Generation of Advanced ERS and Envisat Interferometric SAR Products Using the Stable Point Network Technique

Michele Crosetto, Erlinda Biescas, Javier Duro, Josep Closa, and Alain Arnaud

Abstract e.g., the continuous GPS-based deformation monitoring of The advanced differential interferometric SAR techniques structures, they can usually be used to measure a limited (A-DINSAR), based on time series of SAR images, are powerful number of points or cover relatively small areas. The DINSAR geodetic tools for land deformation monitoring. This paper, technique represents a particular type of monitoring tools, which is focused on a particular A-DINSAR technique, named which can be competitive in different ground deformation Stable Point Network, concisely outlines its characteristics applications. In fact, it offers the typical advantages of the and describes its products: average deformation maps, remote sensing techniques, such as data acquisition over deformation time series, and the maps of the residual inaccessible areas, wide area coverage, periodic and low-cost topographic error used to precisely geocode the A-DINSAR data acquisition, while at the same time, it can potentially products. Furthermore, it illustrates the performance of the monitor deformations at millimeter level of the Earth’s technique on a test area located in Barcelona, Spain. From surface, buildings, and structures. this experiment, interesting features are highlighted: the Much of the DINSAR results published in the last fifteen capability to cover wide areas and at the same time measur- years have been achieved by using the standard DINSAR ing thin infrastructures, such as the main dike of the port; configuration, which is based on a single interferogram the good agreement between the deformation velocities and derived from a pair of SAR images. However, a remarkable the reference values coming from leveling campaigns; the improvement in the quality of the DINSAR results can be high sensitivity of the A-DINSAR estimations, which can achieved by using large sets of SAR images acquired over measure millimeter-level periodical deformations due to the same deformation phenomenon, and by employing thermal dilation, and the precise geocoding of the A-DINSAR Advanced DINSAR (A-DINSAR) data processing and analysis products. tools. In fact, through the use of multiple SAR images the A-DINSAR techniques can achieved improved performances, both in terms of deformation modeling capabilities and Introduction quality of the deformation estimations. This latter character- This paper is focused on the monitoring of ground deforma- istic is due mainly to the available high data redundancy, tion using Differential Interferometric SAR (Synthetic Aper- which allows quantitative DINSAR results to be achieved, ture Radar) techniques, DINSAR. For a general review of SAR both in terms of precision and reliability. The main differ- interferometry, see Rosen et al. (2000). Ground deformation ences between standard DINSAR and A-DINSAR techniques are resulting from either natural processes or human activity described in Crosetto et al. (2005). plays an important role in different fields, e.g., geology, This paper describes a particular A-DINSAR method, hydrogeology, seismology, civil engineering, and urban which is called Stable Point Network (SPN) technique. planning. The monitoring of ground deformation and the In particular, it illustrates the characteristics of the SPN associated motion of manmade objects, such as buildings products by discussing the results achieved on a test area and structures, can be accomplished by several types of over the city of Barcelona, Spain. These results have been techniques: geodetic methods, such as leveling, theodolite obtained in the frame of a project, funded by the European and total station surveying; GPS-based systems; photogram- Space Agency, named “Development of Algorithms for metric methods; and a plethora of specific tools, such as the Exploitation of ERS-Envisat Using the Stable Points inclinometers, extensometers, and creepmeters. These Network” led by Altamira Information, ESA contract techniques have very different characteristics in terms of No. 16702/02/I-LG. The paper begins with a brief descrip- precision, reliability, cost, automation, and real-time capabil- tion of the SPN technique, highlighting its most original ity. In general, the most precise techniques require expensive features and describing its main products. In this paper, it instruments or time consuming procedures. Furthermore, is assumed that the reader is familiar with the fundamen- even if some techniques offer highly automated solutions, tals of DINSAR; see Rosen et al. (2000), Bamler and Hartl (1998), and Hanssen (2001). This is followed by the description of

