Combining InSAR, Levelling and GNSS for the Estimation of 3D Surface Displacements
|
|
|
- Anna Bradley
- 5 years ago
- Views:
Transcription
1 Combining InSAR, Levelling and GNSS for the Estimation of 3D Surface Displacements Thomas Fuhrmann (1), Miguel Caro Cuenca (2), Freek van Leijen (3), Malte Westerhaus (1), Ramon Hanssen (3), Bernhard Heck (1) (1) Karlsruhe Institute of Technology, (2) Netherlands Organisation for Applied Scientific Research (TNO), (3) Delft University of Technology 0 Introduction Database Combination Results KIT University of the State of Baden-Wuerttemberg and National T. Fuhrmann Research Center et al. of thecombining Helmholtz Association InSAR, Levelling and GNSS Fringe Workshop 2015, Frascati March 24, 2015
2 Motivation Major drawbacks of SAR-Interferometry 1-dimensional measurements along the LOS direction Decomposition into horizontal and vertical components not possible Accuracy of estimated displacement/rate not easily accessible (filtering) Results relative to a reference point/area 1 Introduction Database Combination Results
3 Motivation Major drawbacks of SAR-Interferometry 1-dimensional measurements along the LOS direction Decomposition into horizontal and vertical components not possible Accuracy of estimated displacement/rate not easily accessible (filtering) Results relative to a reference point/area 3D velocity field Realistic accuracy information Reference frame 1 Introduction Database Combination Results
4 Research objectives GNSS Levelling InSAR 3D velocity field Derivation of horizontal and vertical surface displacements Robust combination of InSAR, levelling and GNSS Focus on linear movements (displacement rates) Realistic information on the accuracies of the estimates Area of interest: Upper Rhine Graben 2 Introduction Database Combination Results
5 Upper Rhine Graben Most prominent segment of the Cenozoic rift system Significant probability for large earthquakes 3 Introduction Database Combination Results
6 Upper Rhine Graben Most prominent segment of the Cenozoic rift system Significant probability for large earthquakes Basel 1356: M W = (Fäh et al., 2009) 3 Introduction Database Combination Results
7 Upper Rhine Graben Most prominent segment of the Cenozoic rift system Significant probability for large earthquakes Mahlberg 1728: M W = 5.3 (Meidow, 1998) Basel 1356: M W = (Fäh et al., 2009) 3 Introduction Database Combination Results
8 Upper Rhine Graben Most prominent segment of the Cenozoic rift system Significant probability for large earthquakes Mahlberg 1728: M W = 5.3 (Meidow, 1998) Waldkirch 2004: M W = 4.6 (Häge et al., 2009) Basel 1356: M W = (Fäh et al., 2009) 3 Introduction Database Combination Results
9 Upper Rhine Graben Most prominent segment of the Cenozoic rift system Significant probability for large earthquakes Tectonic motion: Small (< 1mm/a), but still not well constrained from Geodesy Mahlberg 1728: M W = 5.3 (Meidow, 1998) Waldkirch 2004: M W = 4.6 (Häge et al., 2009) Basel 1356: M W = (Fäh et al., 2009) 3 Introduction Database Combination Results
10 Database 3 Introduction Database Combination Results
11 Database 3 Introduction Database Combination Results
12 Database 3 Introduction Database Combination Results
13 Database 3 Introduction Database Combination Results
14 Properties of the techniques Spatial distribution: InSAR: high in urban areas Levelling: high along lines GNSS: low (30 40 km) Temporal distribution: InSAR: 35 days Levelling: campaigns ( 20a) GNSS: permanent (daily) 4 Introduction Database Combination Results
15 Properties of the techniques Spatial distribution: InSAR: high in urban areas Levelling: high along lines GNSS: low (30 40 km) Temporal distribution: InSAR: 35 days Levelling: campaigns ( 20a) GNSS: permanent (daily) InSAR desc. InSAR asc. Temporal coverage, representative example GPS Levelling Year 4 Introduction Database Combination Results
16 Single technique analysis InSAR: PS analysis using StaMPS (Hooper et al., JGR 2007) Data: 2 ascending, 1 descending track; ERS-1/2, Envisat Result: LOS displacement w.r.t. a master scene and a reference area Levelling: Kinematic adjustment of repeatedly measured levelling data Data: height differences at levelling benchmarks Result: Linear displacement rates (vertical) w.r.t. a reference point GNSS: Differential processing using Bernese GPS software (Dach et al., 2007) Data: GPS observations, daily coordinates at 76 sites Result: Linear displacement rates (horizontal) w.r.t. ITRF05 (block mean subtracted) 5 Introduction Database Combination Results
17 Single technique analysis InSAR: PS analysis using StaMPS (Hooper et al., JGR 2007) Data: 2 ascending, 1 descending track; ERS-1/2, Envisat Result: LOS displacement w.r.t. a master scene and a reference area Levelling: Kinematic adjustment of repeatedly measured levelling data Data: height differences at levelling benchmarks Result: Linear displacement rates (vertical) w.r.t. a reference point GNSS: Differential processing using Bernese GPS software (Dach et al., 2007) Data: GPS observations, daily coordinates at 76 sites Result: Linear displacement rates (horizontal) w.r.t. ITRF05 (block mean subtracted) 5 Introduction Database Combination Results
18 Single technique analysis InSAR: PS analysis using StaMPS (Hooper et al., JGR 2007) Data: 2 ascending, 1 descending track; ERS-1/2, Envisat Result: LOS displacement w.r.t. a master scene and a reference area Levelling: Kinematic adjustment of repeatedly measured levelling data Data: height differences at levelling benchmarks Result: Linear displacement rates (vertical) w.r.t. a reference point GNSS: Differential processing using Bernese GPS software (Dach et al., 2007) Data: GPS observations, daily coordinates at 76 sites Result: Linear displacement rates (horizontal) w.r.t. ITRF05 (block mean subtracted) 5 Introduction Database Combination Results
19 Combination approach PS levelling / GNSS locations Interferograms 1,2,...,n ERS ascending ERS descending Envisat ascending Envisat descending PS PS grid Estimation of linear velocities Using time series of ERS/Envisat ascending descending Estimation of linear velocities Interpolation of levelling PS grid Interpolation of GNSS PS grid Calculation of Up / East component Step 1 Step 2 Estimation of offset and trend (Up / East) Estimation of East, North and Up components 6 Introduction Database Combination Results
