CHAPTER-7 INTERFEROMETRIC ANALYSIS OF SPACEBORNE ENVISAT-ASAR DATA FOR VEGETATION CLASSIFICATION

Size: px
Start display at page:

Download "CHAPTER-7 INTERFEROMETRIC ANALYSIS OF SPACEBORNE ENVISAT-ASAR DATA FOR VEGETATION CLASSIFICATION"

Transcription

1 147 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 sensing technology that directly measures the phase change between two phase measurements of the Earth s surface (Zhou et al., 2009). Two coherent synthetic aperture radar (SAR) phase images of the same portion of the Earth s surface are required to form a phase difference image, an interferogram in which a fringe pattern appears. These two coherent SAR images can be acquired either from single pass SAR interferometry in which two antennas on the same space platform are separated perpendicularly to the flight direction (azimuth direction), or from repeat-pass interferometry in which two images are acquired in different passes of the same SAR antenna at different times. Applications of InSAR data include surface displacements, land topography, land changes, land subsidence/uplift, water levels, soil moisture, snow accumulation, stem volume of forest, etc. (Fieigl et al., 2002; Arnadottir, 2005) SAR interferometry is based on a coherent combination of images acquired from the same orbit and the complex correlation between above said images is called interferometric coherence (Engdahl, 2003).

2 148 Interferometric data has contributed in improving the land cover classification (Askne, 1997). The coherence band helps in classification between forest and non-forest. Several studies have shown the use of multi-temporal SAR data for land cover changes in boreal forest (Wegmuller et al., 1995 and Floury et al., 1996) and tropical forest (Stussi et al., 1996). It was also shown that the coherence component derived from an interferometric pair gives additional information for land cover classification (Wegmuller et al., 2000). The coherence is generally low over forested areas and high over open fields which make difference between forested and non-forested areas (Wegmuller, 1997 and Hagberg et al., 1995). Further, it was also demonstrated that combined use of coherence band with SAR backscatter improved the sensitivity of SAR system for forestry applications (Srivastava et al., 2006). Using coherence image, multitemporal images and seasonal backscatter change images of appropriate seasons discriminated various classes within the forest with satisfactory accuracy (Martinez et al., 1998; Askne et al., 2003). Environmental conditions at the acquisition time may also lead to the different coherence levels (Santoro et al., 2007, Pullianen et al., 2003, Wagner et al., 2003 and Askne et al., 2005). In the present study, ENVISAT-ASAR repeat pass interferometric data was analysed for vegetation classification. This chapter explains

3 149 interferometric principle, selection of interferometric pair, generation of coherence image and use of coherence image in vegetation classification Interferometry principle - Geometry of insar pair: The geometry of repeat-pass interferometric SAR is shown in figure 7.1. A1 and A2 are the two radar antennas (fig 7.1) that view the same surface separated by a baseline vector with length B and angle, θ with respect to horizontal. B 90+θ-α α A1 90-θ+α θ ρ A2 ρ+ ρ x (azimuth) h P z(y) y (Range) Fig 7.1: Principle of interferometry The distance between A1 and the point, P on the ground that is being imaged is the range vector, ρ while the distance between A2 and the same point is given by the range vector, ρ+ ρ. The topography z(y) is given by z( y) = h ρ * cosθ (7.1)

4 150 The phase difference between two images is given in fringe pattern and is called interferogram. It is proportional to the difference in path delays from two antennas and is given by, Radar phase, 4π φ = * δρ (7.2) where, λ is the wavelength λ Since, λ φ φ is measured, δρ is obtained as δρ = (7.3) 2π Using law of cosines, the topography z(y) is calculated as 2 2 B ( λ φ) z ( y) = h *cosθ (7.4) 2( λ φ Bsin( α θ )) The main factor that affects an interferometric analysis is land cover or vegetation type which includes height of trees, forest biomass or stem volume, type of trees. In forested areas, Wegmuller and Werner, (1995) showed that the coherence between SAR images is very low. In higher biomass regions, the coherence tends to be the lower. 7.2 METHODOLOGY FOR GENERATION OF COHERENCE IMAGE USING INSAR DATA: In general, SAR interferometry processing includes an image co-registration, baseline estimation, interferogram formation, calculation of the interferometric coherence, adaptive filtering and coherence image generation. The generation of coherence image from ASAR SLC data pair is given in fig 7.2 and explained as follows:

5 151 SAR SLC data pair Baseline estimation Coregistration of InSAR data pair Synthetic phase generation Reference DEM Interferogram generation Interferogram flattening Adaptive filtering and Coherence generation Fig 7.2: Methodology flow chart Generation of coherence image Selection of interferometric pair: To generate an interferogram or DEM, there should be a significant correlation and good coherence between two sets of signals recorded during the two repeat passes. The selection of an interferometric pair is made on the basis of baseline length and the time period between two temporal image acquisitions Baseline estimation: The generation of an interferogram is possible when the ground reflectivity was acquired with at least two antenna

6 152 overlap. When the perpendicular component of the baseline (B n ) increases beyond a limit known as the critical baseline, no phase information is preserved, coherence is lost, and interferometry is not possible. Therefore, to generate an interferogramme, the baseline of the acquired datasets should be well below the critical baseline. The critical baseline B ncr is calculated from the equation as follows: B ncr λr tan(θ ) = (7.5) 2R r where R is the range distance of the target from the sensor; R r is the pixel spacing in range, and θ is the incidence angle. For ENVISAT ASAR, the critical baseline is approximately 1.1 km. No suitable interferometric pair with low baseline was available over the two study areas - Dandeli (Karnataka) and Rajpipla (Gujarat). However, interferometric pair with suitable baseline was acquired for Achanakmar- Amarkantak Biosphere Reserve in Bilaspur study area and coherence image was generated from the interferometric pair of the ENVISAT-ASAR data and was used for the vegetation classification. The datasets used for the interferometric analysis of ASAR data were acquired for Achanakmar-Amarkantak Biosphere Reserve in Chattisgarh on 24 th Sep 2006 and 29 th Oct The baseline length for the data pair is 203 m, which is well below the critical baseline. The minimum temporal gap

7 153 between the two acquisitions in the same mode and angle is 35 days for the interferometric data pair acquisition Co registration of the images: Co-registration of two images of the same scene acquired in same mode with same incidence angle at two different time periods makes it possible to use them in the same geometry. One of the images is considered as master and the other, slave in the slant range geometry that have the same orbit and acquisition mode. Co registration of the images was carried out with the orbital parameters. Co registration is carried out to correct relative translational shift, rotational and scale differences. For co-registrtion, pixels in different images should correspond to pixel-by-pixel comparisons Synthetic phase generation: Phase expected for a flat earth or known topography are extracted in the process of synthetic phase generation. This provides an unwrapped phase and better interferogram for SAR data pairs with large baselines. Phase value is estimated from the formula as follows: 4π φ( P) = [ r m r s ] (7.6) λ where, φ (P) is the phase computed for each target, P; r m and r s are the master and slave satellite distances of the object, P.

