1imageryintro(1) GRASS GIS User's Manual imageryintro(1)
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6 Image processing in general
7 Digital numbers and physical values (reflection/radiance-at-sensor):
8 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 The GRASS GIS module i.landsat.toar easily transforms Landsat DN to ra‐
24 diance-at-sensor (top of atmosphere, TOA). The equivalent module for
25 ASTER data is i.aster.toar. For other satellites, r.mapcalc can be em‐
26 ployed.
27 Reflection/radiance-at-sensor and surface reflectance
29 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 im‐
36 ages taken at different times, atmospheric need to be removed convert‐
37 ing at-sensor values which are top of atmosphere to surface reflectance
38 values.
39 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 in‐
47 dices.
48 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 As a general rule in GRASS:
58 1 Raster/imagery output maps have their bounds and resolution
60 equal to those of the current region.
61 2 Raster/imagery input maps are automatically cropped/padded and
63 rescaled (using nearest-neighbor resampling) to match the cur‐
64 rent region.
65 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 op‐
69 tions such as on-the-fly location creation or extension of the default
70 region to match the extent of the imported raster map. For special
71 cases, other import modules are available. Always the full map is im‐
72 ported. Imagery data can be group (e.g. channel-wise) with i.group.
73 For importing scanned maps, the user will need to create a x,y-loca‐
75 tion, scan the map in the desired resolution and save it into an appro‐
76 priate raster format (e.g. tiff, jpeg, png, pbm) and then use r.in.gdal
77 to import it. Based on reference points the scanned map can be recti‐
78 fied to obtain geocoded data.
79 Semantic label information
81 Semantic labels are a description which can be stored as metadata. To
82 print available semantic labels relevant for multispectral satellite
83 data, use i.band.library. r.semantic.label allows assigning of these
84 satellite imagery band references as defined in i.band.library. Seman‐
85 tic labels are also used in signature files of imagery classification
86 tools. Therefore, signature files of one imagery or raster group can be
87 used to classify a different group with identical semantic labels.
88 New enhanced classification workflow involving semantic labels. With
89 r.support any sort of semantic label the user wishes may be added
90 (i.e., not only those registered in i.band.library). Semantic labels
91 are supported also by the temporal GRASS modules.
92 Image processing operations
94 GRASS raster/imagery map processing is always performed in the current
95 region settings (see g.region), i.e. the current region extent and cur‐
96 rent raster resolution is used. If the resolution differs from that of
97 the input raster map(s), on-the-fly resampling is performed (nearest
98 neighbor resampling). If this is not desired, the input map(s) has/have
99 to be resampled beforehand with one of the dedicated modules.
100 Geocoding of imagery data
102 GRASS is able to geocode raster and image data of various types:
103 • unreferenced scanned maps by defining four corner points
105 (i.group, i.target, g.gui.gcp, i.rectify)
106 • unreferenced satellite data from optical and Radar sensors by
108 defining a certain number of ground control points (i.group,
109 i.target, g.gui.gcp, i.rectify)
110 • interactive graphical Ground Control Point (GCP) manager
112 • orthophoto generation based on DEM: i.ortho.photo
114 • digital handheld camera geocoding: modified procedure for i.or‐
116 tho.photo
117 Visualizing (true) color composites
119 To quickly combine the first three channels to a near natural color im‐
120 age, the GRASS command d.rgb can be used or the graphical GIS manager
121 (wxGUI). It assigns each channel to a color which is then mixed while
122 displayed. With a bit more work of tuning the grey scales of the chan‐
123 nels, nearly perfect colors can be achieved. Channel histograms can be
124 shown with d.histogram.
125 Calculation of vegetation indices
127 An example for indices derived from multispectral data is the NDVI
128 (normalized difference vegetation index). To study the vegetation sta‐
129 tus with NDVI, the Red and the Near Infrared channels (NIR) are taken
130 as used as input for simple map algebra in the GRASS command r.mapcalc
131 (ndvi = 1.0 * (nir - red)/(nir + red)). With r.colors an optimized
132 "ndvi" color table can be assigned afterward. Also other vegetation in‐
133 dices can be generated likewise.
