[GRASS-SVN] r49395 - in grass-addons/raster/r.pi: r.pi.energy
r.pi.energy.iter r.pi.searchtime r.pi.searchtime.iter
r.pi.searchtime.mw
svn_grass at osgeo.org
svn_grass at osgeo.org
Sun Nov 27 11:33:22 EST 2011
Author: wegmann
Date: 2011-11-27 08:33:21 -0800 (Sun, 27 Nov 2011)
New Revision: 49395
Modified:
grass-addons/raster/r.pi/r.pi.energy.iter/description.html
grass-addons/raster/r.pi/r.pi.energy/description.html
grass-addons/raster/r.pi/r.pi.searchtime.iter/description.html
grass-addons/raster/r.pi/r.pi.searchtime.mw/description.html
grass-addons/raster/r.pi/r.pi.searchtime/description.html
Log:
fixed and extended manual pages and added various further examples
Modified: grass-addons/raster/r.pi/r.pi.energy/description.html
===================================================================
--- grass-addons/raster/r.pi/r.pi.energy/description.html 2011-11-27 16:23:26 UTC (rev 49394)
+++ grass-addons/raster/r.pi/r.pi.energy/description.html 2011-11-27 16:33:21 UTC (rev 49395)
@@ -8,6 +8,8 @@
The suitability matrix impacts the step direction, while the costmap relates to the depletion of assigned energy.
+If individuals are moving beyond the mapset borders the indivuals are set back to their original source patches.
+
<h2>EXAMPLE</h2>
An example for the North Carolina sample dataset:
@@ -20,7 +22,7 @@
introducing costs for movement results in different immigration counts:
<div class="code"><pre>
-r.mapcalc "cost_raster = if(landclass96==5,0,if(landclass96 == 1, 10, if (landclass96==3,2, if(landclass96==4,1,if(landclass96==6,100)))))"
+r.mapcalc "cost_raster = if(landclass96==5,1,if(landclass96 == 1, 10, if (landclass96==3,2, if(landclass96==4,1,if(landclass96==6,100)))))"
r.pi.energy input=landclass96 output=energy1 keyval=5 n=1000 step_length=5 energy=10 percent=80 costmap=cost_raster
</pre></div>
@@ -28,43 +30,28 @@
<div class="code"><pre>
# the suitability for the next step selection is defined as: class 5 and 3 (forest and grassland) have a high suitability, while shrubland (class 4) only a moderate and water and developed areas (class 6 and 1) have a very low suitability:
r.mapcalc "suit_raster = if(landclass96==5,100,if(landclass96 == 3, 100, if (landclass96==1,1, if(landclass96==6,1,if(landclass96==4,50)))))"
-r.pi.energy input=landclass96 output=energy1 keyval=5 n=1000 step_length=5 energy=10 percent=80 suitability=suit_raster
+r.pi.energy input=landclass96 output=energyiter3 keyval=5 n=1000 step_length=5 energy=10 percent=80 suitability=suit_raster
</pre></div>
further settings can be changed and information retrieved:
setting the perception range to 10 pixel:
<div class="code"><pre>
-r.pi.energy input=landclass96 output=energy1 keyval=5 n=1000 step_length=5 energy=10 percent=80 perception=10
+r.pi.energy input=landclass96 output=energyiter keyval=5 n=1000 step_length=5 energy=10 percent=80 perception=10
</pre></div>
increasing the attraction to move towards patches to 10:
<div class="code"><pre>
-r.pi.energy input=landclass96 output=energy1 keyval=5 n=1000 step_length=5 energy=10 percent=80 multiplicator=10
+r.pi.energy input=landclass96 output=energyiter keyval=5 n=1000 step_length=5 energy=10 percent=80 stats=average multiplicator=10
</pre></div>
output of each movement location for a defined step frequency. Here every 10th step is provided as output raster:
<div class="code"><pre>
-r.pi.energy input=landclass96 output=energy1 keyval=5 n=1000 step_length=5 energy=10 percent=80 out_freq=10
+r.pi.energy input=landclass96 output=energyiter keyval=5 n=1000 step_length=5 energy=10 percent=80 stats=average out_freq=10
</pre></div>
-output of a raster which immigration counts:
-<div class="code"><pre>
-r.pi.energy input=landclass96 output=energy1 keyval=5 n=1000 step_length=5 energy=10 percent=80 out_immi=immi_counts
-</pre></div>
-output of a binary immigration matrix. Each patch emigration and immigration for all patch combinations is recorded as 0 or 1:
-<div class="code"><pre>
-r.pi.energy input=landclass96 output=energy1 keyval=5 n=1000 step_length=5 energy=10 percent=80 binary_matrix=binary_matrix.txt
-</pre></div>
-output of a matrix with immigration counts for each patch:
-<div class="code"><pre>
-r.pi.energy input=landclass96 output=energy1 keyval=5 n=1000 step_length=5 energy=10 percent=80 immi_matrix=immi_counts.txt
-</pre></div>
-
-
-
<h2>SEE ALSO</h2>
<em>
Modified: grass-addons/raster/r.pi/r.pi.energy.iter/description.html
===================================================================
--- grass-addons/raster/r.pi/r.pi.energy.iter/description.html 2011-11-27 16:23:26 UTC (rev 49394)
+++ grass-addons/raster/r.pi/r.pi.energy.iter/description.html 2011-11-27 16:33:21 UTC (rev 49395)
@@ -1,22 +1,58 @@
<h2>DESCRIPTION</h2>
-Individual-based dispersal model for connectivity analysis (energy based)
-using iterative patch removal.
