(1)Istituto per il Rilevamento Elettromagnetico dell'Ambiente, National Research Council, Via Diocleziano 328, 80124 Napoli, Italy

(2)Dipartimento di Ingegneria Elettrica ed Elettronica, Università degli studi di Cagliari, Piazza d’Armi, 09123 Cagliari, Italy

(3)Dipartimento di Ingegneria e Fisica dell'Ambiente, Università degli Studi della Basilicata, Viale dell’Ateneo Lucano 10, 85100 Potenza, Italy

e-mail:{casu.f, manzo.mr, pepe.a, lanari.r}@irea.cnr.it

mazzarella @diee.unica.it

ABSTRACT

We have investigated the capability of the Small BAseline Subset (SBAS) algorithm to survey deformation phenomena of large areas. The key idea is to generate mean deformation velocity maps and displacement time series by exploiting an appropriate set of small baseline multilook interferograms generated from long and, relatively low resolution, SAR image strips obtained by focusing several contiguous raw data frames. Following the generation of the selected interferograms, they are subsequently inverted via the SBAS technique in order to retrieve the mean deformation velocity and the corresponding displacement time series for each coherent pixel of the investigated zone. We present in this work the results of the first experiment we have carried out on a set of 264 descending ERS SAR data frames, spanning the time interval from June 1992 until October 2000. The processed data are relevant to an area in Central Nevada (USA) and they clearly demonstrate the effectiveness of our approach to survey the deformation of the overall zone. 1. INTRODUCTION

Differential Synthetic Aperture Radar Interferometry (DInSAR) is a microwave imaging technique that allows us to investigate surface deformation with a centimeter to millimeter accuracy [1]. The DInSAR methodology has been applied first to investigate single deformation events [2]. However, more recently, it also has been exploited to analyze the temporal evolution of the detected displacements via the generation of deformation time series, computed through the inversion of an appropriate sequence of DInSAR interferograms. The interest on the development of these methodologies is testified by several approaches which have been already presented [3-8]. In this work we concentrate on the technique referred to as Small BAseline Subset (SBAS) approach [4], whose capability to detect and investigate deformation

phenomena has been already shown in different applications, mostly based on exploiting the European Remote Sensing (ERS) SAR data. In particular, the basic SBAS technique has been originally developed to investigate, by using multilook interferograms, areas with a typical extension of 100 x 100 km and with a spatial resolution of the order of 100 x 100 m. We remark that, although the availability of deformation maps extending for 100 x 100 km may be very effective to investigate several displacement phenomena, there are cases where it can be crucial to study much more extended zones in order to detect and analyze the ongoing deformation [9]. Accordingly, we investigate in this study the capability of the SBAS-DInSAR technique to produce deformation maps and the corresponding time series of very extended areas by exploiting relatively low resolution SAR data frames. In particular, we present the results of the first experiment we have carried out on a set of 264 descending ERS SAR data, spanning the time interval from June 1992 until October 2000. The processed data are relevant to an area in Central Nevada (USA) and they clearly demonstrate the capability of our approach to operate as a deformation survey tool. 2. BASIC RATIONALE OF THE EXPLOITED

DINSAR PROCESSING CHAIN

The SBAS technique is a DInSAR algorithm allowing us to detect Earth’s surface deformation and, above all, to analyze their temporal evolution by generating mean deformation velocity maps and time series. In particular, this technique implements a combination of the multilook SAR interferograms generated from an appropriate selection of SAR data pairs characterized by a small spatial and temporal baseline. The key objective of this data selection is to mitigate the decorrelation phenomena, thus maximizing the number of exploited pixels. In this work we have applied the SBAS technique to a set of relatively low resolution multilook stripmap

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Proc. ‘Envisat Symposium 2007’, Montreux, Switzerland 23–27 April 2007 (ESA SP-636, July 2007)

ON THE CAPABILITY OF THE SBAS-DINSAR TECHNIQUE TO INVESTIGATE DEFORMATION PHENOMENA OF LARGE AREAS WITH

LOW RESOLUTION DATA

F. Casu(1,2), M. Manzo(1,3), A. Pepe(1), G. Mazzarella(2), R. Lanari(1)

interferograms (with about 200 x 200 m resolution) that have been used to investigate the algorithm capability to detect and analyze deformation occurring in large areas. In particular, we have first combined several contiguous ERS stripmap SAR data frames (belonging to the same satellite track), to generate long SAR image strips relevant to the investigated area. Subsequently, the azimuthal image resolution has been deliberately degraded to reduce the amount of data to be processed. To achieve this task, the average Doppler centroid (ADC) of the whole data set has been first identified; subsequently, each SAR image has been low pass filtered around the estimated ADC azimuth frequency (thus preserving the spatial coherence of the data) and then downsampled of a properly chosen factor, in our case a factor 5. The small baseline SAR data pairs have been then selected and the multilook interferograms finally generated. At this stage the overall interferogram sequence has been inverted via the SBAS procedure, thus allowing us to retrieve the mean deformation velocity and the corresponding displacement time series for each investigated coherent pixel. 3. RESULTS

