[GRASS-SVN] r61782 - grass/trunk/raster/r.in.xyz
svn_grass at osgeo.org
svn_grass at osgeo.org
Tue Sep 2 00:45:26 PDT 2014
Author: neteler
Date: 2014-09-02 00:45:26 -0700 (Tue, 02 Sep 2014)
New Revision: 61782
Modified:
grass/trunk/raster/r.in.xyz/r.in.xyz.html
Log:
r.in.xyz manual: fix XYZ data URL; example cosmetics
Modified: grass/trunk/raster/r.in.xyz/r.in.xyz.html
===================================================================
--- grass/trunk/raster/r.in.xyz/r.in.xyz.html 2014-09-01 15:32:41 UTC (rev 61781)
+++ grass/trunk/raster/r.in.xyz/r.in.xyz.html 2014-09-02 07:45:26 UTC (rev 61782)
@@ -188,41 +188,50 @@
<p>
Typical commands to create a DEM using a regularized spline fit:
<div class="code"><pre>
- r.univar lidar_min
- r.to.vect -z type=point in=lidar_min out=lidar_min_pt
- v.surf.rst in=lidar_min_pt elev=lidar_min.rst
+r.univar lidar_min
+r.to.vect -z type=point in=lidar_min out=lidar_min_pt
+v.surf.rst in=lidar_min_pt elev=lidar_min.rst
</pre></div>
<h2>EXAMPLE</h2>
-Import the <a href="http://www.grassbook.org/data_menu2nd.phtml">Jockey's
-Ridge, NC, LIDAR dataset</a>, and process into a clean DEM:
+Import the <a href="http://www.grassbook.org/ncexternal/index.html">Jockey's
+Ridge, NC, LIDAR dataset</a> (compressed file "lidaratm2.txt.gz"), and process it
+into a clean DEM:
<div class="code"><pre>
- # scan and set region bounds
- r.in.xyz -s separator="," in=lidaratm2.txt out=test
- g.region n=35.969493 s=35.949693 e=-75.620999 w=-75.639999
- g.region res=0:00:00.075 -a
- # create "n" map containing count of points per cell for checking density
- r.in.xyz in=lidaratm2.txt out=lidar_n separator="," method=n zrange=-2,50
- # check point density [rho = n_sum / (rows*cols)]
- r.univar lidar_n | grep sum
- # create "min" map (elevation filtered for premature hits)
- r.in.xyz in=lidaratm2.txt out=lidar_min separator="," method=min zrange=-2,50
- # zoom to area of interest
- g.region n=35:57:56.25N s=35:57:13.575N w=75:38:23.7W e=75:37:15.675W
- # check number of non-null cells (try and keep under a few million)
- r.univar lidar_min | grep '^n:'
- # convert to points
- r.to.vect -z type=point in=lidar_min out=lidar_min_pt
- # interpolate using a regularized spline fit
- v.surf.rst in=lidar_min_pt elev=lidar_min.rst
- # set color scale to something interesting
- r.colors lidar_min.rst rule=bcyr -n -e
- # prepare a 1:1:1 scaled version for NVIZ visualization (for lat/lon input)
- r.mapcalc "lidar_min.rst_scaled = lidar_min.rst / (1852*60)"
- r.colors lidar_min.rst_scaled rule=bcyr -n -e
+# scan and set region bounds
+r.in.xyz -s separator="," in=lidaratm2.txt out=test
+g.region n=35.969493 s=35.949693 e=-75.620999 w=-75.639999
+g.region res=0:00:00.075 -a
+
+# create "n" map containing count of points per cell for checking density
+r.in.xyz in=lidaratm2.txt out=lidar_n separator="," method=n zrange=-2,50
+
+# check point density [rho = n_sum / (rows*cols)]
+r.univar lidar_n | grep sum
+# create "min" map (elevation filtered for premature hits)
+r.in.xyz in=lidaratm2.txt out=lidar_min separator="," method=min zrange=-2,50
+
+# set computational region to area of interest
+g.region n=35:57:56.25N s=35:57:13.575N w=75:38:23.7W e=75:37:15.675W
+
+# check number of non-null cells (try and keep under a few million)
+r.univar lidar_min | grep '^n:'
+
+# convert to points
+r.to.vect -z type=point in=lidar_min out=lidar_min_pt
+
+# interpolate using a regularized spline fit
+v.surf.rst in=lidar_min_pt elev=lidar_min.rst
+
+# set color scale to something interesting
+r.colors lidar_min.rst rule=bcyr -n -e
+
+# prepare a 1:1:1 scaled version for NVIZ visualization (for lat/lon input)
+r.mapcalc "lidar_min.rst_scaled = lidar_min.rst / (1852*60)"
+r.colors lidar_min.rst_scaled rule=bcyr -n -e
</pre></div>
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