1imageryintro(1)               Grass User's Manual              imageryintro(1)
2
3
4

Image processing in GRASS GIS

6   Image processing in general
7       Digital numbers and physical values (reflection/radiance-at-sensor):
8
9       Satellite  imagery is commonly stored in Digital Numbers (DN) for mini‐
10       mizing the storage volume, i.e. the originally sampled analog  physical
11       value  (color, temperature, etc) is stored a discrete representation in
12       8-16 bits. For example, Landsat data are stored in 8bit  values  (i.e.,
13       ranging  from 0 to 255); other satellite data may be stored in 10 or 16
14       bits. Having data stored in DN, it implies that these data are not  yet
15       the  observed  ground reality. Such data are called "at-satellite", for
16       example the amount of energy sensed by  the  sensor  of  the  satellite
17       platform  is  encoded  in  8  or more bits. This energy is called radi‐
18       ance-at-sensor. To obtain physical values  from  DNs,  satellite  image
19       providers use a linear transform equation (y = a * x + b) to encode the
20       radiance-at-sensor in 8 to 16 bits. DNs can be turned back into  physi‐
21       cal values by applying the reverse formula (x = (y - b) / a).
22
23       The  GRASS  GIS  module  i.landsat.toar easily transforms Landsat DN to
24       radiance-at-sensor (top of atmosphere, TOA). The equivalent module  for
25       ASTER  data  is  i.aster.toar.   For other satellites, r.mapcalc can be
26       employed.
27
28       Reflection/radiance-at-sensor and surface reflectance
29
30       When radiance-at-sensor has been obtained, still the atmosphere  influ‐
31       ences  the  signal as recorded at the sensor. This atmospheric interac‐
32       tion with the sun energy reflected back into  space  by  ground/vegeta‐
33       tion/soil needs to be corrected. The need of removing atmospheric arti‐
34       facts stems from the fact that the atmosphericic conditions are  chang‐
35       ing  over  time.  Hence,  to  gain  comparability between Earth surface
36       images taken at different times, atmospheric need to  be  removed  con‐
37       verting  at-sensor  values  which  are  top  of  atmosphere  to surface
38       reflectance values.
39
40       In GRASS GIS, there are two ways to apply  atmospheric  correction  for
41       satellite  imagery.  A  simple,  less  accurate way for Landsat is with
42       i.landsat.toar, using the DOS correction method. The more accurate  way
43       is  using  i.atcorr  (which supports many satellite sensors). The atmo‐
44       spherically corrected sensor data represent surface reflectance,  which
45       ranges theoretically from 0% to 100%. Note that this level of data cor‐
46       rection is the proper  level  of  correction  to  calculate  vegetation
47       indices.
48
49       In GRASS GIS, image data are identical to raster data.  However, a cou‐
50       ple of commands are explicitly dedicated to image processing. The  geo‐
51       graphic  boundaries  of  the  raster/imagery  file are described by the
52       north, south, east, and west fields. These values  describe  the  lines
53       which  bound  the map at its edges. These lines do NOT pass through the
54       center of the grid cells at the edge of the map, but along the edge  of
55       the map itself.
56
57       As a general rule in GRASS:
58
59       1      Raster/imagery  output  maps  have  their  bounds and resolution
60              equal to those of the current region.
61
62       2      Raster/imagery input maps are automatically  cropped/padded  and
63              rescaled  (using  nearest-neighbor resampling) to match the cur‐
64              rent region.
65
66   Imagery import
67       The module r.in.gdal offers  a  common  interface  for  many  different
68       raster  and  satellite  image  formats.  Additionally,  it  also offers
69       options such as  on-the-fly  location  creation  or  extension  of  the
70       default  region  to  match  the extent of the imported raster map.  For
71       special cases, other import modules are available. Always the full  map
72       is  imported.  Imagery  data  can  be  group  (e.g.  channel-wise) with
73       i.group.
74
75       For importing scanned maps, the user will need to  create  a  x,y-loca‐
76       tion, scan the map in the desired resolution and save it into an appro‐
77       priate raster format (e.g. tiff, jpeg, png, pbm) and then use r.in.gdal
78       to  import  it. Based on reference points the scanned map can be recti‐
79       fied to obtain geocoded data.
80
81   Image processing operations
82       GRASS raster/imagery map processing is always performed in the  current
83       region settings (see g.region), i.e. the current region extent and cur‐
84       rent raster resolution is used. If the resolution differs from that  of
85       the  input  raster  map(s), on-the-fly resampling is performed (nearest
86       neighbor resampling). If this is not desired, the input map(s) has/have
87       to be resampled beforehand with one of the dedicated modules.
88
89   Geocoding of imagery data
90       GRASS is able to geocode raster and image data of various types:
91
92           ·   unreferenced  scanned  maps  by  defining  four  corner  points
93               (i.group, i.target, g.gui.gcp, i.rectify)
94
95           ·   unreferenced satellite data from optical and Radar  sensors  by
96               defining  a  certain  number of ground control points (i.group,
97               i.target, g.gui.gcp, i.rectify)
98
99           ·   interactive graphical Ground Control Point (GCP) manager
100
