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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