t.rast.gapfill.1grass man page

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


temporal, interpolation, raster, time


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



Assign the space time raster dataset start and end time to the output map


Print usage summary


Verbose module output


Quiet module output


Force launching GUI dialog


input=name [required]

Name of the input space time raster dataset


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


Number of interpolation processes to run in parallel
Default: 1


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.


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


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 day size between the raster maps. The values of the maps are chosen so that the interpolated values can be estimated. We expect two maps with values 2 and 4 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=2012-08-24 end=2012-08-25
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
map1|2012-08-20 00:00:00|1.0|1.0
map2|2012-08-22 00:00:00|3.0|3.0
map3|2012-08-24 00:00:00|5.0|5.0
t.rast.list input=precipitation_daily method=deltagaps
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
map1|2012-08-20 00:00:00|1.0|1.0
gap_6|2012-08-21 00:00:00|2.0|2.0
map2|2012-08-22 00:00:00|3.0|3.0
gap_7|2012-08-23 00:00:00|4.0|4.0
map3|2012-08-24 00:00:00|5.0|5.0

See Also

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


Sören Gebbert, Thünen Institute of Climate-Smart Agriculture

Last changed: $Date: 2015-09-22 10:12:20 +0200 (Tue, 22 Sep 2015) $

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