20 Combination approach PS levelling / GNSS locations Estimation of linear velocities Interferograms 1,2,...,n ERS ascending ERS descending Envisat ascending Envisat descending Using time series of ERS/Envisat ascending descending PS PS grid Joint ERS/Envisat Interpolation displacement Interpolation Estimation of levelling of time linear series (Caro Cuenca et al., 2010) of GNSS velocities velocities PS PS grid Calculation of Up / East component Step 1 Step 2 Estimation of offset and trend (Up / East) Estimation of East, North and Up components 6 Introduction Database Combination Results
21 Combination approach PS levelling / GNSS locations Estimation of linear velocities Interferograms 1,2,...,n ERS ascending ERS descending Envisat ascending Envisat descending Using time series of ERS/Envisat ascending descending PS PS grid Joint ERS/Envisat Interpolation displacement Interpolation Estimation of levelling of time linear series (Caro Cuenca et al., 2010) of GNSS velocities velocities PS PS grid Calculation of Up / East component Step 1 Step 2 Estimation of offset and trend (Up / East) Estimation of Different reference East, North frames Residual atmospheric/orbit and Up effects Validation ofcomponents InSAR results 6 Introduction Database Combination Results
22 Combination approach PS levelling / GNSS locations Interferograms 1,2,...,n ERS ascending ERS descending Envisat ascending Envisat descending PS PS grid Estimation of linear velocities Using time series of ERS/Envisat ascending descending Estimation of linear velocities Interpolation of levelling PS grid Interpolation of GNSS PS grid Calculation of Up / East component Step 1 Step 2 Estimation of offset and trend (Up / East) Estimation of East, North and Up components 6 Introduction Database Combination Results
23 Interpolation of PS points (using Kriging) Step 1: At location of levelling/gnss points Step 2: At a 200 m grid (only in vicinity of PS points) 7 Introduction Database Combination Results
24 Interpolation of PS points (using Kriging) Step 1: At location of levelling/gnss points Step 2: At a 200 m grid (only in vicinity of PS points) for every Ifg 7 Introduction Database Combination Results
25 Interpolation of PS points (using Kriging) Step 1: At location of levelling/gnss points Step 2: At a 200 m grid (only in vicinity of PS points) for every Ifg 7 Introduction Database Combination Results
26 Linear velocities from PS time series y A,1 ta,1 3 t 2 A,1 t A, x 3 y A,NA ta,n 3 t y B,1 = A A,N 2 t A,NA 1 0 A x 2 t B,1 3 tb,1 2 t B,1 1 1 x 1 + x. 0 e x..... y B,NB tb,n 3 t 2 B B,N t B,NB 1 1 B y A,i : Displacement in interferogram i, sensor A (ERS) y B,i : Displacement in interferogram i, sensor B (Envisat) N A : Number of interferograms of sensor A N B : Number of interferograms of sensor B t A : Acquisition time of sensor A t B : Acquisition time of sensor B x 0, x 1, x 2, x 3 : Parameters of a polynomial function x : Offset between sensor A and sensor B s 8 Introduction Database Combination Results
27 Linear velocities from PS time series y A,1 ta,1 3 t 2 A,1 t A, x 3 y A,NA ta,n 3 t y B,1 = A A,N 2 t A,NA 1 0 A x 2 t B,1 3 tb,1 2 t B,1 1 1 x 1 + x. 0 e x..... y B,NB tb,n 3 t 2 B B,N t B,NB 1 1 B mm mm Year 20 Envisat 10 0 ERS y A,i : Displacement in interferogram i, sensor A (ERS) y B,i : Displacement in interferogram i, sensor B (Envisat) N A : Number of interferograms of sensor A N B : Number of interferograms of sensor B t A : Acquisition time of sensor A t B : Acquisition time of sensor B x 0, x 1, x 2, x 3 : Parameters of a polynomial function s x : Offset between sensor A and sensor B 8 Introduction Database Combination Results Year
28 Linear velocities from PS time series y A,1 ta,1 3 t 2 A,1 t A, x 10 3 y A,NA ta,n 3 t y B,1 = A A,N 2 t A,NA 1 0 A x 2 20 t B,1 3 tb,1 2 t B,1 1 1 x 1 + x. 0 e Year 20 Envisat x y B,NB tb,n 3 t 2 B B,N t B,NB 1 1 B mm mm ERS 10 0 ERS Envisat Year mm 10 s 20 1st order (linear) Year 8 Introduction Database Combination Results
29 Linear velocities from PS time series mm s y A,1 ta,1 3 t 2 A,1 t A, x 10 3 y A,NA ta,n 3 t y B,1 = A A,N 2 t A,NA 1 0 A x 2 20 t B,1 3 tb,1 2 t B,1 1 1 x 1 + x. 0 e Year 20 Envisat x y B,NB tb,n 3 t 2 B B,N t B,NB 1 1 B st order (linear) 20 2nd order 3rd order Year 8 Introduction Database Combination Results mm mm ERS Envisat ERS Year Statistical test on linearity
30 Linear velocities from PS time series Temporal covariance matrix for the estimation: [mm 2 ] 54 ERS Interferograms 17 Envisat Interferograms Introduction Database Combination Results s
31 Linear velocities from PS time series Temporal covariance matrix for the estimation: [mm 2 ] 54 ERS Interferograms 17 Envisat Interferograms Introduction Database Combination Results Correlation length from atmospheric filtering Variances q ii scaled w.r.t. relative Ifg and PS accuracy s
32 Linear velocities from PS time series mm mm Year ERS/Envisat combination: Accurate estimates for linear rates asc desc 9 Introduction Database Combination Results ERS Envisat ERS Envisat Year
33 Linear velocities from PS time series mm mm Year Separation of non-linear movements asc desc 9 Introduction Database Combination Results ERS Envisat ERS Envisat Year
34 Linear velocities from PS time series LOS velocities (desc) 9 Introduction Database Combination Results
35 Linear velocities from PS time series LOS velocities (desc) + non-linear grid points 9 Introduction Database Combination Results
36 Interpolation of levelling and GPS velocities 10 Introduction Database Combination Results
37 Interpolation of levelling and GPS velocities Standard dev. 10 Introduction Database Combination Results
38 Interpolation of levelling and GPS velocities Standard dev. High weight close to the data points Low weight in between 10 Introduction Database Combination Results
39 Mathematical fusion Using least squares adjustment: y = Ax + e y : Velocities from InSAR (asc and desc), GPS (East and North comp.) and levelling x : Velocities in East, North, Up V asc S asc,1 S asc,2 S asc,3 V desc V GPS,E = S desc,1 S desc,2 S desc,3 v E v N + e V GPS,N v U V lev sin θ asc cos α asc S asc = sin θ asc sin α asc cos θ asc sin θ desc cos α desc S desc = sin θ desc sin α desc cos θ desc 11 Introduction Database Combination Results
40 Mathematical fusion Using least squares adjustment: y = Ax + e y : Velocities from InSAR (asc and desc), GPS (East and North comp.) and levelling x : Velocities in East, North, Up V asc S asc,1 S asc,2 S asc,3 V desc V GPS,E = S desc,1 S desc,2 S desc,3 v E v N + e V GPS,N v U V lev σ 2 V asc σv desc Q yy = 0 0 σv 2 σ VGPS,E,N 0 GPS,E 0 0 σ VGPS,E,N σv 2 0 GPS,N σ 2 V lev sin θ asc cos α asc S asc = sin θ asc sin α asc cos θ asc sin θ desc cos α desc S desc = sin θ desc sin α desc cos θ desc Covariance matrix using standard deviations of single technique estimates 11 Introduction Database Combination Results
