Transportation infrastructure monitoring using satellite remote sensing

Transcription

1 Transport Research Arena 2014, Paris Transportation infrastructure monitoring using satellite remote sensing Edward Hoppe a,*, Brian Bruckno b, Elizabeth Campbell b, Scott Acton c, Andrea Vaccari c, Michael Stuecheli c, Adrian Bohane d, Giacomo Falorni d, Jessica Morgan d a Virginia Center for Transportation Innovation and Research, Charlottesville, USA b Virginia Department of Transportation, Richmond, USA c University of Virginia, Charlottesville, USA d TRE Canada Inc., Vancouver, Canada Abstract The objective of this study was to determine the feasibility of applying commercially available radar remote sensing technology to transportation network monitoring. Synthetic aperture radar data acquired from the Italian COSMO-SkyMed satellite were processed and analyzed. Specific applications included sinkhole detection in karst terrain, slope stability monitoring, and infrastructure assessment. A 40 x 40 km Area of Interest was identified in the proximity to the City of Staunton, in a region geologically prone to sinkhole formation. Satellite data from this area were acquired on a bi-monthly schedule, for a period of 14 months. Radar data were processed with the SqueeSAR algorithm. Additional software tools for automated sinkhole detection were developed. A new approach involving Temporary Coherent Scatterer (TS) was experimented with. Study results indicate that satellite radar remote sensing can be effectively applied to performance monitoring of transportation infrastructure. Keywords: satellite; radar; remote sensing; transport Résumé L'objectif de cette étude était de déterminer la faisabilité de l'application des techniques de télédétection pour la surveillance du réseau de transport. Les données du radar à synthèse d'ouverture (RSO) acquises par le satellite italien COSMO-SkyMed ont été traitées et analysées. Les applications furent spécifiquement pour la détection de dolines en terrain karstique, ainsi que pour la surveillance de la stabilité des pentes et des infrastructures. Une zone d'intérêt de 40 x 40 km a été identifiée à proximité de la ville de Staunton, dans une région géologiquement sujette aux formations de dolines. Les données satellite couvrant cette zone ont été acquises bimensuellement pour une période de 14 mois et traitées avec l'algorithme SqueeSAR. Des outils logiciels supplémentaires pour la détection automatisée des dolines ont été développés. Une nouvelle méthode impliquant le diffuseur temporaire (Temporary Coherent Scatterer; TS) a été expérimentée. Les résultats de l'étude indiquent que la technologie satellite radar peut être utilisée de façon efficace pour la surveillance des infrastructures de transport. Mots-clé: satellite; radar; télédétection; transport * Corresponding author. Tel.: ; fax: address: [email protected].

2 Hoppe et al. / Transport Research Arena 2014, Paris 2 Nomenclature InSAR Interferometric Synthetic Aperture Radar PS Permanent Scatterer DS Distributed Scatterer TS Temporary Scatterer 1. Introduction Various remote sensing technologies can be applied to monitor transportation infrastructure. Satellite-based radar offers potential for wide area remote sensing under all weather and lighting conditions. This technology senses millimeter-level surface displacement by measuring small changes in phase angle of the return signal. Points of interest may include landslides, sinkholes, mines, railways, highway embankments, bridges and structures undergoing deformation. Radar, which is an acronym for RAdio Detection And Ranging, was developed just before World War II for tracking aircraft and ships. Conventional radar systems operate by measuring the time required for the radar pulse to travel to and echo back from the point of interest. Range to target is computed from signal travel time and azimuth is determined by the position of the directional antenna (Schlutz, 2009). The Doppler shift of the reflected radar pulse also carries information about the speed of target. Carl Wiley of Goodyear Aerospace discovered that post-processing the Doppler shift data provided the ability to obtain finer resolution in the direction of beam travel. In 1951, Wiley developed a process known as Synthetic Aperture Radar (SAR) imaging, by which a two-dimensional surface image can be constructed using radar. The term aperture relates to the radar antenna. The aperture would have to be physically large to obtain adequate measurement resolution from high altitude. Instead, with the SAR process, radar data are acquired in a way that takes advantage of the forward motion of a moving platform, resulting in an equivalent synthesized large aperture. Increased access to SAR technology and recent advances in the development of signal processing algorithms have led to a growing demand for applications that require detection of small temporal changes in elevation. Currently, several Earth orbiting radar satellite systems are commercially available for routine SAR data collection. Satellite-based radar systems are capable of detecting surface deformation phenomena on the order of a few millimeters thanks to a technique known as Interferometric Synthetic Aperture Radar (InSAR) becoming a standard tool for remote sensing of displacements (European Space Agency, 2007). The principle behind interferometry is the acquisition and processing of phase shift information obtained from a series of complex SAR images. In each image, every pixel element is processed and the elevation at its centroid is established based on the signal phase response and the satellite altitude information (Rosen et al., 2000). All pixels are georeferenced, allowing GIS processing of InSAR data. When a target area is illuminated by radar waves, energy is dispersed. The resulting spatial distribution of electromagnetic energy is called scattering and the target is commonly referred to as a scatterer. InSAR identifies and provides information on the scatterers that are coherent in space and time. No monitoring stations need to be constructed on the ground. The technique lends itself to large area surveillance. Field validation of the InSAR technique using artificial radar targets (dihedral reflectors) indicates that submillimeter accuracy of displacement measurement can be achieved in both vertical and horizontal directions with satellite-based radar (Ferretti et al., 2007). The interferometry technique is best suited to monitoring a relatively slow rate of movement. Decorrelation is possible if the total displacement between successive radar acquisitions exceeds one-half of the signal s wavelength. 2. Purpose and scope The purpose of this paper is to present the results of some practical applications of InSAR at the Virginia Department of Transportation (VDOT). The focus is primarily on transportation-related applications. Potential applications of InSAR technology as an alternate or complementary diagnostic tool for monitoring the transportation network are outlined.

