Deliverable 3-A: Evaluation of the selected remote sensing techniques to assess the state of geotechnical assets and performance validation with historic geotechnical data Rudiger Escobar Wolf, Thomas Oommen, El Hachemi Bouali, Rick Dobson, Colin Brooks, and Stanley Vitton Michigan Technological University USDOT Cooperative Agreement No. RITARS-14-H-MTU Due on: April 15, 2016 Principal Investigator: Dr. Thomas Oommen, Assistant Professor Department of Geological and Mining Engineering and Sciences Michigan Technological University 1400 Townsend Drive Houghton, MI 49931 (906) 487-2045 [email protected] Program Manager: Caesar Singh, P.E. Director, University Grants Program/Program Manager OST-Office of the Assistant Secretary for Research and Technology U.S. Dept. of Transportation 1200 New Jersey Avenue, SE, E35-336 Washington, DC 20590 (202) 366-3252 [email protected] TABLE OF CONTENTS Executive summary 3 1. Remote sensing applications to measure geotechnical asset surface displacements 4 2. Description of techniques 5 2.1 InSAR 5 2.2 LiDAR 6 2.3 Digital photogrammetry 7 3. Description of test sites 7 3.1 M-10 Highway, Detroit, Michigan 8 3.2 Railroad corridor in Nevada 10 3.3 Trans Alaska Pipeline corridor 11 3.4 Laboratory scaled model setup 12 4. Description of the data 13 4.1 Historic data used in the project 14 4.1.1 InSAR 14 4.1.1 LiDAR 17 4.2 New data collected as part of the project 20 4.2.1 LiDAR 20 4.2.1 Photogrammetry 20 5. Data processing and results 23 5.1 Extracting relevant characteristics for geotechnical asset assessment 23 5.2 Measuring displacement of geotechnical assets 23 5.2.1 InSAR results for the Nevada test sites 25 5.2.2 LiDAR results for the Nevada test sites 30 5.2.3 Photogrammetry results for the Nevada test sites 33 5.2.4 InSAR results for the Michigan sites 35 5.2.5 Photogrammetry results for the M-10 highway site 38 5.2.6 InSAR results for the Alaska sites 40 5.2.7 Photogrammetry results for the Alaska sites 41 5.2.8 Photogrammetry results for the scaled model laboratory tests 45 6. Comparison of results with ground control data and inter-comparison of methods 49 6.1 InSAR results for the Nevada sites 49 6.2. Photogrammetry and LiDAR results for the Nevada sites 51 6.3 Photogrammetry and LiDAR results for the Alaska sites 55 7 Limitations and challenges of the methods 58 7.1 InSAR 58 7.2 LiDAR and digital photogrammetry 61 8. Conclusions and recommendations: what methods seem more appropriate for what 63 9.applications? References 64 Deliverable 3-A RITARS-14-H-MTU 1 GLOSSARY OF TERMS ALOS Advanced Land Observing Satellite ASI Italian Space Agency CSA Canadian Space Agency COSMO-SkyMed Constellation of small Satellites for the Mediterranean basin Observation DEM Digital Elevation Model DLR German Space Agency DSLR Digital single-lens reflex DSI Distributed Scatterer Interferometry ENVISAT Environmental Satellite ERS European Remote Sensing Satellite ESA European Space Agency FOV Field of view GAM Geotechnical Asset Management GNSS Global Navigation Satellite System GPS Global Positioning System ICP Iterative Closest Point InSAR Interferometric Synthetic Aperture Radar JAXA Japanese Aerospace Exploration Agency LiDAR Light Detection and Ranging LOS Line of Sight PALSAR Phased Array type L-band Synthetic Aperture Radar PSI Persistent Scatterer Interferometry RADARSAT-1 and 2 Radar Satellite 1 and 2 SHP Statistically Homogeneous Pixels TIN Triangular Irregular Network TerraSAR-X German radar earth observation satellite TRE Tele-Rilevamento Europa UAV Unmanned aerial vehicle USDOT/OST-R US Department of Transportation, through the Office of the Assistant Secretary for Research and Technology VSM Vertical Support Members Deliverable 3-A RITARS-14-H-MTU 2 EXECUTIVE SUMMARY: DELIVERABLE 3-A Overall Goal of this Deliverable: The strength and weaknesses of different remote sensing methods are evaluated in the context of surface displacement measurements, applied to geotechnical assets. Three remote sensing methods are evaluated: satellite InSAR, LiDAR (terrestrial and aerial), and digital photogrammetry (terrestrial and aerial, both from UAVs and from human piloted helicopters). Field site cases as well as scaled model laboratory tests are performed, and the results of the different methods are compared with ground control data, and between the methods’ results. The methods overall performance is evaluated for different surface deformation measurement cases. A comparison between methods and with ground control points is also presented, considering their precision, data point densities and ease of operation. Recommendations on the applicability for monitoring and characterizing different geotechnical assets are given at the end. Acknowledgements This work is supported by the US Department of Transportation, through the Office of the Assistant Secretary for Research and Technology (USDOT OST-R). 