41 Results Two test areas: Northern part, Southern part 3D velocity field Standard deviations 12 Introduction Database Combination Results
42 Results Northern Upper Rhine Graben 13 Introduction Database Combination Results
43 Results Northern Upper Rhine Graben 13 Introduction Database Combination Results
44 Results Northern Upper Rhine Graben Mean standard deviation Up: 0.10 mm/a 13 Introduction Database Combination Results
45 Results Northern Upper Rhine Graben 13 Introduction Database Combination Results
46 Results Northern Upper Rhine Graben Mean standard deviation East: 0.20 mm/a North: 0.24 mm/a 13 Introduction Database Combination Results
47 Results Northern Upper Rhine Graben Mean standard deviation East: 0.20 mm/a North: 0.24 mm/a 13 Introduction Database Combination Results
48 Results Southern Upper Rhine Graben 14 Introduction Database Combination Results
49 Results Southern Upper Rhine Graben 14 Introduction Database Combination Results
50 Results Southern Upper Rhine Graben Mean standard deviation Up: 0.12 mm/a 14 Introduction Database Combination Results
51 Results Southern Upper Rhine Graben 14 Introduction Database Combination Results
52 Results Southern Upper Rhine Graben Mean standard deviation East: 0.30 mm/a North: 0.36 mm/a 14 Introduction Database Combination Results
53 Results Southern Upper Rhine Graben Mean standard deviation East: 0.30 mm/a North: 0.36 mm/a 14 Introduction Database Combination Results
54 Conclusions Consistent approach to combine velocities from Different SAR sensors (ERS, Envisat) Different SAR tracks (asc, desc) Permanent GNSS sites Repeated levelling measurements Consideration of realistic covariance information Results for two test areas in the URG: Tectonic movements are well below 1.0 mm/a Standard deviations: 0.3 mm/a (horizontal) / 0.1 mm/a (vertical) Next steps: Combined velocity solution for the whole URG area (300 km SAR stripes) Special cases: Overlapping SAR tracks, only ascending/descending 15 Introduction Database Combination Results
55 Conclusions Consistent approach to combine velocities from Different SAR sensors (ERS, Envisat) Different SAR tracks (asc, desc) Permanent GNSS sites Repeated levelling measurements Consideration of realistic covariance information Results for two test areas in the URG: Tectonic movements are well below 1.0 mm/a Standard deviations: 0.3 mm/a (horizontal) / 0.1 mm/a (vertical) Next steps: Combined velocity solution for the whole URG area (300 km SAR stripes) Special cases: Overlapping SAR tracks, only ascending/descending 15 Introduction Database Combination Results
56 Conclusions Consistent approach to combine velocities from Different SAR sensors (ERS, Envisat) Different SAR tracks (asc, desc) Permanent GNSS sites Repeated levelling measurements Consideration of realistic covariance information Results for two test areas in the URG: Tectonic movements are well below 1.0 mm/a Standard deviations: 0.3 mm/a (horizontal) / 0.1 mm/a (vertical) Next steps: Combined velocity solution for the whole URG area (300 km SAR stripes) Special cases: Overlapping SAR tracks, only ascending/descending 15 Introduction Database Combination Results
57 Conclusions Consistent approach to combine velocities from Different SAR sensors (ERS, Envisat) Different SAR tracks (asc, desc) Permanent GNSS sites Repeated levelling measurements Consideration of realistic covariance information Results for two test areas in the URG: Tectonic movements are well below 1.0 mm/a Standard deviations: 0.3 mm/a (horizontal) / 0.1 mm/a (vertical) Next steps: Combined velocity solution for the whole URG area (300 km SAR stripes) Special cases: Overlapping SAR tracks, only ascending/descending 15 Introduction Database Combination Results
Effect of Unmodelled Reference Frame Motion on InSAR Deformation Estimates
Effect of Unmodelled Reference Frame Motion on InSAR Deformation Estimates Hermann Bähr 1, Sami Samiei-Esfahany 2 and Ramon Hanssen 2 1 Karlsruhe Institute of Technology, Germany 2 Delft University of
Atmospheric Phase Screen (APS) estimation and modeling for radar interferometry
Atmospheric Phase Screen (APS) estimation and modeling for radar interferometry Ramon Hanssen¹, Alessandro Ferretti², Marco Bianchi², Rossen Grebenitcharsky¹, Frank Kleijer¹, Ayman Elawar¹ ¹ Delft University
Atmospheric Water Vapor Effect on GNSS Signals and InSAR Data
Atmospheric Water Vapor Effect on GNSS Signals and InSAR Data Basic Concept and Preliminary Results F. Alshawaf 1, S. Hinz 1, A. Thiele 1 T. Fuhrmann 2, B. Heck 2, A. Knöpfler 2, X. Luo 2, M. Mayer 2,
Ground surface deformation of L Aquila. earthquake revealed by InSAR time series
Ground surface deformation of L Aquila earthquake revealed by InSAR time series Reporter: Xiangang Meng Institution: First Crust Monitoring and Application Center, CEA Address: 7 Naihuo Road, Hedong District
Haiti Earthquake (12-Jan-2010) co-seismic motion using ALOS PALSAR
Haiti Earthquake (12-Jan-2010) co-seismic motion using ALOS PALSAR Urs Wegmüller, Charles Werner, Maurizio Santoro Gamma Remote Sensing, CH-3073 Gümligen, Switzerland SAR data: JAXA, METI; PALSAR AO Project
Constructing high-resolution, absolute maps of atmospheric water vapor by combining InSAR and GNSS observations
Constructing high-resolution, absolute maps of atmospheric water vapor by combining InSAR and GNSS observations Fadwa Alshawaf, Stefan Hinz, Michael Mayer, Franz J. Meyer fadwa.alshawaf@kit.edu INSTITUTE
Deformation measurement using SAR interferometry: quantitative aspects
Deformation measurement using SAR interferometry: quantitative aspects Michele Crosetto (1), Erlinda Biescas (1), Ismael Fernández (1), Ivan Torrobella (1), Bruno Crippa (2) (1) (2) Institute of Geomatics,
Modeling of Atmospheric Effects on InSAR Measurements With the Method of Stochastic Simulation
Modeling of Atmospheric Effects on InSAR Measurements With the Method of Stochastic Simulation Z. W. LI, X. L. DING Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, Hung
High-resolution temporal imaging of. Howard Zebker
High-resolution temporal imaging of crustal deformation using InSAR Howard Zebker Stanford University InSAR Prehistory SEASAT Topographic Fringes SEASAT Deformation ERS Earthquake Image Accurate imaging
3-Dimension Deformation Mapping from InSAR & Multiaperture. Hyung-Sup Jung The Univ. of Seoul, Korea Zhong Lu U.S. Geological Survey, U.S.A.