3 Hoppe et al. / Transport Research Arena 2014, Paris 3. Methodology 3.1. Selection of processing algorithm The researchers selected the commercially available SqueeSAR algorithm, recently developed by TRE, for data processing (Ferretti et al., 2011). Central to the modern InSAR technique is the concept of a so-called persistent or permanent scatterer (PS) with the associated time and space coherence properties. A PS point is a pixel which remains coherent through the entire data stack of radar images. It represents behavior of a single natural or artificial physical point that provides consistent radar signal reflections back to the satellite. The SqueeSAR algorithm extends this concept further by comparing neighboring pixels through statistical analysis. If the pixels are statistically similar, then covariance information is used to define a so-called distributed scatterer (DS), representing temporal displacement of some irregular area representing a collection of points with very similar deformation behavior. A by-product of SqueeSAR data processing is the so-called temporary coherence scatterer or temporary scatterer (TS). While PS and DS points exhibit spatial and temporal coherence through the entire data stack, TS points represent only partial coherence. These are points that are coherent for only a subset of the available frames. TS points are generated as raster images of pixels, where each pixel represents the average displacement estimated from the period of coherence but does not have an associated time series. The concept of temporary scatterer is relatively new in InSAR data processing. This approach offers significant overall increase in the target point density Selection of satellite system The researchers selected the COSMO-SkyMed satellite system operated by the Italian Space Agency (E-GEOS, 2013). It presently consists of four identical satellites equipped with X-band SAR. The satellites orbit the Earth every 97.2 minutes at a nominal altitude of 619 km. The COSMO-SkyMed constellation offers global coverage at 3 m pixel resolution when operated in HIMAGE mode, effectively providing time-displacement data at the centroids of consecutive 3 m by 3m square areas on the Earth s surface. SAR data for the area of interest can be acquired by any of the four available satellites. Relative orbital positions allow specifying the revisit time from 1 to 16 days along the same track Selection of area of interest The Area of Interest (AOI) was defined by a 40 by 40 km square, corresponding to a single COSMO-SkyMed radar frame centered on the locality of Middlebrook near Staunton, Virginia. Geologically, the selected AOI is characterized by faulted and folded strata of sedimentary rock, including shale, dolostone, sandstone, and limestone. The topography consists of long, linear ridges separated by valleys. Rock strata at the exposed highway slopes typically range from Cambrian to Devonian in age. Historically, portions of this area have been prone to sinkholes, landslides, and slope failures. The researchers specified this particular AOI mainly because of the prevalence of sinkhole-prone karst terrain. A major thrust of this study was to develop an early warning system for sinkhole detection at the onset of its formation. Other topics of research included rock and soil slope movement detection along highways, and bridge displacement monitoring. VDOT s GIS records indicate that the selected AOI contains 331 bridges, 150 km of Interstate, 360 km of Primary, and 1370 km of Secondary roads. 4. Results 4.1. Data A total of 32 radar frames were acquired by COSMO-SkyMed satellites between 29 August 2011 and 25 October 2012, representing 14 months of monitoring. Frames were typically collected on an 8-day interval for the first 2 months, then on a 16-day interval for the remaining period. The entire AOI was scanned in approximately 5 seconds during each satellite overpass.