+This function is based on <em>r.pi.energy</em> but adds the functionality of iterative patch removal for testing of patch importance to maintain the landscape connectivity integrity. Isolation or connectivity of singular patches of a defined landcover class are analysed using individual-based dispersal models. This functions uses a maximum amount of energy for each individuals dispersing through the landscape which is deminished by a fricition or cost map. Unlike the related function <em>r.pi.energy</em> does this function allows individuals to stay or move within a patch until the energy is depleted.
<h2>NOTES</h2>
-...
+Amount of successful immigrants or emigrants are not taken individual into account which emigrated from and immigrated into the same patch (pseudo immigration).
+The suitability matrix impacts the step direction, while the costmap relates to the depletion of assigned energy.
+
<h2>EXAMPLE</h2>
+An example for the North Carolina sample dataset:
+
+The amount (average) and variance with or without the respective patch of successful emigrants (*_emi), immigrants (*_imi), the percentage of immigrants per patch (*_imi_percent), the amount of lost indivuals (*_lost), the amount of migrants (*_mig), successful (*_mig_succ) and unsuccessful migrants (_mig_unsucc) can be retrieved using this command:
<div class="code"><pre>
-...
+r.pi.energy.iter input=landclass96 output=energyiter1 keyval=5 n=1000 step_length=5 energy=10 percent=80 stats=average,variance
</pre></div>
+introducing costs for movement results in different immigration counts:
+<div class="code"><pre>
+r.mapcalc "cost_raster = if(landclass96==5,1,if(landclass96 == 1, 10, if (landclass96==3,2, if(landclass96==4,1,if(landclass96==6,100)))))"
+r.pi.energy.iter input=landclass96 output=energy1 keyval=5 n=1000 step_length=5 energy=10 percent=80 stats=average costmap=cost_raster
+</pre></div>
+
+introducing a suitability for the movement:
+<div class="code"><pre>
+# the suitability for the next step selection is defined as: class 5 and 3 (forest and grassland) have a high suitability, while shrubland (class 4) only a moderate and water and developed areas (class 6 and 1) have a very low suitability:
+r.mapcalc "suit_raster = if(landclass96==5,100,if(landclass96 == 3, 100, if (landclass96==1,1, if(landclass96==6,1,if(landclass96==4,50)))))"
+r.pi.energy.iter input=landclass96 output=energyiter3 keyval=5 n=1000 step_length=5 energy=10 percent=80 suitability=suit_raster stats=average,variance
+</pre></div>
+
+further settings can be changed and information retrieved:
+
+setting the perception range to 10 pixel:
+<div class="code"><pre>
+r.pi.energy.iter input=landclass96 output=energyiter keyval=5 n=1000 step_length=5 energy=10 percent=80 perception=10 stats=average
+</pre></div>
+
+increasing the attraction to move towards patches to 10:
+<div class="code"><pre>
+r.pi.energy input=landclass96 output=energyiter keyval=5 n=1000 step_length=5 energy=10 percent=80 stats=average multiplicator=10
+</pre></div>
+
+output of each movement location for a defined step frequency. Here every 10th step is provided as output raster:
+<div class="code"><pre>
+r.pi.energy input=landclass96 output=energyiter keyval=5 n=1000 step_length=5 energy=10 percent=80 stats=average out_freq=10
+</pre></div>
+
+
<h2>SEE ALSO</h2>
<em>
-<a href="r.energy.iter.html">r.energy.iter</a>,
+<a href="r.pi.energy.html">r.pi.energy</a>,
+<a href="r.pi.searchtime.html">r.pi.searchtime</a>,
<a href="r.pi.html">r.pi</a>
</em>
Modified: grass-addons/raster/r.pi/r.pi.searchtime/description.html
===================================================================