Michele Crosetto and Erlinda Biescas are with the Institute of Geomatics, Av. del Canal Olimpic, s/n, Castelldefels, Photogrammetric Engineering & Remote Sensing E-08860, Spain ([email protected]). Vol. 74, No. 4, April 2008, pp. 443–450. Javier Duro, Josep Closa, and Alain Arnaud are with 0099-1112/08/7404–0443/$3.00/0 Altamira Information, C/Còrsega, 381–387, 2n 3a, Barcelona, © 2008 American Society for Photogrammetry E-08037, Spain. and Remote Sensing

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING April 2008 443 the experimental results obtained on the above-mentioned are used for accurate deformation analyses, even if deforma- project. The next section describes the experiment, which tion estimations can be derived starting from six SAR images was based on ERS and Envisat SAR images acquired over (Duro et al., 2005; Hu et al., 2005). In this respect, it is more the metropolitan area of Barcelona, Spain followed by flexible than the Permanent Scatterers technique, which the validation results obtained in the monitoring of a requires a set of at least 25 SAR images (Colesanti et al., 2003). Unlike the Permanent Scatterers technique, which particular infrastructure, the main dike of the port. Some generates all the interferograms with respect to a common interesting results over structures are described that show master image and uses large baselines, the SPN exploits a a periodical deformation behavior, which is probably combination of interferograms with different (co-registered) caused by thermal dilation. These results are useful to master images characterized by small baselines in order to appreciate the sensitivity of the given technique to small, limit the geometric decorrelation. This approach is similar to i.e., millimeter-level deformations. Finally, the last section SBAS, although by exploiting the PS the SPN approach is much analyses the quality of the advanced geocoding of the less sensitive to geometric decorrelation, i.e., can exploit interferograms with larger baselines. DINSAR products followed by conclusions. 3. Modeling Capability: The main observable of any DINSAR technique is the DINSAR phase, which besides the component due to deformation, contains other contributions due The SPN Technique to the phase noise, the atmospheric phase component, and the residual topography component, (see Ferretti et al., This section discusses three key features of the SPN technique, namely the pixel selection, the use of multiple images, and 2000 and 2001). The set of observables exploited by the SPN technique is given by N DINSAR phases over each selected the modeling capability, highlighting some of the most PS, where N is the number of generated interferograms. It is important differences with other two important A-DINSAR important to emphasize the 3D nature of the observed approaches: the Permanent Scatterers technique (Ferretti et al., deformation phenomena, which have two dimensions in 2000 and 2001), and the Small Baseline Subset (SBAS) tech- space that are sampled by the SAR system, plus a time nique (Berardino et al., 2002; Lanari et al., 2004). Other dimension, which is sampled temporally by the SAR image A-DINSAR approaches are described in Werner et al. (2003), series. By proper modeling the above set of observables, the Mora et al. (2003), and Hooper et al. (2004). The next section SPN technique is able to estimate the three main components of the DINSAR phases, that is the deformation describes the main products of the SPN technique. Further component, the residual topography, and the atmospheric details of the SPN technique are discussed in Arnaud et al. effects, separating them from the phase noise. Alike the (2003) and Duro et al. (2003 and 2005). Permanent Scatterers and SBAS techniques, the SPN 1. Pixel Selection: Given a set of SAR images, the SPN tech- technique estimates the residual topographic errors by nique exploits only a particular class of pixels, namely exploiting the know relation between the residual topo- those that correspond to points that show a stable electro- graphic component and the normal baseline (the compo- magnetic behavior over the observation period. These nent of the vector that connects the two satellite positions points are typically parts of manmade features, such during image acquisition, measured in the direction as, building and metallic structures, or natural features; perpendicular to the SAR line-of-sight (LOS) of the interfero- exposed rocks can show a stable response over a time scale grams. The standard SPN processing is based on a linear of several years (Usai and Klees, 1999 and 2000). Depend- deformation model to unwrap the phases. With this ing on the available number of SAR images and the type of approach there is no need to directly unwrap the differen- analysis which must be carried out; three different methods tial interferograms, as it is done in the SBAS approach. Note can be used for pixel selection. that the phase unwrapping represents a potential source of errors, especially with low coherence interferograms. This • SAR Amplitude Stability. Given a set of SAR images, the step is followed by a filtering step, which separates the selection can be based on the analysis of the SAR ampli- non-linear deformation component from the atmospheric tude stability over time (Ferretti et al., 2001). Pixels with effects. To this purpose, the algorithm takes advantage of low amplitude standard deviation within the stacking of the different behavior in time and space of the atmospheric images are selected as stable. Note that the same name of artifacts with respect to the deformation. A combination of the technique, SPN, explicitly mentions this property of the sequential temporal and spatial filters is applied in order to points, which in the literature are also named Persistent extract the atmospheric artifacts and the low and high pass Scatterers (PS). This approach is used when many SAR spatial components of the temporally non-linear deforma- images are available (more than 20), and a deformation tion. The low spatial wavelength effects extracted from the analysis at full resolution is needed. This is the standard interferometric phase jointly with the high pass component case for the studies over urban areas. in the temporal domain are gathered in order to obtain the • Interferometric Coherence. The selection is made for all the atmospheric artifacts of every image. This is achieved by multi-look pixels whose coherence is above a given coher- employing adaptive filters in the space and time domains. ence threshold for all selected interferograms. This selection With this approach, both the linear and non-linear compo- method is usually adopted if a medium or low resolution SPN nents of deformation can be estimated, even if the process- analysis is needed. Furthermore, it is employed when only ing is based on a linear model for phase unwrapping. This few SAR images are available. This method is suitable to dependence does not apply in the case of the SBAS analyze sub-urban or country-side areas. technique, where the unwrapping is performed on each • Spectral Coherence. The spectral coherence (for details interferogram. New advanced SPN processing tools are see Altamira 2004), can be used when only few images under development, which do not make use of a linear are available, and it is mandatory to keep the full SAR deformation model for phase unwrapping. resolution. By using the spectral coherence the candidate pixels are identified within each available SAR image. The stable points are then selected by choosing the candidate Products of the SPN Processing Chain pixels that are common to the entire SAR image stack. Some of the parameters that can be estimated by the SPN technique, i.e., the atmospheric contribution of each SAR 2. Multiple Images: In contrast with the standard DINSAR image, which is sometimes referred to as the atmospheric techniques, which are based on a pair of SAR images, the phase screen, are only useful to achieve an accurate DINSAR SPN technique makes use of multiple SAR images acquired over the same observation area. This represents a key fea- modeling; they usually do not have any other specific ture, which allows the SPN technique to achieve advanced application. On the other hand, some other parameters have deformation modeling capabilities and high quality important applications. The three main products of the SPN deformation estimates. Typically, a few tens of SAR images technique are briefly described below.