3-Dimension Deformation Mapping from InSAR & Multiaperture InSAR Hyung-Sup Jung The Univ. of Seoul, Korea Zhong Lu U.S. Geological Survey, U.S.A. Outline Introduction to multiple-aperture InSAR (MAI) 3-D
Dr. Simon Plank. German Remote Sensing Data Center (DFD), German Aerospace Center (DLR)
Pre-survey suitability analysis of the differential and persistent scatterer synthetic ti aperture radar interferometry t method for deformation monitoring of landslides Dr. Simon Plank German Remote Sensing
to: Interseismic strain accumulation and the earthquake potential on the southern San
Supplementary material to: Interseismic strain accumulation and the earthquake potential on the southern San Andreas fault system by Yuri Fialko Methods The San Bernardino-Coachella Valley segment of the
DETECTING ICE MOTION IN GROVE MOUNTAINS, EAST ANTARCTICA WITH ALOS/PALSAR AND ENVISAT/ASAR DATA
DETECTING ICE MOTION IN GROVE MOUNTAINS, EAST ANTARCTICA WITH ALOS/PALSAR AND ENVISAT/ASAR DATA TIAN Xin (1), LIAO Mingsheng (1), ZHOU Chunxia (2), ZHOU Yu (3) (1) State Key Laboratory of Information Engineering
Volcanoes Session. Chairs: Y. Fukushima & G. Puglisi
Volcanoes Session Chairs: Y. Fukushima & G. Puglisi Presentation Summary We had presentations on volcanoes all over the world, more than the last FRINGE workshops: Etna, Campi Flegrei, Ethiopian rift,
The Potential of High Resolution Satellite Interferometry for Monitoring Enhanced Oil Recovery
The Potential of High Resolution Satellite Interferometry for Monitoring Enhanced Oil Recovery Urs Wegmüller a Lutz Petrat b Karsten Zimmermann c Issa al Quseimi d 1 Introduction Over the last years land
APPLICABILITY OF PSINSAR FOR BUILDING HAZARD IDENTIFICATION
APPLICABILITY OF PSINSAR FOR BUILDING HAZARD IDENTIFICATION. STUDY OF THE 29 JANUARY 2006 KATOWICE EXHIBITION HALL COLLAPSE AND THE 24 FEBRUARY 2006 MOSCOW BASMANNY MARKET COLLAPSE Zbigniew Perski (1),
3D temporal evolution of displacements recorded on Mt. Etna from the 2007 to 2010 through the SISTEM method
3D temporal evolution of displacements recorded on Mt. Etna from the 2007 to 2010 through the SISTEM method Bonforte A., Guglielmino F.,, Puglisi G. INGV Istituto Nazionale di Gofisica e vulcanologia Osservatorio
Surface Deformation Measurements Scientific Requirements & Challenges
Surface Deformation Measurements Scientific Requirements & Challenges 1st Science and Application Workshop for Germany-Japan Next-Generation SAR M. Eineder, C. Minet, A. Parizzi Tokyo, 27.6.2013 Tandem-L
TEMPORAL VARIABILITY OF ICE FLOW ON HOFSJÖKULL, ICELAND, OBSERVED BY ERS SAR INTERFEROMETRY
TEMPORAL VARIABILITY OF ICE FLOW ON HOFSJÖKULL, ICELAND, OBSERVED BY ERS SAR INTERFEROMETRY Florian Müller (1), Helmut Rott (2) (1) ENVEO IT, Environmental Earth Observation GmbH, Technikerstrasse 21a,
The March 11, 2011, Tohoku-oki earthquake (Japan): surface displacement and source modelling
The March 11, 2011, Tohoku-oki earthquake (Japan): surface displacement and source modelling Salvatore Stramondo Bignami C., Borgstrom S., Chini M., Guglielmino F., Melini D., Puglisi G., Siniscalchi V.,
The PaTrop Experiment
Improved estimation of the tropospheric delay component in GNSS and InSAR measurements in the Western Corinth Gulf (Greece), by the use of a highresolution meteorological model: The PaTrop Experiment N.
