t.rast.accumulate (1)
NAME
t.rast.accumulate - Computes cyclic accumulations of a space time raster dataset.
KEYWORDS
temporal, accumulation, raster, time
SYNOPSIS
t.rast.accumulate
t.rast.accumulate --help
t.rast.accumulate [-nr] input=name output=name [lower=name] [upper=name] start=string [stop=string] cycle=string [offset=string] [granularity=string] basename=string [suffix=string] limits=lower,upper [scale=float] [shift=float] [method=string] [--overwrite] [--help] [--verbose] [--quiet] [--ui]
Flags:
-n
Register empty maps in the output space time raster dataset, otherwise they will be deleted
-r
Reverse time direction in cyclic accumulation
--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 the input space time raster dataset
output=name [required]
Name of the output space time raster dataset
lower=name
Input space time raster dataset that defines the lower threshold, values lower this threshold are excluded from accumulation
upper=name
Input space time raster dataset that defines the upper threshold, values upper this threshold are excluded from accumulation
start=string [required]
The temporal starting point to begin the accumulation, eg '2001-01-01'
stop=string
The temporal date to stop the accumulation, eg '2009-01-01'
cycle=string [required]
The temporal cycle to restart the accumulation, eg '12 months'
offset=string
The temporal offset to the begin of the next cycle, eg '6 months'
granularity=string
The granularity for accumulation '1 day'
Default: 1 day
basename=string [required]
Basename of the new generated output maps
A numerical suffix separated by an underscore will be attached to create a unique identifier
suffix=string
Suffix to add at basename: set 'gran' for granularity, 'time' for the full time format, 'num' for numerical suffix with a specific number of digits (default %05)
Default: gran
limits=lower,upper [required]
Use these limits in case lower and/or upper input space time raster datasets are not defined or contain NULL values
scale=float
Scale factor for input space time raster dataset
shift=float
Shift factor for input space time raster dataset
method=string
This method will be applied to compute the accumulative values from the input maps in a single granule
Growing Degree Days or Winkler indices; Mean: sum(input maps)/(number of input maps); Biologically Effective Degree Days; Huglin Heliothermal index
Options: mean, gdd, bedd, huglin
Default: mean
DESCRIPTION
t.rast.accumulate is designed to perform temporal accumulations of space time raster datasets. This module expects a space time raster dataset as input that will be sampled by a given granularity. All maps that have the start time during the actual granule will be accumulated with the predecessor granule accumulation result using the raster module r.series.accumulate. The default granularity is 1 day, but any temporal granularity can be set.
The start time and the end time of the accumulation process must be set, eg. start="2000-03-01" end="2011-01-01". In addition a cycle, eg. cycle="8 months", can be specified, that defines after which time interval the accumulation process restarts. The offset option specifies the time between two cycles that should be skipped, eg. offset="4 months".
The lower and upper limits of the accumulation process can be set, either by using space time raster datasets or by using fixed values for all raster cells and time steps. The raster maps that specifies the lower and upper limits of the actual granule will be detected using the following temporal relations: equals, during, overlaps, overlapped and contains. First all maps with equal time stamps to the current granule will be detected, the first lower map and the first upper map that were found are used as limit definitions. If no equal maps are found then maps with a temporal during relation are detected, then maps that temporally overlap the actual granules, until maps are detected that have a temporal contain relation. If no maps are found or lower/upper STRDS are not defined, then the limits option is used, eg. limits=10,30.
The upper limit is only used in the Biologically Effective Degree Days calculation.
The options shift, scale and method are passed to r.series.accumulate. Please refer to the manual page of r.series.accumulate for detailed option description.
The output is a new space time raster dataset with the provided start time, end time and granularity containing the accumulated raster maps. The base name of of the generated maps must always be set. The output space time raster dataset can then be analyzed using t.rast.accdetect to detect specific accumulation patterns.
EXAMPLE
This is an example how to accumulate the daily mean temperature of Europe from 1990 to 2000 using the growing-degree-day method to detect grass hopper reproduction cycles that are critical to agriculture.
