xenial (1) t.rast.series.1grass.gz

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) $

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