UAS mapping for oil spill response in sandy beach environments: Feasibility and best practices




Berryhill, Jacob

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Oil spill events can be catastrophically harmful to coastal ecosystems, causing considerable and long-term environmental and economic impacts before associated consequences are finally eliminated. Conducting timely, flexible, and accurate surveys immediately after a spill incident is of crucial importance for oil spill response in order to locate the spill, determine the size and volume of the spill, monitor and track the oil movement. Traditional survey methods and visual observations are usually performed for investigating the affected shoreline after an oil spill. Field sketches are used to record and convey the state of oiling in the affected areas. Diagnosis of oiling extent is limited to line-of-sight observations on the ground or by expensive manned aircraft operations. Recently, Unmanned Aircraft Systems (UAS) have been increasingly employed in various real-world applications, spanning from military scouting and scientific research to urban planning and entertainment. With the rapid development of miniaturized imaging and positioning technologies, UAS Structure-from-Motion (SfM) photogrammetry has become an emerging, cost-effective, and flexible solution for fulfilling various surveying and mapping needs at local scales. This thesis examined the potential and feasibility of using commercially available rotor copter and fixed-wing UAS platforms with SfM photogrammetric techniques to measure and monitor changes in beach elevation for shoreline oiling surveys. The state of the art of UAS-SfM together with its benefits and generic workflow in oil spill surveying were reviewed. A typical stretch of beach in South Texas was chosen as the study area in the thesis due to abundant historical data collected by the research laboratory from prior projects. The study site contains jetty blocks that provide stable features, as well as beaches that are both maintained and unmaintained. Four objectives were outlined with an effort to develop guidelines for UAS-SfM best practices for Shoreline Cleanup and Assessment Technique (SCAT). Based on the data collected at the study area, research findings suggest that without ground control points (GCPs), SfM processing with post-processing kinematic (PPK)-enabled image locations can achieve remarkably higher accuracy than that with autonomous Global Navigation Satellite System (GNSS) image geotags. Adding more GCPs can exponentially improve the overall accuracy for autonomous GNSS geotagged images. For the specific study area, the accuracy performance of the autonomous GNSS geotagged SfM products is on par with that of the differentially corrected GNSS geotagged SfM products with 10 GCPs used for georeferencing. By comparing against coordinates of the check points, the z residuals of a SfM-generated DSM were found better near the center of the beach and worse towards the water and in the dunes and vegetation. Another benefit of using a rigorous GCP control network is it significantly alleviates the bowling effect. Alternative solution for effectively alleviating the bowling effect in time critical survey missions where surveying GCPs is impossible is the use of high overlap oblique imagery and/or multi-elevation coverage. Height adjustments on erratic height values that occurred within autonomous GNSS geotagged images will not improve the accuracy in DSM rendering, however it is still recommended for reducing time and effort in identifying aerial target locations within the image set. When using the PPK operation mode, special attention needs to be paid because a consistent vertical datum should be maintained for the coordinates of both GCPs and image locations throughout the project. In case rapid SfM processing is considered essential for the sake of time, commercial SfM software demonstrated that several hours may be saved in terms of processing, but overall data quality of the geospatial products may have to be compromised.



Change Detection, DoD, Mapping, oil spill, structure from motion, UAS-SfM



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