Synthetic Aperture Radar Interferometry Data Research Articles

Surface Deformation Assessment due to the Grindulu Fault Activities using Sentinel 1 Data by Applying the Small Baseline Subset (SBAS)-InSAR Technique

The location of Grindulu Fault is in the Pacitan Regency on the Southern Coast of Java, which directly faces the South Java Subduction zone. The activities of the fault could contribute to the surface deformation in the surrounding area. The Meteorology Climatology and Geophysics Council in Indonesia (BMKG) reported a 3.1 Mw earthquake on 7 November 2019, possibly related to the Grindulu fault activities. In this research, we assess the surface deformation around the Grindulu Fault using the Sentinel I data by applying the Small Baseline Subset (SBAS) - Interferometry Synthetic Aperture Radar (InSAR) technique. The SBAS-InSAR is one of the techniques that can provide spatial-temporal continuous analysis of the surface deformation over the extent of InSAR data. The SBAS algorithm was applied to twenty-three (23) ascending Sentinel-1B images to assess the surface deformation in Pacitan between 2019 and 2021 using the GMTSAR software. The results showed surface deformation dominated by subsidence over the whole area, except at the northern part of Grindulu Fault, with the mean velocity values ranging from -2.07 to -43.05 mm/yr.

Using machine learning and satellite data from multiple sources to analyze mining, water management, and preservation of cultural heritage

ABSTRACT Remote sensing, particularly satellite-based, can play a valuable role in monitoring areas prone to geohazards. The high spatial and temporal coverage provided by satellite data can be used to reconstruct past events and continuously monitor sensitive areas for potential hazards. This paper presents a range of techniques and methods that were applied for in-depth analysis and utilization of Earth observation data, with a particular emphasis on: (1) detecting mining subsidence, where a novel approach is proposed by combining an improved U-Net model and Interferometry Synthetic Aperture Radar (InSAR) technology. The results showed that the Efficient Channel Attention (ECA) U-Net model performed better than the U-Net (baseline) model in terms of Mean Intersection over Union (MIoU) and Intersection over Union (IoU) indicators; (2) monitoring water conservancy and hydropower engineering. The Xiaolangdi multipurpose dam complex was monitored using Small BAsline Subsets (SBAS) InSAR method on Sentinel-1 time series data and four small regions with high deformation rates were identified on the slope of the reservoir bank on the north side. The dam body also showed obvious deformation with a velocity exceeding 60 mm/a; (3) the evaluation of the potential of InSAR results to integrate monitoring and warning systems for valuable heritage and architectural preservation. The overall outcome of these methods showed that the use of Artificial Intelligence (AI) techniques in combination with InSAR data leads to more efficient analysis and interpretation, resulting in improved accuracy and prompt identification of potential hazards; and (4) finally, this study also presents a method for detecting landslides in mountainous regions, using optical imagery. The new temporal landslide detection method is evaluated over a 7-year analysis period and unlike conventional bi-temporal change detection methods, this approach does not depend on any prior-knowledge and can potentially detect landslides over extended periods of time such as decades.

Near Real-Time InSAR Deformation Time Series Estimation With Modified Kalman Filter and Sequential Least Squares

The current and planned synthetic aperture radar (SAR) sensors mounted on satellite platforms will continue to operate over the coming years, providing unprecedented SAR data for monitoring wide-range surface deformations. The near real-time processing of SAR interferometry (InSAR) data for the retrieval of ground-deformation time series is urgently required in the current era of big data. The state-of-the-art Kalman filter (KF) and sequential least squares (SLS) algorithms have been proposed to update an InSAR-driven ground-deformation time series. As a contribution of this study, we customize the conventional KF and SLS for big InSAR data for near real-time processing. The development of an accurate prediction model for KF-based InSAR processing is a challenge owing to the large scale of the targets for surface monitoring. We developed a modified KF algorithm, abbreviated as npKF, that does not require any prediction information, abbreviated as npKF. In this context, to avoid occupying a large storage space in SLS-based InSAR processing, we developed a modified SLS algorithm with a truncated cofactor matrix, abbreviated as TSLS. Using both simulated and actual SAR data, we evaluated the performance of these methods under three different aspects: accuracy, computation, and storage performance. With big data, the proposed method can estimate the deformation time series in near real time. It will be a reliable and effective tool for producing near real-time InSAR deformation products in the coming era of processing big SAR data and will play a part in the geologic hazard routine monitoring and early warning system.