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 USDOT OST-R, or any state or other entity. Additional information regarding this project can be found at www.mtri.org/geoasset Deliverable 3-A RITARS-14-H-MTU 3 1. Remote sensing applications to measure geotechnical asset surface displacements The remote sensing techniques selected for assessing the state of geotechnical assets were chosen primarily for three reasons: their ability to produce a precise three dimensional representation of the surface of the assets, their ability to detect changes in the asset’s surface over time, or both. The different technologies have different strengths and weaknesses, which will be analyzed and discussed in this report, in the context of the information they provide about the geotechnical assets’ health and performance, and the quality of that information. InSAR, LiDAR and digital photogrammetry are applied to a series of study cases in different field locations and laboratory scaled models. The results of the tests are evaluated for their precision, data point density, ease of acquisition and potential costs. Different platforms are also evaluated, including terrestrial static, terrestrial mobile, aerial (from unmanned aerial vehicles - UAV- and helicopter), and satellite. Different platforms allow for different “field of view” (FOV) scales, and data point densities, from very large scale FOV and low density data point collections from satellite platforms, to very narrow FOV but high density of data points from terrestrial and UAV platforms. The applicability of each technology and method is also tested in relationship to the type of asset being assessed. Retaining walls require different treatment, and are susceptible to different types of remote sensing methods than rock or soil slopes, or permafrost induced subsidence. A similar consideration also applies to the type of transportation corridor; the performance requirements of geotechnical assets for railroads, roads, and pipelines, will all be different, as the effects of the assets performance on the transportation system (e. g. Maximum allowable ground deformation or the risk of being struck by rockfall) will vary. Drawing on the various field environments, transportation systems, and asset types, we have compiled a series of study cases that show the relative merits of each technology for the specific cases. Nevertheless, general conclusions can be drawn from this series of study cases, and comparisons between systems can be made at a general level. Deliverable 3-A RITARS-14-H-MTU 4 2. Description of techniques Several remote sensing techniques were examined as part of the technology selection process in this project. Some were not used in the end (e. g. panorama stitching photogrammetry and thermal imagery of slopes), so that the analysis was limited in the end to three main technologies: Satellite InSAR, Terrestrial LiDAR, and terrestrial and aerial digital photogrammetry. This section gives a summarized overview of these technologies and their applicability within the scope of the project. 2.1 InSAR Interferometric synthetic aperture radar (InSAR) is a remote sensing technique that utilizes multiple radar images to measure the phase shift between acquisitions. Radar images can be acquired from a terrestrial platform – either stationary or mounted on a mobile vehicle, from an aircraft, or from a satellite (Cutrona, 1990; Zebker et al., 1994; Bürgmann et al., 2000); satellite- based InSAR is the focus of this paper. Satellite-based InSAR is an active, side-looking radar system that transmits and receives radar waves. Sensors attached to the satellite electronically record incoming radar echoes as complex numbers in the form of where A is the amplitude of the radar wave and f is the phase (Dzurisin & Lu, 2007). When multiple radar images are processed as a stack, the phase shift (횫훟) can be calculated at the pixel-scale between a reference image and all other acquired images. The change in distance between the satellite and any given target pixel (횫d) can be calculated with the following relationship: 횫d = ½ 훌(횫훟/2훑) …where 훌 is the radar wavelength, the ½-component is used to eliminate two-way travel time, and the quotient of (횫훟 / 2훑) represents the phase shift in terms of multiples of 2훑, since the phase takes a modulo-2훑 form. Notice if 횫훟 = 2훑, then 횫d = 훌/2, which is the maximum allowable phase shift before the radar image pair is considered de-correlated at that pixel. Two types of InSAR stacking techniques are utilized in this study: (1) persistent scatterer interferometry (PSI) and (2) distributed scatterer interferometry (DSI). PSI requires pixels within large radar image stacks (>20 images recommended) to exhibit consistently high coherence (g), which is defined as the ratio of coherent (e.g., signal) and incoherent (e.g., noise) radar data (Ferretti et al., 2000). Coherence values range from 0 (incoherent) to 1 (coherent), and are a Deliverable 3-A RITARS-14-H-MTU 5 function of systemic spatial decorrelation, natural scene decorrelation, and additive noise (Askne et al., 1999).