8 Interferogram generation: After image co-registration, an interferometric phase φ is generated by multiplying one image by the complex conjugate of the second one. A complex interferogram is formed. Spectral shift filtering is performed on the image pair, and the Hermitian product is calculated. φ = I m I (7.7) * * s where, I m is the master image, I s * is the complex conjugate of second image The intensity of the interferogram is a measure of cross correlation of the images. Closer the fringes more are the topographical changes or height variations Interferogram Flattening: Interferogram is the process in which the fringe and phase effects due to the shape of the earth ellipsoid have been removed. A flattened interferogram contains only the fringes contributed by topographic terrain. Flattening involves the removal of the low frequency phase difference from the interferogram and it also removes the systematic phase difference from the interferogram and to the difference in position of antennas. The phase expected for a flat Earth is separated from the residual phase by estimating the average fringe frequency using Fast Fourier Transform algorithm Interferogram filtering and coherence image generation: A special filter or spectral shift is applied on the input SLC images to provide better results even in SAR pairs with large baseline. Filtering of

9 155 flattened interferogram smoothes the interferometric phase and hence reduces noise. Spectral shift filter used in the processing of interferogram provides capacity to generate a better quality interferogram. Phase noise of an interferogram can be estimated from the complex correlation coefficient of the SAR image pairs. The complex correlation coefficient of the SAR images is known as coherence and it can be used as a measure for the accuracy of the interferometric phase. Coherence for two co-registered SAR images is computed as follows: 1 * s1( x). s2 ( x) γ = (7.8) 2 2 s ( x). s ( x) 2 where s1 and s2 are coregistered complex SAR images and γ is the coherence image Use of coherence based for vegetation classification: The acquired ASAR SLC datasets were preprocessed as discussed in chapter-4 and mean backscatter intensity and backscatter difference images were generated. The interferometric coherence generated was used in conjunction with mean backscatter and backscatter difference for vegetation classification. The methodology flow chart is given in fig 7.3.

10 156 ASAR SLC 1 (24 Sep 2006) ASAR SLC 2 (29 Oct 2006) Co-registration of data pair Interferometric Analysis Co-registered multi-look images Coherence image generation Statistical Analysis Mean backscatter, backscatter difference and Coherence bands Feature extraction Supervised Classification Vegetation classes Fig 7.3: Methodology flowchart Vegetation classification using interferometric coherence A composite image (fig 7.4) was generated using the derived mean backscatter (red), backscatter difference (green) and coherence image (blue). This image was subjected to supervised classification using maximum likelihood classifier, by giving training areas based on groundbased information, and the accuracy assessment of the classified outputs has been carried out by generating confusion matrices and kappa statistics.

11 RESULTS AND DISCUSSIONS: Coherence image was generated from the ASAR InSAR pair and it was used in conjunction with mean backscatter and backscatter difference of the two time period images to delineate different vegetation classes in the study area. The coherence values ranges between 0 and 1 and is a function of systemic spatial decorrelation, the additive noise, and the scene decorrelation that takes place between the two acquisitions. High coherence values i.e., corresponding to values approaching 1 appear bright in the image, while dark values (approaching zero) are those areas where changes (or no radar return, radar facing slope, etc.) occurred during the time interval. The thematic information content decreases with increasing acquisition interval, mainly due to phenological or man-made changes of the object or weather conditions. For dry areas, high coherence information is observed even over long timescales. Figure 7.4 shows a composite image of mean backscatter, backscatter difference and coherence of the ASAR data of the study area. Visual analysis of the composite image showed clear discrimination of barren areas (in blue color), because of high coherence values. Agriculture areas, which were characterized by crop-growth patterns during the study period, were in green color due to differences in backscatter while other areas under agricultural land use were in yellow. Water appeared as black in the composite image (fig 7.3) due to its low

12 158 coherence and low mean backscatter values. Forested regions of the study area were interspersed with agriculture land use '0"N 81 15'0"E 81 20'0"E 81 25'0"E 81 30'0"E 81 35'0"E 81 40'0"E ± 22 10'0"N 22 10'0"N 22 15'0"N 22 15'0"N 22 20'0"N 22 20'0"N 22 25'0"N 22 25'0"N 22 30'0"N 22 30'0"N 22 45'0"N 22 35'0"N 22 35'0"N 22 40'0"N 22 40'0"N 81 15'0"E 81 20'0"E Kms 81 25'0"E 81 30'0"E 81 35'0"E 81 40'0"E Fig 7.4: Composite image of mean intensity (Red), intensity difference (green) and coherence (blue) generated from ASAR images in parts of Achanakmar- Amarkantak Biosphere Reserve

13 159 The discrimination of forest and agriculture classes is attributed to the large coherence values of agricultural areas compared to forests and marked backscatter difference between the two land cover types. Coherence is a function of systemic spatial decorrelation, additive noise, and the scene decorrelation that takes place between the two acquisitions (Wegmuller et al., 1997). The average value of coherence in the study area was very small (<0.5) as the majority area is covered by vegetation. This can be attributed to the wind patterns in the study area that might alter the orientation of scattering objects (leaves, secondary branches) in the vegetation layer. The largest coherence values were obtained in barren areas followed by agricultural areas, which are in accordance with the results reported in earlier studies (Wegmuller and Werner, 1995), which suggested urban areas, agricultural areas, bushes and forests have different correlation characteristics, with urban areas showing the largest correlation and forest the smallest. Agricultural areas in the study area showed medium coherence and large backscatter difference values. As the temporal gap between the two images acquired is 35 days, agriculture areas showed some difference during the two acquisitions. Both the coherence and backscatter difference values of the forested areas were small compared to other vegetation types. This may be attributed to the temporal decorrelation effect that occurs on longer time scales: the growth of the vegetation, human made changes, etc (Srtozzi et al., 2000).

