Small unmanned aerial model accuracy for photogrammetrical fluvial bathymetric survey

Entwistle, NS; Heritage, GL

Abstract

Fluvial systems offer a challenging and varied environment for topographic survey, displaying a rapidly varying morphology, vegetation assemblage and degree of submergence. Traditionally theodolite or GPS based systems have been used to capture cross-section and breakline based topographic data which has subsequently been interpolated. Advances in survey technology has resulted in an improved ability to capture larger volumes of information with infrared terrestrial and aerial LiDAR systems capturing high density (<0.02 m) point data across terrestrial surfaces. The rise of Structure from Motion (SfM) photogrammetry, coupled with small unmanned aerial vehicles (sUAV), has potential to record elevation data at reach scale sub decimetre density. The approach has the additional advantage over LiDAR of seeing through clear water to capture bed detail, whilst also generating ortho-rectified photographic mosaics of the survey reach. However, data accuracy has yet to be comprehensively assessed. Here we present a survey protocol for sUAV deployment and provide a reach scale comparison between a theodolite and SfM sUAV survey on the River Sprint, Kendal, the River Ehen at Egremont, England and the Afon Elwy, at Llanfair Talhaiarn, Wales. Comparative analysis between theodolite survey and SfM suggest similar accuracy and precision across terrestrial surfaces with error lowest over solid surfaces, increasing with vegetation complexity. Submerged SfM data, captured bed levels generally to within ±0.25 m with only a weak relationship recorded between error and flow depth. Significantly, associated error when linked to channel D50 highlights the ability of unmanned aerial vehicles to capture accurate fluvial data across a range of river biotopes and depths to 2.4 m.

Citation

Entwistle, N., & Heritage, G. (2019). Small unmanned aerial model accuracy for photogrammetrical fluvial bathymetric survey. Journal of Applied Remote Sensing, 13(1), 014523. https://doi.org/10.1117/1.JRS.13.014523

• Journal Article Type: Article
• Acceptance Date: Feb 6, 2019
• Online Publication Date: Feb 26, 2019
• Publication Date: Feb 26, 2019
• Deposit Date: Apr 4, 2019
• Publicly Available Date: Apr 4, 2019
• Journal: Journal of Applied Remote Sensing
• Publisher: Society of Photo-optical Instrumentation Engineers
• Volume: 13
• Issue: 1
• Pages: 014523
• DOI: https://doi.org/10.1117/1.JRS.13.014523
• Publisher URL: https://doi.org/10.1117/1.JRS.13.014523
• Related Public URLs: https://www.spiedigitallibrary.org/journals/journal-of-applied-remote-sensing
• Additional Information: Additional Information : Copyright 2019 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.