t.rast.aggregate.ds.1grass man page

t.rast.aggregate.ds — Aggregates data of an existing space time raster dataset using the time intervals of a second space time dataset.

Keywords

temporal, aggregation, raster, time

Synopsis

t.rast.aggregate.ds
t.rast.aggregate.ds --help
t.rast.aggregate.ds [-ns] input=name sample=name [type=name] output=name basename=string method=string [offset=integer] [nprocs=integer] [sampling=name[,name,...]] [where=sql_query] [--overwrite] [--help] [--verbose] [--quiet] [--ui]

Flags

-n
Register Null maps
-s
Use start time - truncated according to granularity - as suffix (overrides offset option)
--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
sample=name [required]
Time intervals from this space time dataset (raster, vector or raster3d) are used for aggregation computation
type=name
Type of the aggregation space time dataset
Options: strds, stvds, str3ds
Default: strds
output=name [required]
Name of the output space time raster dataset
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
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
offset=integer
Offset that is used to create the output map ids, output map id is generated as: basename_ (count + offset)
Default: 0
nprocs=integer
Number of r.mapcalc processes to run in parallel
Default: 1
sampling=name[,name,...]
The method to be used for sampling the input dataset
Options: equal, overlaps, overlapped, starts, started, finishes, finished, during, contains
Default: contains
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’

Description

t.rast.aggregate.ds works like t.rast.aggregate but instead of defining a fixed granularity for temporal aggregation the time intervals of all maps registered in a second space time dataset (can be STRDS, STR3DS or STVDS) are used to aggregate the maps of the input space time raster dataset.

Notes

The sampling method must be specified from the sampler dataset point of view. It defines the temporal relations hips between intervals of the sampling dataset and the input space time raster dataset.

Examples

Precipitation aggregation

In this example we create 7 raster maps that will be registered in a single space time raster dataset named precipitation_daily using a daily temporal granularity. The names of the raster maps are stored in a text file that is used for raster map registration.

A space time vector dataset is created out of two vector maps with different temporal resolution. The maps are created using v.random. The first map has a granule of 3 days the second a granule of 4 days.

The space time raster dataset precipitation_daily with daily temporal granularity will be aggregated using the space time vector dataset resulting in the output space time raster dataset precipitation_agg. The aggregation method is set to sum to accumulate the precipitation values of all intervals in the space time vector dataset. The sampling option assures that only raster maps that are temporally during the time intervals of the space time vector dataset are considered for computation. Hence the option is set to contains (time stamped vector map layers temporally contain the raster map layers):