--- grass-addons/raster/r.pi/r.pi.searchtime/description.html 2011-11-27 16:23:26 UTC (rev 49394)
+++ grass-addons/raster/r.pi/r.pi.searchtime/description.html 2011-11-27 16:33:21 UTC (rev 49395)
@@ -34,7 +34,7 @@
r.pi.searchtime input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 multiplicator=10
</pre></div>
-increasing the attraction to move towards patches to 10:
+limiting the amount of steps to 10:
<div class="code"><pre>
r.pi.searchtime input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 maxsteps=10
</pre></div>
@@ -62,7 +62,7 @@
the previous examples assumed a homogeneous matrix, a heterogenous matrix can be included using a raster file which values are taken as costs for movement (0-100):
<div class="code"><pre>
# it is assumed that our species is a forest species and cannot move through water, hence a cost of 100, does not like urban areas (class: 6, cost: 10) but can disperse through shrubland (class 4, cost=1) better than through grassland (class 3, cost: 2):
-r.mapcalc "suit_raster = if(landclass96==5,0,if(landclass96 == 1, 10, if (landclass96==3,2, if(landclass96==4,1,if(landclass96==6,100)))))"
+r.mapcalc "suit_raster = if(landclass96==5,1,if(landclass96 == 1, 10, if (landclass96==3,2, if(landclass96==4,1,if(landclass96==6,100)))))"
r.pi.searchtime input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 suitability=suit_raster
</pre></div>
Modified: grass-addons/raster/r.pi/r.pi.searchtime.iter/description.html
===================================================================
--- grass-addons/raster/r.pi/r.pi.searchtime.iter/description.html 2011-11-27 16:23:26 UTC (rev 49394)
+++ grass-addons/raster/r.pi/r.pi.searchtime.iter/description.html 2011-11-27 16:33:21 UTC (rev 49395)
@@ -1,25 +1,51 @@
-Individual-based dispersal model for connectivity analysis (time-based) using iterative removal of patches
+Analysis of patch relevance to maintain the landscape connectivity using individual-based dispersal model for connectivity analysis (time-based).
+
<h2>DESCRIPTION</h2>
-This modules aims at generating sampling areas which are only known by the
-coordinate of one corner. The input are single points, while the output are
-areas representing the corresponding area for each of the single
-points/coordinates.
+This modules provides information about the importance of single patches for maintaining the connectivity of individual fragments derived of a landcover classification. Unlike <em>r.pi.energy.iter</em> this module provides information about the differences in time from emigration to immigration. The individual based dispersal model results are based on the step length and range, the perception distance and the attractivity to move towards patches.
+
<h2>NOTES</h2>
-The areas can only be generated horizontally, not diagonal. This can be added
-as wish and might be implemented in the future.
+The suitability matrix impacts the step direction of individuals. If individuals are moving beyond the mapset borders the indivuals are set back to their original source patches.
<h2>EXAMPLE</h2>
An example for the North Carolina sample dataset:
+The patch relevance concerning connectivity are based on patches of the <em>landclass96</em> raster class 5 amd the time (amount of steps) from emigration to immigration is computed. The step length is set to 5 pixel, the output statistics are set to <em>average</em> time and <em>variance</em> of searchtime. For each patch 1000 individuals were released and the model stopped when at least 80% of all individuals sucessfully immigrated:<br>
<div class="code"><pre>
-g.region -d
-...
