Measuring the potential impact of offshore mining on coastal instability through integrated time-series laser scanning and photography
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1 Measuring the potential impact of offshore mining on coastal instability through integrated time-series laser scanning and photography by Neil Slatcher, Roberto Vargas, Chris Cox and Liene Starka, 3D Laser Mapping Abstract Measurement of unstable slopes in actively mined coastal areas is critical to assess the associated hazards and potential impacts upon local infrastructure. Here, we present preliminary results of an automated approach that integrates airborne time-series online-waveform lidar data and image-based terrain feature tracking to quantify coastal slope instability in an area of active offshore mining. We demonstrate that automatic terrain classification using lidar data can be used to exclude areas unsuitable for image-based surface feature tracking from the analysis, enabling the measurement of maximum surface displacement rates. Surface-to-surface comparison of lidar-derived terrain models was also used to determine maximum surface profile changes. Keywords stability monitoring, laser scanning, coastal, time-series Introduction Measurements of unstable slopes in actively mined coastal areas are important to determine the associated hazards and potential impacts upon local infrastructure (Lim et al., 2010). Due to the often inaccessible nature of coastal terrain, survey data is typically captured using periodic airborne laser scanning and photographic surveys. Whilst both techniques offer a number of key advantages, there are also limitations. Laser scanning provides 3D data suitable for accurately measuring surface elevation changes, but the spatial resolution of measurements can limit the ability to track small-scale surface movements (Slatcher et al., 2015). In contrast to this, high-resolution airborne photographic surveys are suitable for identifying small-scale surface features, but time-series data derived from photographs are sensitive to changes in vegetation and can thus limit the ability to consistently track terrain features in consecutive images. Here, we explore the capabilities of integrated time-series online-waveform airborne laser scanning and photography as an approach to measure slope movements, using a case study from a section of the North- Yorkshire coast in the UK. The study site is an area of active subsurface mining that is located beneath a highly dynamic, rapidly eroding coastline. Laser and photographic data were captured at the same time during two airborne surveys performed approximately ten months apart. Changes in surface elevation over time were derived using automated lidar processing techniques. To determine displacement velocities, surface features were tracked in consecutive images in the time-series sequence using computer-vision techniques. After feature tracks were calculated, the 2D pixel-measurements were converted into 3D feature trajectories by re-projecting the image features onto the laser derived topography.
2 Through combining surface elevation changes derived from laser data and displacement velocities calculated from images, we were able to quantify both the magnitude of displacement, and the rate and direction over which the displacement occurred. The results demonstrate that laser scanning and automated feature tracking using photographs can be effectively combined to provide measurements of slope instability. Data capture Study area The study area was a small landslide (~27 m wide and 45 m long) located on the North-Yorkshire coast in the UK, approximately 12 km north-west of the historic town of Whitby (Fig. 1a). The coastal cliffs in this area are formed from middle Lias rocks that consist of the interbedded mudstones, shales, siltstones, ironstones and sandstones that form the Staithes Sandstone and underlying Redcar Mudstone formations (Rawson and Wright, 2000). Coastal instability in this area is significant, with regular rockfall and landslide events leading to elevated rates of coastal recession and associated hazards (Barlow et al., 2012). The study site was chosen as it provided an ideal location to test our automated processing techniques in a challenging, dynamic environment comprising both bare earth and heavily vegetated terrain. Fig. 1: Study area map (a) and survey hardware installed on a helicopter (b). Hardware and survey methodology Two repeat surveys were captured at an interval of approximately ten months (August and June ) using a Mobile Mapping System (MMS) installed on a helicopter (Fig. 1b). The MMS comprised a Riegl VQ- 450 laser scanner coupled with an IGI AeroControl navigation system. The AeroControl system consists of an Inertial Measurement Unit (IMU-IIe) based on fibre-optic gyros and a Sensor Management Unit with integrated
3 high-end GPS receiver. The Riegl VQ-450 laser scanner has a measurement rate of up to 500 khz, a measurement range of up to 800 m and offers online-waveform processing enabling multiple targets to be detected for each individual laser pulse. The laser scanner system was housed in a protective pod giving a 180 downward and sideways-looking field-of-view (inset, Fig. 1b). The field-of-view enabled both the terrain surface (downward looking) and cliff faces (sideways looking) to be scanned. In addition to the laser scanning system, a downward-looking 36,3 megapixel Nikon D-800 camera with a 20 mm lens was also installed in the protective housing to capture optical imagery during the surveys. The average flying height for the surveys was ~100 m AGL giving laser measurement point spacings on the ground of ~15 cm and a GSD of ~1 cm for the optical imagery. Data processing Surface displacement velocity Surface displacement velocity was derived by first using an automatic classifier on the online-waveform laser data to determine bare earth and vegetated terrain. This step was important as seasonal variation and growth of vegetation can limit the ability to consistently track corresponding terrain features in time-series image sets. The terrain classification approach used the online-waveform data from the laser scanner to identify areas of the terrain exhibiting more than one return per laser pulse. Pulses with multiple returns were assumed to be representative of vegetated terrain where backscatter is observed from more than one object (e.g. the ground surface and vegetation above the ground surface) and classified accordingly. Pulses with only a single return were classified as bare-earth To automatically detect and track the movement of key surface features between the two successive time-series image sets, the scale Invariant Feature Transform (SIFT) feature tracking algorithm was implemented to determine the 2D motion of features. The SIFT algorithm automatically identifies and tracks common features in successive time-series images to determine the 2D displacement of each tracked feature. To limit the negative impact of vegetation on the surface displacement results, the terrain classification derived from the laser data was used to exclude any tracked features falling within areas classified as vegetation from the analysis. Threedimensional displacement vectors for the final set of (non-excluded) tracked features were then derived by projecting the feature tracks onto the pointcloud-derived reference surface. Surface elevation change To determine changes in surface elevation over time, automated pointcloud-to-pointcloud processing was implemented using the SiteMonitor4D software package (3D Laser Mapping, 2015). The automated processing workflow in SiteMonitor4D first converts the reference pointcloud into an irregular triangular mesh. The surface normal distance between the reference mesh surface and the comparison pointcloud is then calculated, giving the 3D distance between the reference and comparison surface. The first captured survey (August ) was used as the reference surface and the second captured survey (June ) as the comparison surface.