101           ·   orthophoto generation based on DEM: i.ortho.photo
102
103           ·   digital  handheld  camera  geocoding:  modified  procedure  for
104               i.ortho.photo
105
106   Visualizing (true) color composites
107       To  quickly  combine  the  first three channels to a near natural color
108       image, the GRASS command d.rgb can be used or the graphical GIS manager
109       (wxGUI).  It  assigns each channel to a color which is then mixed while
110       displayed. With a bit more work of tuning the grey scales of the  chan‐
111       nels,  nearly perfect colors can be achieved. Channel histograms can be
112       shown with d.histogram.
113
114   Calculation of vegetation indices
115       An example for indices derived from  multispectral  data  is  the  NDVI
116       (normalized  difference vegetation index). To study the vegetation sta‐
117       tus with NDVI, the Red and the Near Infrared channels (NIR)  are  taken
118       as  used as input for simple map algebra in the GRASS command r.mapcalc
119       (ndvi = 1.0 * (nir - red)/(nir +  red)).  With  r.colors  an  optimized
120       "ndvi"  color  table  can  be assigned afterward. Also other vegetation
121       indices can be generated likewise.
122
123   Calibration of thermal channel
124       The encoded digital numbers of a thermal infrared channel can be trans‐
125       formed  to  degree Celsius (or other temperature units) which represent
126       the temperature of the observed land surface. This requires a few alge‐
127       braic  steps  with  r.mapcalc  which  are outlined in the literature to
128       apply gain and bias values from the image metadata.
129
130   Image classification
131       Single and multispectral data can be classified to  user  defined  land
132       use/land  cover classes. In case of a single channel, segmentation will
133       be used.  GRASS supports the following methods:
134
135           ·   Radiometric classification:
136
137               ·   Unsupervised classification (i.cluster, i.maxlik) using the
138                   Maximum Likelihood classification method
139
140               ·   Supervised classification (i.gensig or g.gui.iclass, i.max‐
141                   lik) using the Maximum Likelihood classification method
142
143           ·   Combined radiometric/geometric (segmentation based) classifica‐
144               tion:
145
146               ·   Supervised classification (i.gensigset, i.smap)
147
148           ·   Object-oriented classification:
149
150               ·   Unsupervised classification (segmentation based: i.segment)
151       Kappa  statistic  can  be calculated to validate the results (r.kappa).
152       Covariance/correlation matrices can be calculated with r.covar.
153
154   Image fusion
155       In case of using multispectral data, improvements of the resolution can
156       be  gained  by  merging  the  panchromatic channel with color channels.
157       GRASS provides the HIS (i.rgb.his, i.his.rgb) and the  Brovey  and  PCA
158       transform (i.pansharpen) methods.
159
160   Radiometric corrections
161       Atmospheric effects can be removed with i.atcorr.  Correction for topo‐
162       graphic/terrain effects is offered in i.topo.corr.  Clouds  in  LANDSAT
163       data  can  be  identified  and removed with i.landsat.acca.  Calibrated
164       digital numbers of LANDSAT  and  ASTER  imagery  may  be  converted  to
165       top-of-atmosphere    radiance    or    reflectance    and   temperature
166       (i.aster.toar, i.landsat.toar).
167
168   Time series processing
169       GRASS also offers support for time series processing  (r.series).  Sta‐
170       tistics  can  be  derived from a set of coregistered input maps such as
171       multitemporal satellite data. The common univariate statistics and also
172       linear regression can be calculated.
173
174   Evapotranspiration modeling
175       In GRASS, several types of evapotranspiration (ET) modeling methods are
176       available:
177
178           ·   Reference   ET:   Hargreaves   (i.evapo.mh),    Penman-Monteith
179               (i.evapo.pm);
180
181           ·   Potential ET: Priestley-Taylor (i.evapo.pt);
182
183           ·   Actual ET: i.evapo.time.
184       Evaporative fraction: i.eb.evapfr, i.eb.hsebal01.
185
186   Energy balance
187       Emissivity  can  be calculated with i.emissivity.  Several modules sup‐
188       port the calculation of the energy balance:
189
190           ·   Actual evapotranspiration for diurnal period  (i.eb.eta);
191
192           ·   Evaporative fraction and root zone soil moisture (i.eb.evapfr);
193
194           ·   Sensible heat flux iteration (i.eb.hsebal01);
195
196           ·   Net radiation approximation (i.eb.netrad);
197
198           ·   Soil heat flux approximation (i.eb.soilheatflux).
199
200   See also
201           ·   GRASS GIS Wiki page: Image processing
202
203           ·   The GRASS 4 Image Processing manual
204
205           ·   Introduction into raster data processing
206
207           ·   Introduction into 3D raster data (voxel) processing
208
209           ·   Introduction into vector data processing
210
211           ·   Introduction into temporal data processing
212
213           ·   Database management
214
215           ·   Projections and spatial transformations
216

SOURCE CODE

218       Available at: Image processing in GRASS GIS source code (history)
219
220       Main index | Imagery index | Topics index | Keywords index |  Graphical
221       index | Full index
222
223       © 2003-2019 GRASS Development Team, GRASS GIS 7.6.0 Reference Manual
224
225
226
227GRASS 7.6.0                                                    imageryintro(1)
Impressum