InSAR practical considerations Understanding the Line of … · InSAR practical considerations Matthew Pritchard ... Was at roipac.org/Modeling and /ContribSoftware ... (requires

InSAR practical considerations

Matthew Pritchard

Cornell UniversityEric FieldingJet Propulsion

Laboratory, Caltech

Understanding the Line of Sight

Sources of error

Resampling/downsampling data

Multi-interferogram methods: time series PSInSAR

Thursday, August 1, 2013

Visualizing the data & preparing for modeling

Was at roipac.org/Modeling and /ContribSoftware

-Perl script: mdx-kml.pl runs mdx to make image from geocoded interferogram or correlation, converts to png and makes kml (requires installation of ImageMagick)-Various GMT scripts-Example Matlab and IDL scripts for loading data

For GIS software, use .rsc file to create metadata file and perhaps rmg2mag_phs to make a single binary file

Make los script: creates file with the satellite heading and incidence angle at each pixel


http://roipac.org/Modeling

http://roipac.org/Modeling

Access to topographic data

Source I use most often: SRTM 1 degree tiles either at 3 arcsec (90m) or 1 arcsec for U.S. (30 m)--new v.3 release has voids filled with GDEMget_SRTM.pl now included with ROI_pac

SRTM also available from seamless USGS server. Can access available Lidar and NED data for U.S. at finer resolution ASTER GDEM -- posted at 1 arcsecond, but various analysis indicates it is closer to 3 arcsec (GDEM2 now available)

Format needs to be I*2 (16 bit) binary file with no headerNeed to create .rsc file with upper left coordinates, # of rows and columns, and pixel spacing


•Previously unmapped fault (right-lateral strike-slip)

Where in the world am I?

•Magnitude 6.6 earthquake: 26 December 2003 in Bam, Iran

• Arid and mountainous region with frequent earthquakes(collision between Arabian and Eurasian plates)

North

Bam

Baravat

10 km20 km

Interferogram courtesy of Yuri FialkoLandsat satellite image from 1999, from Funning et al., 2005

From: Farsinet.com


What am I looking at?

North

20 km

• Each fringe: contour of ground deformation in direction of satellite radar beam

•Each scene:•20 meters per pixel•100’s of km per image•Resolve deformation ~mm/year

•This example: •From European space Agency Envisat satellite (5.6 cm radar wavelength)

•Each fringe is 2.8 cm of deformation


What am I looking at?

North

20 km

• Each fringe: contour of ground deformation in direction of satellite radar beam

•Each scene:•20 meters per pixel•100’s of km per image•Resolve deformation ~mm/year

•This example: •From European space Agency Envisat satellite (5.6 cm radar wavelength)

•Each fringe is 2.8 cm of deformation


Visualizing 3D deformation in a 1D interferogram•Step 1: Fault motion produces 3D deformation field

Both images:Funning et al., 2005



•Step 2: Project 3D deformation onto satellite radar line-of-sightTrade-off between horizontal and vertical deformation creates asymmetric pattern




•Step 2: Project 3D deformation onto satellite radar line-of-sightTrade-off between horizontal and vertical deformation creates asymmetric pattern

•Step 3: Create a fringe every λ/2 centimeters (“wrapped image”)



Reconstructing the full 3D deformation field

•Use interferograms from different satellite look directions

Fialko et al., 2005

Observed interferograms




Fialko et al., 2005

Observed interferograms Observed pixel tracking

Before After

Fault

•PLUS: use the amplitude images to track pixels that moved




Fialko et al., 2005

Inferred vertical displacement Inferred horizontal displacement

Observed interferograms Observed pixel tracking

Before After

Fault

•PLUS: use the amplitude images to track pixels that moved


SAR Track/Frame Geometry

Iran

Modified from Rowena Lohman


Descending Ascending

LOS

LOS

Hector Mine EQ

Modified from Rowena LohmanThursday, August 1, 2013

What are the sources of error?

How do we evaluate them?

• Unwrapping errors: assess by looking a image with different wrap rates

• Atmospheric/ionospheric errors: use multiple images and pairwise logic (e.g., Feigl & Massonnet, 1995)

• Orbital errors: understand their basic characteristics, try different orbital estimates, process tracks of different lengths, tandem pairs can be useful

• DEM errors: inspect the raw DEM, process interferograms with different baselines and timespans, tandem pairs can be useful


Wrapped vs. Unwrapped

Color Cycle = 300 cm

LOS

Color Cycle ~ 3 cm

LOS


Hector Mine EQ








Unwrapped after masking Unwrapped before masking



Orbital Errors (“Ramps”)

• ~0.1-1 m uncertainty in satellite positions

• Orbital fringes not always 100% removed

• In particular, not sensitive to long wavelength deformation

How to overcome?• Simultaneously solve for

position and geophysics

Reported vs. Actual



Atmospheric contamination: Two types

Turbulence

Vertical stratification

Both From: Pritchard & Simons, 2004Thursday, August 1, 2013

Can we remove the atmospheric signal from interferograms?