GEODETIC NETWORK OF SAUDI ARABIA AND FIDUCIAL STATIONS. GFN OF Saudi Arabia in Based on ITRF2000 Datum
STUDIES OF TECTONIC MOVEMENTS IN SAUDI ARABIA USING CORS M. Al Rajhi, Ali Al Omar, R. Yanar, F. Kartal, K. Eren, SUBJECT TO TALKS BACKGROUND GEODETIC NETWORK OF SAUDI ARABIA AND FIDUCIAL STATIONS GFN OF
RADAR TIME SERIES ANALYSIS OVER WEST ANATOLIA
RADAR TIME SERIES ANALYSIS OVER WEST ANATOLIA Mahmut Arıkan, Andrew Hooper, and Ramon Hanssen Delft Institute of Earth Observation and Space Systems (DEOS), Delft University of Technology, Kluyverweg 1,
Improved PSI Performance for Landslide Monitoring Applications. J. Duro, R. Iglesias, P. Blanco-Sánchez, F. Sánchez and D. Albiol
Improved PSI Performance for Landslide Monitoring Applications J. Duro, R. Iglesias, P. Blanco-Sánchez, F. Sánchez and D. Albiol Outline Area of Study Previous PSI (and others) studies Main conclusions
The Santorini Inflation Episode, Monitored by InSAR and GPS
The Santorini Inflation Episode, Monitored by InSAR and GPS Ioannis Papoutsis 1,2, Xanthos Papanikolaou 2, Michael Floyd 3, Kang Hyeun Ji 4, Charalampos Kontoes 1, Demitris Paradissis 2, Demitris Anastasiou
J. Manuel Delgado (1,2), Roberto Cuccu (1), Giancarlo Rivolta (1)
MONITORING GROUND DEFORMATION USING PERSISTENT SCATTERS INTERFEROMETRY (PSI) AND SMALL BASELINES (SBAS) TECHNIQUES INTEGRATED IN THE ESA RSS SERVICE: THE CASE STUDY OF VALENCIA, ROME AND SOUTH SARDINIA
Consequences of the Local Oscillator Drift model for ENVSAT ASAR
CEOS Calibration and Validation Workshop 2015 October 27-29, 2015, at ESA-ESTEC Consequences of the Local Oscillator Drift model for ENVSAT ASAR significant improvement of the localization accuracy Petar
Landslide Monitoring in Three Gorges Area By Joint Use of Phase Based and Amplitude Based Methods
Landslide Monitoring in Three Gorges Area By Joint Use of Phase Based and Amplitude Based Methods Xuguo Shi, Lu Zhang, Mingsheng Liao, Timo Balz LIESMARS, Wuhan University Outlines Introduction InSAR Results
Retrieving 3D deformation pattern of a landslide with hiresolution InSAR and in-situ measurements: Just landslide case-study
Retrieving 3D deformation pattern of a landslide with hiresolution InSAR and in-situ measurements: Just landslide case-study Zbigniew Perski (1), Petar Marinković (2), Yngvar Larsen (3), Tomasz Wojciechowski
Radar Interferometry Dealing with atmospheric errors for individual interferograms
Radar Interferometry Dealing with atmospheric errors for individual interferograms Dr Zhenhong Li COMET, School of Geographical and Earth Sciences University of Glasgow Email: Zhenhong.Li@glasgow.ac.uk
INSAR ATMOSPHERIC DELAY MIGITIGATION BY GPS; CASE STUDY IZMIT EARTQUAKE INTERFEROGRAMS
INSAR ATMOSPHERIC DELAY MIGITIGATION BY GPS; CASE STUDY IZMIT EARTQUAKE INTERFEROGRAMS M.U. Altın a, *, E. Tari a, L. Ge b a ITU, Civil Engineering Faculty, 80626 Maslak Istanbul, Turkey (altinm, tari)@itu.edu.tr
InSAR measurements of volcanic deformation at Etna forward modelling of atmospheric errors for interferogram correction
InSAR measurements of volcanic deformation at Etna forward modelling of atmospheric errors for interferogram correction Rachel Holley, Geoff Wadge, Min Zhu Environmental Systems Science Centre, University
Implementation of Multi-Temporal InSAR to monitor pumping induced land subsidence in Pingtung Plain, Taiwan
Implementation of Multi-Temporal InSAR to monitor pumping induced land subsidence in Pingtung Plain, Taiwan Presenter: Oswald Advisor: Chuen-Fa Ni Date: March 09, 2017 Literature Review Pingtung Plain
The Removal of Lava Flow Subsidence Signals from deformation interferograms at Nyamuragira Volcano, D.R. Congo
The Removal of Lava Flow Subsidence Signals from deformation interferograms at Nyamuragira Volcano, D.R. Congo Andy Toombs & Geoff Wadge Environmental Systems Science Centre National Centre for Earth Observation
Strategies for Measuring Large Scale Ground Surface Deformations: PSI Wide Area Product Approaches
Strategies for Measuring Large Scale Ground Surface Deformations: PSI Wide Area Product Approaches J. Duro (1), R. Iglesias (1), P. Blanco-Sánchez (1), D. Albiol (1), T. Wright (2), N. Adam (3), F. Rodríguez
Preparation for the ITRF2013. Zuheir Altamimi Xavier Collilieux Laurent Métivier IGN, France
Preparation for the ITRF2013 Zuheir Altamimi Xavier Collilieux Laurent Métivier IGN, France 1 Outline Introduction Solicited solutions Analysis Strategy Preparation for the ITRF2013: Combination tests
PERSISTENT SCATTERER SAR INTERFEROMETRY APPLICATIONS ON LANDSLIDES IN CARPATHIANS (SOUTHERN POLAND)
Acta Geodyn. Geomater., Vol. 7, No. 3 (159), 1 7, 2010 PERSISTENT SCATTERER SAR INTERFEROMETRY APPLICATIONS ON LANDSLIDES IN CARPATHIANS (SOUTHERN POLAND) Zbigniew PERSKI 1) *, Tomasz WOJCIECHOWSKI 1)
ERS Track 98 SAR Data and InSAR Pairs Used in the Analysis
ERS Track 98 SAR Data and InSAR Pairs Used in the Analysis Date 1 Date 2 Date 1 Date 2 Date 1 Date 2 Date 1 Date 2 7/17/1992 6/19/2000 7/17/1992 7/2/1993 9/10/1993 10/28/1996 9/3/1995 10/18/1999 9/25/1992
ERS-2 and ENVISAT orbit control Current and future strategy The impact on the interferometric baseline
ERS-2 and ENVISAT orbit control Current and future strategy The impact on the interferometric baseline Berthyl DUESMANN (ESA) & Itziar BARAT (ESA) Contributions by Nuno Miranda, Betlem Rosich, Montse Pinol,
GEORED Project: GNSS Geodesy Network for Geodynamics Research in Colombia, South America. Héctor Mora-Páez
GEORED Project: GNSS Geodesy Network for Geodynamics Research in Colombia, South America. Héctor Mora-Páez Colombian Geological Survey UNAVCO SCIENCE MEETING Feb 27 March 1, 2012 Boulder, CO, TECTONIC
27th Seismic Research Review: Ground-Based Nuclear Explosion Monitoring Technologies
GROUND TRUTH OF AFRICAN AND EASTERN MEDITERRANEAN SHALLOW SEISMICITY USING SAR INTERFEROMETRY AND GIBBS SAMPLING INVERSION Benjamin A. Brooks 1, Francisco Gomez 2, Eric A. Sandvol 2, and Neil Frazer 1
ERS-ENVISAT Cross-interferometry for Coastal DEM Construction
ERS-ENVISAT Cross-interferometry for Coastal DEM Construction Sang-Hoon Hong and Joong-Sun Won Department of Earth System Sciences, Yonsei University, 134 Shinchon-dong, Seodaemun-gu, 120-749, Seoul, Korea
DIFFERENTIAL INSAR STUDIES IN THE BOREAL FOREST ZONE IN FINLAND
DIFFERENTIAL INSAR STUDIES IN THE BOREAL FOREST ZONE IN FINLAND Kirsi Karila (1,2), Mika Karjalainen (1), Juha Hyyppä (1) (1) Finnish Geodetic Institute, P.O. Box 15, FIN-02431 Masala, Finland, Email:
Mitigation of Atmospheric Water-vapour Effects on Spaceborne Interferometric SAR Imaging through the MM5 Numerical Model
PIERS ONLINE, VOL. 6, NO. 3, 2010 262 Mitigation of Atmospheric Water-vapour Effects on Spaceborne Interferometric SAR Imaging through the MM5 Numerical Model D. Perissin 1, E. Pichelli 2, R. Ferretti
ACHIEVING THE ERS-2 ENVISAT INTER-SATELLITE INTERFEROMETRY TANDEM CONSTELLATION.