# Get the temperature data
wget http://www-pool.math.tu-berlin.de/~soeren/grass/temperature_mean_1990_2000_daily_celsius.tar.gz
# Create a temporary location directory
mkdir -p /tmp/grassdata/LL
# Start GRASS and create a new location with PERMANENT mapset
grass72 -c EPSG:4326 /tmp/grassdata/LL/PERMANENT
# Import the temperature data
t.rast.import input=temperature_mean_1990_2000_daily_celsius.tar.gz \
output=temperature_mean_1990_2000_daily_celsius directory=/tmp
# We need to set the region correctly
g.region -p raster=`t.rast.list input=temperature_mean_1990_2000_daily_celsius column=name | tail -1`
# We can zoom to the raster map
g.region -p zoom=`t.rast.list input=temperature_mean_1990_2000_daily_celsius column=name | tail -1`
#############################################################################
#### ACCUMULATION USING GDD METHOD ##########################################
#############################################################################
# The computation of grashopper pest control cycles is based on:
#
# Using Growing Degree Days For Insect Management
# Nancy E. Adams
# Extension Educator, Agricultural Resources
#
# available here: http://extension.unh.edu/agric/gddays/docs/growch.pdf
# Now we compute the Biologically Effective Degree Days
# from 1990 - 2000 for each year (12 month cycle) with
# a granularity of one day. Base temperature is 10C, upper limit is 30C.
# Hence the accumulation starts at 10C and does not accumulate values above 30C.
t.rast.accumulate input="temperature_mean_1990_2000_daily_celsius" \
output="temperature_mean_1990_2000_daily_celsius_accumulated_10_30" \
limits="10,30" start="1990-01-01" stop="2000-01-01" cycle="12 months" \
basename="temp_acc_daily_10_30" method="bedd"
############################################.lf 1 -
t.rast.accumulate(1grass) Grass User's Manual t.rast.accumulate(1grass)
NAME
t.rast.accumulate - Computes cyclic accumulations of a space time raster dataset.
KEYWORDS
temporal, accumulation, raster, time
SYNOPSIS
t.rast.accumulate
t.rast.accumulate --help
t.rast.accumulate [-nr] input=name output=name [lower=name] [upper=name] start=string [stop=string] cycle=string [offset=string] [granularity=string] basename=string [suffix=string] limits=lower,upper [scale=float] [shift=float] [method=string] [--overwrite] [--help] [--verbose] [--quiet] [--ui]
Flags:
-n
Register empty maps in the output space time raster dataset, otherwise they will be deleted
-r
Reverse time direction in cyclic accumulation
--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 the input space time raster dataset
output=name [required]
Name of the output space time raster dataset
lower=name
Input space time raster dataset that defines the lower threshold, values lower this threshold are excluded from accumulation
upper=name
Input space time raster dataset that defines the upper threshold, values upper this threshold are excluded from accumulation
start=string [required]
The temporal starting point to begin the accumulation, eg '2001-01-01'
stop=string
The temporal date to stop the accumulation, eg '2009-01-01'
cycle=string [required]
The temporal cycle to restart the accumulation, eg '12 months'
offset=string
The temporal offset to the begin of the next cycle, eg '6 months'
granularity=string
The granularity for accumulation '1 day'
Default: 1 day
basename=string [required]
Basename of the new generated output maps
A numerical suffix separated by an underscore will be attached to create a unique identifier
suffix=string
Suffix to add at basename: set 'gran' for granularity, 'time' for the full time format, 'num' for numerical suffix with a specific number of digits (default %05)
Default: gran
limits=lower,upper [required]
Use these limits in case lower and/or upper input space time raster datasets are not defined or contain NULL values
scale=float
Scale factor for input space time raster dataset
shift=float
Shift factor for input space time raster dataset
method=string
This method will be applied to compute the accumulative values from the input maps in a single granule
Growing Degree Days or Winkler indices; Mean: sum(input maps)/(number of input maps); Biologically Effective Degree Days; Huglin Heliothermal index
Options: mean, gdd, bedd, huglin
Default: mean
DESCRIPTION
t.rast.accumulate is designed to perform temporal accumulations of space time raster datasets. This module expects a space time raster dataset as input that will be sampled by a given granularity. All maps that have the start time during the actual granule will be accumulated with the predecessor granule accumulation result using the raster module r.series.accumulate. The default granularity is 1 day, but any temporal granularity can be set.
The start time and the end time of the accumulation process must be set, eg. start="2000-03-01" end="2011-01-01". In addition a cycle, eg. cycle="8 months", can be specified, that defines after which time interval the accumulation process restarts. The offset option specifies the time between two cycles that should be skipped, eg. offset="4 months".
