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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.

   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.

   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)

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       © 2003-2019 GRASS Development Team, GRASS GIS 7.8.2 Reference Manual