444 April 2008 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING 1. Average Deformation Map: The SPN technique is able to good PS) formal precisions of less than 1 mm/year can be estimate for each processed PS the temporal evolution of its achieved. Colesanti et al. (2003) indicate similar values for the deformation, i.e., it provides the deformation estimated over Persistent Scatterers technique. a 3D field (2D in space, plus the temporal evolution over the 2. Deformation Time Series of Each PS: This product observed period). The average deformation map is a simplified describes PS-wise the temporal evolution of the deformation. 2D representation of the above 3D field, where for each PS is The deformation time history is estimated in correspon- given the linear deformation velocity estimated over the dence of the used temporal samples, i.e., the acquisition observed period. An example is shown in Plate 1, which dates of the SAR images. Therefore, the quality of the shows the average deformation map of the Barcelona area in temporal description of a given deformation phenomenon the period between February 2003 and March 2004, which mainly depends on number of available SAR images. An was estimated with five Envisat ASAR images. In this example, example of deformation time series is shown in Figure 1. As one may appreciate that most of the city is rather stable, while can be noted in this figure, this product is fundamental to on the bottom left part of the image there is a wide subsiding assess the actual time evolution of the deformation. In fact, area, with deformation rate above 1 cm/year, and which is it includes both the linear and non-linear components of located on the delta of the Llobregat River. A few comments deformation; this represents a remarkable improvement with of explanation are required. First, the velocities of this type of respect to the information contained in the average deforma- mapping are always referred to a reference point located in tion map. Due to the high number of parameters that have the imaged scene. This is due to the fact that the technique to be estimated in order to derive a deformation time series, basically measures differences of velocities between points; all the precision associated with an individual deformation the velocities are relative to a point chosen as a reference. In measurement (i.e., the deformation at a given time) is clearly principle, there is a dependence of the achieved results on the lower than that achievable on the estimation of the linear distance from the reference point, due to residual atmospheric deformation velocity. Colesanti et al. (2003) show, both effects. However, for dimensions comparable with those of analytically and experimentally, that the precision of their the test site discussed in this work, these effects have usually PS A-DINSAR technique achieves values of 1 to 3 mm on a negligible impact on the results. Second, the technique individual deformation measurements. estimates the land deformations along the LOS of the radar; the 3. Map of the Residual Topographic Errors: The residual deformation or deformation velocities are usually referred to topographic error is given by the difference between the the LOS, with the exception of those cases where a priori true height of the scattering phase center of a given pixel, information on the deformation direction is available. In these and the height of the DEM employed in the SPN processing. cases, taking into account the geometry of SAR acquisitions, An example of this error over the Camp Nou Stadium and in particular the LOS direction, the LOS measurements can of Barcelona is shown in Plate 2. Note that the residual be projected on the known deformation direction. The third topographic error is only estimated over the PS, which are comment concerns the precision that can be achieved in such indicated by colored points superposed to an orthoimage products. It depends on several parameters: the electromag- of the Cartographic Institute of Catalonia. This parameter netic stability of the PS, the number of available SAR images, plays a key role for two specific goals. From the modeling and the quality of the linear model used with respect to the viewpoint, it is important to explain the so-called residual actual behavior of the deformation under analysis. In the best topography component of the DINSAR phase; the estima- conditions (large observation periods, high redundancy, tion of the residual topographic error fulfils this purpose.