Applied Research on InSAR and GPS Data Fusion in Deformation Monitoring

With the enrichment of land subsidence monitoring means, data fusion of multisource land subsidence data has gradually become a research hotspot. The Interferometry Synthetic Aperture Radar (InSAR) is a potential Earth observation approach, and it has been verified to have a variety of applications in measuring ground movement, urban subsidence, and landslides but similar to Global Positioning System (GPS). The InSAR observation accuracy and measurements are affected by the tropospheric delay error as well as by the Earth’s ionospheric and tropospheric layers. In order to rectify the InSAR result, there is a need to interpolate the GPS-derived tropospheric delay. Keeping in view of the above, this research study has presented an improved Inverse Distance Weighting (IIDW) interpolation method based on Inverse Distance Weighting (IDW) interpolation by using Sentinel-1 radar satellite image provided by European Space Agency (ESA) and the measured data from the Continuously Operating Reference Stations (CORS) provided by the Survey and Mapping Office of the Lands Department of Hong Kong. Furthermore, the corrected differential tropospheric delay correction is used to correct the InSAR image. The experimental results show that the correction of tropospheric delay by IIDW interpolation not only improves the accuracy of Differential Interferometry Synthetic Aperture Radar (D-InSAR) but also provides a new idea for the solution of InSAR and GPS data fusion.

Estimation of trees height and vertical structure using SAR interferometry in uneven-aged and mixed forests.

Estimation of forest height is an important parameter of stands structure that aids in the determination of forest biomass, successional stage dynamics, and the decision of the type of forest management. In addition, estimating the height of trees especially in uneven-aged, massive, and multi-storied forest stands always faces challenges in kind of inventory and accuracy of the assessment. In this research, the synthetic aperture radar (SAR) interferometry technique was used to estimate the height of trees for determining the vertical structure of forest. For this purpose, we focused on an area at the mixed and uneven-aged forest in Iran and evaluated the potential of Envisat ASAR data to characterize the tree height in the forest patches and the digital surface model (DSM) was produced via SAR interferometry. The height of trees and the vertical structure of the forest stands were estimated using produced DSM and Digital elevation Model (DEM). Furthermore, the accuracy of estimated parameters was evaluated with real ground data (11 × 1ha (100 × 100m) sample plots). The results indicated that the estimated height of trees was meanly 7.69m with a 22m STDV over the reality. Furthermore, the vertical structure in all the plots was three-storied that they are the same as ground truth, but the percentage of the share of trees in the under and middle story was different from the ground truth. In conclusion, the tree height and vertical structure of forest stands can be determined with acceptable accuracy via SAR interferometry and Envisat ASAR data.

Fault motion reversals predating the M w 6.3 2009 L'Aquila earthquake: insights from synthetic aperture radar data

The millimetre accuracy of Synthetic Aperture Radar (SAR) measurements and related multi-temporal data analyses provide fundamental information on surface displacements caused by strong earthquakes. The multi-temporal analysis of SAR interferometry data allows for the geometry, kinematics and temporal behaviour of earthquake-generating faults to be better constrained, and is being acknowledged as a promising technique in the field of earthquake precursors. We used SAR data obtained by multi-temporal interferometric techniques such as Permanent Scatterers (PS) interferometry for the investigation of pre- to post-seismic ground displacements in the region struck by the M w 6.3, 2009 L'Aquila earthquake. We analysed Europen Remote Sensing (ERS) and Envisat PS-datasets from ascending and descending orbits, and COSMO-SkyMed PS-datasets from descending orbit, collectively covering a > 20 year long time span. On a yearly scale, a reversal of motions that affected the hanging-wall and footwall blocks of the earthquake-generating fault is detected. In particular, the hanging-wall block is characterized by pre-seismic uplift – which we document as being independent of any hydrological control – and eastward horizontal motion for about six years, followed by subsidence and westward motion (starting six to eight months prior to the earthquake). We suggest that such a ground displacement pattern may represent an earthquake precursor signal. Supplementary material: Figures showing SAR satellites viewing geometry, examples of raw PS time series of obtained from ERS ascending and descending orbits and maps of LoS-oriented mean velocities obtained from PS datasets from the Envisat ascending and descending orbits and a Table reporting detailed numerical results of the performed statistical analysis are available at https://doi.org/10.6084/m9.figshare.c.5289357