14 160 Figure 7.5 shows a scatter plot of interferometric coherence with SAR backscatter for the four vegetation classes. From the figure, we can infer that the vegetation classes which had poor separability on the SAR backscatter image were clearly separable on the coherence image, which is in accordance to the observations of similar studies (Srivastava et al., 2006). 0.5 Interferometric coherence Backscatter difference (db) Sal MixedForest Mixe d moi st De ciduous Agriculture Barren Fig 7.5: Scatter plot showing variation of interferometric coherence with SAR backscatter for different vegetation classes Barren Coherence Water Agriculture Sal gregarious forests Mixed Moist Deciduous forests Backscatter difference Fig 7.6: Feature space image of different vegetation

15 '0"N 81 15'0"E 81 20'0"E 81 25'0"E 81 30'0"E 81 35'0"E 81 40'0"E ± 22 10'0"N 22 10'0"N 22 15'0"N 22 15'0"N 22 20'0"N 22 20'0"N 22 25'0"N 22 25'0"N 22 45'0"N 22 30'0"N 22 30'0"N 22 35'0"N 22 35'0"N 22 40'0"N 22 40'0"N 81 15'0"E 81 20'0"E 81 25'0"E 81 30'0"E 81 35'0"E 81 40'0"E Kms Water Agriculture Barren Mixed moist deciduous Sal mixed Fig 7.7: Vegetation classified map of parts of Achankmar-Amarkantak Biosphere Reserve generated from interferometric coherence

16 162 Feature space images between the backscatter difference and the coherence band (fig 7.6) clearly distinguishes the different vegetation classes. Forest areas have both the coherence and backscatter difference as low. There is an overlap between the two types of forests i.e., mixed moist deciduous and Sal gregarious forests. Agriculture areas have medium coherence and high backscatter difference. Barren areas have high coherence and low backscatter difference values. Classification into different classes viz.,., water, barren, agriculture, mixed moist deciduous forest and Sal mixed forest was carried out using the bands of mean backscatter, backscatter difference and coherence of the ASAR datasets (fig 7.7). Table 7.1: Confusion matrix (in percentage) of different classes in parts of Bilaspur study area Class name Agriculture Mixed moist deciduous forest Sal gregarious forest Agriculture Mixed moist deciduous forest Sal gregarious forest Barren Barren Confusion matrix for different vegetation classes was derived and is given in Table 7.1. The overall classification accuracy and kappa statistics were 82.5% and 0.76 respectively. Statistical analysis based on Kappa statistics was carried out to test the precision and significance of the results. Accuracy table and kappa statistics are given in table 7.2. The results are in

17 163 correspondence with earlier studies which showed that the mean backscatter, backscatter difference and coherence band increased the classification accuracy (Araujo et al., 1999). Table 7.2: Accuracy table and kappa statistics of different classes in parts of Bilaspur study area Class name Producer's accuracy Users accuracy Kappa statistics Agriculture 71.43% 92.12% 0.89 Barren 100% 69.23% Mixed moist deciduous forest 83.33% 90.91% Sal mixed forest 80.00% 66.67% Though there is marked difference between forest and non-forest, there was difficulty in differentiating mixed moist deciduous and Sal gregarious forests of the study area. In terms of backscatter and coherence, the effect of small changes in species composition within forest is expected to be negligible. The classification accuracy was significant within the forest in plain areas; however, there was some misclassification in hilly regions. This may be due to slopes, which is a major cause for misclassification (Arne, 1995). Forests in the sloping terrain areas were misclassified as the barren areas in sloping regions facing the sensors. The use of coherence for vegetation classification is reasonable since low coherence is observed in forested areas, in comparison with bare soil and agriculture, which have high coherence. Interferometric coherence carries more land-cover related information than the backscattered intensity (Engdahl et al., 2003).

18 CONCLUSIONS: In the present study, an attempt was made to delineate four vegetation classes viz., agriculture, mixed moist deciduous forest, sal dominated forest and barren using ASAR interferometric coherence data over tropical forested regions of central India. It was inferred from the study that coherence from ASAR data can be used to differentiate major vegetation classes with a marked difference between forest and non-forest types. More ground knowledge on sample points of forest types aided with value additions to the information given by ENVISAT ASAR data can be very useful in discriminating different vegetation classes in the Indian region.

Snow Water Equivalent (SWE) of dry snow derived from InSAR -theory and results from ERS Tandem SAR data

Snow Water Equivalent (SWE) of dry snow derived from InSAR -theory and results from ERS Tandem SAR data Snow Water Equivalent (SWE) of dry snow derived from InSAR -theory and results from ERS Tandem SAR data Tore Guneriussen, Kjell Arild Høgda, Harald Johnsen and Inge Lauknes NORUT IT Ltd., Tromsø Science

More information

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 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

More information

RADAR Remote Sensing Application Examples

RADAR Remote Sensing Application Examples RADAR Remote Sensing Application Examples! All-weather capability: Microwave penetrates clouds! Construction of short-interval time series through cloud cover - crop-growth cycle! Roughness - Land cover,

More information

LAND COVER CLASSIFICATION BASED ON SAR DATA IN SOUTHEAST CHINA

LAND COVER CLASSIFICATION BASED ON SAR DATA IN SOUTHEAST CHINA LAND COVER CLASSIFICATION BASED ON SAR DATA IN SOUTHEAST CHINA Mr. Feilong Ling, Dr. Xiaoqin Wang, Mr.Xiaoming Shi Fuzhou University, Level 13, Science Building,No.53 Gongye Rd., 35, Fuzhou, China Email:

More information

ERS-ENVISAT Cross-interferometry for Coastal DEM Construction

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

More information

GEOMORPHOLOGICAL MAPPING WITH RESPECT TO AMPLITUDE, COHERENCEAND PHASE INFORMATION OF ERS SAR TANDEM PAIR

GEOMORPHOLOGICAL MAPPING WITH RESPECT TO AMPLITUDE, COHERENCEAND PHASE INFORMATION OF ERS SAR TANDEM PAIR GEOMORPHOLOGICAL MAPPING WITH RESPECT TO AMPLITUDE, COHERENCEAND PHASE INFORMATION OF ERS SAR TANDEM PAIR AUNG LWIN Assistant Researcher Remote Sensing Department Mandalay Technological University, Myanmar

More information

SAR Data Analysis: An Useful Tool for Urban Areas Applications

SAR Data Analysis: An Useful Tool for Urban Areas Applications SAR Data Analysis: An Useful Tool for Urban Areas Applications M. Ferri, A. Fanelli, A. Siciliano, A. Vitale Dipartimento di Scienza e Ingegneria dello Spazio Luigi G. Napolitano Università degli Studi

More information

THREE DIMENSIONAL DETECTION OF VOLCANIC DEPOSIT ON MOUNT MAYON USING SAR INTERFEROMETRY

THREE DIMENSIONAL DETECTION OF VOLCANIC DEPOSIT ON MOUNT MAYON USING SAR INTERFEROMETRY ABSTRACT THREE DIMENSIONAL DETECTION OF VOLCANIC DEPOSIT ON MOUNT MAYON USING SAR INTERFEROMETRY Francis X.J. Canisius, Kiyoshi Honda, Mitsuharu Tokunaga and Shunji Murai Space Technology Application and

More information

ERS-ENVISAT CROSS-INTERFEROMETRY SIGNATURES OVER DESERTS. Urs Wegmüller, Maurizio Santoro and Christian Mätzler