ACHIEVING THE ERS-2 ENVISAT INTER-SATELLITE INTERFEROMETRY TANDEM CONSTELLATION M. A. Martín Serrano (1), M. A. García Matatoros (2), M. E. Engdahl (3) (1) VCS-SciSys at ESA/ESOC, Robert-Bosch-Strasse
InSAR water vapour correction models: GPS, MODIS, MERIS and InSAR integration
InSAR water vapour correction models: GPS, MODIS, MERIS and InSAR integration Zhenhong Li (1), Eric J. Fielding (2), Paul Cross (1), and Jan-Peter Muller (1) (1): Department of Geomatic Engineering, University
Ground deformation monitoring at the Phlegrean Fields (Naples, Italy) from the exploitation of SAR data in the framework of CAT-1 and DUP activities
Ground deformation monitoring at the Phlegrean Fields (Naples, Italy) from the exploitation of SAR data in the framework of CAT-1 and DUP activities Borgström S., Aquino I., Del Gaudio C., Ricco C., Siniscalchi
Application of satellite InSAR data for hydrocarbon reservoir monitoring
Application of satellite InSAR data for hydrocarbon reservoir monitoring A. Tamburini, A. Belson, A. Ferretti, F. Novali TRE Milano, Italy Copyright - Tele-Rilevamento Europa - 2004 Outline SqueeSAR TM
Journal of Geophysical Research Letters Supporting Information for
Journal of Geophysical Research Letters Supporting Information for InSAR observations of strain accumulation and fault creep along the Chaman Fault system, Pakistan and Afghanistan H. Fattahi 1, F. Amelung
ERAD Water vapor observations with SAR, microwave radiometer and GPS: comparison of scaling characteristics
Proceedings of ERAD (2002): 190 194 c Copernicus GmbH 2002 ERAD 2002 Water vapor observations with SAR, microwave radiometer and GPS: comparison of scaling characteristics D. N. Moisseev 1, R. F. Hanssen
GMES TERRAFIRMA. Validation of existing processing chains in Terrafirma stage 2 TECHNICAL NOTE ON THE TEST SITE SELECTION: ALKMAAR-AMSTERDAM
GMES TERRAFIRMA ESRIN/Contract no. 19366/05/I-E Validation of existing processing chains in Terrafirma stage 2 TECHNICAL NOTE ON THE TEST SITE SELECTION: ALKMAAR-AMSTERDAM 2 nd April 2007 Final version
Application of PSI technique to slope stability monitoring in the Daunia mountains, Italy
ESA ESRIN - Frascati 28 November- 2 December Application of PSI technique to slope stability monitoring in the Daunia mountains, Italy F. Bovenga (1) (fabio.bovenga@ba.infn.it) L. Guerriero (1) R. Nutricato
Geophysical Journal International
Geophysical Journal International Geophys. J. Int. (2013) 193, 29 46 doi: 10.1093/gji/ggs102 Coseismic displacement field and slip distribution of the 2005 Kashmir earthquake from SAR amplitude image correlation
Earth-Oriented Space Research at TU-Delft
Earth-Oriented Space Research at TU-Delft The contribution of DEOS to the South-East Asia: Mastering Environmental Research with Geodetic Space Techniques (SEAMERGES) project Kick-off meeting, Chulalongkorn
MERIS and OSCAR: Online Services for Correcting Atmosphere in Radar
National Aeronautics and Space Administration MERIS and OSCAR: Online Services for Correcting Atmosphere in Radar Eric Fielding and Evan Fishbein Jet Propulsion Laboratory, California Inst. of Tech. Zhenhong
estimated by InSAR, and the temporal evolution of the deformation and possible cause.
QUANTIFICATION OF SUBSIDENCE RATES ASSOCIATED WITH GROUNDWATER FLOW USING SAR INTERFEROMETRY J. Catalao (1), G. Nico (2), V. Conde (1), J.M. Miranda (1) (1) IDL, Universidade Lisboa, Campo Grande, Portugal,
THE INTERPRETATION OF BAM FAULT KINEMATICS USING ENVISAT SAR INTERFEROMETRIC DATA
THE INTERPRETATION OF BAM FAULT KINEMATICS USING ENVISAT SAR INTERFEROMETRIC DATA Zbigniew Perski (1), Ramon Hanssen (1) (1) Delft Institute for Earth Observation and Space Systems, Delft University of
Noise covariance model for time-series InSAR analysis. Piyush Agram, Mark Simons
Noise covariance model for time-series InSAR analysis Piyush Agram, Mark Simons Motivation Noise covariance in individual interferograms has been well studied. (Hanssen, 2001) Extend the noise model to
PERSISTENT SCATTERER INTERFEROMETRY: POTENTIAL AND LIMITS
PERSISTENT SCATTERER INTERFEROMETRY: POTENTIAL AND LIMITS M. Crosetto a, O. Monserrat a, A. Jungner, B. Crippa b a Institute of Geomatics, Av. del Canal Olímpic, s/n, Castelldefels, E-08860, Spain (michele.crosetto,
Real Time Subsidence Monitoring Techniques in Undercity Mining and a Case Study: Zonguldak Undercity Applications-Turkey
Real Time Subsidence Monitoring Techniques in Undercity Mining and a Case Study: Zonguldak Undercity Hakan AKCIN, Hakan S. KUTOGLU, Turkey Keywords: Undercity mining, subsidence, PALSAR, RADARSAT, GNSS.
