t.rast.series(1grass) GRASS GIS User's Manual t.rast.series(1grass)
NAME
t.rast.series - Performs different aggregation algorithms from r.se-
ries on all or a subset of raster maps in a space time raster dataset.
KEYWORDS
temporal, aggregation, series, raster, time
SYNOPSIS
t.rast.series
t.rast.series --help
t.rast.series [-tn] input=name method=string[,string,...] [quan-
tile=float[,float,...]] [order=string[,string,...]]
[where=sql_query] output=name[,name,...] [--overwrite] [--help]
[--verbose] [--quiet] [--ui]
Flags:
-t
Do not assign the space time raster dataset start and end time to
the output map
-n
Propagate NULLs
--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
method=string[,string,...] [required]
Aggregate operation to be performed on the raster maps
Options: average, count, median, mode, minimum, min_raster, maxi-
mum, max_raster, stddev, range, sum, variance, diversity, slope,
offset, detcoeff, quart1, quart3, perc90, quantile, skewness, kur-
tosis
Default: average
quantile=float[,float,...]
Quantile to calculate for method=quantile
Options: 0.0-1.0
order=string[,string,...]
Sort the maps by category
Options: id, name, creator, mapset, creation_time, modifica-
tion_time, start_time, end_time, north, south, west, east,
min, max
Default: start_time
where=sql_query
WHERE conditions of SQL statement without ’where’ keyword used in
the temporal GIS framework
Example: start_time > ’2001-01-01 12:30:00’
output=name[,name,...] [required]
Name for output raster map(s)
DESCRIPTION
The input of this module is a single space time raster dataset, the
output is a single raster map layer. A subset of the input space time
raster dataset can be selected using the where option. The sorting of
the raster map layer can be set using the order option. Be aware that
the order of the maps can significantly influence the result of the ag-
gregation (e.g.: slope). By default the maps are ordered by start_time.
t.rast.series is a simple wrapper for the raster module r.series. It
supports a subset of the aggregation methods of r.series.
EXAMPLES
Estimate the average temperature for the whole time series
Here the entire stack of input maps is considered:
t.rast.series input=tempmean_monthly output=tempmean_average method=average
Estimate the average temperature for a subset of the time series
Here the stack of input maps is limited to a certain period of time:
t.rast.series input=tempmean_daily output=tempmean_season method=average \
where="start_time >= ’2012-06’ and start_time <= ’2012-08’"
Climatology: single month in a multi-annual time series
By considering only a single month in a multi-annual time series the
so-called climatology can be computed. Estimate average temperature
for all January maps in the time series:
t.rast.series input=tempmean_monthly \
method=average output=tempmean_january \
where="strftime(’%m’, start_time)=’01’"
# equivalently, we can use
t.rast.series input=tempmean_monthly \
output=tempmean_january method=average \
where="start_time = datetime(start_time, ’start of year’, ’0 month’)"
# if we want also February and March averages
t.rast.series input=tempmean_monthly \
output=tempmean_february method=average \
where="start_time = datetime(start_time, ’start of year’, ’1 month’)"
t.rast.series input=tempmean_monthly \
output=tempmean_march method=average \
where="start_time = datetime(start_time, ’start of year’, ’2 month’)"
Generalizing a bit, we can estimate monthly climatologies for all
months by means of different methods
for i in `seq -w 1 12` ; do
for m in average stddev minimum maximum ; do
t.rast.series input=tempmean_monthly method=${m} output=tempmean_${m}_${i} \
where="strftime(’%m’, start_time)=’${i}’"
done
done
SEE ALSO
r.series, t.create, t.info
Temporal data processing Wiki
AUTHOR
Sören Gebbert, Thünen Institute of Climate-Smart Agriculture
SOURCE CODE
Available at: t.rast.series source code (history)
Accessed: unknown
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