ERS-ENVISAT CROSS-INTERFEROMETRY SIGNATURES OVER DESERTS. Urs Wegmüller, Maurizio Santoro and Christian Mätzler ERS-ENVISAT CROSS-INTERFEROMETRY SIGNATURES OVER DESERTS Urs Wegmüller, Maurizio Santoro and Christian Mätzler Gamma Remote Sensing AG, Worbstrasse 225, CH-3073 Gümligen, Switzerland, http://www.gamma-rs.ch,

More information

GLOBAL FOREST CLASSIFICATION FROM TANDEM-X INTERFEROMETRIC DATA: POTENTIALS AND FIRST RESULTS

GLOBAL FOREST CLASSIFICATION FROM TANDEM-X INTERFEROMETRIC DATA: POTENTIALS AND FIRST RESULTS GLOBAL FOREST CLASSIFICATION FROM TANDEM-X INTERFEROMETRIC DATA: POTENTIALS AND FIRST RESULTS Michele Martone, Paola Rizzoli, Benjamin Bräutigam, Gerhard Krieger Microwaves and Radar Institute German Aerospace

More information

Generation and Validation of Digital Elevation Model using ERS - SAR Interferometry Remote Sensing Data

Generation and Validation of Digital Elevation Model using ERS - SAR Interferometry Remote Sensing Data Jour. Agric. Physics, Vol. 7, pp. 8-13 (2007) Generation and Validation of Digital Elevation Model using ERS - SAR Interferometry Remote Sensing Data SHELTON PADUA 1, VINAY K. SEHGAL 2 AND K.S. SUNDARA

More information

Radar Remote Sensing: Monitoring Ground Deformations and Geohazards from Space

Radar Remote Sensing: Monitoring Ground Deformations and Geohazards from Space Radar Remote Sensing: Monitoring Ground Deformations and Geohazards from Space Xiaoli Ding Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic University A Question 100 km 100 km

More information

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 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

More information

COAL MINE LAND SUBSIDENCE MONITORING BY USING SPACEBORNE INSAR DATA-A CASE STUDY IN FENGFENG, HEBEI PROVINCE, CHINA

COAL MINE LAND SUBSIDENCE MONITORING BY USING SPACEBORNE INSAR DATA-A CASE STUDY IN FENGFENG, HEBEI PROVINCE, CHINA COAL MINE LAND SUBSIDENCE MONITORING BY USING SPACEBORNE INSAR DATA-A CASE STUDY IN FENGFENG, HEBEI PROVINCE, CHINA Li Cao a, Yuehua Zhang a, Jianguo He a, Guang Liu b,huanyin Yue b, Runfeng Wang a, Linlin

More information

DEM GENERATION AND ANALYSIS ON RUGGED TERRAIN USING ENVISAT/ASAR ENVISAT/ASAR MULTI-ANGLE INSAR DATA

DEM GENERATION AND ANALYSIS ON RUGGED TERRAIN USING ENVISAT/ASAR ENVISAT/ASAR MULTI-ANGLE INSAR DATA DEM GENERATION AND ANALYSIS ON RUGGED TERRAIN USING ENVISAT/ASAR ENVISAT/ASAR MULTI-ANGLE INSAR DATA Li xinwu Guo Huadong Li Zhen State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing

More information

Polarimetry-based land cover classification with Sentinel-1 data

Polarimetry-based land cover classification with Sentinel-1 data Polarimetry-based land cover classification with Sentinel-1 data Banqué, Xavier (1); Lopez-Sanchez, Juan M (2); Monells, Daniel (1); Ballester, David (2); Duro, Javier (1); Koudogbo, Fifame (1) 1. Altamira-Information

More information

The Potential of High Resolution Satellite Interferometry for Monitoring Enhanced Oil Recovery

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

More information

Haiti Earthquake (12-Jan-2010) co-seismic motion using ALOS PALSAR

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

More information

Analysis of ERS Tandem SAR Coherence From Glaciers, Valleys, and Fjord Ice on Svalbard

Analysis of ERS Tandem SAR Coherence From Glaciers, Valleys, and Fjord Ice on Svalbard IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 39, NO. 9, SEPTEMBER 2001 2029 Analysis of ERS Tandem SAR Coherence From Glaciers, Valleys, and Fjord Ice on Svalbard Dan Johan Weydahl Abstract

More information

MONITORING OF GLACIAL CHANGE IN THE HEAD OF THE YANGTZE RIVER FROM 1997 TO 2007 USING INSAR TECHNIQUE

MONITORING OF GLACIAL CHANGE IN THE HEAD OF THE YANGTZE RIVER FROM 1997 TO 2007 USING INSAR TECHNIQUE MONITORING OF GLACIAL CHANGE IN THE HEAD OF THE YANGTZE RIVER FROM 1997 TO 2007 USING INSAR TECHNIQUE Hong an Wu a, *, Yonghong Zhang a, Jixian Zhang a, Zhong Lu b, Weifan Zhong a a Chinese Academy of

More information

DIFFERENTIAL INSAR STUDIES IN THE BOREAL FOREST ZONE IN FINLAND

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:

More information

SAR interferometry Status and future directions. Rüdiger Gens

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

More information

POLARIMETRY-BASED LAND COVER CLASSIFICATION WITH SENTINEL-1 DATA

POLARIMETRY-BASED LAND COVER CLASSIFICATION WITH SENTINEL-1 DATA POLARIMETRY-BASED LAND COVER CLASSIFICATION WITH SENTINEL-1 DATA Xavier Banqué (1), Juan M Lopez-Sanchez (2), Daniel Monells (1), David Ballester (2), Javier Duro (1), Fifame Koudogbo (1) (1) Altamira

More information

Measuring rock glacier surface deformation using SAR interferometry

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

More information

Dr. Simon Plank. German Remote Sensing Data Center (DFD), German Aerospace Center (DLR)

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

More information

GEOGRAPHICAL DATABASES FOR THE USE OF RADIO NETWORK PLANNING

GEOGRAPHICAL DATABASES FOR THE USE OF RADIO NETWORK PLANNING GEOGRAPHICAL DATABASES FOR THE USE OF RADIO NETWORK PLANNING Tommi Turkka and Jaana Mäkelä Geodata Oy / Sanoma WSOY Corporation Konalantie 6-8 B FIN-00370 Helsinki tommi.turkka@geodata.fi jaana.makela@geodata.fi

More information

This module presents remotely sensed assessment (choice of sensors and resolutions; airborne or ground based sensors; ground truthing)

This module presents remotely sensed assessment (choice of sensors and resolutions; airborne or ground based sensors; ground truthing) This module presents remotely sensed assessment (choice of sensors and resolutions; airborne or ground based sensors; ground truthing) 1 In this presentation you will be introduced to approaches for using

More information

Snow property extraction based on polarimetry and differential SAR interferometry