Continuous Multitrack Assimilation of Sentinel‐1 Precipitable Water Vapor Maps for Numerical Weather Prediction: How Far Can We Go With Current InSAR Data?

AbstractThe present study assesses the viability of including water vapor data from Interferometry Synthetic Aperture Radar (InSAR) in the initialization of numerical weather prediction (NWP) models, using already available Sentinel‐1 A and B products. Despite the limitations resulting from the 6‐day return period of images produced by the 2‐satellite system, it is found that for a sufficiently large domain designed to contain a set of images every 12 h (at varying locations), the impact on model performance is beneficial or at least neutral. The proposed methodology is tested in 24 consecutive 12 h forecasts, covering two cycles of the Sentinel‐1 system and 214 images, for a domain containing Iberia. A statistical analysis of the forecast precipitable water vapor (PWV) against independent GNSS observations concluded for relevant improvements in the different scores, especially during a consecutive 3‐day period where the standard initial data were less accurate. An analysis of the rain forecasts against gridded remote sensing observations further indicates an overall improvement in the grid‐point distribution of different precipitation classes throughout the simulation, even when the mean impact of PWV assimilation was not significant. It is suggested that current InSAR data are already a useful source of NWP data and will only become more relevant as new systems are put into operation.

The use of satellite data to support the structural health monitoring in areas affected by slow-moving landslides: a potential application to reinforced concrete buildings

The building stock around the world is exposed to different types of natural actions such as earthquakes or landslides. In particular, Italy is one of the countries worldwide most affected by landslides. Mitigation of landslide risk is a topic of great interest for the evaluation and management of its consequences. Periodical monitoring of the landslide-induced damage on structures require high costs due to the large number of exposed elements. With respect to the reinforced concrete structures, slow-moving landslides can affect primary structural elements, but more frequently damage occurs on the most vulnerable elements of the structure such as infills. The aim of this work is to demonstrate the potential utility of satellite data derived from a remote sensing technique, known as differential synthetic aperture radar interferometry, to support the structural health monitoring of reinforced concrete buildings affected by landslides. This article shows the structural health monitoring process for a reinforced concrete infilled building within a landslide-affected area, using the differential synthetic aperture radar interferometry data as input for the structural analysis in order to investigate the evolution of damage over the years. Three-dimensional structure, including the explicit infills consideration, has been modeled based on the information available from a visual survey, obtaining the missing parameters from a simulated design process and from the literature. In the field of the civil protection programs for the landslide risk reduction, this methodology can be quickly repeated for large sets of reinforced concrete buildings. Evidence of the visual survey showed a significant damage pattern in some infills. A good agreement has been found between analytical previsions and existing damage. Moreover, a global infills damage assessment of the case study building is proposed. Finally, assuming a constant increase in displacements in future years, a prediction of the future expected damage is shown.

Application of Gray-Markov Model to Land Subsidence Monitoring of a Mining Area

Land subsidence monitoring in mining areas is one of the main applications of surface deformation monitoring, which is of great significance for safety production. Using the IPTA (Interferometric Point Target Analysis) time-series InSAR (Interferometry Synthetic Aperture Radar) method, land subsidence data from the new exploration area in the Weizhou mining area were analyzed and compared with static GPS (Global Positioning System) monitoring data for 2017–2020. Gray-Markov model was established by combining the gray prediction model with the Markov model to predict the surface subsidence of the mining area. The results show that (1) InSAR data have high accuracy and application potential in prediction of long-term surface deformation in mining areas; (2) The Gray-Markov model can better reflect the volatility and practicality of subsidence data in mining areas; (3) The prediction results have high accuracy, and the Gray-Markov model can serve as an effective guide for long-term surface deformation monitoring and safety management.

