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

[Sajid Pareeth]

Thanks Markus,

Will try it soon.

Sajid

On Mon, Nov 28, 2016 at 9:03 AM, Markus Metz <markus.metz.giswork at gmail.com>
wrote:

> There is a new addon r.seasons which determines the number of seasons per
> pixel and extracts start and end dates for the given number of seasons from
> a time series.
>
> The module is designed for noisy input, e.g.
>
> time|value
> 0|0
> 1|0
> 2|1
> 3|0
> 4|1
> 5|1
> 6|1
> 7|0
> 8|1
> 9|1
> 10|1
> 11|1
> 12|0
> 13|0
>
> with threshold 0.5 and minimum season length set to 3 would detect one
> season. Core season start is at 4, end at 10. Full season start is at 2,
> end at 10.
>
> Markus M
>
>
> On Wed, Nov 9, 2016 at 4:17 PM, Nikos Alexandris <nik at nikosalexandris.net>
> wrote:
>
>> Nikos Alexandris:
>>
>> 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.
>>>>>
>>>>
>> Veronica Andreo:
>>
>> Yes, maybe it is not well explained, but it is here:
>>>> https://grasswiki.osgeo.org/wiki/Temporal_data_processing#Fi
>>>> lling_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 :)
>>>>
>>>
>> Nikos:
>>
>> Yes. It was even before -- just me read through quickly.
>>>
>>> r.hants is awesome!
>>>
>>> We have 24 Landsat 8 derived EVI2 maps (2 for each month) for one year.
>>> Clouds and Water surfaces removed. Another set of "relatively normalised
>>> images, using `i.histo.match` is also ready.  We really need to "fix"
>>> `i.histo.match` to crunch floats directly.  The resulting ranges worry
>>> me a bit -- I just followed the "old" way as discussed some years ago in
>>> a relevant thread (floats > integers > histo-matching > floats).
>>>
>>> Anyhow, I am testing the following:
>>>
>>> for NF in 4 5 6 7 8 ;do r.hants file=evi2_maps nf=$NF fet=0.05 dod=3
>>> base_period=24 suffix=_hants_nf_$NF amplitude=amplitude_hants_nf_$NF
>>> phase=phase_hants_nf_$NF ;done
>>>
>>> (thinking loudly... it would be super-nice to have `t.rast.hants`)
>>>
>>> Not sure about `dod`. Perhaps it should also follow a patten like 3 4 5
>>> 6 and 7 for the above?
>>>
>>> for NF in 5 6 7 8 ; do t.rast.series evi2_hants_nf_$NF method=max_raster
>>> output=dominant_frequencies_hants_nf_$NF --o
>>>
>>
>> Just in case, the above *should* read:
>>
>> for NF in 4 5 6 7 8 ;do r.series input=`g.list rast
>> pattern=amplitude*nf_${NF}* separator=comma` method=max_raster
>> output=dominant_frequencies_hants_nf_${NF} --o ;done
>>
>>
>> "Dominant" frequencies 0, 1, 2 and 3 appear to be within the areas
>>> of our interest (agricultural surfaces).  Very good.  And convincing.
>>> Yet I am learning to interpret this correctly. And the "phase" as well.
>>> Reminds math studies, years ago.
>>>
>>
>> It is a "good" thing to actually have similar dominant frequency "maps"
>> for all of the experimented Number of Frequencies.
>>
>> A confirmation would be reassuring.
>>
>> Thanks, Nikos
>>
>> [rest deleted]
>>
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>>
>
>
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