+r.pi.searchtime.iter input=landclass96 output=searchtime_iter1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 dif_stats=average
</pre></div>
+setting the perception range to 10 pixel:
+<div class="code"><pre>
+r.pi.searchtime.iter input=landclass96 output=searchtime_iter1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 dif_stats=average perception=10
+</pre></div>
+
+increasing the attraction to move towards patches to 10:
+<div class="code"><pre>
+r.pi.searchtime.iter input=landclass96 output=searchtime_iter1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 dif_stats=average multiplicator=10
+</pre></div>
+
+output of each movement location for a defined step frequency. Here every 10th step is provided as output raster:
+<div class="code"><pre>
+r.pi.searchtime.iter input=landclass96 output=searchtime_iter1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 dif_stats=average out_freq=10
+</pre></div>
+
+output of a raster which immigration counts:
+<div class="code"><pre>
+r.pi.searchtime.iter input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 dif_stats=average out_immi=immi_counts
+</pre></div>
+
+the previous examples assumed a homogeneous matrix, a heterogenous matrix can be included using a raster file which values are taken as costs for movement (0-100):
+<div class="code"><pre>
+# it is assumed that our species is a forest species and cannot move through water, hence a cost of 100, does not like urban areas (class: 6, cost: 10) but can disperse through shrubland (class 4, cost=1) better than through grassland (class 3, cost: 2):
+r.mapcalc "suit_raster = if(landclass96==5,1,if(landclass96 == 1, 10, if (landclass96==3,2, if(landclass96==4,1,if(landclass96==6,100)))))"
+r.pi.searchtime.iter input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 dif_stats=average suitability=suit_raster
+</pre></div>
+
+
<h2>SEE ALSO</h2>
<em>
Modified: grass-addons/raster/r.pi/r.pi.searchtime.mw/description.html
===================================================================
--- grass-addons/raster/r.pi/r.pi.searchtime.mw/description.html 2011-11-27 16:23:26 UTC (rev 49394)
+++ grass-addons/raster/r.pi/r.pi.searchtime.mw/description.html 2011-11-27 16:33:21 UTC (rev 49395)
@@ -1,21 +1,47 @@
+Analysis of landscape connectivity using individual-based dispersal model inside a moving window.
+
<h2>DESCRIPTION</h2>
-Individual-based dispersal model for connectivity analysis (time-based)
-using moving window.
+This modules provides information about the patch connectivity within a moving window
+
<h2>NOTES</h2>
-...
+The suitability matrix impacts the step direction of individuals. If individuals are moving beyond the mapset borders the indivuals are set back to their original source patches.
<h2>EXAMPLE</h2>
An example for the North Carolina sample dataset:
+The patch relevance concerning connectivity are based on patches of the <em>landclass96</em> raster class 5 amd the time (amount of steps) from emigration to immigration is computed. The step length is set to 5 pixel, the output statistics are set to <em>average</em> time and <em>variance</em> of searchtime. For each patch 1000 individuals were released and the model stopped when at least 80% of all individuals sucessfully immigrated. The command below is computing the time for the whole landscape and the result is prompted to the screen<br>
<div class="code"><pre>
-g.region -d
-...
+r.pi.searchtime.mw input=landclass96 output=searchtime_mw1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000
</pre></div>
+for a graphical output a moving window size needs to be defined (here 7x7 pixel) and the amount of steps until immigration per moving window is provided in the output raster:
+<div class="code"><pre>
+r.pi.searchtime.mw input=landclass96 output=searchtime_mw1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 size=7
+</pre></div>
+
+
+setting the perception range to 10 pixel:
+<div class="code"><pre>
+r.pi.searchtime.mw input=landclass96 output=searchtime_mw1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 size=7 perception=10
+</pre></div>
+
+increasing the attraction to move towards patches to 10:
+<div class="code"><pre>
+r.pi.searchtime.mw input=landclass96 output=searchtime_mw1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 size=7 multiplicator=10
+</pre></div>
+
+the previous examples assumed a homogeneous matrix, a heterogenous matrix can be included using a raster file which values are taken as costs for movement (0-100):
+<div class="code"><pre>
+# it is assumed that our species is a forest species and cannot move through water, hence a cost of 100, does not like urban areas (class: 6, cost: 10) but can disperse through shrubland (class 4, cost=1) better than through grassland (class 3, cost: 2):
+r.mapcalc "suit_raster = if(landclass96==5,1,if(landclass96 == 1, 10, if (landclass96==3,2, if(landclass96==4,1,if(landclass96==6,100)))))"
+r.pi.searchtime.mw input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 size=7 suitability=suit_raster
+</pre></div>
+
+
<h2>SEE ALSO</h2>
<em>
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