A Nonlocal Noise Reduction Method Based on Fringe Frequency Compensation for SAR Interferogram

Phase noise reduction is one of the key steps for synthetic aperture radar interferometry data processing. In this article, a novel phase filtering method is proposed. The main innovation and contribution of this research is to 1) incorporate local fringe frequency (LFF) compensation technique into the nonlocal phase filtering method to include more independent and identically distributed samples for filtering; 2) modify the nonlocal phase filter from three aspects: 1) executing nonlocal filtering in the complex domain of the residual phase to avoid gray jumps in phase, 2) adaptively calculating the smoothing parameter based on the LFF and the coherence coefficient, and 3) using the integral image in similarity calculation to improve the efficiency; 3) perform Goldstein filter in high coherence areas to reduce the computation expense. Experiments based on both simulated and real data have shown that the proposed method has achieved a better performance in terms of both noise reduction and edge preservation than some existing phase filtering methods.

Volume coherence function optimization method for extracting vegetation and terrain parameters from polarimetric synthetic aperture radar interferometry images

An advanced algorithm is introduced to enhance the efficiency in measuring vegetation parameters using L-band polarimetric synthetic aperture radar interferometry data. In this method, the combination of the eigenvalue decomposition technique and the coherence matrix optimization method is to achieve higher accuracy for ground phase estimation. Then an exhaustive search method based on the optimal polarization coherence channel is developed to retrieve the forest parameters. To evaluate the effectiveness of this method, we first apply it to simulated data obtained from PolSARProSim software. Next, the experimental L-band single-baseline single-frequency polarimetric interferometric data are used to assess the applicability of the proposed method for the actual forest topography in Malaysia and Kudara, Lake Baikal, Russia. The obtained results indicate that the proposed algorithm significantly overcomes the disadvantages of the recently published methods.

Land Subsidence Susceptibility Mapping in Jakarta Using Functional and Meta-Ensemble Machine Learning Algorithm Based on Time-Series InSAR Data

Areas at risk of land subsidence in Jakarta can be identified using a land subsidence susceptibility map. This study evaluates the quality of a susceptibility map made using functional (logistic regression and multilayer perceptron) and meta-ensemble (AdaBoost and LogitBoost) machine learning algorithms based on a land subsidence inventory map generated using the Sentinel-1 synthetic aperture radar (SAR) dataset from 2017 to 2020. The land subsidence locations were assessed using the time-series interferometry synthetic aperture radar (InSAR) method based on the Stanford Method for Persistent Scatterers (StaMPS) algorithm. The mean vertical deformation maps from ascending and descending tracks were compared and showed a good correlation between displacement patterns. Persistent scatterer points with mean vertical deformation value were randomly divided into two datasets: 50% for training the susceptibility model and 50% for validating the model in terms of accuracy and reliability. Additionally, 14 land subsidence conditioning factors correlated with subsidence occurrence were used to generate land subsidence susceptibility maps from the four algorithms. The receiver operating characteristic (ROC) curve analysis showed that the AdaBoost algorithm has higher subsidence susceptibility prediction accuracy (81.1%) than the multilayer perceptron (80%), logistic regression (79.4%), and LogitBoost (79.1%) algorithms. The land subsidence susceptibility map can be used to mitigate disasters caused by land subsidence in Jakarta, and our method can be applied to other study areas.