134 Calibration of thermal channel
136 The encoded digital numbers of a thermal infrared channel can be trans‐
137 formed to degree Celsius (or other temperature units) which represent
138 the temperature of the observed land surface. This requires a few alge‐
139 braic steps with r.mapcalc which are outlined in the literature to ap‐
140 ply gain and bias values from the image metadata.
141 Image classification
143 Single and multispectral data can be classified to user defined land
144 use/land cover classes. In case of a single channel, segmentation will
145 be used. GRASS supports the following methods:
146 • Radiometric classification:
148 • Unsupervised classification (i.cluster, i.maxlik) using the
150 Maximum Likelihood classification method
151 • Supervised classification (i.gensig or g.gui.iclass, i.max‐
153 lik) using the Maximum Likelihood classification method
154 • Combined radiometric/geometric (segmentation based) classifica‐
156 tion:
157 • Supervised classification (i.gensigset, i.smap)
159 • Object-oriented classification:
161 • Unsupervised classification (segmentation based: i.segment)
163 Kappa statistic can be calculated to validate the results (r.kappa).
164 Covariance/correlation matrices can be calculated with r.covar.
165 Note - signatures generated for one scene are suitable for classifica‐
167 tion of other scenes as long as they consist of same raster bands (se‐
168 mantic labels match). This comes handy when classifying multiple scenes
169 from a single sensor taken in different areas or different times.
170 Image fusion
172 In case of using multispectral data, improvements of the resolution can
173 be gained by merging the panchromatic channel with color channels.
174 GRASS provides the HIS (i.rgb.his, i.his.rgb) and the Brovey and PCA
175 transform (i.pansharpen) methods.
176 Radiometric corrections
178 Atmospheric effects can be removed with i.atcorr. Correction for topo‐
179 graphic/terrain effects is offered in i.topo.corr. Clouds in LANDSAT
180 data can be identified and removed with i.landsat.acca. Calibrated
181 digital numbers of LANDSAT and ASTER imagery may be converted to
182 top-of-atmosphere radiance or reflectance and temperature
183 (i.aster.toar, i.landsat.toar).
184 Time series processing
186 GRASS also offers support for time series processing (r.series). Sta‐
187 tistics can be derived from a set of coregistered input maps such as
188 multitemporal satellite data. The common univariate statistics and also
189 linear regression can be calculated.
190 Evapotranspiration modeling
192 In GRASS, several types of evapotranspiration (ET) modeling methods are
193 available:
194 • Reference ET: Hargreaves (i.evapo.mh), Penman-Monteith
196 (i.evapo.pm);
197 • Potential ET: Priestley-Taylor (i.evapo.pt);
199 • Actual ET: i.evapo.time.
201 Evaporative fraction: i.eb.evapfr, i.eb.hsebal01.
202 Energy balance
204 Emissivity can be calculated with i.emissivity. Several modules sup‐
205 port the calculation of the energy balance:
206 • Actual evapotranspiration for diurnal period (i.eb.eta);
208 • Evaporative fraction and root zone soil moisture (i.eb.evapfr);
210 • Sensible heat flux iteration (i.eb.hsebal01);
212 • Net radiation approximation (i.eb.netrad);
214 • Soil heat flux approximation (i.eb.soilheatflux).
216 See also
218 • GRASS GIS Wiki page: Image processing
219 • The GRASS 4 Image Processing manual
221 • Introduction into raster data processing
223 • Introduction into 3D raster data (voxel) processing
225 • Introduction into vector data processing
227 • Introduction into temporal data processing
229 • Database management
231 • Projections and spatial transformations
233
235 Available at: Image processing in GRASS GIS source code (history)
236 Accessed: Saturday Jan 21 17:40:37 2023
238 Main index | Imagery index | Topics index | Keywords index | Graphical
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241 © 2003-2023 GRASS Development Team, GRASS GIS 8.2.1 Reference Manual
243GRASS 8.2.1 imageryintro(1)