actual ET: i.evapo.time.
Evaporative fraction: i.eb.evapfr, i.eb.hsebal01.
Energy balance
Emissivity can be calculated with i.emissivity. Several modules sup-
port the calculation of the energy balance:
• Actual evapotranspiration for diurnal period (i.eb.eta);
• Evaporative fraction and root zone soil moisture (i.eb.evapfr);
• Sensible heat flux iteration (i.eb.hsebal01);
• Net radiation approximation (i.eb.netrad);
• Soil heat flux approximation (i.eb.soilheatflux).
See also
• GRASS GIS Wiki page: Image processing
• The GRASS 4 Image Processing manual
• Introduction into raster data processing
• Introduction into 3D raster data (voxel) processing
• Introduction into vector data processing
• Introduction into temporal data processing
• Database management
• Projections and spatial transformations
SOURCE CODE
Available at: Image processing in GRASS GIS source code (history)
Accessed: unknown
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