MAPS="map_1 map_2 map_3 map_4 map_5 map_6 map_7"
for map in ${MAPS} ; do
    r.mapcalc expression="${map} = 1"
    echo ${map} >> map_list.txt
done
t.create type=strds temporaltype=absolute \
         output=precipitation_daily \
         title="Daily precipitation" \
         description="Test dataset with daily precipitation"
t.register -i type=raster input=precipitation_daily \
           file=map_list.txt start="2012-08-20" increment="1 days"
t.info type=strds input=precipitation_daily
 +-------------------- Space Time Raster Dataset -----------------------------+
 |                                                                            |
 +-------------------- Basic information -------------------------------------+
 | Id: ........................ precipitation_daily@PERMANENT
 | Name: ...................... precipitation_daily
 | Mapset: .................... PERMANENT
 | Creator: ................... soeren
 | Temporal type: ............. absolute
 | Creation time: ............. 2014-11-23 16:48:17.686979
 | Modification time:.......... 2014-11-23 16:48:18.302978
 | Semantic type:.............. mean
 +-------------------- Absolute time -----------------------------------------+
 | Start time:................. 2012-09-10 00:00:00
 | End time:................... 2012-09-17 00:00:00
 | Granularity:................ 1 day
 | Temporal type of maps:...... interval
 +-------------------- Spatial extent ----------------------------------------+
 | North:...................... 80.0
 | South:...................... 0.0
 | East:.. .................... 120.0
 | West:....................... 0.0
 | Top:........................ 0.0
 | Bottom:..................... 0.0
 +-------------------- Metadata information ----------------------------------+
 | Raster register table:...... raster_map_register_3225725979b14b5db343a00835b882c7
 | North-South resolution min:. 10.0
 | North-South resolution max:. 10.0
 | East-west resolution min:... 10.0
 | East-west resolution max:... 10.0
 | Minimum value min:.......... 1.0
 | Minimum value max:.......... 1.0
 | Maximum value min:.......... 1.0
 | Maximum value max:.......... 1.0
 | Aggregation type:........... None
 | Number of registered maps:.. 7
 |
 | Title:
 | Daily precipitation
 | Description:
 | Test dataset with daily precipitation
 | Command history:
 | # 2014-11-23 16:48:17
 | t.create type="strds" temporaltype="absolute"
 |     output="precipitation_daily" title="Daily precipitation"
 |     description="Test dataset with daily precipitation"
 | # 2014-11-23 16:48:18
 | t.register -i type="rast" input="precipitation_daily"
 |     file="map_list.txt" start="2012-08-20" increment="1 days"
 |
 +----------------------------------------------------------------------------+
v.random output=points_1 n=20
v.random output=points_2 n=20
t.create type=stvds temporaltype=absolute \
         output=points \
         title="Points" \
         description="Points for aggregation"
t.register -i type=vector input=points \
           map=points_1 start="2012-08-20" increment="3 days"
t.register -i type=vector input=points \
           map=points_2 start="2012-08-23" increment="4 days"
t.info type=stvds input=points
 +-------------------- Space Time Vector Dataset -----------------------------+
 |                                                                            |
 +-------------------- Basic information -------------------------------------+
 | Id: ........................ points@PERMANENT
 | Name: ...................... points
 | Mapset: .................... PERMANENT
 | Creator: ................... soeren
 | Temporal type: ............. absolute
 | Creation time: ............. 2014-11-23 16:48:49.193903
 | Modification time:.......... 2014-11-23 16:48:50.185671
 | Semantic type:.............. mean
 +-------------------- Absolute time -----------------------------------------+
 | Start time:................. 2012-08-20 00:00:00
 | End time:................... 2012-08-27 00:00:00
 | Granularity:................ 1 day
 | Temporal type of maps:...... interval
 +-------------------- Spatial extent ----------------------------------------+
 | North:...................... 79.283411
 | South:...................... 5.724954
 | East:.. .................... 118.881168
 | West:....................... 0.016755
 | Top:........................ 0.0
 | Bottom:..................... 0.0
 +-------------------- Metadata information ----------------------------------+
 | Vector register table:...... vector_map_register_6f02d33e0ee243d1a521aaaca39ecb31
 | Number of points ........... 40
 | Number of lines ............ 0
 | Number of boundaries ....... 0
 | Number of centroids ........ 0
 | Number of faces ............ 0
 | Number of kernels .......... 0
 | Number of primitives ....... 40
 | Number of nodes ............ 0
 | Number of areas ............ 0
 | Number of islands .......... 0
 | Number of holes ............ 0
 | Number of volumes .......... 0
 | Number of registered maps:.. 2
 |
 | Title:
 | Points
 | Description:
 | Points for aggregation
 | Command history:
 | # 2014-11-23 16:48:49
 | t.create type="stvds" temporaltype="absolute"
 |     output="points" title="Points" description="Points for aggregation"
 | # 2014-11-23 16:48:49
 | t.register -i type="vect" input="points"
 |     map="points_1" start="2012-08-20" increment="3 days"
 | # 2014-11-23 16:48:50
 | t.register -i type="vect" input="points"
 |     map="points_2" start="2012-08-23" increment="4 days"
 |
 +----------------------------------------------------------------------------+
t.rast.aggregate.ds input=precipitation_daily \
                    output=precipitation_agg \
                    sample=points type=stvds \
                    basename=prec_agg \
                    method=sum sampling=contains
t.support input=precipitation_agg \
          title="Aggregated precipitation" \
          description="Aggregated precipitation dataset"
t.info type=strds input=precipitation_agg
 +-------------------- Space Time Raster Dataset -----------------------------+
 |                                                                            |
 +-------------------- Basic information -------------------------------------+
 | Id: ........................ precipitation_agg@PERMANENT
 | Name: ...................... precipitation_agg
 | Mapset: .................... PERMANENT
 | Creator: ................... soeren
 | Temporal type: ............. absolute
 | Creation time: ............. 2014-11-23 16:53:23.488799
 | Modification time:.......... 2014-11-23 16:53:28.714886
 | Semantic type:.............. mean
 +-------------------- Absolute time -----------------------------------------+
 | Start time:................. 2012-08-20 00:00:00
 | End time:................... 2012-08-27 00:00:00
 | Granularity:................ 1 day
 | Temporal type of maps:...... interval
 +-------------------- Spatial extent ----------------------------------------+
 | North:...................... 80.0
 | South:...................... 0.0
 | East:.. .................... 120.0
 | West:....................... 0.0
 | Top:........................ 0.0
 | Bottom:..................... 0.0
 +-------------------- Metadata information ----------------------------------+
 | Raster register table:...... raster_map_register_7b025eb7431747c98c5c1ad971e8c282
 | North-South resolution min:. 10.0
 | North-South resolution max:. 10.0
 | East-west resolution min:... 10.0
 | East-west resolution max:... 10.0
 | Minimum value min:.......... 3.0
 | Minimum value max:.......... 4.0
 | Maximum value min:.......... 3.0
 | Maximum value max:.......... 4.0
 | Aggregation type:........... sum
 | Number of registered maps:.. 2
 |
 | Title:
 | Aggregated precipitation
 | Description:
 | Aggregated precipitation dataset
 | Command history:
 | # 2014-11-23 16:53:23
 | t.rast.aggregate.ds input="precipitation_daily"
 |     output="precipitation_agg" sample="points" type="stvds" basename="prec_agg"
 |     method="sum" sampling="contains"
 | # 2014-11-23 16:53:28
 | t.support input="precipitation_agg"
 |     title="Aggregated precipitation"
 |     description="Aggregated precipitation dataset"
 |
 +----------------------------------------------------------------------------+

MODIS satellite sensor daily data aggregation to 8 days

In this example the aggregation from daily data to eight days is shown. This "eight-day week" is used in some MODIS satellite sensor products.