Geomorphological and geophysical surveys with InSAR analysis applied to the Picerno earth flow (southern Apennines, Italy)

Landslides represent a major geomorphological feature influencing the evolution of the southern Apennine slopes with earth flows being one of the most representative types of landslide. The development of earth flows in the southern Apennines is facilitated by the widespread occurrence of clayey lithologies. Earth flows can either produce marked scars along the slopes, when their activity is high, or give rise to bumpy areas that are frequently covered by vegetation, when their activity is low. In this latter case, although the risk is significantly reduced, the earth flow can still represent a problem for buildings and infrastructure. The Picerno earth flow in the Basilicata region is an example of a low-activity earth flow. This NW-SE–oriented landslide extends for a length of ~ 5.5 km, has an average width of ~ 680 m, and affects the village of Picerno. Our investigations mainly focused on the terminal portion of the landslide and revealed that some significant linear infrastructures (e.g., the Potenza–Naples railway) and important provincial and municipal roads are affected by the earth flow. In order to better define the amount of displacement characterizing the Picerno earth flow and gain insights into the lateral extension and the depth of the main detachment surface, we undertook a detailed geological and geomorphological survey. Interferometry synthetic aperture radar data provided by the processing of SAR images, with the aim of highlighting the regions of the landslide that are currently active, together with electrical resistivity tomography data which have been utilized to define the geometry of the landslide body. Our results provide useful suggestions for planning appropriate actions aimed at stabilizing the landslide body.

Fault Geometry and Slip Distribution at Depth of the 1997 Mw 7.2 Zirkuh Earthquake: Contribution of Near‐Field Displacement Data

AbstractIn this study, we reestimate the source model of the 1997 Mw 7.2 Zirkuh earthquake (northeastern Iran) by jointly optimizing intermediate‐field Interferometry Synthetic Aperture Radar data and near‐field optical correlation data using a two‐step fault modeling procedure. First, we estimate the geometry of the multisegmented Abiz fault using a genetic algorithm. Then, we discretize the fault segments into subfaults and invert the data to image the slip distribution on the fault. Our joint‐data model, although similar to the Interferometry Synthetic Aperture Radar‐based model to the first order, highlights differences in the fault dip and slip distribution. Our preferred model is ∼80° west dipping in the northern part of the fault, ∼75° east dipping in the southern part and shows three disconnected high slip zones separated by low slip zones. The low slip zones are located where the Abiz fault shows geometric complexities and where the aftershocks are located. We interpret this rough slip distribution as three asperities separated by geometrical barriers that impede the rupture propagation. Finally, no shallow slip deficit is found for the overall rupture except on the central segment where it could be due to off‐fault deformation in quaternary deposits.

Activity and kinematic behaviour of deep-seated landslides from PS-InSAR displacement rate measurements

Large landslides and deep-seated gravitational slope deformations (DSGSD) represent an important geo-hazard in relation to the deformation of large structures and infrastructures and to the associated secondary landslides. DSGSD movements, although slow (from a few millimetres to several centimetres per year), can continue for very long periods, producing large cumulative displacements and undergoing partial or complete reactivation. Therefore, it is important to map the activity of such phenomena at a regional scale. Ground surface displacements at DSGSD typically range close to the detection limit of monitoring equipment but are suitable for synthetic aperture radar (SAR) interferometry. In this paper, permanent scatterers (PSInSAR™) and SqueeSAR™ techniques are used to analyse the activity of 133 DSGSD, in the Central Italian Alps. Statistical indicators for assigning a degree of activity to slope movements from displacement rates are discussed together with methods for analysing the movement and activity distribution within each landslide. In order to assess if a landslide is active or not, with a certain degree of reliability, three indicators are considered as optimal: the mean displacement rate, the activity index (ratio of active PS, displacement rate larger than standard deviation, overall PS) and the nearest neighbor ratio, which allows to describe the degree of clustering of the PS data. According to these criteria, 66% of the phenomena are classified as active in the monitored period 1992–2009. Finally, a new methodology for the use of SAR interferometry data to attain a classification of landslide kinematic behaviour is presented. This methodology is based on the interpretation of longitudinal ground surface displacement rate profiles in the light of numerical simulations of simplified failure geometries. The most common kinematic behaviour is rotational, amounting to 41 DSGSDs, corresponding to the 62.1% of the active phenomena.

