diff --git a/raster/r.carve/README b/raster/r.carve/README deleted file mode 100644 index b40508ea206..00000000000 --- a/raster/r.carve/README +++ /dev/null @@ -1,37 +0,0 @@ -http://fatra.cnr.ncsu.edu/~hmitaso/gmslab/reports/cerl99/rep99.html -Terrain modeling and Soil Erosion Simulations for Fort Hood and Fort Polk -test areas - -Geographic Modeling and Systems Laboratory, -University of Illinois atUrbana-Champaign -Helena Mitasova, Lubos Mitas, William M. Brown, Douglas M. Johnston - - - rast=name raster input file - vect=name vector input file containing stream - - output=name raster output file - [points=name] vector output file for adjusted stream points - [width=value] width of streams (in meters) - [depth=value] additional depth - --q quiet - do not show progress --n no flat areas allowed in flow direction - -r.carve does not create a depressionless DEM because many depressions are in -flat areas and not in the streams. - -http://fatra.cnr.ncsu.edu/~hmitaso/gmslab/reports/cerl99/demstr.gif - -The program will take the vector stream data, transform them to raster, and -subtracts a defaultdepth + additionaldepth from the DEM. If the given -width is more than 1 cell it will carve the stream with the given width. -With -n option it should eliminate all flat cells within the stream, so -when and if the water gets into the stream it will flow. Vector -('points') option generates x,y,z for points which define the stream -with the z value of the bottom of the carved-in stream. These points -can then be combined with contour points to interpolate a new DEM with -better representation of valleys. - -It has not been thoroughly tested so not all option may work properly - -but this was the intention. diff --git a/raster/r.fill.stats/r.fill.stats.html b/raster/r.fill.stats/r.fill.stats.html index 87fb0c045df..6776740af0c 100644 --- a/raster/r.fill.stats/r.fill.stats.html +++ b/raster/r.fill.stats/r.fill.stats.html @@ -222,50 +222,6 @@

Smoothing

using the r.param.scale module which operates with variable-size cell neighborhoods.) - -

Spatial weighting scheme

The key to getting good gap filling results is to understand the @@ -389,11 +345,6 @@

Gap-filling of a dataset using spatially weighted mean (IDW)

Lidar point cloud example

- - Inspect the point density and determine the extent of the point cloud using the r.in.pdal module: diff --git a/raster/r.flow/r.flow.html b/raster/r.flow/r.flow.html index da06e63bf5c..ebc6876df0f 100644 --- a/raster/r.flow/r.flow.html +++ b/raster/r.flow/r.flow.html @@ -2,9 +2,9 @@

DESCRIPTION

r.flow generates flowlines using a combined raster-vector approach -(see Mitasova +(see Mitasova and Hofierka 1993 -and Mitasova +and Mitasova et al. 1995) from an input elevation raster map (integer or floating point), and optionally an input aspect raster map and/or an input barrier raster map. @@ -189,12 +189,12 @@

REFERENCES