SAR Interferometry Data Research Articles

Surface displacement measurement and modeling of the Shah-Gheyb salt dome in southern Iran using InSAR and machine learning techniques

Salt domes play a crucial role in hydrocarbon storage, underground construction, solution mining, and mineralization. Therefore, deformation monitoring is essential for analyzing the kinematics and impact of salt domes. This study aims to measure the temporal displacements of the Shah-Gheyb salt dome from 2016 to 2019 and from 2020 to 2022 using the New Small Baseline Subset (NSBAS) Interferometric Synthetic Aperture Radar (InSAR) technique and to predict future displacements through machine learning models. A total of 14 data layers, including topography, remote sensing, hydrology, and geology group were used in Machine Learning (ML). Random Forest Regression (RFR) and Support Vector Regression (SVR) models were employed to project displacements in both the East-West (E-W) and Up-Down (U-D) components through 29 scenarios.In the E-W direction, the salt dome exhibits a displacement rate of 39 mm/year, while in the U-D direction, it varies between −18 and +6 mm/year. ML predictions and SAR interferometry data processing results for the period 2020–2022 were validated using Root Mean Square Error (RMSE) and the correlation coefficient (R). The RFR model demonstrated the lowest RMSE of 1.9 mm for the E-W component, achieving a maximum R-value of 97.3 %. For the U-D component, the RMSE was 2.8 mm, with an R-value of 55.8 %. Evaluation of the predictive performance of the ML models and a comparison of InSAR and ML outcomes indicated that the RFR model predicted displacement along the E-W and U-D directionsbetween 2020 and 2022 with greater accuracy than the SVR. Furthermore, comparing the displacement predictedby the RFR model using SAR interferometry along two perpendicular profiles confirmedthe model's precision.

SAR Interferometry Data Exploitation for Infrastructure Monitoring Using GIS Application

Monitoring structural stability in urban areas and infrastructure networks is emerging as one of the dominant socio-economic issues for population security. The problem is accentuated by the age of the infrastructure because of increasing risks due to material deterioration and loss of load capacity. In this case, SAR satellite data are crucial to identify and assess the deteriorating conditions of civil infrastructures. The large amount of data available from SAR satellite sensors leads to the exploitation and development of new GIS-based procedures for rapid responses and decision making. In recent decades, the DInSAR technique has been used efficiently for the monitoring of structures, providing measurement points located on structures with millimeter precision. Our study has analyzed the behavior of structures in settlements, attempting to discuss the interactions of soil and structures, and examining the behavior of different types of structures, such as roads and buildings. The method used is based on long-term SAR interferometry data and a semi-automatic procedure to measure the displacement (mm/year) of structures, through a GIS-based application performed in the “Implemented MOnitoring DIsplacement” I.MODI platform. The analysis provides extensive information on long-term spatial and temporal continuity of up to 25 years of record, using satellite SAR multi-sensors from ERS, Envisat, and COSMO-SkyMed. The interpretation uses time series spatial analysis, supported by orthophotos, and layers of the DBTR (regional topographic database), Digital Surface model (DSM), and hydrogeological map to show anomalous areas with a high displacement rate and to observe the correlation of settlements in the sediments. With the satellite information and Geographic Information System (GIS), we were able to observe relevant parameters, such as the velocity of advance in the direction of the slope (deformation profiles), the cumulative displacement, and the trend changes in structures. The results illustrate an innovative procedure that allows the management of DInSAR data to facilitate the effective management of structures in which a monitoring protocol was developed at different spatial scales, integrating the information into a GIS.

