[GRASS-user] Extracting vegetation phenology from Landsat-based time series

[Veronica Andreo]

Hello Nikos,

Yes, maybe it is not well explained, but it is here:
https://grasswiki.osgeo.org/wiki/Temporal_data_processing#Filling_and_reconstructing_time_series_data_with_gaps_-_HANTS

and also in the manual under "NOTES"
https://grass.osgeo.org/grass70/manuals/addons/r.hants.html

The idea is, once you are happy with the result of hants, you ask for
amplitude outputs and with those amplitude maps (you'll have one map per
frequency) you run r.series method=max_raster. This will give you the most
important frequency (according to amplitude value of that frequency) in
each pixel. If the result is that the most important frequency is 0, then
you have one cycle per year (that, if you use a base period of one year of
course).

Hope it is clearer now :)

Best,
Vero

2016-11-01 8:30 GMT+01:00 Nikos Alexandris <nik at nikosalexandris.net>:

> * Veronica Andreo <veroandreo at gmail.com> [2016-10-31 10:40:33 +0100]:
>
> Hello Nikos and all :)
>>
>> Don't know exactly which parameters you would like to extract from your
>> time series, Nikos, but if helpful, what I did was to use a combination of
>> r.hants and temporal modules to get some phenological indicators such as,
>> number of cycles per year,...
>>
>
> Vero,
>
> Is the number of cycles per year in the Wiki as well?  Going through,
> last time, I think I didn't grasp that.  Can you pin-point?  It's
> exactly what we need at the moment.
>
> Nikos
>
> ...yearly max and min values, dates of yearly max
>> and min values, period that the variable was above a certain threshold,
>> max
>> rate of change (slope between every pair of maps and then aggregate per
>> year with method=maximum). Some of those examples are in the wiki [0]. I
>> believe that much more could be done with t.rast.algebra (it seems very
>> powerfull), but I haven't yet tested enough.
>>
>> @Sajid, I agree it would be great to have such functionalities as
>> "ready-to-use" module in GRASS, too. Therefore, we could avoid all the
>> steps of moving a time series into r and then back again into GRASS [1]
>>
>> @MarkusM, local weighted regression sounds cool. +1 for that! It would be
>> also very useful to have DINEOF [2] natively implemented. It is very nice
>> when you want to keep the variation of the series instead of smoothing it
>> out [1].
>>
>> Best,
>> Vero
>>
>> [0] https://grasswiki.osgeo.org/wiki/Temporal_data_processing
>> [1]
>> https://grasswiki.osgeo.org/wiki/Temporal_data_processing/GR
>> ASS_R_raster_time_series_processing
>> [2] http://modb.oce.ulg.ac.be/mediawiki/index.php/DINEOF
>>
>
> [rest deleted]
>
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