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[,string,...]   [quantile=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,  maximum,  max_raster,
           stddev,  range,  sum,  variance,  diversity,  slope, offset, detcoeff, quart1, quart3,
           perc90, quantile, skewness, kurtosis
           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,    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[,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
       aggregation (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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       © 2003-2022 GRASS Development Team, GRASS GIS 7.8.7 Reference Manual