4 Hoppe et al. / Transport Research Arena 2014, Paris 4 After SqueeSAR processing, the resulting data containing PS and DS points were delivered in the ESRI shapefile format. The file contained unique attributes for each PS and DS point, including date-stamped displacements at successive frame acquisitions. Data processing identified 167,801 PS points and 131,285 DS points, for a total of 299,086 unique physical targets, coherent across all 32 radar frames. The locations of scatterers were superimposed on 0.3 m resolution orthophotos using ArcGIS software. The projection was State Plane Virginia North FIPS 4501 (Feet) with NAD 1983 datum. The size of the representative DS area ranged from 76 to 891 m 2. In addition, TS raster data was provided as a grayscale TIF image file, with each pixel assigned 8-bit resolution value representing line of sight displacement of the centroid. TS data was projected on the same coordinate system as PS and DS points Scatterer density and distribution Figure 1 shows locations of permanent (PS) and distributed (DS) scatterers discovered within the AOI after 32 image acquisitions. An average density of 187 points per km 2 was achieved, but it is evident that the coverage is non-uniform. As expected, forested ridges and steep slopes do not produce many coherent targets for the X-band radar signals. High point density area near the eastern edge of the AOI represents Staunton and the surrounding road network. Fig. 1. Distribution of PS and DS scatterers within the 40 x 40 km AOI. Figure 2 shows a close-up view of PS and DS scatterers near the intersection of Routes 262 and 250 in Augusta County, with an adjoining housing subdivision and water treatment plant. This view is fairly representative of observed results. It appears that InSAR data processing can detect a lot of radar scatterers along the highways, railways, and other infrastructure objects, but the scatterer density decreases rapidly in the surrounding fields and forested areas. It indicates that the InSAR technique can be highly relevant when focused directly on infrastructure monitoring Sinkhole detection The primary focus area of the study was the automated detection of subsidence behavior potentially indicative of sinkhole formation. The majority of karst features encountered in the Valley and Ridge physiographic province are of the solution-type sinkhole. They are characterized by water-soluble bedrock with overlying soils. As joints and discontinuities within the soluble bedrock material widen, they are filled in by surface soils, resulting in a characteristic depression. In such cases, sinkhole development is a relatively slow and progressive process (Ritter, 2011).

5 Hoppe et al. / Transport Research Arena 2014, Paris Fig. 2. Distribution of PS and DS scatterers near the intersection of Routes 262 and 250. The approach pursued by the researchers was to model the shape of subsidence using a Gaussian function with the amplitude increasing linearly with time (Vaccari et al., 2013). While this approach is clearly inappropriate in the latter stages of sinkhole development, the use of the error function curve appears to be suitable for modeling the onset (Aoyagi, 1995). Figure 3 shows an example displacement profile constructed by binning data into concentric annuli and calculating average displacement within each of these rings. The problem was to process 300,000 scatterers across 32 time frames. Normalized cross-correlations between observed displacement profile, obtained from InSAR data, and its corresponding Gaussian fit, produced fairly high correlation coefficients. The largest observed settlements over the measurement period were of the of the order of 30 to 40 mm. A simplified risk function was developed to identify a risk factor or severity based on the computed rate of growth of local subsidence. Severe, moderate, and slight risk areas with the corresponding location coordinates were identified. It should be noted that while these locations were field verified as probable areas of localized subsidence, they could not be automatically classified as sinkholes. The processing algorithm may be viewed as a tool to identify likely precursors to potential sinkhole formation. Ultimately, a detailed field assessment is required to determine the actual cause of subsidence. Figure 4 shows a sinkhole detected in a residential subdivision near Staunton based on the spatiotemporal modeling of InSAR PS and DS data points. Generally, surface features of approximately 3 x 3 m laterally, corresponding to a radar image pixel size, were detectable. Fig. 3. Profile extraction.

