t.rast.accdetect(1grass)

💖 t.rast.accdetect 💖

okay bestie, gather round, because this is the manual page for t.rast.accdetect from the GRASS User's Manual, and she is iconic. Buckle up. 🎀

✨ NAME ✨

t.rast.accdetect — literally there to detect accumulation patterns in temporally accumulated space time raster datasets that were created by t.rast.accumulate. that's the whole job. she's a detective. 🔍

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🔑 KEYWORDS 🔑

temporal, accumulation, raster, time — the four horsemen, no cap. 🦋

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🌸 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=name]
[range=min,max]   [staend=start,intermediate,end]   [--overwrite]
[--help]  [--verbose]  [--quiet]  [--ui]

🚩 Flags 🚩

• -n — registers the empty maps in the output space time raster dataset. without this, those empty maps get yeeted (deleted). 💀
• -r — reverses the time direction in cyclic accumulation. backwards girlie behavior, we support it. 🦋
• --overwrite — allow output files to overwrite existing files. permission slip granted. 💅
• --help — prints the usage summary. the "what do i do" button.
• --verbose — verbose module output. she will NOT stop talking and honestly? same.
• --quiet — quiet module output. the introvert setting. 🤫
• --ui — forces launching the GUI dialog. for the visual learners. ✨

🎀 Parameters 🎀

• input=name [required] — name of the input space time raster dataset. your starting material, bestie.
• minimum=name — an input space time raster dataset that specifies the minimum values used to detect the accumulation pattern. the floor. 📉
• maximum=name — an input space time raster dataset that specifies the maximum values used to detect the accumulation pattern. the ceiling. 📈
• occurrence=name [required] — the output space time raster dataset that stores the occurrence of the accumulation pattern using the provided data range. this is giving receipt. 🧾
• indicator=name — the output space time raster dataset that stores the indication of the start, intermediate and end of the specified data range. beginning, middle, end — a whole trilogy. 📚
• 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'. she resets. she rebrands. she comes back. 💅
• 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=name — 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
• 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. your fallback bestie. 🫶
• 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

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💜 DESCRIPTION 💜

So t.rast.accdetect is designed to detect accumulation patterns in temporally accumulated space time raster datasets created by t.rast.accumulate. Its input is a space time raster dataset resulting from a t.rast.accumulate run. That's the pipeline, and she is serving it. ✨

The start time and the end time of the pattern detection process must be set, eg. start="2000-03-01" end="2011-01-01". 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. 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".

🚨 OK PAUSE. 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. not "a little off", not "vibes slightly different". WRONG. results. this is the one rule, bestie, do not freestyle it. 😭

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. crop rotation said "i contain multitudes" 🌾

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.

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. If no equal maps are found, then maps with a temporal during relation will be detected, then maps that temporally overlap the actual granules 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 we wrote "of of". we're keeping it. it's part of her charm. 🙈)

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. three little numbers, three big moods. 🌱✨🍂

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🌈 EXAMPLE 🌈

Please have a look at the t.rast.accumulate example. (she's not being lazy, she's being ✨efficient✨ and pointing you at the whole story.)

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👀 SEE ALSO 👀

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

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👑 AUTHOR 👑

Sören Gebbert, Thünen Institute of Climate-Smart Agriculture. legend behavior, fr fr. 🫶

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💾 SOURCE CODE 💾

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

Latest change: Tuesday Jan 27 12:40:13 2026 in commit: 5e8b13d47dd4fc81efa5abefe90c34f7a8291fb9

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© 2003-2026 GRASS Development Team, GRASS 8.5.0 Reference Manual

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okay that's the whole page, you read all of it, I'm so proud of you 🥹💖 she runs on GRASS 8.5.0, and this whole glow-up was last touched Tuesday Jan 27 12:40:13 2026 — so she's fresh, she's current, she's that girl. go detect some accumulation patterns bestie, you've got this. slay, periodt, bye!! 💅🌸🦋