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NAME

       t.rast.gapfill   -  Replaces  gaps in a space time raster dataset with interpolated raster
       maps.

KEYWORDS

       temporal, interpolation, raster, time, no-data filling

SYNOPSIS

       t.rast.gapfill
       t.rast.gapfill --help
       t.rast.gapfill  [-t]  input=name   [where=sql_query]    basename=string    [suffix=string]
       [nprocs=integer]   [--help]  [--verbose]  [--quiet]  [--ui]

   Flags:
       -t
           Assign the space time raster dataset start and end time to the output map

       --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

       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’

       basename=string [required]
           Basename of the new generated output maps
           A numerical suffix separated by an underscore will be  attached  to  create  a  unique
           identifier

       suffix=string
           Suffix  to  add  at  basename:  set  ’gran’  for granularity, ’time’ for the full time
           format, ’num’ for numerical suffix with a specific number of digits (default %05)
           Default: gran

       nprocs=integer
           Number of interpolation processes to run in parallel
           Default: 1

DESCRIPTION

       t.rast.gapfill  fills  temporal  gaps  in  space  time  raster   datasets   using   linear
       interpolation.  Temporal  all gaps will be detected in the input space time raster dataset
       automatically. The predecessor and successor maps of the gaps will be identified and  used
       to linear interpolate the raster map between them.

NOTES

       This  module uses r.series.interp to perform the interpolation for each gap independently.
       Hence several interpolation processes can be run in parallel.

       Each gap is re-sampled by the space time raster dataset  granularity.   Therefore  several
       time  stamped  raster  map layers will be interpolated if the gap is larger than the STRDS
       granularity.

Examples

       In this example we will create 3 raster maps and register them in the temporal database an
       then  in  the  newly created space time raster dataset.  There are gaps of one and two day
       size between the raster maps. The values of the maps are chosen so that  the  interpolated
       values  can  be  estimated.  We expect one map with a value of 2 for the first gap and two
       maps (values 3.666 and 4.333) for the second gap after interpolation.
       r.mapcalc expression="map1 = 1"
       r.mapcalc expression="map2 = 3"
       r.mapcalc expression="map3 = 5"
       t.register type=raster maps=map1 start=2012-08-20 end=2012-08-21
       t.register type=raster maps=map2 start=2012-08-22 end=2012-08-23
       t.register type=raster maps=map3 start=2012-08-25 end=2012-08-26
       t.create type=strds temporaltype=absolute \
                output=precipitation_daily \
                title="Daily precipitation" \
                description="Test dataset with daily precipitation"
       t.register type=raster input=precipitation_daily maps=map1,map2,map3
       t.rast.list input=precipitation_daily columns=name,start_time,min,max
       name|start_time|min|max
       map1|2012-08-20 00:00:00|1.0|1.0
       map2|2012-08-22 00:00:00|3.0|3.0
       map3|2012-08-25 00:00:00|5.0|5.0
       t.rast.list input=precipitation_daily method=deltagaps
       id|name|mapset|start_time|end_time|interval_length|distance_from_begin
       map1@PERMANENT|map1|PERMANENT|2012-08-20 00:00:00|2012-08-21 00:00:00|1.0|0.0
       None|None|None|2012-08-21 00:00:00|2012-08-22 00:00:00|1.0|1.0
       map2@PERMANENT|map2|PERMANENT|2012-08-22 00:00:00|2012-08-23 00:00:00|1.0|2.0
       None|None|None|2012-08-23 00:00:00|2012-08-24 00:00:00|1.0|3.0
       map3@PERMANENT|map3|PERMANENT|2012-08-24 00:00:00|2012-08-25 00:00:00|1.0|4.0
       t.rast.gapfill input=precipitation_daily basename=gap
       t.rast.list input=precipitation_daily columns=name,start_time,min,max
       name|start_time|min|max
       map1|2012-08-20 00:00:00|1.0|1.0
       gap_6_1|2012-08-21 00:00:00|2.0|2.0
       map2|2012-08-22 00:00:00|3.0|3.0
       gap_7_1|2012-08-23 00:00:00|3.666667|3.666667
       gap_7_2|2012-08-24 00:00:00|4.333333|4.333333
       map3|2012-08-25 00:00:00|5.0|5.0

SEE ALSO

        r.series.interp, t.create, t.info

AUTHOR

       Sören Gebbert, Thünen Institute of Climate-Smart Agriculture

       Last changed: $Date: 2015-09-22 10:10:38 +0200 (Tue, 22 Sep 2015) $

SOURCE CODE

       Available at: t.rast.gapfill source code (history)

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