Monitoring the Dynamics of Ephemeral Rivers from Space: An Example of the Kuiseb River in Namibia

Ephemeral rivers are characterized by brief episodic flood events, which recharge subterraean alluvial aquifers that sustain humans, riparian vegetation, and wildlife in the hyper-arid Namib Desert. Yet we only have a poor understanding of the dynamics and feedback mechanisms in these hydrological systems as arid and semi-arid zones are typically poorly equipped with reliable in situ monitoring stations to provide necessary information. The main objective of our study is to show the potential of satellite data to monitor the dynamics of ephemeral rivers, such as the Kuiseb located in Namibia, since remotesensing offers the advantage of adapted spatial and temporal resolutions. For this study, multi-spectral imagery (Sentinel-2), Synthetic Aperture Radar (SAR, Sentinel-1), and SAR interferometry (Sentinel-1) data were used to produce Normalized Difference Vegetation Index (NDVI) maps, backscattering maps (as σ0), and interferograms, respectively. These parameters provide information on the hydrologic and vegetation dynamics of the river. Strong variations in NDVI, σ0, and interferograms are observed during March–April 2017 and June–July 2018 in a tributary of the Kuiseb in the central Namib Desert. In those years, rain events caused the reactivation of the tributary. However, during a major flood in 2021, when no rain occured, no variations in NDVI were detected in this tributary, unlike the σ0 and interferogram anomalies after the flood. Thus, these variations cannot be explained by rains, which were non-existent during this period, but seem to be linked to the dynamics of the aquifer of the Kuiseb River, wherein floods recharge the alluvial aquifers and the rising water table levels produce a signal that is measurable by satellite radar sensors. All these results present a preliminary work that might be used by water resource managers to automate the processing and methods used to create an ephemeral river monitoring tool.

Measuring Coastal Subsidence after Recent Earthquakes in Chile Central Using SAR Interferometry and GNSS Data

Coastal areas concentrate a large portion of the country’s population around urban areas, which in subduction zones commonly are affected by drastic tectonic processes, such as the damage earthquakes have registered in recent decades. The seismic cycle of large earthquakes primarily controls changes in the coastal surface level in these zones. Therefore, quantifying temporal and spatial variations in land level after recent earthquakes is essential to understand shoreline variations better, and to assess their impacts on coastal urban areas. Here, we measure the coastal subsidence in central Chile using a multi-temporal differential interferometric synthetic aperture radar (MT-InSAR). This geographic zone corresponds to the northern limit of the 2010 Maule earthquake (Mw 8.8) rupture, an area affected by an aftershock of magnitude Mw 6.8 in 2019. The study is based on the exploitation of big data from SAR images of Sentinel-1 for comparison with data from continuous GNSS stations. We analyzed a coastline of ~300 km by SAR interferometry that provided high-resolution ground motion rates from between 2018 and 2021. Our results showed a wide range of subsidence rates at different scales, of analyses on a regional scale, and identified the area of subsidence on an urban scale. We identified an anomalous zone of subsidence of ~50 km, with a displacement <−20 mm/year. We discuss these results in the context of the impact of recent earthquakes and analyze the consequences of coastal subsidence. Our results allow us to identify stability in urban areas and quantify the vertical movement of the coast along the entire seismic cycle, in addition to the vertical movement of coast lands. Our results have implications for the planning of coastal infrastructure along subduction coasts in Chile.

An Integrated Platform for Ground-Motion Mapping, Local to Regional Scale; Examples from SE Europe

Ground and infrastructure stability are important for our technologically based civilization. Infrastructure projects take into consideration the risk posed by ground displacement (e.g., seismicity, geological conditions and geomorphology). To address this risk, earth scientists and civil engineers employ a range of measurement technologies, such as optical/laser leveling, GNSS and, lately, SAR interferometry. Currently there is a rich source of measurement information provided in various formats that covers most of the industrialized world. Integration of this information becomes an issue that will only increase in importance in the future. This work describes a practical approach to address and validate integrated stability measurements through the development of a platform that could be easily used by a variety of groups, from geoscientists to civil engineers and also private citizens with no training in this field. The platform enables quick cross-validation between different data sources, easy detection of critical areas at all scales (from large-scale individual buildings to small-scale tectonics) and can be linked to end-users from various monitoring fields and countries for automated notifications. This work is closing the gap between the specialized monitoring work and the general public, delivering the full value of technology for societal benefits in a free and open manner. The platform is calibrated and validated by an application of SAR interferometry data to specific situations in the general area of the Romanian Carpathians and their foreland. The results demonstrate an interplay between anthropogenically induced changes and high-amplitude active tectono–sedimentary processes creating rapid regional and local topographic variations.

