Dear forum contributors,
I am very grateful i could be part of this forum to complete my thesis regarding seasonal subsidence calculation using Sentinel-1 and StaMPS. In the progress of completing my bsc I have created a Readme that should cover a large part of installation and basic processing of SNAP-StaMPS. The documentation will be sufficient for any beginner trying to start and extra links within the paper show you to more in depth articles. I hope I can help some people with their problems getting started with these methods. From experience i can say it takes time to generate, interpret, regenerate and conclude on these results. PS-InSAR has a complex data chain with multiple uncertainties and possible problems so be careful jumping into the deep end.
you can find all help from installing, data downloading, pre-processing, StaMPS processing and LOS convertion in this github:
I can also recommend reading through a different Gitlab of our fellow contributors, which has been updated extensively over the last month I just saw.
Don’t forget to read through some basic PS-InSAR before starting this adventure and giving those contributors the scientific credit they really deserve!
please notify me if any information is not sufficient or misleading, this GitHub is just a start but is definitely not complete nor perfect.
Good luck processing,
Gijs van Leeuwen
very nice documentation, this will surely help people getting into it. I added it at the top of the list here: About the StaMPS category
I have a qusrtion, I exported to stamps every pairs of my images with single master, now I don’t understand this step correctly:
3) Replace the former directories to the directory name (INSAR_master_data)
NOTE: In case of several Stacks export each Stack and then copy results to the directories directories /diff0, /geo,/dem,/rslc.
I dont know what to do cause there are 4 pair of images, I mean should I just paste all diff0s without any other caption for it? and also i didnt understand the name of it! is INSAR_20191108_data true?!
I believe you are not using the snap2stamps package, right?
In theory each image should have different name, equal to its acquisition day. So the only equals would be the master. It should not be a problem
no unfortunately i don’t have any knowledge in python and linux and it’s really taking too much time from me!I tried to process these steps in windows and then do the other steps on vm. i’m really confused
in the documentation i am confused where i should install conda environment. should i install it on windows or linux? i installed the python packages on my virtual environment on ubuntu, is that correct?
I dowloaded all data via my windows pc and had my conda environment there, for both that and the processing steps in python after and the running of snap2stamps.
you can do all these processes on your linux pc as well so if you install a conda environment there you can run it from there. if you have a virtual python environment on your ubuntu machine you can use this as well. conda is just a way to create a python environment, which i used to create the seperate python 2 and 3 environments.
i installed them on windows and linux virtual environment. but i am doing the snap preprocessing on windows and the stamps will be on the linux virtual environments.
It’s very informative. I want to calculate Vertical displacement form LOS, but my processed data contain only descending orbit only.
Please help me to calculate the Vertical displacement using only descending orbit data?
You can only compute vertical displacement combining both ascending and descending orbits or potentially by using a separate Ground Control Point vertical height change time series. You can search on these forums for more posts on how to exactly compute this or look in to the referenced papers in my thesis.
This post may help you: StaMPS-Visualizer, SNAP-StaMPS Workflow
It explains how to compute vertical displacements from only 1 orbit, please be aware that if you are not using 2 orbits the result will probably be incorrect.
Thank you so much. I will go through it