Flags:

-c

Copy input cell values on output

-a

Accumulate input values along the path

-n

Count cell numbers along the path

--overwrite

Allow output files to overwrite existing files

--help

Print usage summary

--verbose

Verbose module output

--quiet

Quiet module output

--ui

Force launching GUI dialog

Parameters:

input=name [required]

Name of input direction
Direction in degrees CCW from east, or bitmask encoded

format=string [required]

Format of the input direction map
Options: auto, degree, 45degree, bitmask
Default: auto
auto: auto-detect direction format
degree: degrees CCW from East
45degree: degrees CCW from East divided by 45 (e.g. r.watershed directions)
bitmask: bitmask encoded directions (e.g. r.cost -b)

values=name

Name of input raster values to be used for output
Name of input raster map

raster_path=name

Name for output raster path map
Name for output raster map

vector_path=name

Name for output vector path map
Name for output vector map

start_coordinates=east,north[,east,north,...]

Coordinates of starting point(s) (E,N)

start_points=name[,name,...]

Name of starting vector points map(s)
Or data source(s) for direct OGR access

Hydrological path

We are using the full North Carolina sample dataset. First we create the two points from a text file using v.in.ascii module (here the text file is CSV and we are using unix here-file syntax with EOF, in GUI just enter the values directly for the parameter input):

v.in.ascii input=- output=start format=point separator=comma <<EOF
638667.15686275,220610.29411765
638610.78431373,220223.03921569
EOF

We need to supply a direction raster map to the r.path module. To get these directions, we use the r.watershed module:

r.watershed elevation=elev_lid792_1m accumulation=accum drainage=drain_dir

The directions are categorical and we convert them to degrees using raster algebra:

r.mapcalc "drain_deg = if(drain_dir != 0, 45. * abs(drain_dir), null())"

Now we are ready to extract the drainage paths starting at the two points.

r.path input=drain_deg raster_path=drain_path vector_path=drain_path start_points=start

Before we visualize the result, we set a color table for the elevation we are using and create a shaded relief map:

r.colors map=elev_lid792_1m color=elevation
r.relief input=elev_lid792_1m output=relief

We visualize the input and output data:

d.shade shade=relief color=elev_lid792_1m
d.vect map=drain_path color=0:0:61 width=4 legend_label="drainage paths"
d.vect map=start color=none fill_color=224:0:0 icon=basic/circle size=15 legend_label=origins
d.legend.vect -b

Figure: Drainage paths from two points where directions from r.watershed were used

Least-cost path

We compute bitmask encoded movement directions using r.walk:

g.region swwake_30m -p
# create friction map based on land cover
r.recode input=landclass96 output=friction rules=- << EOF
1:3:0.1:0.1
4:5:10.:10.
6:6:1000.0:1000.0
7:7:0.3:0.3
EOF
# without Knight’s move
r.walk -b elevation=elev_ned_30m friction=friction output=walkcost \


    outdir=walkdir start_coordinates=635576,216485
r.path input=walkdir start_coordinates=640206,222795 \


    raster_path=walkpath vector_path=walkpath
# with Knight’s move
r.walk -b -k elevation=elev_ned_30m friction=friction output=walkcost_k \


    outdir=walkdir_k start_coordinates=635576,216485
r.path input=walkdir_k start_coordinates=640206,222795 \


    raster_path=walkpath_k vector_path=walkpath_k
# without Knight’s move and without bitmask encoding (single direction)
r.walk elevation=elev_ned_30m friction=friction output=walkcost_s \


    outdir=walkdir_s start_coordinates=635576,216485
r.path input=walkdir_s start_coordinates=640206,222795 \


    raster_path=walkpath_s vector_path=walkpath_s

The extracted least-cost path splits and merges on the way from the start point to the stop point (start point for r.walk). Note the gaps in the raster path when using the Knight’s move.

Figure: Comparison of shortest paths using single directions and multiple bitmask encoded directions without and with Knight’s move