A nation-wide system for landslide mapping and risk management in Italy: The second Not-ordinary Plan of Environmental Remote Sensing

Landslides are frequent events that may cause human casualties and injuries as well as damage to urban and man-made structures, with extensive loss of economic resources. For this reason, landslide mapping is a primary tool for hazard and risk assessment. Italian Ministry of Environment, thanks to great availability and functionality of Synthetic Aperture Radar (SAR) data promoted the Not-ordinary Plan of Environmental Remote Sensing (Piano Straordinario di Telerilevamento Ambientale, PST-A in Italian) in 2008, as to constitute a national database of active or potential instability phenomena affecting the Italian territory, based on the exploitation of interferometric products (ERS and ENVISAT). In this paper, the PST-A-3 is described. A procedure based on the integration of engineering-geological approaches and SAR interferometry data belonging to COSMO-SkyMed constellation (100 frames 40×40km) has been here implemented over 7,400km2 of the Italian territory. First, landslides have been mapped by field geologists, defining type and state of activity. Simultaneously to field surveys, remote sensing data have been analyzed as to detect areas with considerable displacement registered by the satellite. Both products have been overlaid, also quantifying the coincidence between the events reported according to the two detection methodologies and subtracting those landslide not recordable by the satellite, finally obtaining an updated landslide inventory map with 4,522 newly detected phenomena. Therefore, PST-A-3 proves to be a valuable system for local authorities, in order to provide a contribution to risk management but also for the forecasting of landslide events, as testified by two case studies selected. Thanks to the PST-A experience, the use of such strategy to other countries could represent a valid contribution to land management at worldwide scale.

ASSESSING GROUND SUBSIDENCE PHENOMENA WITH PERSISTENT SCATTERER INTERFEROMETRY DATA IN WESTERN THESSALY, GREECE

The main objective of the present study was to investigate ground subsidence in the wider area of Farsala, western Thessaly basin, by means of remote sensing techniques and to identify potential geo environmental mechanisms that contribute to the development of the observed surface fractures affecting the site. In this context, a set of Synthetic Aperture Radar (SAR) images, acquired in 1995-2003 by the European Space Agency (ESA) satellites ERS1 and ERS2 and processed with the Persistent Scatterer Interferometry (PSI) technique by the German Space Agency (DLR) during the Terrafirma project, were evaluated in order to investigate spatial and temporal patterns of deformation. Groundwater table levels of three water boreholes within the research area were processed providing the mean piezometric level drawdown and the mean annual drawdown rate. In addition, a quantitative comparison between the deformation subsidence rate and the thickness of the compressible sediments was also performed. The outcomes of the present study indicated a clear relationship in the subsidence deformation rate and the groundwater fluctuation and also a correlation between the depth of the bedrock and the deformation subsidence rate. Overall, the multitemporal SAR interferometry (DInSAR) data are proved as a valuable and suitable technique for increasing knowledge about the extent and the rate of the deformations in the current study area, proved to be affected with an increasing intensity.

Comparison of Laser and Stereo Optical, SAR and InSAR Point Clouds from Air- and Space-Borne Sources in the Retrieval of Forest Inventory Attributes

