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:
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.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.
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.i.rgb.his, i.his.rgb) and the Brovey and PCA transform (i.pansharpen) methods. 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). 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.
Latest change: Wednesday Oct 04 09:39:09 2023 in commit: 0bd5c8d434532a31f890a307ce4c08273b8d70eb
© 2003-2023 GRASS Development Team, GRASS GIS 8.2.2dev Reference Manual