4 Results Surface displacement velocity Fig. 2 shows the output of the automated terrain classification. To provide a reference image for the terrain classification, Fig. 2a shows a coloured 3D pointcloud of the study area highlighting the landslide boundary (dashed line) and the extensive vegetation across the site. The results of the automated terrain classification are shown in Fig. 2b, revealing a good general correspondence between areas of vegetated and bare-earth terrain (as shown in Fig. 2a) and the final classification. Fig. 2: 3D coloured point cloud of the study area (a). The dashed line highlights the landslide boundary. The results of the terrain surface classification (b). Fig. 3 shows the SIFT keypoints that were identified and tracked between successive time-series images. The full set of automatically detected surface features are shown in Fig. 4a. It is evident that a significant number of the surface features selected for tracking fell within vegetated areas and were thus problematic for surface
5 velocity estimation. Fig. 4b shows the selected surface features that remained after the application of the classification mask. Displacement vectors for tracked features that exhibited significant surface displacement are also shown in Fig. 4b. There is a clear downslope displacement trend within the landslide boundary, with maximum displacement distances of ~2,1 m over the 10 month study period (~2,5 m/yr -1 ). The displacement vectors shown in Fig. 4b also highlight the issue of using automated feature tracking in vegetated areas. Fig. 3: Aerial view of the study area showing SIFT keypoint tracking between successive image sets. Whilst the correspondence between the terrain surface and the automatic classification was generally good (Fig. 2b), there were vegetated areas falsely classified as bare earth and were thus not excluded from the analysis. Consequently, there are a number of irregularly large displacement vectors shown in Fig. 4b representing incorrectly tracked surface feature correspondences. Fig. 4: Aerial view of the study area showing all features selected for surface tracking (a). Calculated displacement vectors after the application of the lidar-derived terrain classification mask (b).
6 Surface elevation change The results of the time-series surface elevation change analysis are shown in Fig. 5. There is a clearly visible area of surface change at the base of the landslide with maximum surface-to-surface distances of ~4,9 m. This zone was interpreted as an area of accumulation at the base of the landslide comprising both rockfall debris and eroded material transported and deposited through surface run-off. Seasonal variability and growth of the vegetation outside the landslide boundary are also visible in Fig. 4, with notable changes in surface-to-surface distances of up to ~2,0 m. Fig. 5: 3D surface distance comparison map. Conclusions and future work This paper presents preliminary results of an automated approach that integrates time-series online-waveform lidar data and image-based terrain feature tracking to quantify coastal slope instability. It has been demonstrated that automatic terrain classification using lidar data can be used to exclude areas unsuitable for image-based surface feature tracking. Through utilizing the high spatial resolution (~1 cm) of images and the surface classification capability of online-waveform lidar data, it was possible to estimate maximum surface displacement rates of ~2,5 m/yr -1. Changes in surface-to-surface distances of up to ~4,9 m were also automatically derived from the lidar data. Future work will focus on using this method over larger spatial extents and different terrains to further validate the technique. In addition, more sophisticated terrain classification and surface tracking techniques will be tested to identify an optimal approach for integration into automated slope stability analysis.
7 References [1] J Barlow, M Lim, NJ Rosser, DN Petley, MJ Brain, EC Norman, and M Geer: Modeling cliff erosion using negative power law scaling of rockfalls. Geomorphology, , , [2] M Lim, NJ Rosser, R Allison, and DN Petley: Erosional processes in the hard rock coastal cliffs at Staithes, North Yorkshire. Geomorphology, 114, 12-21, [3] PF Rawson and JK Wright: The Yorkshire Coast. The Yorkshire Coast Geologists Association Guide No. 34. Geologists' Association, London. p.117, [4] N Slatcher, M James, S Calvari, G Ganci, and J Browning: Quantifying effusion rates at active volcanoes through integrated time-lapse laser scanning and photography. Remote Sensing, 7(11), , Contact Sam Bentley, 3D Laser Mapping, sam.bentley@3dlasermapping.com
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