0) Use interferograms themselves to estimate linear or exponential phase with elevation: constant for image or spatially variable

1) Direct water vapor and “dry delay” observations: From satellite (e.g., Li et al., 2005) and OSCAR.jpl.nasa.gov From GPS & other ground sensors (e.g., Webley et al., 2002)

2) Data stacks or APS: Assume atmosphere random in time or low-pass time domain filtering (e.g., Ferretti et al., 2001; Simons and Rosen, 2007)

3) Global and Regional Models computed by data center (~100 km horizontal resolution by ECMWF, NCEP; North American RR ~ 32 km) (e.g., Doin et al., 2007; Elliott et al., 2007)

4) Regional or Local Model computed by user (<3 km horizontal resolution) (e.g., Foster et al., 2006)


Can we remove the atmospheric signal from interferograms?

0) Use interferograms themselves to estimate linear or exponential phase with elevation: constant for image or spatially variable

1) Direct water vapor and “dry delay” observations: From satellite (e.g., Li et al., 2005) and OSCAR.jpl.nasa.gov From GPS & other ground sensors (e.g., Webley et al., 2002)

2) Data stacks or APS: Assume atmosphere random in time or low-pass time domain filtering (e.g., Ferretti et al., 2001; Simons and Rosen, 2007)

3) Global and Regional Models computed by data center (~100 km horizontal resolution by ECMWF, NCEP; North American RR ~ 32 km) (e.g., Doin et al., 2007; Elliott et al., 2007)

4) Regional or Local Model computed by user (<3 km horizontal resolution) (e.g., Foster et al., 2006)

Based on several studies, we can’t remove everything. Will likely always need to account for atmosphere via covariance matrix


Ionosphere: C- and L- bands, spanning 4000 km

“Azimuthal streaking” seen in polar regions (e.g., Joughin et al., 1996; Gray et al., 2000)

ERS: 1993-1997From: Pritchard, 2003

ALOS: Feb. 2008-Aug. 2007500 m B-perp

ALOS: Dec. 2007- Dec. 2006B-perp 1.1 km

ALOS: Mar. 2008-Mar. 2007600 m B-perp


Ionosphere

From: NOAA

From: Xu et al.,, 2004; originally from Aarons, 1982

Types of effects:

1) Broad phase delay (Ecuador example?)2) Turbulent effects (scintillation or

spread F: origin of Chile and Wenchuan examples?)

3) Faraday rotation


Ionosphere

From: NOAA

From: Xu et al.,, 2004; originally from Aarons, 1982

Types of effects:

1) Broad phase delay (Ecuador example?)2) Turbulent effects (scintillation or

spread F: origin of Chile and Wenchuan examples?)

3) Faraday rotation

Ionospheric corrective measures:1) “Throw out bad scenes”2) Split spectrum3) Optimium time of day


Unexpected deformation can cause errors

Vertical component of deformation from Southern California GPS station (in mm)

Annual and sub-annual cycles

Not a perfect sinusoid:Amplitude varies from year to year

Presumably related to natural and human-induced groundwater changes

From: Dong, JPL webpage


GPS: Reference frames Antenna environment: multipathing, antenna changes, cutting down trees

InSAR: atmospheric effects: (can mitigate with multiple interferograms, dense GPS, etc.) Ionospheric effects (mostly a problem near magnetic poles/equator; near dawn/dusk) Digital Elevation Model errors Orbital errors (hard to measure long-wavelength signals like post-glacial rebound)

How we can be fooled by space geodesy?

Both from: Bawden et al., 2001Thursday, August 1, 2013

GPS: Reference frames Antenna environment: multipathing, antenna changes, cutting down trees

InSAR: atmospheric effects: (can mitigate with multiple interferograms, dense GPS, etc.) Ionospheric effects (mostly a problem near magnetic poles/equator; near dawn/dusk) Digital Elevation Model errors Orbital errors (hard to measure long-wavelength signals like post-glacial rebound)

How we can be fooled by space geodesy?

GPS stations measure contraction in LA Basin …

Both from: Bawden et al., 2001

… But interferograms reveal other deformation that can contaminate tectonic signal

Both: Contamination by signals that were not expected


Should we believe GPS/InSAR?: Part 1How well do coincident measurements agree?