Snow property extraction based on polarimetry and differential SAR interferometry Snow property extraction based on polarimetry and differential SAR interferometry S. Leinß, I. Hajnsek Earth Observation and Remote Sensing, Institute of Enviromental Science, ETH Zürich TerraSAR X and

More information

Making a case for full-polarimetric radar remote sensing

Making a case for full-polarimetric radar remote sensing Making a case for full-polarimetric radar remote sensing Jeremy Nicoll Alaska Satellite Facility, University of Alaska Fairbanks 1 Polarization States of a Coherent Plane Wave electric field vector vertically

More information

MONITORING SUBSURFACE COAL FIRES USING SATELLITE-BASED OBSERVATIONS

MONITORING SUBSURFACE COAL FIRES USING SATELLITE-BASED OBSERVATIONS MONITORING SUBSURFACE COAL FIRES USING SATELLITE-BASED OBSERVATIONS Tajdarul H Syed Department of Applied Geology, Indian School of Mines, Dhanbad, India. CANEUS SSTDM 2014 SSTDM-2014 IISc, Bangalore Jharia

More information

PHASE UNWRAPPING. Sept. 3, 2007 Lecture D1Lb4 Interferometry: Phase unwrapping Rocca

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

More information

Ground surface deformation of L Aquila. earthquake revealed by InSAR time series

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

More information

Slow Deformation of Mt. Baekdu Stratovolcano Observed by Satellite Radar Interferometry

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,

More information

Feasibility of snow water equivalent retrieval by means of interferometric ALOS PALSAR data

Feasibility of snow water equivalent retrieval by means of interferometric ALOS PALSAR data Feasibility of snow water equivalent retrieval by means of interferometric ALOS PALSAR data, Florian Müller, Helmut Rott, and Markus Heidinger ENVEO Technikerstrasse 21a, A 6020 Innsbruck, Austria www.galahad-euproject.org

More information

Utilizing Persistent Scatterer Interferometry to Investigate the Nature and Factors Controlling Nile Delta Subsidence

Utilizing Persistent Scatterer Interferometry to Investigate the Nature and Factors Controlling Nile Delta Subsidence Western Michigan University ScholarWorks at WMU Master's Theses Graduate College 12-2013 Utilizing Persistent Scatterer Interferometry to Investigate the Nature and Factors Controlling Nile Delta Subsidence

More information

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 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

More information

Model based forest height estimation with ALOS/PalSAR: A first study.

Model based forest height estimation with ALOS/PalSAR: A first study. Model based forest height estimation with ALOS/PalSAR: A first study. K.P. Papathanassiou*, I. Hajnsek*, T.Mette*, S.R. Cloude** and A. Moreira* * (DLR) (DLR-HR) Oberpfaffenhofen, Germany ** AEL Consultants

More information

InSAR atmospheric effects over volcanoes - atmospheric modelling and persistent scatterer techniques

InSAR atmospheric effects over volcanoes - atmospheric modelling and persistent scatterer techniques InSAR atmospheric effects over volcanoes - atmospheric modelling and persistent scatterer techniques Rachel Holley 1,2, Geoff Wadge 1, Min Zhu 1, Ian James 3, Peter Clark 4 Changgui Wang 4 1. Environmental

More information

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 1, NO. 2, APRIL

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 1, NO. 2, APRIL IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 1, NO. 2, APRIL 2004 57 Delta-K Interferometric SAR Technique for Snow Water Equivalent (SWE) Retrieval Geir Engen, Tore Guneriussen, and Øyvind Overrein

More information

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 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

More information

Application of PSI technique to slope stability monitoring in the Daunia mountains, Italy

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

More information

Publication I American Society for Photogrammetry and Remote Sensing (ASPRS)

Publication I American Society for Photogrammetry and Remote Sensing (ASPRS) Publication I Leena Matikainen, Juha Hyyppä, and Marcus E. Engdahl. 2006. Mapping built-up areas from multitemporal interferometric SAR images - A segment-based approach. Photogrammetric Engineering and

More information

THE PYLA 2001 EXPERIMENT : EVALUATION OF POLARIMETRIC RADAR CAPABILITIES OVER A FORESTED AREA

THE PYLA 2001 EXPERIMENT : EVALUATION OF POLARIMETRIC RADAR CAPABILITIES OVER A FORESTED AREA THE PYLA 2001 EXPERIMENT : EVALUATION OF POLARIMETRIC RADAR CAPABILITIES OVER A FORESTED AREA M. Dechambre 1, S. Le Hégarat 1, S. Cavelier 1, P. Dreuillet 2, I. Champion 3 1 CETP IPSL (CNRS / Université

More information

MODELING INTERFEROGRAM STACKS FOR SENTINEL - 1

MODELING INTERFEROGRAM STACKS FOR SENTINEL - 1 MODELING INTERFEROGRAM STACKS FOR SENTINEL - 1 Fabio Rocca (1) (1) Politecnico di Milano, Via Ponzio 34/5, 20133 Milano, Italy, Email: rocca@elet.polimi.it ABSTRACT The dispersion of the optimal estimate

More information

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 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

More information

Multi-temporal archaeological and environmental prospection in Nasca (Peru) with ERS-1/2, ENVISAT and Sentinel-1A C-band SAR data

Multi-temporal archaeological and environmental prospection in Nasca (Peru) with ERS-1/2, ENVISAT and Sentinel-1A C-band SAR data 12-13 November 215 ESA-ESRIN, Frascati (Rome), Italy Day 1 Session: Historical Landscapes and Environmental Analysis Multi-temporal archaeological and environmental prospection in Nasca (Peru) with ERS-1/2,

More information

APPEARANCE OF PERSISTENT SCATTERERS FOR DIFFERENT TERRASAR-X ACQUISITION MODES

APPEARANCE OF PERSISTENT SCATTERERS FOR DIFFERENT TERRASAR-X ACQUISITION MODES APPEARANCE OF PERSISTENT SCATTERERS FOR DIFFERENT TERRASAR-X ACQUISITION MODES Stefan Gernhardt a, Nico Adam b, Stefan Hinz c, Richard Bamler a,b a Remote Sensing Technology, TU München, Germany b Remote

More information

Mapping small reservoirs in semi-arid regions using multitemporal SAR: methods and applications

Mapping small reservoirs in semi-arid regions using multitemporal SAR: methods and applications Mapping small reservoirs in semi-arid regions using multitemporal SAR: methods and applications Donato Amitrano Gerardo Di Martino Antonio Iodice Daniele Riccio Giuseppe Ruello University of Napoli, Italy

More information

RADAR BACKSCATTER AND COHERENCE INFORMATION SUPPORTING HIGH QUALITY URBAN MAPPING

RADAR BACKSCATTER AND COHERENCE INFORMATION SUPPORTING HIGH QUALITY URBAN MAPPING RADAR BACKSCATTER AND COHERENCE INFORMATION SUPPORTING HIGH QUALITY URBAN MAPPING Peter Fischer (1), Zbigniew Perski ( 2), Stefan Wannemacher (1) (1)University of Applied Sciences Trier, Informatics Department,

