Quantifying the spatial error of low-cost 3D monitoring

The development of low-cost sensing systems for 3D data collection has revolutionised geomorphic research. High-resolution 3D data can now be collected, processed on-site, and analysed with ease, thanks to advancements in devices such as iPhone/iPad LiDAR sensors and the processing of Structure-from-Motion (SfM) data on a mobile phone. Despite these advances, systematic comparisons between low-cost methods and industry-standard techniques, such as Terrestrial Laser Scanning (TLS), remain limited, particularly in fluvial environments.

This study evaluates the efficacy of multiple low-cost 3D monitoring methods, including iPad LiDAR, mobile phone SfM, and digital SLR SfM, against TLS in a fluvial context. Six leaky wooden dams, widely used as natural flood management interventions across the UK, were selected as monitoring subjects. These dams significantly impact river hydrology, sediment transport, and geomorphic evolution, yet the lack of repeat monitoring limits our empirical understanding of their effects.

Our results quantify the spatial errors associated with each low-cost technique, offering critical insights into their applicability for geomorphic data collection. Additionally, this work establishes the first accessible, spatially distributed database of high-resolution surveys of leaky wooden dams. This database provides a valuable foundation for future research and enables academics, industry, the third sector, and the public to contribute to a global record of geomorphic change. By demonstrating the untapped potential of low-cost sensing technologies, this study promotes more widespread, cost-effective monitoring of geomorphic processes.