SOD Spread tutorial - Revision history

Annakrat: change from where the dataset is downloaded, location to project

2025-03-14T17:43:51Z

change from where the dataset is downloaded, location to project

← Older revision		Revision as of 17:43, 14 March 2025
Line 1:		Line 1:
	{{AddonCmd\|r.pops.spread}} is a model for stochastic landscape spread of the pest and pathogens. It uses [https://github.com/ncsu-landscape-dynamics/pops-core PoPS] (Pest or Pathogen Spread) Core library. In this tutorial we will use it specifically to model the spread of Sudden Oak Death tree disease.		{{AddonCmd\|r.pops.spread}} is a model for stochastic landscape spread of the pest and pathogens. It uses [https://github.com/ncsu-landscape-dynamics/pops-core PoPS] (Pest or Pathogen Spread) Core library. In this tutorial we will use it specifically to model the spread of Sudden Oak Death tree disease.
	This tutorial shows how to run the model. You can download the [~~http~~://~~fatra~~.cnr.ncsu.edu/pops/data/PoPS_SOD_tutorial.zip sample dataset] and simulate sudden oak death spread in the [https://goo.gl/maps/b1ZPn3abtW82 Rouge River-Siskiyou National Forest region] of western Oregon.		This tutorial shows how to run the model. You can download the [https://tilia.cnr.ncsu.edu/pops/data/PoPS_SOD_tutorial.zip sample dataset] and simulate sudden oak death spread in the [https://goo.gl/maps/b1ZPn3abtW82 Rouge River-Siskiyou National Forest region] of western Oregon.

	= Software =		= Software =
Line 9:		Line 9:

	= Input data used in this tutorial =		= Input data used in this tutorial =
	Download the [~~http~~://~~fatra~~.cnr.ncsu.edu/pops/data/PoPS_SOD_tutorial.zip sample dataset] containing:		Download the [https://tilia.cnr.ncsu.edu/pops/data/PoPS_SOD_tutorial.zip sample dataset] containing:
	* digital elevation model		* digital elevation model
	* orthophoto of study area		* orthophoto of study area
Line 16:		Line 16:
	* roads and rivers		* roads and rivers
	* mapset containing weather coefficients		* mapset containing weather coefficients
	The sample dataset is a GRASS ~~GIS Location~~, so it goes into your GRASS ~~GIS~~ Database which is usually a directory called grassdata in your home directory or your Documents directory.		The sample dataset is a GRASS project, so it goes into your GRASS Database which is usually a directory called grassdata in your home directory or your Documents directory.

	= Workflow =		= Workflow =
	Download sample data and unzip it into your grassdata. Launch GRASS GIS and select ~~Location~~ <tt>PoPS_SOD_tutorial</tt> and Mapset <tt>tutorial</tt>.		Download sample data and unzip it into your grassdata. Launch GRASS GIS and select project <tt>PoPS_SOD_tutorial</tt> and Mapset <tt>tutorial</tt>.

	Change working directory by		Change working directory by

⚠️Wenzeslaus: Old PoPS repo is PoPS Core now

2020-09-07T20:35:10Z

Old PoPS repo is PoPS Core now

← Older revision		Revision as of 20:35, 7 September 2020
Line 1:		Line 1:
	{{AddonCmd\|r.pops.spread}} is a model for stochastic landscape spread of the pest and pathogens. It uses [https://github.com/ncsu-landscape-dynamics/~~PoPS~~ PoPS] (Pest or Pathogen Spread) library. In this tutorial we will use it specifically to model the spread of Sudden Oak Death tree disease.		{{AddonCmd\|r.pops.spread}} is a model for stochastic landscape spread of the pest and pathogens. It uses [https://github.com/ncsu-landscape-dynamics/pops-core PoPS] (Pest or Pathogen Spread) Core library. In this tutorial we will use it specifically to model the spread of Sudden Oak Death tree disease.
	This tutorial shows how to run the model. You can download the [http://fatra.cnr.ncsu.edu/pops/data/PoPS_SOD_tutorial.zip sample dataset] and simulate sudden oak death spread in the [https://goo.gl/maps/b1ZPn3abtW82 Rouge River-Siskiyou National Forest region] of western Oregon.		This tutorial shows how to run the model. You can download the [http://fatra.cnr.ncsu.edu/pops/data/PoPS_SOD_tutorial.zip sample dataset] and simulate sudden oak death spread in the [https://goo.gl/maps/b1ZPn3abtW82 Rouge River-Siskiyou National Forest region] of western Oregon.

