Provided by: grass-doc_7.0.3-1build1_all
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, 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
DESCRIPTION
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.
EXAMPLE
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
SEE ALSO
r.series, t.create, t.info Temporal data processing Wiki
AUTHOR
Sören Gebbert, Thünen Institute of Climate-Smart Agriculture Last changed: $Date: 2016-01-13 00:30:14 +0100 (Wed, 13 Jan 2016) $ Main index | Temporal index | Topics index | Keywords index | Full index © 2003-2016 GRASS Development Team, GRASS GIS 7.0.3 Reference Manual