Plate 1. Average deformation map of the Barcelona area estimated with five ASAR SAR images in the period between February 2003 and March 2004. This map, in color, is superposed to an amplitude SAR image, which is shown in gray values. The white frame indicates the location of the main dike of the port shown in Figure 3.

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING April 2008 445 Figure 1. Estimation of the deformation time series of the roof of an industrial building located in the area of Barcelona. Input data: 49 ERS images that cover the period 1995 to 2000. Figure 1a and 1b are two temperature time series, which correspond to the medium and maximum temperatures in the acquisition days of the SAR images. The deformation is probably due to thermal dilation of the portion of the observed roof.

The second goal is the implementation of advanced due to attitude control problems of the ERS2 satellite (see geocoding procedures for the A-DINSAR products. The www.earth.esa.int/pcs/ers/sar/doppler_query). standard methods employ an a priori known DEM to 2. ASAR-0304 is formed by five Envisat ASAR images acquired geocode these products, i.e., they use an approximate value between February 2003 and March 2004. This represents of the true height of the scattering phase center of each a very small dataset, which however was the only one pixel, which results in a location error during the geocod- available during the project. Since the number of ASAR ing. By using the estimated residual topographic error this images is insufficient to estimate both deformation and kind of error can be largely reduced, thus achieving a more residual topographic error, for this dataset the residual precise geocoding. This may considerably help the topographic error was considered as a known parameter, interpretation and the exploitation of the A-DINSAR results. taking its value estimated by using ERS-9500. This forced The formal precision that can be achieved in the estimation the ASAR-0304 analysis to be performed on the same PS of the residual topographic error is a function of the set than the ERS-9500 analysis. An example of average distribution of the normal baselines. Using large baselines, deformation map derived with ASAR-0304 is shown in between 1200 m, Colesanti et al. (2003) achieve a Plate 1. standard deviation that is less than 1 m. It is important to 3. ERSASAR-9804 contains a mixed group of 47 ERS1/2 and note that this parameter describes a rather specific feature, five ASAR images that range from January 1998 until March i.e., the height of the radar scattering phase center. This 2004. Starting from 2000 this group contains only the means that in general, it cannot be used to improve the ERS2 images that have small Doppler Centroid variations quality of the DEM used in the SPN procedure; it is only (differences less then 700 HZ were accepted). As for the useful to get a kind of improved “radar DEM” only over the previous dataset, the residual topographic error was not PS selected in the SPN procedure. estimated using this dataset; the value estimated by using ERS-9500 was used. It is worth to note that the combination of ASAR in IS2 mode (the ERS-like configuration) and Experiment Description ERS data is not straightforward, due to the difference of Three datasets were considered in the Barcelona test site: 31 MHZ between the radar central frequencies of the two sensors. Under certain conditions it is possible to make 1. ERS-9500 is formed by 49 ERS1 and ERS2 images acquired ASAR and ERS cross-interferograms. Altamira Information, between April 1995 and December 2000. This is a redundant jointly with DLR (Deutsche Forschungsanstalt fur Luft-und dataset, useful to derive high quality estimations of the Raumfahrt), generated the first-ever produced ERS ASAR parameters, which at the same time does not include those cross-interferograms, over Paris and Las Vegas (Arnaud ERS2 images that are affected by Doppler Centroid variations et al., 2003).