More information

USE OF INTERFEROMETRIC SATELLITE SAR FOR EARTHQUAKE DAMAGE DETECTION ABSTRACT

USE OF INTERFEROMETRIC SATELLITE SAR FOR EARTHQUAKE DAMAGE DETECTION ABSTRACT USE OF INTERFEROMETRIC SATELLITE SAR FOR EARTHQUAKE DAMAGE DETECTION Masashi Matsuoka 1 and Fumio Yamazaki 2 ABSTRACT Synthetic Aperture Radar (SAR) is one of the most promising remote sensing technologies

More information

GEOGG141 Principles & Practice of Remote Sensing (PPRS) RADAR III: Applications Revision

GEOGG141 Principles & Practice of Remote Sensing (PPRS) RADAR III: Applications Revision UCL DEPARTMENT OF GEOGRAPHY GEOGG141 Principles & Practice of Remote Sensing (PPRS) RADAR III: Applications Revision Dr. Mathias (Mat) Disney UCL Geography Office: 113, Pearson Building Tel: 7670 0592

More information

Available online at GHGT-9. Detection of surface deformation related with CO 2 injection by DInSAR at In Salah, Algeria

Available online at   GHGT-9. Detection of surface deformation related with CO 2 injection by DInSAR at In Salah, Algeria Available online at www.sciencedirect.com Energy Procedia 100 (2009) (2008) 2177 2184 000 000 Energy Procedia www.elsevier.com/locate/procedia www.elsevier.com/locate/xxx GHGT-9 Detection of surface deformation

More information

Forest mapping and monitoring with interferometric. Synthetic Aperture Radar (InSAR)

Forest mapping and monitoring with interferometric. Synthetic Aperture Radar (InSAR) Citation: Balzter, H. (2001): Forest mapping and monitoring with interferometric Synthetic Aperture Radar (InSAR). Progress in Physical Geography 25, 159-177. Forest mapping and monitoring with interferometric

More information

THE THEMATIC INFORMATION EXTRACTION FROM POLINSAR DATA FOR URBAN PLANNING AND MANAGEMENT

THE THEMATIC INFORMATION EXTRACTION FROM POLINSAR DATA FOR URBAN PLANNING AND MANAGEMENT THE THEMATIC INFORMATION EXTRACTION FROM POLINSAR DATA FOR URBAN PLANNING AND MANAGEMENT D.Amarsaikhan a, *, M.Sato b, M.Ganzorig a a Institute of Informatics and RS, Mongolian Academy of Sciences, av.enkhtaivan-54b,

More information

SURFACE DEFORMATION OF ALPINE TERRAIN DERIVED BY PS-INSAR TECHNIQUE ON THE SIACHEN GLACIER

SURFACE DEFORMATION OF ALPINE TERRAIN DERIVED BY PS-INSAR TECHNIQUE ON THE SIACHEN GLACIER SURFACE DEFORMATION OF ALPINE TERRAIN DERIVED BY PS-INSAR TECHNIQUE ON THE SIACHEN GLACIER Junchao Shi and Ling Chang Department of Remote Sensing, Delft University of Technology, the Netherlands. ABSTRACT

More information

Generating an InSAR DEM using ASF software tools. Rüdiger Gens

Generating an InSAR DEM using ASF software tools. Rüdiger Gens Generating an InSAR DEM using ASF software tools Rüdiger Gens 2 Outline! AKDEM production system! SAR interferometric processing chain " general setup " examples 3 AKDEM production system! driver program

More information

DAMAGE DETECTION OF THE 2008 SICHUAN, CHINA EARTHQUAKE FROM ALOS OPTICAL IMAGES

DAMAGE DETECTION OF THE 2008 SICHUAN, CHINA EARTHQUAKE FROM ALOS OPTICAL IMAGES DAMAGE DETECTION OF THE 2008 SICHUAN, CHINA EARTHQUAKE FROM ALOS OPTICAL IMAGES Wen Liu, Fumio Yamazaki Department of Urban Environment Systems, Graduate School of Engineering, Chiba University, 1-33,

More information

http://topex.ucsd.edu/gmtsar amplitude and phase coherence and pixel matching resolution: optical vs. microwave D s = 2H sinθ r = 2H λ L H = 800km. Optical : L = 1m λ = 0.5µm D s = 0.8m Microwave : L

More information

The PaTrop Experiment

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.

More information

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 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,

More information

Rice Monitoring using Simulated Compact SAR. Kun Li, Yun Shao Institute of Remote Sensing and Digital Earth

Rice Monitoring using Simulated Compact SAR. Kun Li, Yun Shao Institute of Remote Sensing and Digital Earth Rice Monitoring using Simulated Compact SAR Kun Li, Yun Shao Institute of Remote Sensing and Digital Earth Outlines Introduction Test site and data Results Rice type discrimination Rice phenology retrieval

More information

K&C Phase 4 Status report. Use of short-period ALOS-2 observations for vegetation characterization and classification

K&C Phase 4 Status report. Use of short-period ALOS-2 observations for vegetation characterization and classification K&C Phase 4 Status report Use of short-period ALOS-2 observations for vegetation characterization and classification Paul Siqueira University of Massachusetts, Amherst Science Team meeting #22 Tokyo, Japan,

More information

OBJECT-BASED CLASSIFICATION USING HIGH RESOLUTION SATELLITE DATA AS A TOOL FOR MANAGING TRADITIONAL JAPANESE RURAL LANDSCAPES

OBJECT-BASED CLASSIFICATION USING HIGH RESOLUTION SATELLITE DATA AS A TOOL FOR MANAGING TRADITIONAL JAPANESE RURAL LANDSCAPES OBJECT-BASED CLASSIFICATION USING HIGH RESOLUTION SATELLITE DATA AS A TOOL FOR MANAGING TRADITIONAL JAPANESE RURAL LANDSCAPES K. Takahashi a, *, N. Kamagata a, K. Hara b a Graduate School of Informatics,

More information

ANALYSIS OF ASAR POLARISATION SIGNATURES FROM URBAN AREAS (AO-434)

ANALYSIS OF ASAR POLARISATION SIGNATURES FROM URBAN AREAS (AO-434) ANALYSIS OF ASAR POLARISATION SIGNATURES FROM URBAN AREAS (AO-434) Dan Johan Weydahl and Richard Olsen Norwegian Defence Research Establishment (FFI), P.O. Box 25, NO-2027 Kjeller, NORWAY, Email: dan-johan.weydahl@ffi.no

More information

PSI Precision, accuracy and validation aspects

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

More information

ACHIEVING THE ERS-2 ENVISAT INTER-SATELLITE INTERFEROMETRY TANDEM CONSTELLATION.