The lower and upper limits of the accumulation process can be set, either by using space time raster datasets or by using fixed values for all raster cells and time steps. The raster maps that specifies the lower and upper limits of the actual granule will be detected using the following temporal relations: equals, during, overlaps, overlapped and contains. First all maps with equal time stamps to the current granule will be detected, the first lower map and the first upper map that were found are used as limit definitions. If no equal maps are found then maps with a temporal during relation are detected, then maps that temporally overlap the actual granules, until maps are detected that have a temporal contain relation. If no maps are found or lower/upper STRDS are not defined, then the limits option is used, eg. limits=10,30.
The upper limit is only used in the Biologically Effective Degree Days calculation.
The options shift, scale and method are passed to r.series.accumulate. Please refer to the manual page of r.series.accumulate for detailed option description.
The output is a new space time raster dataset with the provided start time, end time and granularity containing the accumulated raster maps. The base name of of the generated maps must always be set. The output space time raster dataset can then be analyzed using t.rast.accdetect to detect specific accumulation patterns.
EXAMPLE
This is an example how to accumulate the daily mean temperature of Europe from 1990 to 2000 using the growing-degree-day method to detect grass hopper reproduction cycles that are critical to agriculture.
# Get the temperature data
wget http://www-pool.math.tu-berlin.de/~soeren/grass/temperature_mean_1990_2000_daily_celsius.tar.gz
# Create a temporary location directory
mkdir -p /tmp/grassdata/LL
# Start GRASS and create a new location with PERMANENT mapset
grass72 -c EPSG:4326 /tmp/grassdata/LL/PERMANENT
# Import the temperature data
t.rast.import input=temperature_mean_1990_2000_daily_celsius.tar.gz \
output=temperature_mean_1990_2000_daily_celsius directory=/tmp
# We need to set the region correctly
g.region -p raster=`t.rast.list input=temperature_mean_1990_2000_daily_celsius column=name | tail -1`
# We can zoom to the raster map
g.region -p zoom=`t.rast.list input=temperature_mean_1990_2000_daily_celsius column=name | tail -1`
#############################################################################
#### ACCUMULATION USING GDD METHOD ##########################################
#############################################################################
# The computation of grashopper pest control cycles is based on:
#
# Using Growing Degree Days For Insect Management
# Nancy E. Adams
# Extension Educator, Agricultural Resources
#
# available here: http://extension.unh.edu/agric/gddays/docs/growch.pdf
# Now we compute the Biologically Effective Degree Days
# from 1990 - 2000 for each year (12 month cycle) with
# a granularity of one day. Base temperature is 10C, upper limit is 30C.
# Hence the accumulation starts at 10C and does not accumulate values above 30C.
t.rast.accumulate input="temperature_mean_1990_2000_daily_celsius" \
output="temperature_mean_1990_2000_daily_celsius_accumulated_10_30" \
limits="10,30" start="1990-01-01" stop="2000-01-01" cycle="12 months" \
basename="temp_acc_daily_10_30" method="bedd"
############################################.lf 1 -
t.rast.accumulate(1grass) Grass User's Manual t.rast.accumulate(1grass)
NAME
t.rast.accumulate - Computes cyclic accumulations of a space time raster dataset.
KEYWORDS
temporal, accumulation, raster, time
SYNOPSIS
t.rast.accumulate
t.rast.accumulate --help
t.rast.accumulate [-nr] input=name output=name [lower=name] [upper=name] start=string [stop=string] cycle=string [offset=string] [granularity=string] basename=string [suffix=string] limits=lower,upper [scale=float] [shift=float] [method=string] [--overwrite] [--help] [--verbose] [--quiet] [--ui]
Flags:
-n
Register empty maps in the output space time raster dataset, otherwise they will be deleted
-r
Reverse time direction in cyclic accumulation
--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 the input space time raster dataset
output=name [required]
Name of the output space time raster dataset
lower=name
Input space time raster dataset that defines the lower threshold, values lower this threshold are excluded from accumulation
upper=name
Input space time raster dataset that defines the upper threshold, values upper this threshold are excluded from accumulation
start=string [required]
The temporal starting point to begin the accumulation, eg '2001-01-01'
stop=string
The temporal date to stop the accumulation, eg '2009-01-01'
cycle=string [required]
The temporal cycle to restart the accumulation, eg '12 months'
offset=string
The temporal offset to the begin of the next cycle, eg '6 months'
granularity=string
The granularity for accumulation '1 day'
Default: 1 day
basename=string [required]
Basename of the new generated output maps
A numerical suf