Annakrat: /* Compute the spread of SOD for default values */

2020-03-31T19:13:33Z

Compute the spread of SOD for default values

← Older revision		Revision as of 19:13, 31 March 2020
Line 54:		Line 54:

	== Compute the spread of SOD for default values ==		== Compute the spread of SOD for default values ==
	~~[[File:Spread sod.png\|150px\|thumb\|Example output of SOD spread. Overlaid onto the orthophoto image.]]~~

	Run the model using the text files created and setting only the required parameters. For this analysis we used a wind direction of NE and are looking at the first 5 years of spread from the initial 2019 infection discovery.		Run the model using the text files created and setting only the required parameters. For this analysis we used a wind direction of NE and are looking at the first 5 years of spread from the initial 2019 infection discovery.

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	You can visualize the spread series in Animation Tool, do not display 0 values for infected maps.		You can visualize the spread series in Animation Tool, do not display 0 values for infected maps.

			[[File:SOD_spread_example.png\|upright\|center\|Example output of SOD spread. Overlaid onto the orthophoto image.]]

	We can run multiple stochastic runs and aggregate the results into a probability layer (0.1 if cell was infected once in 10 runs), average layer (average number of infected trees per cell) and standard deviation layer.		We can run multiple stochastic runs and aggregate the results into a probability layer (0.1 if cell was infected once in 10 runs), average layer (average number of infected trees per cell) and standard deviation layer.
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	Explore results in Map Display or using Animation Tool, omit displaying 0 values of probability.		Explore results in Map Display or using Animation Tool, omit displaying 0 values of probability.


	== Effect of dispersal kernel ==		== Effect of dispersal kernel ==

Annakrat at 17:54, 31 March 2020

2020-03-31T17:54:06Z

← Older revision		Revision as of 17:54, 31 March 2020
Line 88:		Line 88:
	Explore results in Map Display or using Animation Tool, omit displaying 0 values of probability.		Explore results in Map Display or using Animation Tool, omit displaying 0 values of probability.


	~~[[File:Spread sodk1.png\|150px\|thumb\|Example output of SOD spread for kappa=0. Overlaid onto the orthophoto image.]]~~
	~~[[File:Spread sodk2.png\|150px\|thumb\|Example output of SOD spread for kappa=4. Overlaid onto the orthophoto image.]]~~

	== Effect of dispersal kernel ==		== Effect of dispersal kernel ==
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	<source lang="bash">		<source lang="bash">
	r.pops.spread host=host total_plants=max_host infected=eu_infection_2019 start_date=2019-01-01 end_date=2023-12-31 step_unit=week reproductive_rate=1.6 weather_coefficient_file=weather.txt natural_direction=NE natural_dispersal_kernel=exponential natural_distance=400 natural_direction_strength=3 single_series=spread_distance output_frequency=yearly runs=1 random_seed=1		r.pops.spread host=host total_plants=max_host infected=eu_infection_2019 start_date=2019-01-01 end_date=2023-12-31 step_unit=week reproductive_rate=1.6 weather_coefficient_file=weather.txt natural_direction=NE natural_dispersal_kernel=exponential natural_distance=400 natural_direction_strength=3 single_series=spread_distance output_frequency=yearly runs=1 random_seed=1

	g.list type=raster pattern="spread_distance*" output=series.txt --o		g.list type=raster pattern="spread_distance*" output=series.txt --o
	r.colors rules=color_infected.txt file=series.txt		r.colors rules=color_infected.txt file=series.txt
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	<source lang="bash">		<source lang="bash">
	r.pops.spread host=host total_plants=max_host infected=eu_infection_2019 start_date=2019-01-01 end_date=2023-12-31 step_unit=week reproductive_rate=1.6 weather_coefficient_file=weather.txt natural_direction=E natural_dispersal_kernel=exponential natural_distance=242 natural_direction_strength=10 single_series=spread_direction output_frequency=yearly runs=1 random_seed=1		r.pops.spread host=host total_plants=max_host infected=eu_infection_2019 start_date=2019-01-01 end_date=2023-12-31 step_unit=week reproductive_rate=1.6 weather_coefficient_file=weather.txt natural_direction=E natural_dispersal_kernel=exponential natural_distance=242 natural_direction_strength=10 single_series=spread_direction output_frequency=yearly runs=1 random_seed=1