6 Hoppe et al. / Transport Research Arena 2014, Paris 6 Fig. 4. Sinkhole detected near a housing subdivision in Staunton. Another approach to local subsidence detection involved analysis of TS raster data. The underlying assumption is that a point that was coherent at one time may have lost its coherence due to excessive displacement, possibly due to a sinkhole development or other type of surface movement. Figure 5 shows an example of a possible sinkhole formation under a paved area in Staunton. The original TS data were processed using ArcGIS software. Grayscale image was converted to a color scheme (red intensity indicates the relative magnitude of settlement) and the amplitudes of neighboring pixels were averaged. There is a clear evidence of surface distress at this site. Subsequent interviews with the city officials indicated that approximately a year ago there was a sewer line break in the vicinity. One of the local DS points (A4ZY0) shows approximately 15 mm of settlement during the monitoring period, corroborating TS data. This area of Staunton has been known for catastrophic sinkhole events in the past. The site is located almost directly over an underground stream (Lewis Creek), which flows parallel to the nearby street in the southerly direction Rock slope monitoring Fig. 5. TS and DS data indicating localized subsidence in Staunton. Figure 6 shows example of highway rock slope monitoring along a portion of Route 600, near the western boundary of the AOI. The site consists of dipping slopes of dark blue-gray, fine to medium-grained cherty

7 Hoppe et al. / Transport Research Arena 2014, Paris limestone of the Silurian and Devonian age. The slope height and angle are approximately 37 m and 40 degrees, respectively. The rock mass is heavily jointed. Material is released where the joints intersect the bedding planes. This highway slope poses a continuous maintenance problem for VDOT. Figure 6 shows TS results (color tiles) with some DS points superimposed (cyan circles). Example timedisplacement graph is shown for DS point (A002Z). TS data are presented using a color scheme, with the red intensity proportional to displacement. TS points provide significant increase in the information density at this location. In addition to InSAR analysis, this slope was also subjected to ground-based Light Detection and Ranging (LiDAR) and Digital Photogrammetry (DP) field validation studies. The results obtained from InSAR, LiDAR, and DP data are fairly consistent and indicate approximately 20 mm change in rock surface elevation during the monitoring period. Fig. 6. Route 600 slope monitoring using TS and DS data Bridge monitoring The use of InSAR for bridge monitoring was found to be challenging. Modern bridges are typically integral or semi-integral constructions, with no expansion joints present on the superstructure. Usually, there are no distinct natural scatterers that can be identified on the deck surface. This makes it difficult to obtain bridge displacement data corresponding to specific points of interest. Figure 7 shows example InSAR results obtained from a simple span bridge located at Route 635 over Interstate 81. PS points labeled as PS1 and PS2 align with the expansion joints over bridge piers. The corresponding timedisplacement PS data indicate progressive settlement, approaching approximately 5 mm during the monitoring period. Figure 7 also shows the underside of pier cap at location PS1, with exposed reinforcing bars due to delaminated concrete cover. The inspection report documents significant deterioration to concrete and bearings. The bridge is already scheduled for maintenance work. It is possible that there may be some correlation between the InSAR results and the overall bridge condition. The evidence of concrete delamination may be indicative of deterioration taking place at the pier cap, joint, and bearings, most likely due to extensive chloride intrusion from de-icing chemicals.

8 Hoppe et al. / Transport Research Arena 2014, Paris 8 Fig. 7. PS data at the Route 635 bridge over I-81 and view of underside of pier cap at location PS Pavement monitoring One of the most promising applications of TS data discovered in the course of field validation appears to be pavement condition monitoring. Figure 8 shows examples of TS results obtained from the junction of Route 262 and Middlebrook Avenue, west of Staunton. Significantly different TS responses were recorded on these two roads, correlating with the visible evidence of surface distress along Middlebrook Avenue (top right). Similar patterns were observed at other locations. Figure 9 shows TS data associated with pavement distress at a truck parking lot near Interstate 81. Fig. 8. TS responses corresponding to pavement condition.

