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@@ -86,10 +86,11 @@ elevation map. Each point will receive the elevation value from the
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elevation raster, as if it came from a point survey.
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<div class="code"><pre>
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- g.region rast=elevation.10m res=150 -p
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- v.random output=rand2k_elev n=2000
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- v.db.addtable map=rand2k_elev column="elevation double precision"
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- v.what.rast vect=rand2k_elev rast=elevation.10m column=elevation
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+# reduce resolution for this example
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+g.region rast=elevation -p res=100
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+v.random output=rand2k_elev n=2000
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+v.db.addtable map=rand2k_elev column="elevation double precision"
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+v.what.rast map=rand2k_elev rast=elevation column=elevation
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</pre></div>
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<b>Part 2: remove points lacking elevation attributes</b>. Points
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@@ -101,18 +102,18 @@ mean...). In the following line of code, points with no data are
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removed from the map.
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<div class="code"><pre>
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- v.extract rand2k_elev output=rand2k_elev_filt where="elevation not NULL"
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+v.extract rand2k_elev output=rand2k_elev_filt where="elevation not NULL"
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</pre></div>
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Check the result of previous line ("number of NULL attributes" must be
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0):
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<div class="code"><pre>
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- v.univar rand2k_elev_filt type=point column=elevation
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+v.univar rand2k_elev_filt type=point column=elevation
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</pre></div>
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<b>Part 3: reconstruct DEM through kriging</b>. Using automatic
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-variogram fit is the simplest way to run v.krige from CLI (note:
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+variogram fit is the simplest way to run <em>v.krige</em> from CLI (note:
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requires R's automap package). Output map name is optional, the
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modules creates it automatically appending "_kriging" the the input
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map name and also checks for overwrite. If output_var is specified,
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@@ -123,29 +124,32 @@ parametrisation. A wider range of models is available from gstat
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package and can be tested on the GUI via the variogram plotting. If
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model is specified in the CLI, also sill, nugget and range values are
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to be provided, otherwise an error is raised (see second example of
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-v.krige command).
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+<em>v.krige</em> command).
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<div class="code"><pre>
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- v.krige input=rand2k_elev_filt column=elevation output=rand2k_elev_kriging \
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- output_var=rand2k_elev_kriging_var
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- v.krige input=rand2k_elev_filt column=elevation \
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- output=rand2k_elev_filt_kriging output_var=rand2k_elev_filt_kriging_var\
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- model=Mat sill=2500 nugget=0 range=1000
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+# automatic variogram fit
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+v.krige input=rand2k_elev_filt column=elevation \
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+ output=rand2k_elev_kriging output_var=rand2k_elev_kriging_var
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+
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+# define variogram model, create variance map as well
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+v.krige input=rand2k_elev_filt column=elevation \
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+ output=rand2k_elev_filt_kriging output_var=rand2k_elev_filt_kriging_var \
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+ model=Mat sill=2500 nugget=0 range=1000
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</pre></div>
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Or run wxGUI, to interactively fit the variogram and explore options:
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<div class="code"><pre>
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- v.krige
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+v.krige
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</pre></div>
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<b>Calculate prediction error</b>:
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<div class="code"><pre>
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- r.mapcalc "rand2k_elev_kriging_pe = sqrt(rand2k_elev_kriging_var)"
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- r.univar elevation.10m
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- r.univar rand2k_elev_kriging
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- r.univar rand2k_elev_kriging_pe
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+r.mapcalc "rand2k_elev_kriging_pe = sqrt(rand2k_elev_kriging_var)"
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+r.univar elevation
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+r.univar rand2k_elev_kriging
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+r.univar rand2k_elev_kriging_pe
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</pre></div>
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The results show high errors, as the kriging techniques (ordinary and
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@@ -167,10 +171,10 @@ package <a href="http://cran.r-project.org/web/packages/spgrass6/index.html">spg
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mantained by Roger Bivand
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<br>
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-The <a href="http://grass.osgeo.org/statsgrass/grass6_r_install.html">Short
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-Introduction to Geostatistical and Spatial Data Analysis with GRASS 6
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-and R statistical data language</a> at the GRASS website. (includes
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-installation tips)
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+The <a href="http://grasswiki.osgeo.org/wiki/R_statistics">Short
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+Introduction to Geostatistical and Spatial Data Analysis with GRASS GIS
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+and R statistical data language</a> at the GRASS Wiki (includes
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+installation tips).
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<br><br>
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