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<< Click to Display Table of Contents >> Interferometric Stacking - Post Processing Tools - Time Series Classification - Phenomenological Analysis |
  
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Interferometric Stacking - Post Processing Tools - Time Series Classification - Phenomenological Analysis
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<< Click to Display Table of Contents >> Interferometric Stacking - Post Processing Tools - Time Series Classification - Phenomenological Analysis |
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Purpose
This tool fits the SBAS raster deformation time series using an external temporal phenomenological magnitude (i.e.: M= temperature, rain, etc). It retrieves the best fitting coefficients and its RMS for each pixel in the time series. These coefficients represent different characteristics of the displacement such as speed, acceleration, coupling between both time series and delay.
These results can be sequentially used to perform a displacement classification using the Model Classification panel.
Technical Notes
The phenomenological model is defined by:
With:
the displacement [mm]
time
external temporal phenomenological data [M unit]
an initial displacement offset [mm]
the deformation speed independent phenomenological component [mm/year]*
a scaling constant [mm/M unit]**
a delay between the phenomenological event and the displacement [days]
* This estimated speed corresponds to the deformation process not described by phenomenological process. For example a structure may sink at constant speed and thermally dilate at the same time. This velocity corresponds to the sinking process.
** This scaling constant represents the coupling between the deformation and the external phenomenological magnitude. For example, in the case of external temperature measurements, this magnitude can be interpreted as the coefficient of thermal expansion of the target.
Input Files
Input File
The displacement meta file (*_meta) . Both decomposed or LOS meta files are supported.
Phenomenological Data File
A list of external measurements in csv and/or meta files format such as the ones obtained from the ERA5 or ECMWF download tools.
The csv files should include a header which is skipped, and the data lines must be in the following format:
d-m-yyyy,x
with
d the day number
m the month number
yyyy the year
- the date separator
, the column separator
x the measured quantity
Example:
date,rain
1-1-2014,86.2
1-2-2014,73.3
1-3-2014,51.8
1-4-2014,15.5
…
Parameters - Principal Parameters
Analyze time subset
By setting this flag only the period defined by From and To will be analyzed.
Generate fitted TS
By setting this flag the fitted time series for each model will be saved. This may be useful to analyze different model performance.
Parameters - Other Parameters
Limits for the possible fitting parameters can be introduced.
Output Files
A directory is created combining the Output Root Name and the model’s name from the Phenomenological Data File name (I.e.: projectXXX_temperature). In that directory the files are:
modelName_aN
Images with fitting parameter of order N.
modelName_chi
Image with the fitting chi.
modelName_rms
Image with the fitting RMS error.
modelName_meta
Meta file of fitting parameters and the fitted time series (if selected).
modelName_input_TS
Fitted time series images (if selected).
General Functions
Cancel
The window will be closed.
Help
Specific help document section.
Specific Function(s)
None.
Task, SARscapeBatch object, SARscapeBatch script example
References A. De Grandi (2019): PASTA - Phenomena Aware Spatial-Temporal Analysis, Bsc Thesis, Università degli Studi dell’Insubria, Italy.