Note: A new GRASS GIS stable version has been released: GRASS GIS 7.6, available here.
Updated manual page: here
NAME
t.rast.series - Performs different aggregation algorithms from r.series 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 [order=string[,string,...]] [where=sql_query] output=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 [required]
- Aggregate operation to be performed on the raster maps
- Options: average, count, median, mode, minimum, min_raster, maximum, max_raster, stddev, range, sum, variance, diversity, slope, offset, detcoeff, quart1, quart3, perc90, quantile, skewness, kurtosis
- Default: average
- order=string[,string,...]
- Sort the maps by category
- Options: id, name, creator, mapset, creation_time, modification_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 [required]
- Name for output raster map
t.rast.series is a simple wrapper for the raster module
r.series. It supports a subset of the aggregation methods of
r.series.
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 aggregation (e.g.: slope). By default the maps are
ordered by start_time.
Estimate average temperature for the whole time series
t.rast.series input=tempmean_monthly output=tempmean_general method=average
Estimate average temperature for all January maps in the time series, the
so-called climatology
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
r.series,
t.create,
t.info
Temporal data processing Wiki
Sören Gebbert, Thünen Institute of Climate-Smart Agriculture
Last changed: $Date: 2016-01-12 15:28:48 -0800 (Tue, 12 Jan 2016) $
SOURCE CODE
Available at: t.rast.series source code (history)
Note: A new GRASS GIS stable version has been released: GRASS GIS 7.6, available here.
Updated manual page: here
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