	g.list type=raster pattern="spread_direction*" output=series.txt --o		g.list type=raster pattern="spread_direction*" output=series.txt --o
	r.colors rules=color_infected.txt file=series.txt		r.colors rules=color_infected.txt file=series.txt
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	<source lang="bash">		<source lang="bash">
	r.pops.spread host=host total_plants=max_host infected=eu_infection_2019 start_date=2019-01-01 end_date=2023-12-31 step_unit=week reproductive_rate=1.6 weather_coefficient_file=weather.txt natural_direction=NE natural_dispersal_kernel=cauchy natural_distance=242 natural_direction_strength=3 single_series=spread_type output_frequency=yearly runs=1 random_seed=1		r.pops.spread host=host total_plants=max_host infected=eu_infection_2019 start_date=2019-01-01 end_date=2023-12-31 step_unit=week reproductive_rate=1.6 weather_coefficient_file=weather.txt natural_direction=NE natural_dispersal_kernel=cauchy natural_distance=242 natural_direction_strength=3 single_series=spread_type output_frequency=yearly runs=1 random_seed=1

	g.list type=raster pattern="spread_type*" output=series.txt --o		g.list type=raster pattern="spread_type*" output=series.txt --o
	r.colors rules=color_infected.txt file=series.txt		r.colors rules=color_infected.txt file=series.txt
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	<source lang="bash">		<source lang="bash">
	r.pops.spread host=host total_plants=max_host infected=eu_infection_2019 start_date=2019-01-01 end_date=2023-12-31 step_unit=week reproductive_rate=3 weather_coefficient_file=weather.txt natural_direction=NE natural_dispersal_kernel=exponential natural_distance=242 natural_direction_strength=3 single_series=spread_rate output_frequency=yearly runs=1 random_seed=1		r.pops.spread host=host total_plants=max_host infected=eu_infection_2019 start_date=2019-01-01 end_date=2023-12-31 step_unit=week reproductive_rate=3 weather_coefficient_file=weather.txt natural_direction=NE natural_dispersal_kernel=exponential natural_distance=242 natural_direction_strength=3 single_series=spread_rate output_frequency=yearly runs=1 random_seed=1

	g.list type=raster pattern="spread_rate*" output=series.txt --o		g.list type=raster pattern="spread_rate*" output=series.txt --o
	r.colors rules=color_infected.txt file=series.txt		r.colors rules=color_infected.txt file=series.txt
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	<source lang="bash">		<source lang="bash">
	r.pops.spread host=host total_plants=max_host infected=eu_infection_2019 start_date=2019-01-01 end_date=2023-12-31 step_unit=week reproductive_rate=3 weather_coefficient_file=weather.txt natural_direction=NE natural_dispersal_kernel=exponential natural_distance=242 natural_direction_strength=3 single_series=spread_rate output_frequency=yearly runs=1 random_seed=1		r.pops.spread host=host total_plants=max_host infected=eu_infection_2019 start_date=2019-01-01 end_date=2023-12-31 step_unit=week reproductive_rate=3 weather_coefficient_file=weather.txt natural_direction=NE natural_dispersal_kernel=exponential natural_distance=242 natural_direction_strength=3 single_series=spread_rate output_frequency=yearly runs=1 random_seed=1

	g.list type=raster pattern="probability*" output=series.txt --o		g.list type=raster pattern="probability*" output=series.txt --o
	r.colors color=magma file=series.txt		r.colors color=magma file=series.txt
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	<source lang="bash">		<source lang="bash">
	r.pops.spread host=host total_plants=max_host infected=eu_infection_2019 start_date=2019-01-01 end_date=2023-12-31 step_unit=week reproductive_rate=1.6 weather_coefficient_file=weather.txt natural_direction=NE natural_dispersal_kernel=exponential natural_distance=242 natural_direction_strength=3 probability_series=probtreatmentA output_frequency=yearly runs=10 nprocs=2 random_seed=1 treatments=treatment_A treatment_date=2019-12-31 treatment_length=0 treatment_application=ratio_to_all		r.pops.spread host=host total_plants=max_host infected=eu_infection_2019 start_date=2019-01-01 end_date=2023-12-31 step_unit=week reproductive_rate=1.6 weather_coefficient_file=weather.txt natural_direction=NE natural_dispersal_kernel=exponential natural_distance=242 natural_direction_strength=3 probability_series=probtreatmentA output_frequency=yearly runs=10 nprocs=2 random_seed=1 treatments=treatment_A treatment_date=2019-12-31 treatment_length=0 treatment_application=ratio_to_all