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

More information

Estimation of Velocity of the Polar Record Glacier, Antarctica Using Synthetic Aperture Radar (SAR)

Estimation of Velocity of the Polar Record Glacier, Antarctica Using Synthetic Aperture Radar (SAR) Proceedings Estimation of Velocity of the Polar Record Glacier, Antarctica Using Synthetic Aperture Radar (SAR) Prashant H. Pandit 1, *, Shridhar D. Jawak 2 and Alvarinho J. Luis 2 1 Department of Natural

More information

CNES R&D and available software for Space Images based risk and disaster management

CNES R&D and available software for Space Images based risk and disaster management CNES R&D and available software for Space Images based risk and disaster management 1/21 Contributors: CNES (Centre National d Etudes Spatiales), Toulouse, France Hélène Vadon Jordi Inglada 2/21 Content

More information

to: Interseismic strain accumulation and the earthquake potential on the southern San

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

More information

SNOW MASS RETRIEVAL BY MEANS OF SAR INTERFEROMETRY

SNOW MASS RETRIEVAL BY MEANS OF SAR INTERFEROMETRY SNOW MASS RETRIEVAL BY MEANS OF SAR INTERFEROMETRY Helmut Rott (1), Thomas Nagler (1), Rolf Scheiber (2) (1) ENVEO, Environmental Earth Observation OEG, Exlgasse 39, A-6020 Innsbruck, Austria E-mail: Helmut.Rott@enveo.at

More information

Module 2.1 Monitoring activity data for forests using remote sensing

Module 2.1 Monitoring activity data for forests using remote sensing Module 2.1 Monitoring activity data for forests using remote sensing Module developers: Frédéric Achard, European Commission (EC) Joint Research Centre (JRC) Jukka Miettinen, EC JRC Brice Mora, Wageningen

More information

The financial and communal impact of a catastrophe instantiated by. volcanoes endlessly impact on lives and damage expensive infrastructure every

The financial and communal impact of a catastrophe instantiated by. volcanoes endlessly impact on lives and damage expensive infrastructure every Chapter 1 Introduction The financial and communal impact of a catastrophe instantiated by geophysical activity is significant. Landslides, subsidence, earthquakes and volcanoes endlessly impact on lives

More information

Vegetation Change Detection of Central part of Nepal using Landsat TM

Vegetation Change Detection of Central part of Nepal using Landsat TM Vegetation Change Detection of Central part of Nepal using Landsat TM Kalpana G. Bastakoti Department of Geography, University of Calgary, kalpanagb@gmail.com Abstract This paper presents a study of detecting

More information

Interferometric Synthetic Aperture Radar (InSAR) Study of Coastal Wetlands Over Southeastern Louisiana

Interferometric Synthetic Aperture Radar (InSAR) Study of Coastal Wetlands Over Southeastern Louisiana 2 Interferometric Synthetic Aperture Radar (InSAR) Study of Coastal Wetlands Over Southeastern Louisiana Zhong Lu and Oh-Ig Kwoun CONTENTS 2.1 Introduction... 26 2.2 Study Site... 28 2.3 Radar Mapping

More information

7.1 INTRODUCTION 7.2 OBJECTIVE

7.1 INTRODUCTION 7.2 OBJECTIVE 7 LAND USE AND LAND COVER 7.1 INTRODUCTION The knowledge of land use and land cover is important for many planning and management activities as it is considered as an essential element for modeling and

More information

Infrastructure monitoring using SAR interferometry

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

More information

INSAR DEM CALIBRATION FOR TOPOGRAPHIC MAPPING IN EASTERN UGANDA

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

More information

SNOW COVER MONITORING IN ALPINE REGIONS WITH COSMO-SKYMED IMAGES BY USING A MULTITEMPORAL APPROACH AND DEPOLARIZATION RATIO

SNOW COVER MONITORING IN ALPINE REGIONS WITH COSMO-SKYMED IMAGES BY USING A MULTITEMPORAL APPROACH AND DEPOLARIZATION RATIO SNOW COVER MONITORING IN ALPINE REGIONS WITH COSMO-SKYMED IMAGES BY USING A MULTITEMPORAL APPROACH AND DEPOLARIZATION RATIO B. Ventura 1, T. Schellenberger 1, C. Notarnicola 1, M. Zebisch 1, T. Nagler

More information

IMPROVING REMOTE SENSING-DERIVED LAND USE/LAND COVER CLASSIFICATION WITH THE AID OF SPATIAL INFORMATION

IMPROVING REMOTE SENSING-DERIVED LAND USE/LAND COVER CLASSIFICATION WITH THE AID OF SPATIAL INFORMATION IMPROVING REMOTE SENSING-DERIVED LAND USE/LAND COVER CLASSIFICATION WITH THE AID OF SPATIAL INFORMATION Yingchun Zhou1, Sunil Narumalani1, Dennis E. Jelinski2 Department of Geography, University of Nebraska,

More information

Multi-Baseline SAR interferometry

Multi-Baseline SAR interferometry ulti-baseline SAR interferometry A. onti Guarnieri, S. Tebaldini Dipartimento di Elettronica e Informazione - Politecnico di ilano G. Fornaro, A. Pauciullo IREA-CNR ESA cat-1 project, 3173. Page 1 Classical

More information

DEMONSTRATION OF TERRASAR-X SCANSAR PERSISTENT SCATTERER INTERFEROMETRY

DEMONSTRATION OF TERRASAR-X SCANSAR PERSISTENT SCATTERER INTERFEROMETRY DEMONSTRATION OF TERRASAR-X SCANSAR PERSISTENT SCATTERER INTERFEROMETRY Fernando Rodriguez Gonzalez (1) Ramon Brcic (1) Nestor Yague-Martinez (1) Robert Shau (1) Alessandro Parizzi (1) Nico Adam (1) (1)

More information

Deformation measurement using SAR interferometry: quantitative aspects

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,

More information

Examination Questions & Model Answers (2009/2010) PLEASE PREPARE YOUR QUESTIONS AND ANSWERS BY USING THE FOLLOWING GUIDELINES;

Examination Questions & Model Answers (2009/2010) PLEASE PREPARE YOUR QUESTIONS AND ANSWERS BY USING THE FOLLOWING GUIDELINES; Examination s & Model Answers (2009/2010) PLEASE PREPARE YOUR QUESTIONS AND ANSWERS BY USING THE FOLLOWING GUIDELINES; 1. If using option 2 or 3 use template provided 2. Use Times New Roman 12 3. Enter

More information

DLR s TerraSAR-X contributes to international fleet of radar satellites to map the Arctic and Antarctica