Forest parameters inversion by mean coherence set from single-baseline PolInSAR data

The effectiveness of the forest height inversion methods using polarimetric SAR interferometry data is affected by the estimation accuracy of ground phase, volume only coherence and model prediction for forest canopy layer. Finding the forest model that considers the effects of the signal penetration in the forest medium as well as optimum volume coherence, which is an important purpose of the forest inversion method. This paper suggests a novel inversion algorithm to increase the accuracy of forest height estimation as well as ground phase estimation based on mean of coherence set. For this purpose, an adaptive total least square line fit is proposed to estimate the ground phase and coherence line. Then, the combination between the VE-RVoG model and an optimization volume only coherence algorithm based on mean coherence set and polarization signature is developed to find more accurate forest parameter values. The proposed algorithm applies forest height estimation using L-band PolInSAR data acquired by PolSARProSim software, spaceborne SIR-C and UAVSAR system. The method was further validated by using UAV L-band PolInSARata and the reference data of LiDAR canopy height model over rainforest Lope National Park, Gabon. Results showed that RMSE of results approximate 2.91 m and R 2 is 0.909. The obtained results demonstrate the potential of proposed method in forest parameters estimation.

Assessment of the temporal–spatial evolution of subsidence and its driving mechanism in the Beijing Plain (China) by using SAR interferometry and geological data

Based on the four types of SAR images from Envisat, Radarsat-2, and TerraSAR-X between 2007 and 2019, a linear time-series analysis of Coherence Targets method is employed to acquire the 13-year deformation velocity and cumulative deformation maps of the Beijing Plain. Such measurement firstly provides the annual subsidence rate and spatial evolution characteristics of the subsidence areas and the maximum subsidence rates in Laiguangying–Ciqu, which is the largest subsidence area of the Beijing Plain, were beyond 14 cm/year from 2009 to 2018. We correlate the observed subsidence with land use, Quaternary strata and pre-existing faults to assess the driving mechanisms of land subsidence. We find that large-scale land subsidence or patchy subsidence in the Beijing Plain takes on a similar variation to dry land and rural settlements/manufacturing enterprise land use. We also observe that the subsiding areas are mainly distributed in the edge of Alluvial–Proluval Fan or in the lacustrine or swampy sediments among Alluvial–Proluval Fans with much thicker compressible deposits, and the high subsidence gradients orientate along active faults. Therefore, this research could contribute to propose effective control measures for subsidence and ground fissure and achieve better hazard mitigation programme in Beijing.

A Slope Three-Layer Scattering Model for Forest Parameter Inversion of PolInSAR

The canopy vertical structure, especially the average height, has always been regarded as an important factor in monitoring forest changes. The coherent scattering model links forest canopy information with radar observations and the random volume over ground (RVoG) model has been extensively applied to the polarimetric SAR interferometry (PolInSAR) data since it was proposed. The complex coherence of the RVoG model was originally derived in a simplified way by neglecting some factors due to the complexity of the scattering process. Thence, this letter proposed a slope three-layer scattering (STLS) model for forest parameters' estimation in sloping mountain forest region. This model separates the vertical structure of the forest into three layers: the ground layer, the tree-trunk layer, and the canopy layer which account for the simultaneous effects of three scattering components on complex coherence. Moreover, it also corrects the distortion caused by the local terrain slope. The STLS model provides a better understanding of the microwave scattering process in the terrain slope area compared with the traditional RVoG model, S-RVoG model, and general three-layer scattering model (GTLSM) model. Finally, the STLS model has been quantitatively tested with the simulated PolInSAR data with different terrain slopes from PolSARProSim software and qualitatively tested with the spaceborne SIR-C data in Tian-Shan Mount area. The results validate the potential of the proposed STLS model in forest parameter inversion.