The DInSAR processing chain described in the previous section has been applied to a set of 264 ERS-1/2 SAR data frames (track 442, frames: from 2781 to 2871), spanning the 1992-2000 time interval, that are relevant to an area located in Central Nevada (USA) and extends for about 600 x 100 km; moreover, the generated DInSAR products have a spatial resolution of about 200 x 200 m. In order to provide an overall picture of the detected deformation, we present in Fig. 1A the computed mean deformation velocity map, that has been geocoded and superimposed on a SAR amplitude image of the area. Note that this representation allows us to provide a mean information (with respect to time) on the detected deformation; note also that areas where the deformation measurement accuracy is affected by decorrelation noise have been excluded from the color map. Fig. 1A clearly shows significant deformation patterns present in the area due to natural (earthquake, microseismicity) and anthropogenic (mining, water pumping, agricultural exploitation) causes. Let us provide some details on the detected displacement and, at the same time, to show the deformation time series retrieval capability of the SBAS inversion. To achieve this task, we present in Figs. 1B-F the computed time series relevant to five selected coherent pixels identified in Fig. 1A by the black triangles labeled as B, C, D, E and F, respectively. In particular, the plots of Figs. 1B-C show subsidence due to water pumping in support of open-pit gold mining at Lone Tree Gold Mine and Cortez Gold Mine, respectively; Fig. 1D represents subsidence due to the agricultural exploitation of

Antelope Valley; Fig. 1E illustrates the deformation relevant to the Eureka Valley Earthquake (M6.1; May, 17 1993); finally, Fig. 1F shows the deformation associated with geothermal production and intense microseismicity at Coso Geothermal area. Let us now carry out a validation of the presented DInSAR products; this is not an easy task because of the lack, in the investigated area, of available geodetic data (GPS, leveling, etc.) ensuring a significant temporal overlap with the computed DInSAR measurements. Accordingly, we decided to focus on a cross-comparison between the presented results and those obtained from alternatively computed DInSAR data. In particular, we compared our results with those achieved by applying the standard SBAS approach to process the data of one of the frames involved in the long strip processing. In this case, we focused on the SAR images relevant to the 2799 frame (whose location is highlighted in Fig. 1A) by considering the same acquisition dates used for producing the results of Fig. 1A. On these data, a standard SBAS inversion has been carried out but on DInSAR multilook interferograms generated from the full resolution SAR images. On these results, that we have considered as a reference, a spatial downsampling step has been finally performed in order to make them comparable with those shown in Fig. 1. Note that, for these results, we expect standard deviations of the order of σv=1 mm/year and σd=5 mm for the mean deformation velocity and the displacement time series, respectively [10]. We carried out our comparison by computing, in correspondence to the common coherent pixels of the two DInSAR products, the difference between the mean deformation velocities and the standard deviation of the difference between the homologous deformation time series. In particular, we evaluated the percentage of the pixels included within the (−σv, σv) and (−2σv, 2σv) intervals, obtaining values of 87% and 99%, respectively. In addition, we performed a similar operation for the deformation time series; in this case, we obtained that 98% and 99.9% of the differences between the two DInSAR time series were included within the (−σd, σd) and (−2σd, 2σd) intervals, respectively. This analysis allows us to conclude that there is a good agreement between the long strip results and those relevant to the reference data. 4. CONCLUSIONS

We have presented the results of the first experiment we have carried out to investigate the capability of the SBAS-DInSAR technique to produce deformation maps and the corresponding time series of very extended areas, by exploiting 200 x 200 m resolution SAR data. The overall analysis is based on the exploitation of 264 descending SAR data frames, acquired by the ERS-1/2 sensors during the 1992–2000 time interval, relevant to

Figure 1. SBAS-DInSAR results relevant to a 600 x 100 km area in Central Nevada (USA). A) mean deformation velocity map (in color) superimposed on a SAR amplitude image (grey scale) of the zone; the location of the 2799 frame

has been highlighted as well as the DInSAR reference point (white square). B-F) DInSAR deformation time series for the pixels marked by triangles in Fig. 1A and labeled by the letters from B to F. The inset in upper-right corner shows

the area location.

an area of Central Nevada (USA) with a spatial coverage of approximately 600 by 100 km. The presented results demonstrate the capability of the proposed solution to provide, in such extended areas, space–time information on the detected deformation with an accuracy that is comparable with that achieved for the conventional SBAS-DInSAR processing. We further remark that the proposed approach can be also extended to analyze low resolution SAR data, as those acquired by the ScanSAR systems [11], that allow to increase coverage also in the across-track direction at some expenses of azimuth resolution. The availability of a burst-synchronized long ScanSAR time series is foreseen in the future because it may allow, based on solutions like the one applied in this study, to exploit the DInSAR technology for continental base deformation analysis. 5. ACKNOWLEDGMENTS

This work has been partially sponsored by ASI and by the European Union on Provision 3.16 under the project of the Campania Regional Center of Competence “Analysis and Monitoring of the Environmental Risk”. We thank the European Space Agency (ESA), which provided the ERS SAR data relevant to the Central Nevada area, that we obtained through the Cat-1 2616 and WInSAR in collaboration with Dr. Falk Amelung and Noel Gourmelen (University of Miami). Precise satellite orbits are provided by the University of Delft (The Netherlands). The DEM of the investigated zone was acquired through the SRTM archive.

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