Urban land and infrastructure deformation monitoring by satellite radar interferometry
Urban land and infrastructure deformation monitoring by satellite radar interferometry Lei Zhang and Xiaoli Ding Department of Land Surveying and Geo-Informatics (LSGI) The Hong Kong Polytechnic University
Infrastructure monitoring using SAR interferometry
Infrastructure monitoring using SAR interferometry Hossein Nahavandchi Roghayeh Shamshiri Norwegian University of Science and Technology (NTNU), Department of Civil and Environmental Engineering Geodesy
A Unique Reference Frame: Basis of the Global Geodetic Observing System (GGOS) for Geodynamics and Global Change
SRTM (InSAR) A Unique Reference Frame: Basis of the Global Geodetic Observing System (GGOS) for Geodynamics and Global Change Hermann Drewes President IAG Commission 1 Reference Frames Deutsches Geodätisches
MAPPING DEFORMATION OF MAN-MADE LINEAR FEATURES USING DINSAR TECHNIQUE
MAPPING DEFORMATION OF MAN-MADE LINEAR FEATURES USING DINSAR TECHNIQUE H. Wu a, *, Y. Zhang a, J. Zhang a, X. Chen b a Key Laboratory of Mapping from Space of State Bureau of Surveying and Mapping, Chinese
Università di Roma La Sapienza, Facoltà d Ingegneria, Dipartimento di Idraulica,Trasporti e Strade (D.I.T.S.) 00185, Roma, Italy
DORIS FP7-EU PROJECT: EXPLOITATION OF 20 YEARS DINSAR DATA ARCHIVE FOR LANDSLIDE MONITORING Manunta M. (1), Calò F. (1), Paglia L., (1), Bonano M. (1,2), Lanari R. (1) (1) IREA-CNR, Via Diocleziano 328,
Slow Deformation of Mt. Baekdu Stratovolcano Observed by Satellite Radar Interferometry
Slow Deformation of Mt. Baekdu Stratovolcano Observed by Satellite Radar Interferometry Sang-Wan Kim and Joong-Sun Won Department of Earth System Sciences, Yonsei University 134 Shinchon-dong, Seodaemun-gu,
PSI Precision, accuracy and validation aspects
PSI Precision, accuracy and validation aspects Urs Wegmüller Gamma Remote Sensing AG, Gümligen, Switzerland, wegmuller@gamma-rs.ch Contents - Precision - Accuracy - Systematic errors - Atmospheric effects
CAPABILITY OF DETECTING RAPID SUBSIDENCE WITH COSMO SKYMED AND SENTINEL-1 DATASET OVER KONYA CITY
CAPABILITY OF DETECTING RAPID SUBSIDENCE WITH COSMO SKYMED AND SENTINEL-1 DATASET OVER KONYA CITY Fatma CANASLAN ÇOMUT (1), Aydın USTUN (2), Milan LAZECKY (3), Daniele PERISSIN (4) (1) Disaster & Emergency
Journal of Geophysical Research - Solid Earth
Journal of Geophysical Research - Solid Earth Supporting Information for Transpressional Rupture Cascade of the 2016 M w 7.8 Kaikoura Earthquake, New Zealand Wenbin Xu 1*, Guangcai Feng 2*, Lingsen Meng
DETECTION OF GROUND MOTION IN THE LISBON REGION WITH PERSISTENT SCATTERER INTERFEROMETRY (PSI)
DETECTION OF GROUND MOTION IN THE LISBON REGION WITH PERSISTENT SCATTERER INTERFEROMETRY (PSI) Sandra HELENO 1, Afonso LOUREIRO 1,2, João FONSECA 1, João MATOS 1, João CARVALHO 3, Geraint COOKSLEY 4, Ana
Geodesy (InSAR, GPS, Gravity) and Big Earthquakes
Geodesy (InSAR, GPS, Gravity) and Big Earthquakes Mathew Pritchard Teh-Ru A. Song Yuri Fialko Luis Rivera Mark Simons UJNR Earthquake Research Panel, Morioka, Japan - Nov 6, 2002 Goals Accurate and high
Karlsruhe Integrated Displacement Analysis Approach Towards a rigorous combination of different geodetic methods
Karlsruhe Integrated Displacement Analysis Approach Towards a rigorous combination of different geodetic s Bernhard HECK, Michael MAYER, Malte WESTERHAUS, and Karl ZIPPELT (Germany) Key words: Deformation
SAR interferometry Status and future directions. Rüdiger Gens
SAR interferometry Status and future directions Rüdiger Gens Polarimetric InSAR Polarimetric InSAR InSAR - Status and future directions sensitivity to changes in surface scattering, even in the presence
ALOS PI Symposium 2009, 9-13 Nov 2009 Hawaii MOTION MONITORING FOR ETNA USING ALOS PALSAR TIME SERIES
ALOS PI Symposium 2009, 9-13 Nov 2009 Hawaii ALOS Data Nodes: ALOS RA-094 and RA-175 (JAXA) MOTION MONITORING FOR ETNA USING ALOS PALSAR TIME SERIES Urs Wegmüller, Charles Werner and Maurizio Santoro Gamma
GPS measurements of current crustal movements along the Gulf of Suez, Egypt.
GPS measurements of current crustal movements along the Gulf of Suez, Egypt. Presented By Nadia Abo-Ali Assistant Researcher At National Research Institute of Astronomy and Geophysics (NRIAG), Helwan,
Pilot service: Southern Emilia Romagna (Italy)
Pilot service: Southern Emilia Romagna (Italy) Surface deformation service provider (PSInSAR TM ) Geodetic and optical remote sensing data integration Final user The study area The Southern Emilia-Romagna
APLICATION OF INSAR TO THE STUDY OF GROUND DEFORMATION IN THE MEXICALI VALLEY, B. C., MEXICO.
APLICATION OF INSAR TO THE STUDY OF GROUND DEFORMATION IN THE MEXICALI VALLEY, B. C., MEXICO. O. Sarychikhina (1), R. Mellors (2), E. Glowacka (1). (1) Centro de Investigacion Cientifica y Educaccion Superior
INGV. Giuseppe Pezzo. Istituto Nazionale di Geofisica e Vulcanologia, CNT, Roma. Sessione 1.1: Terremoti e le loro faglie
Giuseppe Pezzo Istituto Nazionale di Geofisica e Vulcanologia, CNT, Roma giuseppe.pezzo@ingv.it The study of surface deformation is one of the most important topics to improve the knowledge of the deep
The Tohoku Earthquake 2011 Case.
