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Date of Graduation
5-2026
Description
Monitoring stream morphology is crucial for maintaining the health and safety of fluvial environments, particularly as these naturally dynamic systems are shifted out of equilibrium due to climate and land-use change. Quantifying these changes in stream geometry is useful for infrastructure design, water quality assessment, water resource management, and stream restoration, among other applications. Traditional stream surveying methods contain limitations that inhibit affordable and accessible stream surveying needed for evaluating fluvial systems with high accuracy and large data samples. In recent years, several advancements have been made in the use of Light Detection and Ranging (LiDAR) technology as an alternative method for stream surveying, increasing the efficiency and accuracy of monitoring these systems. However, the accessibility of these methods is hindered by their high cost and the requirement of highly trained users. This study investigated the use of the Apple iPhone’s LiDAR scanning technology as an affordable and accessible alternative method for stream surveying. The objectives of this study were to develop a workflow for data collection with the iPhone LiDAR scanner and to assess the accuracy of the developed method. The iPhone LiDAR method was evaluated at three locations in and near Fayetteville, Arkansas, where accelerated erosion was observed. To increase the accuracy of the iPhone LiDAR scanning technology, additional hardware and software were applied in the data collection and processing workflows. The repeatability of the iPhone LiDAR method was evaluated by comparing multiple scans at the same location, and results suggest that iPhone LiDAR combined with additional software was successful.
Publication Date
2026
Document Type
Book
Degree Name
Bachelor of Science in Biomedical Engineering
Degree Level
Undergraduate
Department
Biomedical Engineering
Advisor/Mentor
Haggard, Brian
Disciplines
Biomedical Engineering and Bioengineering
Keywords
Engineering
Citation
Stitt, L. C. (2026). Monitoring Streambank Morphology Using iPhone LiDAR Technology. 2026 Research Poster Competition. Retrieved from https://scholarworks.uark.edu/hnrcsturpc26/68