Using StaMPS/MTI for PSI Analysis – Part 3: StaMPS

Dear all,

I have to apologize for the long hiatus. I am busy finishing my PhD thesis, and some time-demanding projects at work.

However, as promised, I have managed to continue the StaMPS-workflow documentation. I think that StaMPS is quite demanding for inexperienced people (mostly students starting to work with PSI) who struggle with this complex methodological approach. The number of steps necessary to obtain reasonable results and the large number of parameter settings that are available in StaMPS might appear overwhelming to new users.

I have therefore prepared a full StaMPS workflow on GitLab. This includes a documentation of all StaMPS steps, associated parameters and some settings that might be useful as a starting point.

In this context, I have also updated the first two parts (Installation and Setup as well as Pre-Processing with SNAP) on this blog. Please note that I will only actively maintain the GitLab documentation, as markdown and script files are easier to handle there. I will edit StaMPS-related blog posts here, but only in case of major changes. Please refer to the documentation on GitLab as a main reference.

You may also have a look at the thread dealing with this topic which I opened at ESA’s STEP forum.

About This Author

Matthias studied Environmental Information Management at the University of Natural Resources and Life Sciences Vienna and holds a PhD in environmental statistics. The focus of his thesis was on the statistical modelling of rare (extreme) events as a basis for vulnerability assessment of critical infrastructure. He is working at the Austrian national weather and geophysical service (ZAMG) and at the Institute of Mountain Risk Engineering at BOKU University. He currently focuses the (statistical) assessment of adverse weather events and natural hazards, and disaster risk reduction. His main interests are statistical modelling of environmental phenomena as well as open source tools for data science, geoinformation and remote sensing.

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