Wide Area Deformation map generation with TerraSAR-X Data. The Tohoku Earthquake 2011 Case. N. Yague-Martinez (1), C. Minet (2), M. Eineder (2), B. Schättler (2) (1) Starlab, Spain (2) Remote Sensing Technology
Introduction to geodetic VLBI
Introduction to geodetic VLBI David Mayer Andreas Hellerschmied Johannes Böhm Harald Schuh and Johannes Böhm, Very Long Baseline Interferometry for Geodesy and Astrometry, in Guochang Xu (editor): Sciences
Advanced interpretation of land subsidence by validating multiinterferometric SAR data: the case study of Anthemountas basin (Northern Greece)
Revision of paper Advanced interpretation of land subsidence by validating multiinterferometric SAR data: the case study of Anthemountas basin (Northern Greece) By Raspini et al. General comments This
Introduction: the Abruzzo earthquake The network and the processing strategies. displacements estimation at earthquake epoch. horizontal displacements
The Abruzzo earthquake: temporal and spatial analysis of the first geodetic results L. Biagi, S. Caldera, D. Dominici, F. Sansò Politecnico di Milano Università degli studi de L Aquila Outline Introduction:
INSAR DEM CALIBRATION FOR TOPOGRAPHIC MAPPING IN EASTERN UGANDA
INSAR DEM CALIBRATION FOR TOPOGRAPHIC MAPPING IN EASTERN UGANDA Siefko SLOB *, François KERVYN **, Johan LAVREAU **, John ODIDA *** and David KYAGULANYI *** * International Institute for Aerospace Survey
Determination of Current Velocity Field (Rate) of North Anatolian Fault in Izmit-Sapanca Segment
Determination of Current Velocity Field (Rate) of North Anatolian Fault in Izmit-Sapanca Segment Cetin MEKIK, Bulent TURGUT, Haluk OZENER, Turkish Republic INTRODUCTION Turkey is geologically the part
Ivana Zinno, Francesco Casu, Claudio De Luca, Riccardo Lanari, Michele Manunta. CNR IREA, Napoli, Italy
An Unsupervised Implementation of the P-SBAS DiNSAR Algorithm for Processing Large Data Volumes through Distributed Computing Infrastructures within Operational Environments Ivana Zinno, Francesco Casu,
Castaldo R. (1) De Novellis V. (1) Lollino P. (2) Manunta M. (1) Tizzani P. (1) 1: IREA-CNR, Napoli, Italy. 2: IRPI-CNR, Bari, Italy.
Advanced Numerical Modelling for Slow Landslide Analyses through the Effective Integration of SBAS-DInSAR and in situ Observations: the Case Study of Ivancich Landslide (Assisi, Italy) Castaldo R. (1)
Sentinel-1A SAR Interferometry Verification
Sentinel-1A SAR Interferometry Verification Dirk Geudtner1, Pau Prats2, Nestor Yaguee-Martinez2, Andrea Monti Guarnieri3, Itziar Barat1, Björn Rommen1 and Ramón Torres1 1ESA ESTEC 2DLR, Microwave and Radar
CRUSTAL DEFORMATION MAPPED BY COMBINED GPS AND INSAR
CRUSTAL DEFORMATION MAPPED BY COMBINED GPS AND INSAR Sverrir Guðmundsson LYNGBY 000 SUBMITTED FOR THE DEGREE OF M.Sc. E. IMM Preface This thesis was made in collaboration between the Institute for Mathematical
A new era of geodetic big data - challenges and opportunities Tim Wright (COMET, University of
A new era of geodetic big data - challenges and opportunities Tim Wright (COMET, University of Leeds) S @NERC_COMET N A New Era of Geodetic Big Data? GEM Strain Rate Model 17,491 GNSS sites included in
Ice & Snow Session. Chairs: J. Mouginot & N. Gourmelen
Ice & Snow Session Chairs: J. Mouginot & N. Gourmelen Session 12 talks and 10 posters Antarctic ice motion, ground-line detection and monitoring, dynamics ice-fluctuations in Antarctica and Greenland,
Continuous Risk Assessment of Structures and Plants in Areas of Ground Deformation Susceptibility by Space-based SAR/ASAR Interferometry
CCN4 Project Continuous Risk Assessment of Structures and Plants in Areas of Ground Deformation Susceptibility by Space-based SAR/ASAR Interferometry Issaak Parcharidis*, Michael Foumelis and Penelope
Land subsidence induced by groundwater extraction in the Alto Guadalentín aquifer, SE Spain.
Land subsidence induced by groundwater extraction in the Alto Guadalentín aquifer, SE Spain. Pablo Ezquerro, Marta Béjar-Pizarro, Gerardo Herrera, José A. Fernández Merodo, Roberto Tomás, Carolina Guardiola-Albert,
Monitoring of landslide dynamics with LIDAR, SAR interferometry and photogrammetry. Case study of Kłodne landslide (Southern Poland)
Proceedings of World Landslide Forum 3, 2-6 June 2014, Beijing Monitoring of landslide dynamics with LIDAR, SAR interferometry and photogrammetry. Case study of Kłodne landslide (Southern Poland) Zbigniew
NOTES AND CORRESPONDENCE Segmented Faulting Process of Chelungpu Thrust: Implication of SAR Interferograms
, Vol. 14, No.2, 241-247, June 2003 NOTES AND CORRESPONDENCE Segmented Faulting Process of Chelungpu Thrust: Implication of SAR Interferograms Chien-Chih Chen 1,*, Chung-Pai Chang 2, and Kun-Shan Chen
Dealing with significant differential tectonic plate velocities within an RTK-network: The case of HEPOS
Dealing with significant differential tectonic plate velocities within an RTK-network: The case of HEPOS M. Gianniou, E. Mitropoulou, I. Stavropoulou National Cadastre and Mapping Agency S.A. Mesogion
CHAPTER-7 INTERFEROMETRIC ANALYSIS OF SPACEBORNE ENVISAT-ASAR DATA FOR VEGETATION CLASSIFICATION
147 CHAPTER-7 INTERFEROMETRIC ANALYSIS OF SPACEBORNE ENVISAT-ASAR DATA FOR VEGETATION CLASSIFICATION 7.1 INTRODUCTION: Interferometric synthetic aperture radar (InSAR) is a rapidly evolving SAR remote
PHASE UNWRAPPING. Sept. 3, 2007 Lecture D1Lb4 Interferometry: Phase unwrapping Rocca
PHASE UNWRAPPING 1 Phase unwrapping 2 1D Phase unwrapping Problem: given the wrapped phase ψ=w(φ) find the unwrapped one ψ. The wrapping operator: W(φ)=angle(exp(j φ)), gives always solution -π π and is
MEASURING VOLCANIC DEFORMATION AT UNIMAK ISLAND FROM 2003 TO 2010 USING
MEASURING VOLCANIC DEFORMATION AT UNIMAK ISLAND FROM 2003 TO 2010 USING WEATHER MODEL-ASSISTED TIME SERIES INSAR Gong, W. a, Meyer, F. J. a, Lee, C. W. b, Lu, Z. c, Freymueller, J a. a. Geophysical Institute,
A Deformation Model to support a Next Generation Australian Geodetic Datum
A Deformation Model to support a Next Generation Australian Geodetic Datum Richard Stanaway, Surveying and Geospatial Engineering School of Civil and Environmental Engineering multi-gnss + augmentation
Measuring rock glacier surface deformation using SAR interferometry
Permafrost, Phillips, Springman & Arenson (eds) 2003 Swets & Zeitlinger, Lisse, ISBN 90 5809 582 7 Measuring rock glacier surface deformation using SAR interferometry L.W. Kenyi Institute for Digital Image