9 Hoppe et al. / Transport Research Arena 2014, Paris Fig. 9. TS results corresponding to pavement distress at a truck parking lot. 5. Discussion The results indicate that there are potential practical applications of InSAR technology to transportation infrastructure monitoring. The scatterer density is generally sufficient for characterizing deformation phenomena along the transportation corridor. Sinkhole detection and slope stability monitoring are some of the most obvious geohazard applications to pursue. Others are likely to be developed as the technology becomes more widely implemented. Potential uses include monitoring settlements at bridge approaches, tunnel entrances, drainage structures, retaining walls, and railway lines. Typical deformation patterns for bridges and structures could be established and if out-of-range displacements are detected, a warning could be issued. The apparent applicability of TS data to pavement monitoring requires more detailed exploration. At this point the actual mechanism affecting TS response from pavements is not evident. It is possible that the dielectric constant of the pavement material increases as pavement deteriorates and the reflected radar signal is thus modified in a measurable way (Shang & Umana, 1999). One of the main attractions of the satellite-based InSAR is the ability to cover very large areas with a predictable and ongoing schedule, making it suitable as a network level monitoring tool. While the satellite remote sensing can be used for identifying potential trouble spots, other ground-based remote sensing technologies, such as LiDAR, Digital Photogrammetry or high frequency radar, can be subsequently applied to carry out detailed condition assessment at a particular site. With the current interest in performance-based specifications and the ever increasing use of design-build contracts, InSAR technology offers an effective post-construction monitoring tool. Also, with the increasing availability and access to historical radar data, it may be possible to look back in time and analyze a specific site anywhere on Earth that has already undergone deformations. Despite the fact that InSAR is now a mature, commercially available technology, its documented transportation applications are still very sparse. Part of the problem has been the relatively high cost of entry, mainly associated with numerically-intensive data processing. Presently, the cost of one year of monitoring is approximately $100/km 2. It is anticipated that in near future, with the upcoming availability of Sentinel satellite data and more efficient processing methods, the cost will drop to around $30/km 2. InSAR is a technology that clearly requires a multidisciplinary team approach to be effectively implemented. As more research is carried out, the number and the complexity of transportation applications of InSAR are likely to increase. The availability of millimeter-scale remote sensing of deformation offers potential new opportunities for effective implementation in transportation monitoring, and especially in geohazard assessment. What makes this technology particularly attractive is that the accuracy of results increases with the number of frame acquisitions, as random atmospheric errors become progressively minimized.

10 Hoppe et al. / Transport Research Arena 2014, Paris Conclusions Increased availability of civilian radar satellites, combined with rapid progress in digital signal processing, renders InSAR technology attractive for long-term performance monitoring of transport infrastructure. The ability of InSAR to measure surface displacements at a millimeter scale over a large land mass creates the potential for network level implementation. The application of InSAR technology to sinkhole detection, slope stability monitoring, and bridge deformation monitoring has been proven effective. Temporary scatterer (TS) provides additional detail of InSAR data. Acknowledgements This study was supported in part by the U.S. Department of Transportation (US DOT) under Cooperative Agreement #RITARS-11-H-UVA. The Research and Innovative Technology Administration (RITA) of the US DOT administers the Commercial Remote Sensing and Spatial Information Technologies Program, which was established to facilitate implementation of commercially available remote sensing products for transportation infrastructure development and construction. Disclaimer The views, opinions, findings and conclusions reflected in this paper are the responsibility of the authors only and do not represent the official policy or position of the US DOT/RITA, or any State or other entity. References Aoyagi, T. (1995). Representing Settlement for Soft Ground Tunneling. A Thesis presented to the Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge. E-GEOS (2013). COSMO-SkyMed. Accessed July 21, European Space Agency (2007). InSAR Principles: Guidelines for SAR Interferometry Processing and Interpretation (TM-19). ESA Publications, The Netherlands. Ferretti, A., Savio, G., Barzaghi, R., Borghi, A., Musazzi, S., Novali, F., Prati, C., & Rocca, F. (2007). Submillimeter Accuracy of InSAR Time Series: Experimental Validation. IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 5, pp Ferretti, A., Fumagalli, A., Novali, F., Prati, C., Rocca, F., & Rucci, A. (2011). A New Algorithm for Processing Interferometric Data-Stacks: SqueeSAR. IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 9, pp Ritter, D. F., Kockel, R.C., & Miller, J. R. (2011). Process Geomorphology. Waveland Press, Long Groove. Rosen, P. A., Hensley, S., Joughin, I. R., Li, F. K., Madsen, S. N., Rodriguez, E., & Goldstein, R. M. (2000). Synthetic Aperture Radar Interferometry. Proc. of the IEEE, vol. 88, no. 3, pp Shang, J. Q., & Umana, J. A. (1999). Dielectric Constant and Relaxation Time of Asphalt Pavement Materials. Journal of Infrastructure Systems, December 1999, pp Schlutz, M. (2009). Synthetic Aperture Radar Imaging Simulated in MATLAB. A Thesis presented to the Faculty of the California Polytechnic State University, San Louis Obispo. Vaccari, A., Stuecheli, M., Hoppe, E., Bruckno, B., & Acton, S. (2013). Detection of geophysical features in InSAR point cloud data sets using spatiotemporal models. International Journal of Remote Sensing, vol. 34, no. 22, pp