[GRASS-user] detrending time series maps

[Veronica Andreo]

That's right, Ivan!
End of year mental death, sorry for the noise...

Vero

El vie, 29 dic 2023 a las 15:34, Ivan Marchesini (<ivan.marchesini at gmail.com>)
escribió:

> Hi veronica
>
>
> I see what you did with creating the days time series. In that way you
> acknowledge irregular gaps, right?
>
> yes this is the reason
>
>
> However, why do you multiply by days strds? From my understanding,
> detrending by subtracting the results of a model obbeys this rule: value(t)
> = observed(t) - predicted(t). Then, this
> mystrds-(regression_offset+regression_slope*days) should be
> mystrds-(regression_offset+regression_slope*mystrds).
>
> hmm
>
> may be I'm wrong but I used r.regression.series to assess a relationship
> between mystrds values and time (days)
>
> As a consequence the offset and slope maps I obtain are b and a in the
> following linear equation
>
> y=ax+b
>
> i.e.
>
> predicted_mystrds=slope*days+offset
>
> This is why I suppose that for detrending I need to do:
>
> mystrds-(predicted_mystrds)
>
> i.e.
>
> mystrds-(slope*days+offset)
>
>
> I'm I wrong?
>
> thank you
>
> Ivan
>
>
>
>
> Best,
> Vero
>
> El jue, 28 dic 2023 a las 8:14, Ivan Marchesini (<
> ivan.marchesini at gmail.com>) escribió:
>
>> Dear Veronica
>>
>> I think I found a simple solution using temporal raster modules. Here is
>> an example:
>>
>> #evaluating info of the strds
>> eval `t.info mystrds -g`
>>
>> #getting the starting day (of the year, 0-365) of my strds
>> startday=$(date -d "$start_time" "+%j")
>>
>> #Creating a new strds where each pixel has the value of the count of the
>> days starting from the start_day of my strds (the start day in my dataset
>> is in 2016)
>> t.rast.mapcalc inputs=mystrds
>> expression="(start_year()-2016)*365-${startday} +start_doy()" output=days
>> basename=days nprocs=xxx --o
>>
>> #fitting the trend equation
>> r.regression.series xseries="`t.rast.list in=days columns=name sep=,
>> format=line`" yseries="` t.rast.list in=mystrds columns=name sep=,
>> format=line`"
>> out=regression_offset,regression_slope,regression_rsq,regression_t
>> meth=offset,slope,rsq,t
>>
>> #detrending
>> t.rast.mapcalc input=mystrds,days expression="mystrds_detrend
>> = mystrds-(regression_offset+regression_slope*days)" output=mystrds_detrend
>> basename=mystrds_detrend nprocs=xxx method=start --o
>>
>>
>> Best
>>
>> Ivan
>>
>>
>>
>>
>> On 23/12/23 14:53, Ivan Marchesini wrote:
>>
>> Hi Veronica
>>
>> Thank you. It goes in the direction of my idea evn if  my problem is
>> exactly trying to take into account the correct gaps between that data
>>
>> I have another idea.
>>
>> if it works I will come back here to explain how I did
>>
>> thank you again
>>
>> Ivan
>>
>>
>> On 22/12/23 13:45, Veronica Andreo wrote:
>>
>> Hello Ivan,
>>
>> AFAIU you could use the slope and offset maps from t.rast.series within
>> t.rast.algebra to detrend the values of the maps within the strds,
>> something like "detrended_strds = trend_strds - (trend_strds*map(slope) +
>> map(offset))". Others suggest, to detrend by subtracting the previous
>> value, i.e. that would imply using the temporal algebra with the temporal
>> index, something like "detrended_strds = trend_strds[1] - trend_strds[0]".
>>
>> I haven't tested any of these, just a couple of ideas ;-) However, I do
>> not know how this might interact with seasonality within data, or irregular
>> gaps.
>>
>> hth somehow
>> Vero
>>
>> El vie, 22 dic 2023 a las 5:10, Ivan Marchesini via grass-user (<
>> grass-user at lists.osgeo.org>) escribió:
>>
>>> Dear colleagues
>>>
>>> I would like to the advantage of the t.* modules for detrending a strd.
>>>
>>> In the strd I have earth observation data irregularly sampled (2 or 3
>>> times per month), in the period November-February, for 7 years. They are
>>> not equally spaced (i.e gaps have different duration)
>>>
>>> A simple t.rast.series analysis (opion=slope,offset) highlights that
>>> probably there is a descending trend when considering the maps ordered
>>> by id.
>>>
>>> I would like to fit a proper time depending fitting curve for each pixel
>>> and then subtract the function from the real data.
>>>
>>> any hints on how I can do this task exploiting the GRASS GIS modules or
>>> some simple bash/python scripting?
>>>
>>> thank you
>>>
>>> Ivan
>>>
>>>
>>>
>>>
>>> _______________________________________________
>>> grass-user mailing list
>>> grass-user at lists.osgeo.org
>>> https://lists.osgeo.org/mailman/listinfo/grass-user
>>>
>>
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