	g.list type=raster pattern="probtreatmentA*" output=series.txt --o		g.list type=raster pattern="probtreatmentA*" output=series.txt --o
	r.colors color=magma file=series.txt		r.colors color=magma file=series.txt
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	<source lang="bash">		<source lang="bash">
	r.pops.spread host=host total_plants=max_host infected=eu_infection_2019 start_date=2019-01-01 end_date=2023-12-31 step_unit=week reproductive_rate=1.6 weather_coefficient_file=weather.txt natural_direction=NE natural_dispersal_kernel=exponential natural_distance=242 natural_direction_strength=3 probability_series=probtreatmentB output_frequency=yearly runs=10 nprocs=2 random_seed=1 treatments=treatment_B treatment_date=2019-12-31 treatment_length=0 treatment_application=ratio_to_all		r.pops.spread host=host total_plants=max_host infected=eu_infection_2019 start_date=2019-01-01 end_date=2023-12-31 step_unit=week reproductive_rate=1.6 weather_coefficient_file=weather.txt natural_direction=NE natural_dispersal_kernel=exponential natural_distance=242 natural_direction_strength=3 probability_series=probtreatmentB output_frequency=yearly runs=10 nprocs=2 random_seed=1 treatments=treatment_B treatment_date=2019-12-31 treatment_length=0 treatment_application=ratio_to_all

	g.list type=raster pattern="probtreatmentB*" output=series.txt --o		g.list type=raster pattern="probtreatmentB*" output=series.txt --o
	r.colors color=magma file=series.txt		r.colors color=magma file=series.txt
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	<source lang="bash">		<source lang="bash">
	r.pops.spread host=host total_plants=max_host infected=eu_infection_2019 start_date=2019-01-01 end_date=2023-12-31 step_unit=week reproductive_rate=1.6 weather_coefficient_file=weather.txt natural_direction=NE natural_dispersal_kernel=exponential natural_distance=242 natural_direction_strength=3 probability_series=probtreatmentC output_frequency=yearly runs=10 nprocs=2 random_seed=1 treatments=treatment_C treatment_date=2019-12-31 treatment_length=0 treatment_application=ratio_to_all		r.pops.spread host=host total_plants=max_host infected=eu_infection_2019 start_date=2019-01-01 end_date=2023-12-31 step_unit=week reproductive_rate=1.6 weather_coefficient_file=weather.txt natural_direction=NE natural_dispersal_kernel=exponential natural_distance=242 natural_direction_strength=3 probability_series=probtreatmentC output_frequency=yearly runs=10 nprocs=2 random_seed=1 treatments=treatment_C treatment_date=2019-12-31 treatment_length=0 treatment_application=ratio_to_all

	g.list type=raster pattern="probtreatmentC*" output=series.txt --o		g.list type=raster pattern="probtreatmentC*" output=series.txt --o
	r.colors color=magma file=series.txt		r.colors color=magma file=series.txt
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	<source lang="bash">		<source lang="bash">
	r.pops.spread host=host total_plants=max_host infected=eu_infection_2019 start_date=2019-01-01 end_date=2023-12-31 step_unit=week reproductive_rate=1.6 weather_coefficient_file=weather.txt natural_direction=NE natural_dispersal_kernel=exponential natural_distance=242 natural_direction_strength=3 probability_series=probtreatmentAC output_frequency=yearly runs=10 nprocs=2 random_seed=1 treatments=treatment_A,treatment_C treatment_date=2019-12-31,2021-12-31 treatment_length=0,0 treatment_application=ratio_to_all		r.pops.spread host=host total_plants=max_host infected=eu_infection_2019 start_date=2019-01-01 end_date=2023-12-31 step_unit=week reproductive_rate=1.6 weather_coefficient_file=weather.txt natural_direction=NE natural_dispersal_kernel=exponential natural_distance=242 natural_direction_strength=3 probability_series=probtreatmentAC output_frequency=yearly runs=10 nprocs=2 random_seed=1 treatments=treatment_A,treatment_C treatment_date=2019-12-31,2021-12-31 treatment_length=0,0 treatment_application=ratio_to_all