Improving Forest Height Retrieval by Reducing the Ambiguity of Volume-Only Coherence Using Multi-Baseline PolInSAR Data

Nonvolume decorrelation ( $\gamma _{\mathrm {Nonvol}}$ ) united with the unknown ground contribution will bring a 2-D ambiguity to volume-only coherence, making the inversion underdetermined even when multiple baselines are available. In the context of random volume over ground (RVoG) model and three-stage algorithm, this paper theoretically presented the varied response of different baselines to both $\gamma _{\mathrm {Nonvol}}$ and ground contribution, and then proposed a new multi-baseline inversion method to reduce the 2-D ambiguity. The proposed method includes two steps, calculating the common overlapped ambiguity from different baselines and fixing the extinction coefficient, to more accurately retrieve the volume-only coherence and forest height. It makes no assumptions on $\gamma _{\mathrm {Nonvol}}$ and ground contribution. The method was validated and compared with three single-baseline inversions and two published multi-baseline inversions by using the airborne P-band polarimetric SAR interferometry (PolInSAR) data and the reference data of LiDAR canopy height model (CHM) over a dense rainforest site. Results showed that the developed multi-baseline method successfully reduced the combined influence of both $\gamma _{\mathrm {Nonvol}}$ and ground contribution, and performed better than any single baseline, improving the ${R}^{2}$ from 0.60 to 0.77 and unbiased root-mean-square error (RMSE) from 1.32 to 1.04 m at the scale of ca. $100 \times 140\,\,\text{m}^{2}$ . Moreover, the multi-baseline scheme is relatively robust among different baseline combinations.

An efficient feature optimization for wetland mapping by synergistic use of SAR intensity, interferometry, and polarimetry data

Wetlands are home to a great variety of flora and fauna species and provide several unique environmental services. Knowledge of wetland species distribution is critical for sustainable management and resource assessment. In this study, multi-temporal single- and full-polarized RADARSAT-2 and single-polarized TerraSAR-X data were applied to characterize the wetland extent of a test site located in the north east of Newfoundland and Labrador, Canada. The accuracy and information content of wetland maps using remote sensing data depend on several factors, such as the type of data, input features, classification algorithms, and ecological characteristics of wetland classes. Most previous wetland studies examined the efficiency of one or two feature types, including intensity and polarimetry. Fewer investigations have examined the potential of interferometric coherence for wetland mapping. Thus, we evaluated the efficiency of using multiple feature types, including intensity, interferometric coherence, and polarimetric scattering for wetland mapping in multiple classification scenarios. An ensemble classifier, namely Random Forest (RF), and a kernel-based Support Vector Machine (SVM) were also used to determine the effect of the classifier. In all classification scenarios, SVM outperformed RF by 1.5–5%. The classification results demonstrated that the intensity features had a higher accuracy relative to coherence and polarimetric features. However, an inclusion of all feature types improved the classification accuracy for both RF and SVM classifiers. We also optimized the type and number of input features using an integration of RF variable importance and Spearman’s rank-order correlation. The results of this analysis found that, of 81 input features, 22 were the most important uncorrelated features for classification. An overall classification accuracy of 85.4% was achieved by incorporating these 22 important uncorrelated features based on the proposed classification framework.

Potential of Space-Borne PolInSAR for Forest Canopy Height Estimation Over India—A Case Study Using Fully Polarimetric L-, C-, and X-Band SAR Data

