StaMPS-Visualizer, SNAP-StaMPS Workflow

Hi @hm1u16, have a look at the posts around this one…sounds familiar, if the error remains, ask again here.

If I remember correctly, you have to give a radius big enough to get all your points selected, by doing that the dimensions are consistent and you can proceed.

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Hi Everyone.
I tried the code in R which comes with the Shiny Visualizer to subset my data in R. In order to visualize it in Shiny Visualizer, however, it fails. Due to availability of ‘rgdal’ and ‘rgeos’ packages in R version 3.4.4. I updated R to the latest version 3.6.2. It still tells me that the packages are not available.
My question is this:
Has anyone faced similar problems with R version 3.4 installing rgdal and rgeos packages?
Thank you

This is solved. Thanks to @thho.

hello,I am a newbie in PSInSAR,so if I can take few
of your time,I hope to communite with you about some questions in this.

feel free to go ahead.

Just one hint to this post which collects the most important information PS InSAR:

For all having the same problem, use this as a starting point:

Hi Thho, thankyou for all your posts.
I’m trying for the first time to visualize my PS just processed.

I copied the script you suggested ad I got this error

Deramping computed on the fly.
**** z = ax + by+ c
619688 ref PS selected

savename =


Color Range: to 109 m/yr

ans =

[ ]

Undefined function or variable ‘lon2’.

Do you know how to fix this parameter?
thank you very much

have you executed it line by line, including the plot command beforehand?

So at best you follow this order

ps_plot('v-do', 'ts');

wait for the image to load

load parms.mat;

then execute the rest as a whole.

Greetings StaMPS experts,

I have a question I hope someone can help with. I have processed both ascending and descending Sentinel-1 data for a volcano. The processing worked great as I can clearly identify inflation on part of the volcano for both datasets - a great result. Then I selected only the inflating area (about 300 PS points), and averaged these time series into a single time series for ascending, and another for descending . I notice the descending data provides much better “smoother” average time series curve (below). Can I ask what are the possible reasons for the poor quality/scatter in the ascending time series? Could it be atmospheric effects, or something about that orbit? Is there anyway to analyse the data for an explanation? I would like to explain this.

Thank you kindly,

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My first thought on that is a question:

May it be that the radar looks downslope in descending and against the slope in ascending orbit?

If so, in the ascending images the forshortening/layover effects may dominate your signal, hence the poor results…

But this is indeed a good case study to present it, would love to see the studyside in a map and how you images are oriented.

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Thanks for your response. The PS selection area is relatively flat (crater floor - see black dash boundary below).

Below is a map showing the descending PS data interpolated by kriging . Both datasets show strong inflation in this area . White area is crater lake.

Please can I ask how to easily show determine the orientation of the satellite look angle? Is this available in the metadata?


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Concerning my thoughts of forshortening and layover:

  • Ascending (south north) looks (always right) downslope the area you use for the plot
  • Descending (north south) looks (also always right) “against” the slope (foreshortening/layover)

Hence, Ascending should provide better results, but in your case it performs worse (taken the displacement plot). Why?

Here I am not sure, but my thoughts on that are:

  • The slope is not that steep (as you already said) hence the orbit direction (and therefore foreshortening and layover, where the last my not occurre at all) is not crucial in your case, therefore other contributions affects the phase signal
  • surely, atmospheric effects could be one point, it would be random, that just the ascending images are affected but it is possible though. Anyway, imagin the case, where the master (since it must be different for both time series) of ascending is highly affected by an atmospheric phase contribution…that could lead to overall more noisy phase signal in the PSI approach, when the atm phase can not be identified correctly during processing.
  • What about the amount (number) and distribution (equal or clustered) of PS compared of ascending and descending images…when there are differences in both or one of these aspects, that may be another answer, since your timeline seems to be an average of the area.
  • Do you use the same reference point for both? (I assume that, but just to be sure, but anyway, where is it, I am interested and a sign in your map would be helpful :slight_smile: )

These are my first thoughts on that, hence PS processing can be very case specific, there are some more details to think about but it is hard to tell, without seeing the interferograms.


The suggestion of a atmospherically disturbed master acquisition for ascending mode could make sense indeed. Would be worth a try to select a different one.

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Yes this is a good point!

I am confused by your question - what do you mean by reference point? The time series y-axis is not relative to any reference point. It is LOS distance specific to the orbit path (average of all PS distance is set to zero for each observation in the time series).

Great answer, thank you.

I assume you process in StaMPS (and if not, still, I think also this is a sota thing to do in each PSI or SBAS like processing). Before processing, you should define a reference area in your study site. page 32

A reference area is an area you consider to be stable. This can be found by knowledge of the study area or by producing a first set of ifg and checking them. Looking at your map, the greenish areas in the eastern part or on the most southern part might be such areas. Those areas are used for reference for displacement. Imagine the case (I think your case) where every single point enters the reference, then, extreme values are also part of a reference value which might lead to strange results…When we now come back to your case, it might be that in your ascending images, you have more extreme values, which then might lead to a more unstable displacement signal, when used for reference…

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Thank you Thorsten. I understand now, and it is good to know about this feature!
Best, Mark

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what did you use to visualise you results. is it in QGIS?

I used the StaMPS visualizer to export csv, then interpolated the points by ordinary kriging (SAGA), then QGIS to display results.