	g.list type=raster pattern="probtreatmentAC*" output=series.txt --o		g.list type=raster pattern="probtreatmentAC*" output=series.txt --o
	r.colors color=magma file=series.txt		r.colors color=magma file=series.txt
	</source>		</source>

	~~<source lang="bash">~~
	~~</source>~~
	~~<source lang="bash">~~
	~~</source>~~

	<--		<!--
	== Wind dispersal ==		== Wind dispersal ==
	=== Direction ===		=== Direction ===
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	</source>		</source>

	~~<!--~~
	~~[[File:Spread sods1 1.png\|150px\|thumb\|Output of SOD spread for scale_1=1 for the years 2016-2021. Overlaid onto the orthophoto image.]]~~
	~~[[File:Spread sods1 2.png\|150px\|thumb\|Output of SOD spread for scale_1=50 for the years 2016-2021. Overlaid onto the orthophoto image.]]~~
	~~-->~~

	== Treatments ==		== Treatments ==

Annakrat at 17:50, 31 March 2020

2020-03-31T17:50:36Z

@@ Line 95: / Line 95: @@
 The choice and parametrization of dispersal kernel significantly influences the spread and should be informed by calibration. The natural dispersal kernel (required) typically represents wind dispersal, additionally, we can optionally add the anthropogenic kernel which represents more human affected spread possibly over longer distances.
-Each kernel is defined by type (cauchy, exponential), direction (none, N, NE, E, ...), direction strength (concentration around the direction using von mises distribution) and scale (distance).
+Each kernel is defined by type (cauchy, exponential), direction (none, N, NE, E, ...), direction strength (concentration around the direction using [https://en.wikipedia.org/wiki/Von_Mises_distribution von mises distribution]) and scale (distance).
 <source lang="bash">
@@ Line 122: / Line 122: @@
 == Effect of reproductive rate ==
-Similarly to kernel, reproductive rate should be informed by calibration. In this example we double it:
+Similarly to kernel, reproductive rate should be informed by calibration.
 <source lang="bash">
 r.pops.spread host=host total_plants=max_host infected=eu_infection_2019 start_date=2019-01-01 end_date=2023-12-31 step_unit=week reproductive_rate=3 weather_coefficient_file=weather.txt natural_direction=NE natural_dispersal_kernel=exponential natural_distance=242 natural_direction_strength=3 single_series=spread_rate output_frequency=yearly runs=1 random_seed=1
 </source>
 == Wind dispersal ==
 === Direction ===
@@ Line 194: / Line 258: @@
 r.pops.spread host=host total_plants=all_trees infected=inf_2016 output=spread_sod_treatB_barrier output_series=spread_sod_treatB_barrier wind=N moisture_coefficient_file=precipitation.txt temperature_coefficient_file=temperature.txt start_time=2016 end_time=2021 step=week  treatments=treatment_B,barrier treatment_year=2016,2018 random_seed=4
 </source>

Annakrat: update

2020-03-31T17:37:33Z

update

Show changes

Annakrat at 12:55, 31 March 2020

2020-03-31T12:55:55Z

← Older revision		Revision as of 12:55, 31 March 2020
Line 1:		Line 1:
	{{AddonCmd\|r.pops.spread}} is a model for stochastic landscape spread of the pest and pathogens. It uses [https://github.com/ncsu-landscape-dynamics/PoPS PoPS] (Pest or Pathogen Spread) library. In this tutorial we will use it specifically to model the spread of Sudden Oak Death tree disease.		{{AddonCmd\|r.pops.spread}} is a model for stochastic landscape spread of the pest and pathogens. It uses [https://github.com/ncsu-landscape-dynamics/PoPS PoPS] (Pest or Pathogen Spread) library. In this tutorial we will use it specifically to model the spread of Sudden Oak Death tree disease.
	This tutorial shows how to run the model. You can download the [http://fatra.cnr.ncsu.edu/pops/data/~~Oregon~~.zip sample dataset] and simulate sudden oak death spread in the [https://goo.gl/maps/b1ZPn3abtW82 Rouge River-Siskiyou National Forest region] of western Oregon.		This tutorial shows how to run the model. You can download the [http://fatra.cnr.ncsu.edu/pops/data/PoPS_SOD_tutorial.zip sample dataset] and simulate sudden oak death spread in the [https://goo.gl/maps/b1ZPn3abtW82 Rouge River-Siskiyou National Forest region] of western Oregon.

