Date of Graduation

5-2026

Document Type

Thesis

Degree Name

Bachelor of Science in Biological Engineering

Degree Level

Undergraduate

Department

Biological and Agricultural Engineering

Advisor/Mentor

Dr. Brian Haggard

Committee Member

Dr. Joshua Blackstock

Second Committee Member

Dr. Dongyi Wang

Abstract

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 changes in stream geometry are useful for infrastructure design, water quality assessment, water resource management, and stream restoration, among other applications. Traditional stream surveying methods contain numerous cost limitations that inhibit affordable and accessible stream surveying needed for evaluating stream geometry with high accuracy and larger spatial scales (>100s of ). 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, accessibility of these methods is hindered by affordability and the requirement of highly trained users. In response, this study investigated the use of the Apple iPhone’s LiDAR scanning technology and a relatively low-cost real-time kinetic global navigation satellite system receiver (RTK-GNSS) as an affordable and accessible alternative method for stream surveying. The objectives of this study were to develop a workflow for data collection using the iPhone LiDAR scanner and to assess the accuracy of the developed method. The iPhone LiDAR method was evaluated at three locations in Fayetteville, Arkansas, where accelerated erosion along stream banks was observed. To increase the spatial accuracy of the iPhone LiDAR scanning technology, additional hardware (i.e. RTK-GNSS, survey rod, ground control points), and software were applied in the data collection and processing workflows. The repeatability of the iPhone LiDAR method was also evaluated by comparing subsequent scans at the same location, resulting in a mean scan-to-scan distance on the order of cm.  The use of the iPhone for assessing stream erosion change was evaluated by comparing the iPhone derived point cloud with aerial imagery from 2017 to 2023 along a stream reach of the Middle Fork of the White River. Our analysis determined an average yearly bank migration rate of approximately 1 m per year. The rate of data collection using the developed iPhone LiDAR method in the field was approximately 30 square meters of survey area per minute while maintaining a resolution of 10 mm and a positional RMSE of 7 cm, which we considered suitable for monitoring streambank erosion at the scale of 1  and greater.

Keywords

Fluvial Geomorphology; LiDAR; Stream Surveying; Streambank Erosion

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