This paper aims to demonstrate the potential of space-borne datasets for forest height estimation over Indian tropical forests. Fully polarimetric space-borne SAR interferometry (PolInSAR) data is acquired at the L -, C -, and X -band frequencies. The X -band data are acquired in two modes—single pass and repeat pass. The datasets are compensated for decorrelation due to SNR and varying spatial baselines. The remaining major decorrelation is the volumetric decorrelation, which is modeled using random volume over ground model to estimate the PolInSAR height for all the three frequencies. The modified Three-stage inversion algorithm is utilized for forest stand height estimation. The effect of forest biomass on height inversion accuracy is assessed. Furthermore, a first-order estimate of the absolute temporal decorrelation is demonstrated for the repeat-pass space-borne PolInSAR datasets. Extensive field validation campaigns are carried out in the tropical forest ranges. The forest height is inverted using data at all the three frequencies and validated with field measured values. The zero-temporal baseline bistatic TerraSAR-X/TanDEM-X and the L -band ALOS-2/PALSAR-2 result in a good height inversion with ${\boldsymbol{r}^2}$ and RMSE of 0.77 and 1.86 m ( X -band) and 0.75 and 1.94 m ( L -band), respectively. Furthermore, a comparison of estimated height of ALOS-2/PALSAR-2, TerraSAR-X, and RadarSAT-2 has been done with respect to the estimated height of TerraSAR-X/TanDEM-X. It is observed that RMSE of height inversion with respect to TerraSAR-X/TanDEM-X height is found to be 5.4 m, 7.6 m, and 12.8 m for ALOS-2/PALSAR-2, RadarSAT-2, and TerraSAR-X, respectively.

New Data on the Olyutorskii Earthquake Acquired via SAR Interferometry

A coseismic displacement field based on SAR interferometry data was determined for the area of the April 20, 2006 Olyutorskii earthquake. The resulting image shows displacements toward the satellite (“uplifts”) to the northwest of the surface rupture area where the epicenters of most aftershocks lie. The displacement- affected area extends as far as the Vyvenka–Vatyna tectonic suture. We have developed a model for the rupture surface that is in agreement with the hypothesis of A.V. Lander and T.K. Pinegina stating that the largest displacements occurred along a fault northwest of the surface rupture zone; the fault dips southeast and is not exposed. The slip on the fault is close to a pure thrust type. These results furnish another confirmation of the fact that advanced satellite technologies can provide important additional information on the dynamics of seismic regions, especially where the existing observing networks are sparse.

Using Sentinel-1A DInSAR interferometry and Landsat 8 data for monitoring water level changes in two lakes in Crete, Greece

Differential Interferometric Synthetic Aperture Radar (DInSAR) methodology has been successfully employed to detect water level changes and produce corresponding water level variation maps. In this study, Agia and Kournas lakes, located in Western Crete, Greece, were used as pilot areas to monitor water level change with means of SAR interferometry and auxiliary Earth Observation (EO) data. The water level variation was monitored for the period 2015–2016, using Sentinel-1A imageries and corresponding stage water level data. Landsat 8 data were additionally used to study vegetation regime and surface water extent and how these parameters affect interferograms performance. The results highlighted the fact that the combination of SAR backscattering intensity and unwrapped phase can provide additional insight into hydrological studies. The overall analysis of both interferometric characteristics and backscattering mechanism denoted their potential in enhancing the reliability of the water-level retrieval scheme and optimizing the capture of hydrological patterns spatial distribution.

Subsidence Trends of Volturno River Coastal Plain (Northern Campania, Southern Italy) Inferred by SAR Interferometry Data

The Volturno Plain is one of the largest alluvial plains of peninsular Italy, which is one of the most susceptible plains to coastal hazards. This area is characterized by both natural and human-induced subsidence. This present study is based on the post-processing, analysis and mapping of the available Persistent Scatterer interferometry datasets. The latter were derived from the combination of both ascending and descending orbits of three different radar satellite systems during an observation period of almost two decades (June 1992–September 2010). The main output of this study is a map of vertical deformation, which provides new insights into the areal variability of the ground deformation processes (subsidence/uplift) of Volturno plain over the last few decades. The vertical displacement values obtained by the post-processing of the interferometric data show that the Volturno river plain is characterized by significant subsidence in the central axial sectors and in the river mouth area. Moderate uplift is detected in the eastern part of the plain, whereas other sectors of the study area are characterized by moderate subsidence and/or stability. On the basis of the analyzed subsoil stratigraphy, we inferred that the subsidence recorded in the Volturno plain is mainly a consequence of a natural process related to the compaction of the fluvial and palustrine deposits that form the alluvial plain. The anthropic influences (e.g., water exploitation, urbanization) are substantially considered to be an additional factor that may enhance subsidence only locally. The uplift mapped in the eastern sector of the plain is related to the tectonic activity. The study of the subsidence in the Volturno plain is a valuable tool for river flood analyses and the assessment of the coastal inundation hazards and related risk mitigation.

