t.rast.accdetect(1)

📦
version 8.4.2 · 8 versions
, in fedora 44

💖 t.rast.accdetect 💖

okay bestie, welcome to the t.rast.accdetect manual page from the GRASS GIS User's Manual — this is the one that clocks accumulation patterns in your temporally accumulated space time raster datasets, the ones your girl t.rast.accumulate made. periodt.

✨ KEYWORDS ✨

temporal, accumulation, raster, time — the four horsemen, but make it cute 🎀

🦋 SYNOPSIS 🦋

t.rast.accdetect
t.rast.accdetect --help
t.rast.accdetect [-nr] input=name  [minimum=name]   [maximum=name]
occurrence=name  [indicator=name]  start=string  [stop=string]
cycle=string  [offset=string]  basename=string  [suffix=string]
[range=min,max]   [staend=start,intermediate,end]   [--overwrite]
[--help]  [--verbose]  [--quiet]  [--ui]

🚩 Flags 🚩

  • -n — register empty maps in the output space time raster dataset. if you don't use this, those empty maps get deleted, no cap 💀
  • -r — reverse time direction in cyclic accumulation. going backwards, iconic behaviour
  • --overwrite — let output files overwrite files that already exist, live your truth
  • --help — print the usage summary, the lil TL;DR
  • --verbose — verbose module output, she's chatty
  • --quiet — quiet module output, she's mysterious
  • --ui — force launching the GUI dialog, for the visual girlies

🌸 Parameters 🌸

  • input=name [required] — name of the input space time raster dataset. yes bestie, required, we need the goods
  • minimum=name — input space time raster dataset that specifies the minimum values for detecting the accumulation pattern
  • maximum=name — input space time raster dataset that specifies the maximum values for detecting the accumulation pattern
  • occurrence=name [required] — the output space time raster dataset that stores the occurrence of the accumulation pattern using the data range you provided
  • indicator=name — the output space time raster dataset that stores the indication of the start, intermediate and end of the specified data range
  • start=string [required] — the temporal starting point to begin the accumulation, eg '2001-01-01'
  • stop=string — the temporal date to stop the accumulation, eg '2009-01-01'
  • cycle=string [required] — the temporal cycle to restart the accumulation, eg '12 months'
  • offset=string — the temporal offset to the begin of the next cycle, eg '6 months'
  • 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, 'count' for numerical suffix with a specific number of digits (default %05) Default: gran
  • range=min,max — the minimum and maximum value of the occurrence of accumulated values. these values will be used if the min/max space time raster datasets are not specified
  • staend=start,intermediate,end — the user defined values that indicate start, intermediate and end status in the indicator output space time raster dataset Default: 1,2,3

👑 DESCRIPTION 👑

so t.rast.accdetect is designed to detect accumulation patterns in temporally accumulated space time raster datasets created by t.rast.accumulate. this module's input? a space time raster dataset resulting from a t.rast.accumulate run. it's giving sequel energy.

the start time and the end time of the pattern detection process must be set, eg. start="2000-03-01" end="2011-01-01". and no, the start and end time do not need to be the same as for the accumulation run that produced the input space time raster dataset — you have freedom, you have range 💅

in addition, a cycle, eg. "8 months", can be specified, that defines after which time interval the accumulation pattern detection process restarts. the offset option specifies the time that should be skipped between two cycles, eg. "4 months".

🚨 bestie listen. the cycle and offset options must be exactly the same that were used in the accumulation process that generated the input space time raster dataset. otherwise the accumulation pattern detection will produce wrong results. this is not a drill, this is not a vibe check you can skip 😭

the minimum and maximum values for the pattern detection process can be set either by using space time raster datasets, or by using fixed values for all raster cells and time steps.

using space time raster datasets allows specifying minimum and maximum values for each raster cell and each time step. for example: we want to detect the germination (minimum value) and harvesting (maximum value) dates for different crops in Germany using the growing-degree-day (GDD) method for several years. different crops may grow in different raster cells and change with time because of crop rotation. hence we need to specify different GDD germination/harvesting (minimum/maximum) values for different raster cells and different years. it's a whole situation, very main character.

the raster maps that specify the minimum and maximum values of the actual granule will be detected using the following temporal relations: equals, during, overlaps, overlapped and contains. here's the order of operations, slay:

  1. first, all maps with time stamps equal to the current granule of the input STRDS will be detected. the first minimum map and the first maximum map that are found will be used as range definitions.
  2. if no equal maps are found, then maps with a temporal during relation will be detected.
  3. then maps that temporally overlap the actual granules.
  4. and finally, maps that have a temporal contain relation will be detected.

if no maps are found, or minimum/maximum STRDS are not set, then the range option is used, eg. range=480,730.

the base name of of the generated maps must always be set. (yes the original says "of of", we're keeping that, it's part of the lore 🙈)

this module produces two output space time raster datasets: occurrence and indicator.

  • the occurrence output STRDS stores the time in days from the beginning of a given cycle for each raster cell and time step that has a value within the minimum and maximum definition. these values can be used to compute the duration of the recognized accumulation pattern. ✨
  • the indicator output STRDS uses three integer values to mark raster cells as beginning, intermediate state or end of an accumulation pattern. by default, the module uses 1 to indicate the start, 2 for the intermediate state and 3 to mark the end of the accumulation pattern in a cycle. these default values can be changed using the staend option.

🍓 EXAMPLE 🍓

please have a look at the t.rast.accumulate example. she did it first, go pay her a visit 💖

🌈 SEE ALSO 🌈

  • t.rast.accumulate
  • t.rast.aggregate
  • t.rast.mapcalc
  • t.info
  • r.series.accumulate
  • g.region

🧚 AUTHOR 🧚

Sören Gebbert, Thünen Institute of Climate-Smart Agriculture — thank you for your service, legend 👑

⭐ SOURCE CODE ⭐

available at: t.rast.accdetect source code (history)

accessed: Saturday Jan 17 05:41:53 2026

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© 2003-2025 GRASS Development Team, GRASS GIS 8.4.2 Reference Manual

okay that's the whole thing bestie, you ate that. this was t.rast.accdetect(1) from GRASS GIS 8.4.2, dated Saturday Jan 17 05:41:53 2026 — now go detect some patterns, ily, mwah 💜✨🎀

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