	= Software =		= Software =
	Required software includes:		Required software includes:
	* GRASS GIS >=7.2		* GRASS GIS >=7.8
	* Addon module:		* Addon module:
	** {{AddonCmd\|r.pops.spread}}		** {{AddonCmd\|r.pops.spread}}

	= Input data used in this tutorial =		= Input data used in this tutorial =
	Download the [http://fatra.cnr.ncsu.edu/pops/data/~~Oregon~~.zip sample dataset] containing:		Download the [http://fatra.cnr.ncsu.edu/pops/data/PoPS_SOD_tutorial.zip sample dataset] containing:
	* digital elevation model		* digital elevation model
	* orthophoto of study area		* orthophoto of study area
Line 15:		Line 15:
	* host layer		* host layer
	* layer of all trees		* layer of all trees
	* mapset containing ~~moisture and temperature~~ coefficients		* mapset containing weather coefficients
	The sample dataset is a GRASS GIS Location, so it goes into your GRASS GIS Database which is usually a directory called grassdata in your home directory or your Documents directory.		The sample dataset is a GRASS GIS Location, so it goes into your GRASS GIS Database which is usually a directory called grassdata in your home directory or your Documents directory.

	= Workflow =		= Workflow =
	Download sample data and unzip it. Launch GRASS GIS and select ~~the unzipped <tt>Oregon</tt> directory,~~ Location <tt>~~Oregon_spread~~</tt> and ~~create a new~~ Mapset <tt>tutorial</tt>.		Download sample data and unzip it into your grassdata. Launch GRASS GIS and select Location <tt>PoPS_SOD_tutorial</tt> and Mapset <tt>tutorial</tt>.

	~~<!--~~ Change working directory:		Change working directory:

	Type <tt>cd</tt> (stands for change directory) into the GUI Console and hit Enter. ~~Navigate to~~ where ~~ever~~ you ~~have saved the example data~~ and ~~change~~ directory ~~to the <tt>tutorial</tt> mapset~~. ~~-->~~		Type <tt>cd</tt> (stands for change directory) into the GUI Console and hit Enter. You can create a directory for this exercise (outside of grassdata) anywhere where you can write and read. Navigate to that directory.

	Install addon:		Install addon:
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	</source>		</source>

	First, we will set computational region of our analyses to an extent covering our study area ~~and so that the cells are aligned with our host raster~~:		First, we will set computational region of our analyses to an extent covering our study area:
	<source lang="bash">		<source lang="bash">
	g.region ~~vector~~=~~EU1_12x8 align=host~~ -p		g.region region=small_study_area -p
	</source>		</source>

	== Create files with lists of input maps ==		== Create files with lists of input maps ==
	Here we used already prepared weather coefficients, these can be created using this [https://github.com/ncsu-landscape-dynamics/weather-coefficient workflow].		Here we used already prepared weather coefficients, these can be created using this [https://github.com/ncsu-landscape-dynamics/weather-coefficient workflow].
	We need ~~two~~ text ~~files, one with temperature coefficients and one with precipitation coefficients~~.		Mapset weather includes raster layers per each week of a simulation. With that, we need to prepare a text file. List and write the maps in a file using g.list:
	List and write the maps in a file using g.list~~. Search for ppt for precipitation and tmean or temperature mean for temperature, we list only the years we need for the simulation (2016 to 2021)~~:
	<source lang="bash">		<source lang="bash">
	g.list -m type=raster pattern="ppt_201[6-9],ppt_202[0-2]" mapset=weather output~~=precipitation.txt~~		g.list -m type=raster pattern="average_weather_*" mapset=weather output=weather.txt
	~~g.list -m type=raster pattern="tmean_201[6-9],tmean_202[0-2]" mapset~~=weather ~~output=temperature~~.txt
	</source>		</source>
			The file should be written to your current working directory.