446 April 2008 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING Plate 2. Example of residual topographic error map over the Camp Nou Stadium of Barcelona. This error, which is estimated over the PS, is indicated by colored points superposed to an orthoimage of the Cartographic Institute of Catalonia.

The SPN technique has been the first A-DINSAR tool (subsidence, i.e., vertical deformation) the LOS measure- capable to merge ERS and ASAR data inside the same process- ments of the above PS were projected onto the vertical ing (Duro et al., 2003). This was achieved without making direction. The validation results are summarized in Figure 3, use of ASAR and ERS cross-interferograms. A similar strategy where the deformation time series of the four PS are com- has been described by Pepe et al. (2005). In this work, pared with the reference value. Despite the slightly different the cross-interferograms were not used due to the severe observed periods (April 1995 to December 2000 for ERS-9500 decorrelation effects that these interferograms suffer in urban areas. However, the ASAR and ERS interferograms were used together in order to jointly contribute to the estimation of land deformation. An example of deformation profile jointly estimated with ERSASAR-9804 is shown in Figure 2.

A-DINSAR Validation Over the Main Dike of the Port This section is focused on the validation of the A-DINSAR results obtained over a particular infrastructure: the main dike of the port of Barcelona, whose location is shown in Plate 1 by a white frame. The reason to choose this specific feature for validation purposes is threefold: first, it is an important infrastructure of the city which is know to be subject to subsidence; second, leveling data taken by the Topographic Service of the Port are available for validation; and finally, it has an elongated and very narrow form, see Figure 2. Main dike of the port of Barcelona: deforma- Figure 3, which is comparable with the SAR resolution, and tion time series of two PS derived by a joint estimation which makes it very difficult to be monitored by A-DINSAR. based on ERS and ASAR images. For the two time series Using the ERS-9500 dataset four PS were measured over are indicated the observations in correspondence to the the main dike (see their location in Figure 3). These points ERS and ASAR images. One PS has a very high temporal are quite close to a reference point, measured by leveling, coherence (0.98), while the other shows a much noisier which was used for the validation. Using the a priori time series, has much lower coherence (0.71). available knowledge about the deformation direction

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING April 2008 447 Figure 3. Main dike of the port of Barcelona: 1:5 000 map of the Cartographic Institute of Catalonia (a) with the location of the reference points, indicated by crosses, and of the four measured PS, which is indicated by a single circle, and (b) subsidence profiles of the four PS, measured with the ERS-9501 and the ASAR-0304 datasets. These profiles are compared with the reference one, coming from leveling data.