DLR s TerraSAR-X contributes to international fleet of radar satellites to map the Arctic and Antarctica DLR s TerraSAR-X contributes to international fleet of radar satellites to map the Arctic and Antarctica The polar regions play an important role in the Earth system. The snow and ice covered ocean and

More information

Interferometric SAR analysis for Characterizing Surface Changes of an Active Volcano using Open Source Software

Interferometric SAR analysis for Characterizing Surface Changes of an Active Volcano using Open Source Software Interferometric SAR analysis for Characterizing Surface Changes of an Active Volcano using Open Source Software Asep SAEPULOH1, Katsuaki KOIKE1, Makoto OMURA2 1 Department of Life and Environmental Sciences,

More information

Applications of SAR Interferometry in Earth and Environmental Science Research

Applications of SAR Interferometry in Earth and Environmental Science Research Sensors 2009, 9, 1876-1912; doi:10.3390/s90301876 OPEN ACCESS sensors ISSN 1424-8220 www.mdpi.com/journal/sensors Review Applications of SAR Interferometry in Earth and Environmental Science Research Xiaobing

More information

Urban land and infrastructure deformation monitoring by satellite radar interferometry

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

More information

AGOG 485/585 /APLN 533 Spring Lecture 5: MODIS land cover product (MCD12Q1). Additional sources of MODIS data

AGOG 485/585 /APLN 533 Spring Lecture 5: MODIS land cover product (MCD12Q1). Additional sources of MODIS data AGOG 485/585 /APLN 533 Spring 2019 Lecture 5: MODIS land cover product (MCD12Q1). Additional sources of MODIS data Outline Current status of land cover products Overview of the MCD12Q1 algorithm Mapping

More information

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 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

More information

Preparation of LULC map from GE images for GIS based Urban Hydrological Modeling

Preparation of LULC map from GE images for GIS based Urban Hydrological Modeling International Conference on Modeling Tools for Sustainable Water Resources Management Department of Civil Engineering, Indian Institute of Technology Hyderabad: 28-29 December 2014 Abstract Preparation

More information

EE/Ge 157 b. Week 2. Polarimetric Synthetic Aperture Radar (2)

EE/Ge 157 b. Week 2. Polarimetric Synthetic Aperture Radar (2) EE/Ge 157 b Week 2 Polarimetric Synthetic Aperture Radar (2) COORDINATE SYSTEMS All matrices and vectors shown in this package are measured using the backscatter alignment coordinate system. This system

More information

Synthetic Aperture Radars for Humanitarian Purposes: Products and Opportunities

Synthetic Aperture Radars for Humanitarian Purposes: Products and Opportunities Synthetic Aperture Radars for Humanitarian Purposes: Products and Opportunities Donato Amitrano, Gerardo Di Martino, Antonio Iodice, Daniele Riccio, Giuseppe Ruello Department of Electrical and Information

More information

CHANGE DETECTION USING REMOTE SENSING- LAND COVER CHANGE ANALYSIS OF THE TEBA CATCHMENT IN SPAIN (A CASE STUDY)

CHANGE DETECTION USING REMOTE SENSING- LAND COVER CHANGE ANALYSIS OF THE TEBA CATCHMENT IN SPAIN (A CASE STUDY) CHANGE DETECTION USING REMOTE SENSING- LAND COVER CHANGE ANALYSIS OF THE TEBA CATCHMENT IN SPAIN (A CASE STUDY) Sharda Singh, Professor & Programme Director CENTRE FOR GEO-INFORMATICS RESEARCH AND TRAINING

More information

Application of Sentinel-1 SAR for monitoring surface velocity of Greenland outlet glaciers

Application of Sentinel-1 SAR for monitoring surface velocity of Greenland outlet glaciers pplication of Sentinel-1 SR for monitoring surface velocity of Greenland outlet glaciers Thomas Nagler, Markus Hetzenecker, Helmut Rott and Jan Wuite ENVEO IT GmbH Fringe 2015 OUTLINE Ice Surface Velocity

More information

Tandem-L: A Mission Proposal for Monitoring Dynamic Earth Processes

Tandem-L: A Mission Proposal for Monitoring Dynamic Earth Processes Tandem-L: A Mission Proposal for Monitoring Dynamic Earth Processes A. Moreira, G. Krieger, M. Younis, I. Hajnsek, K. Papathanassiou, M. Eineder, P. Dekker, F. De Zan German Aerospace Center (DLR) Dynamic

More information

III. Publication III. c 2004 Authors

III. Publication III. c 2004 Authors III Publication III J-P. Kärnä, J. Pulliainen, K. Luojus, N. Patrikainen, M. Hallikainen, S. Metsämäki, and M. Huttunen. 2004. Mapping of snow covered area using combined SAR and optical data. In: Proceedings

More information

Application of differential SAR interferometry for studying eruptive event of 22 July 1998 at Mt. Etna. Abstract

Application of differential SAR interferometry for studying eruptive event of 22 July 1998 at Mt. Etna. Abstract Application of differential SAR interferometry for studying eruptive event of 22 July 1998 at Mt. Etna Coltelli M. 1, Puglisi G. 1, Guglielmino F. 1, Palano M. 2 1 Istituto Nazionale di Geofisica e Vulcanologia,

More information

Transactions on Information and Communications Technologies vol 18, 1998 WIT Press, ISSN

Transactions on Information and Communications Technologies vol 18, 1998 WIT Press,  ISSN Ready-to-use GIS information from remotely sensed data G. Sylos Labini*, S. Samarelli*, G. Pasquariello^ G. Nico*, A. Refice* & J. Bequignon ' Planetek Italia, Tecnopolis, 70010 Valenzano, Bari, Italy

More information

Land cover/land use mapping and cha Mongolian plateau using remote sens. Title. Author(s) Bagan, Hasi; Yamagata, Yoshiki. Citation Japan.

Land cover/land use mapping and cha Mongolian plateau using remote sens. Title. Author(s) Bagan, Hasi; Yamagata, Yoshiki. Citation Japan. Title Land cover/land use mapping and cha Mongolian plateau using remote sens Author(s) Bagan, Hasi; Yamagata, Yoshiki International Symposium on "The Imp Citation Region Specific Systems". 6 Nove Japan.

More information

EFFECT OF ANCILLARY DATA ON THE PERFORMANCE OF LAND COVER CLASSIFICATION USING A NEURAL NETWORK MODEL. Duong Dang KHOI.

EFFECT OF ANCILLARY DATA ON THE PERFORMANCE OF LAND COVER CLASSIFICATION USING A NEURAL NETWORK MODEL. Duong Dang KHOI. EFFECT OF ANCILLARY DATA ON THE PERFORMANCE OF LAND COVER CLASSIFICATION USING A NEURAL NETWORK MODEL Duong Dang KHOI 1 10 Feb, 2011 Presentation contents 1. Introduction 2. Methods 3. Results 4. Discussion

More information