Allintitle Lidar Embankment
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An Analysis of Preprocessing Techniques for the Removal of Transportation Embankments and Surface Roughness in Fine-Resolution LiDAR DEMs
The use of fine-resolution DEMs is becoming increasingly commonplace in environmental modelling. Depending on the application and scale of analysis, some of the information in these DEMs may interfere with the derivation of terrain attributes and therefore needs to be removed without impacting application-relevant information. This research examines preprocessing techniques for two surface features in fine-resolution DEMs: transportation embankments and surface roughness. A region growing algorithm was developed to map and remove embankments in LiDAR DEMs which demonstrates moderate to high efficacy for identifying embankments. An analysis of roughness smoothing techniques was performed to determine the impact of smoothing techniques on the distribution of flow path lengths, which indicates that the choice of smoothing technique should be based on the degree of smoothing required and landscape relief. The results of this research highlight the need for the development of new DEM preprocessing techniques and the careful consideration of existing techniques.
Monitoring Deformations of Embankments in Arctic Regions Using Unmanned Aerial Vehicle Photogrammetry and Terrestrial Laser Scanning
Embankments in Arctic regions are typically constructed during winter with no cuts in the ground to preserve the permafrost foundation. These embankments are susceptible to deformations in the summer immediately following construction as ice within the embankment fill melts and in subsequent years as permafrost at the embankment toe thaws. Unmanned aerial vehicle (UAV) photogrammetry and terrestrial laser scanning (TLS) were used to monitor deformations of four high-fill embankment sections along the newly constructed Inuvik-Tuktoyaktuk Highway (ITH). Two UAVs (senseFly albris and DJI Phantom 4 Pro) and one laser scanner (FARO Focus3D X 330) were used. One of the high-fill sections was reinforced with wicking woven geotextiles to improve slope stability and instrumented to displacements within the embankment. UAV photogrammetry and TLS are both relatively new technologies being used to monitor deformations of structures. Significant effort was dedicated to learning about the technologies, developing best operating practices, calibrating the technologies to quantify their accuracies, and designing the on-site surveys. UAV and TLS surveys were conducted during summer in three consecutive years (2017-2019). UAV imagery and TLS data were processed using specialized software to generate point clouds of the high-fill sections. An RTK system was used to measure positions of checkerboard ground control points (GCP) for georeferencing point clouds. The accuracy of UAV and TLS point clouds was quantified based on GCP errors. Alignment of point clouds was required because of poor quality GCP measurements. Point clouds from each year were compared using multiscale model-to-model cloud comparison (M3C2) to determine deformations. A cross-section analysis was also performed for each high-fill section. High-fill sections along ITH showed deformations including toe subsidence and lateral spreading. Some of the high-fill sections showed positive change (e.g. heave, deposition) at the upper-slope and negative change (e.g. erosion, settlement) at the lower-slope, while other sections showed the opposite behaviour. The behaviours and magnitudes of UAV and TLS deformations were highly sensitive to the point cloud alignment methods. UAV measured deformations underestimated the instrumentation displacement data at KM-82 by approximately 30 mm, while TLS measured deformations were reasonably close to the instrumentation data after point cloud alignment. Due to the novelty of research methods and technologies used, a few mistakes were made during data acquisition. These mistakes were discovered while analyzing the field data. The data acquisition methods and accuracy of reconstructed point clouds were improved year-to-year; however, embankment deformations were too small to be detected by the UAV or TLS and the identification of deformation mechanisms was limited. Although the results were not as conclusive as originally intended, several lessons were learned that will be valuable for future researchers and practitioners. UAV photogrammetry is better suited for monitoring larger areas with greater deformation magnitudes and TLS is better suited for monitoring smaller areas with small-scale deformations. Generally, UAV photogrammetry is better suited for monitoring deformations of embankments. Results obtained by the albris were slightly more accurate than the Phantom, but the high cost of the albris is not justified by its overall performance compared to the Phantom.