versus February 1995 to July 2002 for the reference data), etc.) with the given DINSAR phases. The same coherence is there is a good agreement between the SPN estimations referred to as the multi-image phase coherence by Ferretti and the reference value; the maximum difference equals et al. (2000). The PS shown in Figure 2 have a coherence of 0.7 mm/year. 0.98 and 0.71 (noisier PS), respectively. In Figure 3 the results obtained are reported with the ASAR-0304. In order to compare the ERS and ASAR results, the same four PS mentioned above were considered. In the Periodical Deformation Behavior ASAR analysis, the residual topographic error estimated by This section describes a particular type of deformation ERS-9500 was used. One may notice that the deformation behavior: a periodical deformation pattern, which is proba- rates estimated by ASAR are very close to the ERS rates and to bly caused by the seasonal temperature variation of the the reference value. This result is particularly valuable due observed scene. An example of periodical deformation to the limited number of available ASAR observations. It is pattern is shown in Figure 1a. It corresponds to the temporal important to note that this good result strongly depends on evolution of deformation of the roof of an industrial build- the linear deformation behavior of the studied feature. Even ing located in Barcelona, which was estimated by using the if the SPN technique is able of measuring highly non-linear ERS-9500 dataset. A similar behavior is shown by several deformations (see next section), in this case the linearity other PS over the analyzed area. In the same plot (Figure 1a) of deformation makes possible the direct comparison of are reported two temperature time series, which correspond deformation rates that refer to different observed periods, to the medium and maximum temperatures of Barcelona in e.g., February 1995 to July 2002 for the reference data, and the acquisition days of the 49 SAR images. One may notice a April 2003 to March 2004 for the ASAR data. strong correlation between the deformation pattern and the Finally, a joint estimation of land deformation based temperature time series: there is a high correlation coeffi- on ERS and ASAR observations was performed over the cient (0.84) between deformation and maximum tempera- Barcelona test site. Again, in this analysis the residual tures. Figure 1b shows the deformation time series and the topographic error was fixed, taking the estimation from the time series of the maximum temperatures, which has been ERS-9500 dataset. Figure 2 shows the deformation time series rescaled in order to make possible the direct comparison of of two of the above four PS. The general deformation rate of the time series. Again, one may appreciate that both time both time series is consistent. However, one may appreciate series show a similar pattern: the standard deviation of the that one time series is much smoother than the other one. difference between the two time series is 0.8 mm. This This can be explained by the temporal stability of the indicates that the deformation is probably due to thermal electromagnetic properties of the two PS. One of the two PS dilation of the portion of the observed roof. has a very stable behavior over the observed period; the The importance of this result is twofold. First, it is useful dispersion of estimated deformation time series is very low. to understand the potentiality of the considered A-DINSAR On the other hand, the second PS shows a much noisier technique to fully describe the temporal evolution of deforma- behavior, due to the higher noise of its DINSAR phases. In tions. In fact, in this example the seasonal behavior of defor- the SPN procedure, it is possible to assess the quality of mation, which is highly non-linear, can only be appreciated each PS through the estimation of the so-called temporal by analyzing the deformation time series of the given PS. This coherence, which is based on a linear deformation model. capability of A-DINSAR techniques may play a fundamental role The temporal coherence measures the goodness of fit of the in several applications where the temporal dimension of estimated model (deformation, residual topographic error, deformation is required to understand the deformation driving

