Hello Ivan,
Thanks for coming back to this :)
I see what you did with creating the days time series. In that way you
acknowledge irregular gaps, right? Otherwise, as t.rast.series
method=slope,offset uses r.series in the background, it will use index as
independent variable and therefore maps are considered equally separated in
time.
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).
Best,
Vero
El jue, 28 dic 2023 a las 8:14, Ivan Marchesini ()
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@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
>>
>>
>>
>>
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>
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