# NOTE: the example is written in shell language
# create maps every 8 days as seed maps
for year in `seq 2000 2001` ; do
   for doy in `seq -w 1 8 365` ; do
      r.mapcalc -s expression="8day_${year}_${doy} = rand(0.0,40.0)"
   done
done
# From de name of each map, we take year and doy, and convert it
# to a YYYY-MM-DD date for start and end, and create a file with
# mapnames, start date and end date
g.list type=raster pattern=8day_20??_* > names_list
for NAME in `cat names_list` ; do
   # Parse
   YEAR=`echo $NAME | cut -d’_’ -f2`
   DOY=`echo $NAME | cut -d’_’ -f3`
   # convert YYYY_DOY to YYYY-MM-DD
   DOY=`echo "$DOY" | sed ’s/^0*//’`
   doy_end=0
   if [ $DOY -le "353" ] ; then
      doy_end=$(( $DOY + 8 ))
   elif [ $DOY -eq "361" ] ; then
      if [ $[$YEAR % 4] -eq 0 ] && [ $[$YEAR % 100] -ne 0 ] || [ $[$YEAR % 400] -eq 0 ] ; then
         doy_end=$(( $DOY + 6 ))
      else
	     doy_end=$(( $DOY + 5 ))
      fi
   fi
   DATE_START=`date -d "${YEAR}-01-01 +$(( ${DOY} - 1 ))days" +%Y-%m-%d`
   DATE_END=`date -d "${YEAR}-01-01 +$(( ${doy_end} -1 ))days" +%Y-%m-%d`
   # text file with mapnames, start date and end date
   echo "$NAME|$DATE_START|$DATE_END" >> list_map_start_end_time.txt
done
# check the list created.
cat list_map_start_end_time.txt
8day_2000_001|2000-01-01|2000-01-09
8day_2000_009|2000-01-09|2000-01-17
...
8day_2000_353|2000-12-18|2000-12-26
8day_2000_361|2000-12-26|2001-01-01
8day_2001_001|2001-01-01|2001-01-09
8day_2001_009|2001-01-09|2001-01-17
...
8day_2001_345|2001-12-11|2001-12-19
8day_2001_353|2001-12-19|2001-12-27
8day_2001_361|2001-12-27|2002-01-01
# all maps except for the last map in each year represent 8-days
# intervals. But the aggregation starts all over again every
# January 1st.
# create 8-day MODIS-like strds
t.create type=strds temporaltype=absolute \
   output=8day_ts title="8 day time series" \
   description="STRDS with MODIS like 8 day aggregation"
# register maps
t.register type=raster input=8day_ts \
   file=list_map_start_end_time.txt
# check
t.info input=8day_ts
t.rast.list input=8day_ts
# finally, copy the aggregation to a daily time series
t.rast.aggregate.ds -s input=daily_ts sample=8day_ts \
   output=8day_agg basename=8day_agg \
   method=average sampling=contains
# add metadata
t.support input=8day_agg \
   title="8 day aggregated ts" \
   description="8 day MODIS-like aggregated dataset"
# check map list in newly created aggregated strds
t.rast.list input=8day_agg
name|mapset|start_time|end_time
8day_agg_2000_01_01|modis|2000-01-01 00:00:00|2000-01-09 00:00:00
8day_agg_2000_01_09|modis|2000-01-09 00:00:00|2000-01-17 00:00:00
8day_agg_2000_01_17|modis|2000-01-17 00:00:00|2000-01-25 00:00:00
...
8day_agg_2000_12_18|modis|2000-12-18 00:00:00|2000-12-26 00:00:00
8day_agg_2000_12_26|modis|2000-12-26 00:00:00|2001-01-01 00:00:00
8day_agg_2001_01_01|modis|2001-01-01 00:00:00|2001-01-09 00:00:00
...
8day_agg_2001_12_11|modis|2001-12-11 00:00:00|2001-12-19 00:00:00
8day_agg_2001_12_19|modis|2001-12-19 00:00:00|2001-12-27 00:00:00
8day_agg_2001_12_27|modis|2001-12-27 00:00:00|2002-01-01 00:00:00

See Also

t.rast.aggregate, t.create, t.info

Author

Sören Gebbert, Thünen Institute of Climate-Smart Agriculture

Last changed: $Date: 2016-01-15 00:20:43 +0100 (Fri, 15 Jan 2016) $

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