Single tree identification using airborne multibaseline SAR interferometry data

Remote sensing data allowlarge scale observation of forested ecosystems. Forest assessment benefits from information about individual trees. Multibaseline SAR interferometry (InSAR) is able to generate dense point clouds of forest canopies, similar to airborne laser scanning (ALS). This type of point cloud was generated using data from the Ka-bandMEMPHIS system, acquired over a mainly coniferous forest near Vordemwald in the Swiss Midlands. This point cloud was segmented using an advanced clustering technique to detect individual trees and derive their positions, heights, and crown diameters. To evaluate the InSAR point cloud properties and limitations, it was compared to products derived from ALS and stereo-photogrammetry. All point clouds showed similar geolocation accuracies with 0.2–0.3mrelative shifts. Both InSAR and photogrammetry techniques yielded points predominantly located in the upper levels of the forest vegetation, while ALS provided points fromthe top of the canopy down to the understory and forest floor. The canopy height models agreed very well with each other, with R² values between 0.84 and 0.89. The detected trees and their estimated physical and structural parameters were validated by comparing them to reference forestry data. A detection rate of ~90% was achieved for larger trees, corresponding to half of the reference trees. The smaller trees were detected with a success rate of ~50%. The tree height was slightly underestimated, with a R² value of 0.63. The estimated crown diameter agreed on an average sense, however with a relatively low R² value of 0.19. Very high success rates (N90%) were obtained when matching the trees detected fromthe InSAR-datawith those detected fromthe ALS- and photogrammetry-data. There, InSAR tree heightswere in themean 1–1.5mlower,with high R² values ranging between 0.8 and 0.9. Our results demonstrate the use of millimeter wave SAR interferometry data as an alternative to ALS- and photogrammetry-based data for forest monitoring.

Geodetic accuracy observations of regional land deformations caused by the 2011 Tohoku Earthquake using SAR interferometry and GEONET data

Geodetic accuracy observations of regional land deformations caused by the 2011 Tohoku Earthquake using SAR interferometry and GEONET data

Forest aboveground biomass estimation using polarization coherence tomography and PolSAR segmentation

Forest aboveground biomass (AGB) is essential for monitoring the carbon cycle budget and climate change. This study proposes a method for the estimation of forest AGB based on polarization coherence tomography (PCT) and polarimetric synthetic aperture radar (PolSAR) segmentation. The data used are the single-baseline polarimetric SAR interferometry data acquired by the German Aerospace Center’s E-SAR sensor at the L-band over the city of Traunstein and its vicinity in 2003. First, vertical structure tomographic profiles (relative reflectivity distribution in vertical direction) were produced by PCT for each pixel and then averaged within each field-surveyed forest stand to obtain the mean tomographic profile. Next, the mean vertical tomographic profiles were parameterized by 10 shape parameters. Several models for biomass estimation were designed based on the relationships between the definition parameters and the in situ measurements using backward step-wise regression and the M-fold (10-fold) cross-validation method. The best model was chosen by evaluation criteria such as R, root mean square error (RMSE), R2, etc. Forest polygons (objects) were produced by image segmentation using forest heights, (produced by coherence optimization representing double-bounce scattering mechanism), (produced by coherence optimization representing the volume scattering mechanism), and the volume scattering mechanism class (produced by Freeman–Durden decomposition and Whishart classification). Finally, the selected model was used to estimate the AGB of each forest polygon and was validated using ground-measured biomass with R2 of 0.883 and RMSE of 39.98 tons ha−1. The results show that the proposed method works well for the estimation of forest AGB. No saturation phenomena have been observed even for the forest stands with AGB larger than 500 tons ha−1.