448 April 2008 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING mechanisms. Second, with this result it is possible to appreci- notice that the measures PS are very well aligned over the ate the sensitivity of the technique at hand, which is able to bridge. Although this represents a qualitative analysis, it is sense small, i.e., millimeter-level, deformations. In fact, it is sufficient to appreciate the magnitude of the geocoding errors worth noting the magnitude of the deformation oscillation, that in this case are smaller than 2 m. The same occurs in which ranges in the interval 3 mm; this result is useful the second case (Figure 4, botom portion). Note that in this to get an idea of the sensitivity of the A-DINSAR results. case the PS are apparently shifted on the left with respect to the bridge. However, in the topographic map only the axe of the bridge is indicated; again the geocoding errors are below Advanced Geocoding of the DINSAR Products 2 m, and as geocoding of remotely sensed satellite data, this An important feature of the SPN technique is its capability of represents an excellent result. Note that these geocoding performing an advanced geocoding of the DINSAR products, errors do not include the global positioning error that usually which is based on estimated residual topographic errors. affects the DINSAR products geocoded without ground control The advanced geocoding represents the key to operationally points, and which is typically of the order of few tens of exploit the DINSAR results. In fact, only if they are properly meters. In fact, this error, which in the Barcelona test case geocoded they can be fused with other type of data, e.g., was constant over the entire area of study, was previously coming from geographical information systems and geologi- removed by using few ground control points. cal databases, in order to effectively support the decision These results indirectly confirm the good quality of the process associated with land deformation management. In residual topographic error estimated by the SPN technique. the Barcelona test site the DINSAR products were geocoded In fact, due to the SAR geometry, an error in the estimated by using the residual topographic error estimated with residual topographic error (Res_error) results in an amplified the ERS-9500 dataset. An example of geocoded product is geocoding error (G) in the direction perpendicular to the track illustrated in Figure 4, which shows few geocoded PS direction: G Res_error/tan, where is the off-nadir angle. (indicated by black squares) superposed to two portions of With 23°, G is 2.35 times the Res_error. This means that a 1:5 000 topographic map of the Cartographic Institute of for the PS shown in Figure 4, the Res_error is well below 1 m. Catalonia. One of the goals of this study was the evaluation of the quality of the advanced geocoding. To this end, few particu- Conclusions lar cases were analyzed, where it was possible to interpret In recent years different Advanced DINSAR (A-DINSAR) tech- the physical nature of the PS. Two examples are shown in niques for land deformation monitoring have been proposed. Figure 4; there are two thin linear structures that correspond This paper describes a particular A-DINSAR implementation, to two pedestrian bridges where some PS were measured. In which is named the SPN technique. The main features of both cases it is reasonable to assume that the PS correspond this technique have been briefly outlined: the pixel selec- to some parts of these bridges. In one case (Figure 4, top tion based on the so-called Persistent Scatterers, the use of portion) the bridge is very thin, i.e., less than 3 m; one may multiple SAR images acquired over the same observation area, and its modeling capabilities. Some of the most original features of the SPN approach have been compared with other A-DINSAR techniques described in the literature, such as, the PS and SBAS techniques. The most important products of the SPN processing chain have been described. They include the average deformation maps, the deformation time history of each measured point, and the so-called residual topographic error map, which plays a key role in deriving precise geocoded DINSAR products. The performance of the SPN technique have been illustrated by analyzing the results achieved on a test site located over the metropolitan area of Barcelona, Spain, where a rich set of ERS and Envisat SAR data was available. The following aspects have been highlighted:

• The dense coverage of measured PS: about 519,000 PS were measured over the test site (temporal coherence above 0.5), with an average density of about 2,250 PS/km2. • The capability of covering the entire metropolitan area of Barcelona, measuring at the same time very thin infrastructures compared with the SAR resolution, for example, the main dike of the port. • The good agreement of the A-DINSAR estimations and the reference values coming from leveling campaigns, with discrepancies in the estimated deformation rates below 1 mm/year. • The capability to jointly estimate the terrain deformation using ERS and ASAR images. • The high sensitivity of the DINSAR estimates to millimeter- Figure 4. Example of advanced geocoded DINSAR product. level deformations, such as, the case of the periodical Two portions of a 1:5 000 topographic map of the deformations due to thermal dilation of buildings. Cartographic Institute of Catalonia with superposed • The high precision achieved in the geocoding of the measured black squares, which represent the geocoded PS. The PS, with errors below 2 m in the illustrated cases, which in corresponding portions of the orthoimage produced by turn confirms the quality of the estimated residual topographic errors. the same Cartographic Institute are also reported. • It is important to emphasize that the above results were generated through a highly automated data processing chain.