It is anticipated that many of the future forest mapping applications will be based on three-dimensional (3D) point clouds. A comparison study was conducted to verify the explanatory power and information contents of several 3D remote sensing data sources on the retrieval of above ground biomass (AGB), stem volume (VOL), basal area (G), basal-area weighted mean diameter (Dg) and Lorey’s mean height (Hg) at the plot level, utilizing the following data: synthetic aperture radar (SAR) Interferometry, SAR radargrammetry, satellite-imagery having stereo viewing capability, airborne laser scanning (ALS) with various densities (0.8–6 pulses/m2) and aerial stereo imagery. Laser scanning is generally known as the primary source providing a 3D point cloud. However, photogrammetric, radargrammetric and interferometric techniques can be used to produce 3D point clouds from space- and air-borne stereo images. Such an image-based point cloud could be utilized in a similar manner as ALS providing that accurate digital terrain model is available. In this study, the performance of these data sources for providing point cloud data was evaluated with 91 sample plots that were established in Evo, southern Finland within a boreal forest zone and surveyed in 2014 for this comparison. The prediction models were built using random forests technique with features derived from each data sources as independent variables and field measurements of forest attributes as response variable. The relative root mean square errors (RMSEs) varied in the ranges of 4.6% (0.97 m)–13.4% (2.83 m) for Hg, 11.7% (3.0 cm)–20.6% (5.3 cm) for Dg, 14.8% (4.0 m2/ha)–25.8% (6.9 m2/ha) for G, 15.9% (43.0 m3/ha)–31.2% (84.2 m3/ha) for VOL and 14.3% (19.2 Mg/ha)–27.5% (37.0 Mg/ha) for AGB, respectively, depending on the data used. Results indicate that ALS data achieved the most accurate estimates for all forest inventory attributes. For image-based 3D data, high-altitude aerial images and WorldView-2 satellite optical image gave similar results for Hg and Dg, which were only slightly worse than those of ALS data. As expected, spaceborne SAR data produced the worst estimates. WorldView-2 satellite data performed well, achieving accuracy comparable to the one with ALS data for G, VOL and AGB estimation. SAR interferometry data seems to contain more information for forest inventory than SAR radargrammetry and reach a better accuracy (relative RMSE decreased from 13.4% to 9.5% for Hg, 20.6% to 19.2% for Dg, 25.8% to 20.9% for G, 31.2% to 22.0% for VOL and 27.5% to 20.7% for AGB, respectively). However, the availability of interferometry data is limited. The results confirmed the high potential of all 3D remote sensing data sources for forest inventory purposes. However, the assumption of using other than ALS data is that there exist a high quality digital terrain model, in our case it was derived from ALS.

Experimental Results and Algorithm Analysis of DEM Generation Using Bistatic SAR Interferometry With Stationary Receiver

This paper presents the theory, algorithm, and results of a new bistatic interferometry synthetic aperture radar (InSAR) method. It employs the data acquired in an innovative bistatic configuration, which uses the orbital sensors as transmitters of opportunity and the stationary receivers on the ground, to generate a digital elevation model (DEM). In the bistatic spaceborne/stationary InSAR configuration, the interferometric phase only depends on the target-receiver range, which could not be obtained directly from the measured bistatic range. Therefore, the conventional transforming relationship between the interferometric phase and the topographic height is no longer practical. In order to solve the problem, we introduce a new conversion relationship between the interferometric phase and the topographic height, which is derived by the model of the ellipsoidal projection in the bistatic configuration. Meanwhile, the error analysis of the new conversion is carried out through a simulation. Both the simulated and measured data are used to test and verify the feasibility of the new bistatic InSAR method. In the spaceborne/stationary InSAR experiment, YaoGan-3 (an L-band spaceborne SAR system launched by China) was selected as the transmitter and two stationary receivers were mounted on the top of a tall building. The generated DEM of high quality shows that the presented method performs very well in the bistatic InSAR data process.

Land Subsidence in Bandung Basin and its Possible Caused Factors

Bandung basin which located in West Java, Indonesia is a large intra-montane basin surrounded by volcanic highlands. The basin is a highly urbanized area whose population has been rapidly increasing since few decades ago. It is widely believed that the increase of population, as well as industrial activities, will increase the degree of groundwater extraction and will eventually lead to land subsidence over the area of Bandung basin. According to results of ten champaign of GPS (Global Positioning System) surveys, which were conducted within the period of 2000 to 2012 it was found that several locations in Bandung basin experienced land subsidence, with an average subsidence rate of about –8cm/year and it can reach to about –16.9cm/year in a certain location and time period. The large subsidences occur in Cimahi, Gedebage, Dayeuhkolot, Rancaekek, Majalaya, and Katapang districts. Similar results are also achieved from InSAR (Interferometry Synthetic Aperture Radar) data. During the period of 1999 to 2010, maximum rate of land subsidence in the Bandung basin reached 2 meters that occurred in industrial areas such as in Cimahi, Katapang, Dayeuhkolot, Gedebage, and Rancaekek. It is indicated that there is a strong correlation between the land subsidence and groundwater extraction in Cimahi, Dayeuhkolot, Majalaya, and Rancaekek. Furthermore, there could also be possible existence of other causes due to natural compaction in almost all subsidence areas and tectonic processes in Dayeuhkolot, Gedebage, Cimahi, dan Majalaya.