•Compare large earthquakes in South America: RMS different few cm

90 InSAR and GPS points for Mw 8.1 Antofagasta, Chile earthquake. GPS stations first occupied in 1992, so GPS was immature (Pritchard et al., 2002)

10 InSAR and GPS points for Mw 8.4 Arequipa, Peru earthquake. Only 4 different GPS stations included(Pritchard et al., 2007)

•For other earthquakes also agree to few cm: Landers, Northridge, Hector Mine (Massonnet et al., 1993, 1998; Zebker et al., 1994; Fialko et al., 2001; Jonsson et al., 2002)


Compare resampling methods

The problem: reduce 106-107points to 102-103

Compare: 1. Data resolution matrix method (Lohman & Simons, 2005)

2. Curvature based (Simons et al., 2002)

3. “Quadtree” (Jonsson et al., 2002)

4. Uniform

Potential downsides: 1. Need to specify a model 2 & 3: Sensitive to noisy data 4. Need too many points to get near model detail

From: Lohman & Simons, 2005

1 2

3 4


Data available in southern California

From: Yuri Fialko

Multiple interferograms: Time series of interferograms


Possible pairs with Perpendicular baseline < 200 m

From: Yuri Fialko

New techniques: Time series of interferograms


Strategies for combining multiple interferograms

Prerequisite: Need to co-register interferograms either in radar or geographic coordinate. You can do this in ROI_PAC 3.0.1 using process_2pass_master.pl by setting the Do_sim flag in the *.proc file--time-invariant view is stacking: Just take co-registered interferograms and add them together -- divide by the total time interval to get a rate

--time-variable view is called time-series: including methods called SBAS, PSInSAR, etc. More in lectures on GIAnT.Advantages: Deformation is time-dependent separate signal & error (atmosphere, unwrapping, DEM)Thursday, August 1, 2013

The Basic Idea…

Date

Inte

rfer

ogra

m N

umbe

r



The Basic Idea…

Date

Inte

rfer

ogra

m N

umbe

r

A stack of interferograms provides multiple constraints on a given time interval



The Basic Idea…

Date

Inte

rfer

ogra

m N

umbe

r

Goal: Solve for the deformation history that, in a least-squared sense, fits the set of observations (i.e., interferograms),

Many different methods (e.g., Lundgren et al. (2001), Schmidt & Burgmann, 2003), but SBAS (Berardino et al. (2002)) is perhaps most common one



Persistent scatterers (PS or PSInSAR)

Long Valley Caldera,Hooper et al. 2004

• Select pixels with stable scattering behavior over time

• Only focus on “good” pixels

InSAR– Spatial coherence @ 1 time– Need neighborhoods of good pts

PS– Coherence @ 1 point– Need > 15-20 scenes

• Added bonus:DEM errors!Works with large baselines

From: Rowena LohmanThursday, August 1, 2013

StaMPS method (Hooper et al., 2004)

Example: Imperial Valley, CAFrom: Rowena Lohman


Review: Will InSAR work for you?

• What is the local rate of deformation?

– Sensitivity of single igram ~1cm – How many years to get signal this big and will it be overcome by noise?– Can you stack several igrams together?

• What is the scale of deformation?

– Pixel size ~10m, but generally need to average many together– Image size is ~100 km, but if too broad worry about precision of orbits

• What is the local noise?

– How much vegetation/precipitation/water vapor/human cultivation?– Can you only make igrams with data from the same seasons?– Can you get L-band data and find persistent scatterers?

• What data is available? • Is there data from multiple satellites and/or imaging geometries?

• Is a digital elevation model available?

• Do you need rapid response for hazard assessment?


Review: How to set up InSAR capability?

1) Establish access to data • Main sources: see next slide

• How? Can be purchased commercially. Lower cost/no-cost data available with restrictions. In Europe, through ESA. In U.S., through ASF and UNAVCO. Some foreign access is allowed to UNAVCO

Can useful interferograms be made with available data? Worry about ground conditions, radar wavelength, frequency of observations, perpendicular baseline, availability of advanced processing techniques

2) Purchase/Install software to process and visualize data • Open source: ROI_PAC, DORIS, GMTSAR, NEST, RAT and IDIOT (TU Berlin) • Commercial: Gamma, MDA Earthview, DIAPASON, SARscape

3) Download/create DEM (SRTM is only +/- 60 degrees latitude, but ASTER G-DEM to 89)

4) Download precise orbital information & instrument files (Only ERS & Envisat)

5) Interpret results, create stacks, time series, persistent scatterers. May need to buy/downoad/create new software

6) Publish new discoveries and software tools!


•Good overview of classical & space based geodesy (but no InSAR): John Wahr’s online textbook http://samizdat.mines.edu/geodesy

•Introductions to InSAR:•2 page overview from Physics Today http://www.geo.cornell.edu/eas/PeoplePlaces/Faculty/matt/vol59no7p68_69.pdf

•Overviews of applications: Massonnet & Feigl, Rev. Geophys., 1998; Burgmann et al., AREPS, 2000.

•More advanced InSAR:•The definitive SAR book: Curlander & Mcdonough, 1990

•More technical reviews: Rosen et al., IEEE 2000; Hanssen’s Radar Interferometry book, 2001; Simons & Rosen, Treatise on Geophysics, 2007;

•Time series analysis: Berardino et al., IEEE, 2002; Schmidt & Burgmann, JGR, 2003

•Persistent scatterers: Ferretti IEEE, 2001; Hooper et al., GRL, 2004; Kampes’ Persistent Scatterers book, 2006

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InSAR practical considerations Understanding the Line of … · InSAR practical considerations Matthew Pritchard ... Was at roipac.org/Modeling and /ContribSoftware ... (requires