			== Download color ramp for infected ==
			Download [https://raw.githubusercontent.com/ncsu-landscape-dynamics/pops-intro-grass-notebook/master/color_infected.txt color ramp] into your current working directory to be used for better visualization of infected cells.

	== Compute the spread of SOD for default values ==		== Compute the spread of SOD for default values ==

Annakrat: /* Compute the spread of SOD for default values */

2019-03-27T01:36:12Z

Compute the spread of SOD for default values

← Older revision		Revision as of 01:36, 27 March 2019
Line 63:		Line 63:

	<source lang="bash">		<source lang="bash">
	r.pops.spread host=host total_plants=all_trees infected=inf_2016 ~~probability_series~~=probability wind=NE moisture_coefficient_file=precipitation.txt temperature_coefficient_file=temperature.txt start_time=2016 end_time=2021 step=week random_seed=4 runs=5 nprocs=5		r.pops.spread host=host total_plants=all_trees infected=inf_2016 probability=probability wind=NE moisture_coefficient_file=precipitation.txt temperature_coefficient_file=temperature.txt start_time=2016 end_time=2021 step=week random_seed=4 runs=5 nprocs=5
	</source>		</source>

Annakrat: /* Compute the spread of SOD for default values */

2019-03-14T20:22:38Z

Compute the spread of SOD for default values

← Older revision		Revision as of 20:22, 14 March 2019
Line 57:		Line 57:
	d.rast -i map=inf_2016 values=0		d.rast -i map=inf_2016 values=0
	d.rast -i spread_sod values=0		d.rast -i spread_sod values=0
			</source>
			You can visualize the spread series in Animation Tool.

			So far the result represents one stochastic run, here we inspect the probability surface from 5 stochastic runs:

			<source lang="bash">
			r.pops.spread host=host total_plants=all_trees infected=inf_2016 probability_series=probability wind=NE moisture_coefficient_file=precipitation.txt temperature_coefficient_file=temperature.txt start_time=2016 end_time=2021 step=week random_seed=4 runs=5 nprocs=5
	</source>		</source>

Line 67:		Line 74:
	-->		-->

	~~You can visualize the spread series in Animation Tool.~~

	[[File:Spread sodk1.png\|150px\|thumb\|Example output of SOD spread for kappa=0. Overlaid onto the orthophoto image.]]		[[File:Spread sodk1.png\|150px\|thumb\|Example output of SOD spread for kappa=0. Overlaid onto the orthophoto image.]]
	[[File:Spread sodk2.png\|150px\|thumb\|Example output of SOD spread for kappa=4. Overlaid onto the orthophoto image.]]		[[File:Spread sodk2.png\|150px\|thumb\|Example output of SOD spread for kappa=4. Overlaid onto the orthophoto image.]]

	== Wind dispersal ==		== Wind dispersal ==
	=== Direction ===		=== Direction ===

Annakrat at 20:08, 14 March 2019

2019-03-14T20:08:03Z

@@ Line 101: / Line 101: @@
 <source lang="bash">
 r.pops.spread host=host total_plants=all_trees infected=inf_2016 output=spread_sod_sr1 wind=N moisture_coefficient_file=precipitation.txt temperature_coefficient_file=temperature.txt start_time=2016 end_time=2021 step=week reproductive_rate=1 random_seed=4
-r.pops.spread host=host total_plants=all_trees infected=inf_2016 output=spread_sod_sr10 wind=N moisture_coefficient_file=precipitation.txt temperature_coefficient_file=temperature.txt start_time=2016 end_time=2021 step=week reproductive_rate=10 random_seed=4
+r.pops.spread host=host total_plants=all_trees infected=inf_2016 output=spread_sod_sr6 wind=N moisture_coefficient_file=precipitation.txt temperature_coefficient_file=temperature.txt start_time=2016 end_time=2021 step=week reproductive_rate=6 random_seed=4
 </source>
@@ Line 108: / Line 108: @@
 [[File:Spread sods1 2.png|150px|thumb|Output of SOD spread for scale_1=50 for the years 2016-2021. Overlaid onto the orthophoto image.]]
 -->