Persistent Scatterers Interferometry Technique for Urban Subsidence Monitoring in Kazakhstan Republic

Abstract Methods of SAR interferometry data processing can detect and monitor low-amplitude motion of the earth's surface having the natural or man-made nature. The method of Persistent Scatterers Interferometry (PSI) successfully used for monitoring above mentioned processes in many cities around the world. The results of PSI technologies allow to providing geodynamic zoning of urban areas and identifying the objects exposed to linear deformation - separate objects, extended linear features (e.g. subway). The paper present the results of PSI COSMO-SkyMed data processing for Astana city (capital of Kazakhstan), including the displacement distribution for individual buildings and structures, and areal coverage analysis of surface movements. Data of ENVISAT ASAR images for Almaty city received from ESA grant C1.P15390 “Spatio-temporal analysis of surface displacements in urban areas with high seismic risk by SAR interferometry data”.

Quantification of Temporal Decorrelation Effects at L-Band for Polarimetric SAR Interferometry Applications

Temporal decorrelation is the most critical issue for the successful inversion of polarimetric SAR interferometry (Pol-InSAR) data acquired in an interferometric repeat-pass mode, typical for satellite or lower frequency airborne SAR systems. This paper provides a quantitative estimation of temporal decorrelation effects at L-band for a wide range of temporal baselines based on a unique set of multibaseline Pol-InSAR data. A new methodology that allows to quantify individual temporal decorrelation components has been developed and applied. Temporal decorrelation coefficients are estimated for temporal baselines ranging from 10 min to 54 days and converted to height inversion errors caused by them. The temporal decorrelations of γ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TV</sub> (volume temporal decorrelation) and γ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TG</sub> (ground temporal decorrelation) depend not only on the wind-induced movement but also strongly on the rain-induced dielectric changes in volume and on the ground at temporal baseline on the order of day or longer. At temporal baselines on the order of minutes, the wind speed is a critical parameter and the speed of 2 m/s already hampers the application of Pol-InSAR forest parameter inversion. The approach is supported and validated by using L-band E-SAR repeat-pass data acquired in the frame of three dedicated campaigns, BioSAR 2007, TempoSAR 2008, and TempoSAR 2009.

Assimilation of DEMs Generated from Optical Stereo and InSAR Pair through Data Fusion

Main objective of this study is to compare and assimilate the digital elevation models (DEMs) generated using C-band Envisat, L-band ALOS PALSAR Interferometric SAR (InSAR) datasets and Cartosat-1 optical stereo data. The study area is Dehradun city and surroundings including lower Shivalik hills i.e. the younger Himalayas in the south and higher Himalayas on the north. The general elevation of the area ranges from 400m to 2000m AMSL. Ground control points (GCPs) collected from Differential Global Positioning System (DGPS) survey, were used for satellite triangulation of optical data & geocoding of InSAR data. RMSE for satellite triangulation for cartosat-1 is 0.745 using 6 ground control points and 3 check points. It is found that when GCP’s are used along with reference DEM (SRTM), the DSM generated by ALOS PALSAR and DSM generated from Cartosat-1 stereo data are well representing the terrain. The fusion of imagery from multiple sensors has been in use in remote sensing for scientific exploration in the past but there is a little work done on data fusion application for generation of topography (DEM) through Optical stereo and SAR interferometry data sources. The defined framework for data fusion uses three fundamental steps in the analysis of the system namely, identification, estimation, and validation for algorithm preparation. The knowledge of the study area, i.e. the field, and the processes involved in the DEM generation of optical and InSAR data are cognitively used for rule-based decision making in the algorithm for mathematical generation of resultant DEM through data fusion implemented at feature level.