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING April 2008 449 Acknowledgments Ferretti, A., C. Prati, and F. Rocca, 2001. Permanent scatterers in This work was supported under European Space Agency SAR interferometry, IEEE Transactions on Geoscience and Contract Number 16702/02/I-LG. Moreover, it has been Remote Sensing, 39(1):8–20. partially supported by the Spanish Ministry of Science and Hanssen, R., 2001. Radar Interferometry, Kluwer Academic Publish- Technology, through the research project REN2003–00742, ers, Dordrecht, Holland, 308 p. AURORAE. Hooper, A., H. Zebker, P. Segall, and B. Kampes, 2004. A new method for measuring deformation on volcanoes and other natural terrains using InSAR Persistent Scatterers, Geophysical References Research Letters, 31, L23611, doi: 10.1029/2004GL021737. Hu, J.C., B. Fruneau, B. Deffontaines, E. Pathier, A. Arnaud, C.T. Altamira, 2004. Development of Algorithms for the Exploitation of Lee, and J. Angelier, 2005. Tainan neotectonics from Persistent ERS-Envisat Using the Stable Points Network, Final report, ESA Scatterer interferometry (SW Taiwan), Proceedings of Fringe Contract No. 16702/02/I-LG. 2005, 28 November-02 December, Frascati, Italy. Arnaud, A., N. Adam, R. Hanssen, J. Inglada, J. Duro, J. Closa, and Lanari, R., O. Mora, M. Manunta, J.J. Mallorquí, P. Berardino, and M. Eineder, 2003. ASAR ERS interferometric phase continuity, E. Sansosti, 2004. A small-baseline approach for investigating Proceedings of the International Geoscience and Remote Sensing deformations on full-resolution differential SAR interferograms, Symposium, 21–25 July, Toulouse, France, unpaginated CD-ROM. IEEE Transactions on Geosciences and Remote Sensing, Bamler, R., and P. Hartl, 1998. Synthetic aperture radar interferome- 42(7):1377–1386. try, Inverse Problems, 14:R1–R54. Mora, O., J.J. Mallorquí, and A. Broquetas, 2003. Linear and Berardino, P., G. Fornaro, R. Lanari, and E. Sansosti, 2002. A new nonlinear terrain deformation maps from a reduced set of algorithm for surface deformation monitoring based on small interferometric SAR images, IEEE Transactions on Geosciences baseline differential SAR interferograms, IEEE Transactions on and Remote Sensing, 41(10):2243–2253. Geoscience and Remote Sensing, 40(11):2375–2383. Pepe, A., E. Sansosti, P. Berardino, and R. Lanari, 2005. On the Colesanti, C., A. Ferretti, F. Novali, C. Prati, and F. Rocca, 2003. generation of ERS/ENVISAT DInSAR time-series via the SBAS SAR monitoring of progressive and seasonal ground deformation technique, IEEE Geoscience and Remote Sensing Letters, using the Permanent Scatterers technique, IEEE Transactions on 2(3):265–269. Geoscience and Remote Sensing, 41(7):1685–1701. Rosen, P.A., S. Hensley, I.R. Joughin, F.K. Li, S.N. Madsen, Crosetto, M., B. Crippa, E. Biescas, O. Monserrat, M. Agudo, and E. Rodríguez, and R.M. Goldstein, 2000. Synthetic aperture P. Fernandez, 2005. Land deformation monitoring using SAR radar interferometry, Proceedings of the IEEE, 88(3):333–382. interferometry: State-of-the-art, Photogrammetrie Fernerkundung Usai, S., and R. Klees, 1999. SAR interferometry on very long time und Geoinformation, 6:497–510. scale: A study of the interferometric characteristics of man- Duro, J., J. Inglada, J. Closa, N. Adam, and A. Arnaud, 2003. High made features, IEEE Transactions on Geoscience and Remote resolution differential interferometry using time series of ERS Sensing, 37(4):2118–2123. and ENVISAT SAR data, Proceedings of Fringe 2003, 01–05 Usai, S., and R. Klees, 2000. An analysis of the interferometric December, Frascati, Italy. characteristics of anthropogenic features, IEEE Transactions Duro, J., J. Closa, E. Biescas, M. Crosetto, and A. Arnaud, 2005. High on Geoscience and Remote Sensing, 38(3):1491–1497. resolution differential interferometry using time series of ERS th Werner, C., U. Wegmüller, T. Strozzi, and A. Wiesmann, 2003. and ENVISAT SAR data, Proceedings of the 6 Geomatics Week Interferometric point target analysis for deformation mapping, Conference, February, Barcelona, Spain, unpaginated CD-ROM. Proceedings of IGARSS 2003, 7:4362–4364. Ferretti, A., C. Prati, and F. Rocca, 2000. Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry, IEEE Transactions on Geoscience and Remote (Received 06 January 2006; accepted 12 July 2006; revised 14 Sensing, 38(5):2202–2212. September 2006)

450 April 2008 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING