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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 minimizing 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
radiance-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 physical values by applying the reverse formula (x = (y
- b) / a).
The GRASS GIS module i.landsat.toar easily transforms Landsat DN to radiance-at-sensor
(top of atmosphere, TOA). The equivalent module for ASTER data is i.aster.toar. For other
satellites, r.mapcalc can be employed.
Reflection/radiance-at-sensor and surface reflectance
When radiance-at-sensor has been obtained, still the atmosphere influences the signal as
recorded at the sensor. This atmospheric interaction with the sun energy reflected back
into space by ground/vegetation/soil needs to be corrected. The need of removing
atmospheric artifacts stems from the fact that the atmosphericic conditions are changing
over time. Hence, to gain comparability between Earth surface images taken at different
times, atmospheric need to be removed converting 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 atmospherically corrected sensor data represent surface reflectance, which ranges
theoretically from 0% to 100%. Note that this level of data correction is the proper level
of correction to calculate vegetation indices.
In GRASS GIS, image data are identical to raster data. However, a couple of commands are
explicitly dedicated to image processing. The geographic 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 current region.
Imagery import
The module r.in.gdal offers a common interface for many different raster and satellite
image 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. Always the full map is imported.
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-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 rectified to obtain geocoded data.
Semantic label information
Semantic labels are a description which can be stored as metadata. To print available
semantic labels relevant for multispectral satellite data, use i.band.library.
r.semantic.label allows assigning of these satellite imagery band references as defined in
i.band.library. Semantic labels are also used in signature files of imagery classification
tools. Therefore, signature files of one imagery or raster group can be used to classify a
different group with identical semantic labels.
New enhanced classification workflow involving semantic labels. With r.support any sort
of semantic label the user wishes may be added (i.e., not only those registered in
i.band.library). Semantic labels are supported also by the temporal GRASS modules.
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 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.
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.ortho.photo
Visualizing (true) color composites
To quickly combine the first three channels to a near natural color image, 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 channels, 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 status 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 indices can be generated likewise.
Calibration of thermal channel
The encoded digital numbers of a thermal infrared channel can be transformed to degree
Celsius (or other temperature units) which represent the temperature of the observed land
surface. This requires a few algebraic steps with r.mapcalc which are outlined in the
literature to apply 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.maxlik) using the
Maximum Likelihood classification method
• Combined radiometric/geometric (segmentation based) classification:
• 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.
Note - signatures generated for one scene are suitable for classification of other scenes
as long as they consist of same raster bands (semantic labels match). This comes handy
when classifying multiple scenes from a single sensor taken in different areas or
different times.
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 topographic/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). Statistics 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 support 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: Fri Jun 3 13:27:06 2022
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© 2003-2022 GRASS Development Team, GRASS GIS 8.2.0 Reference Manual