Quantifying Fluvial Substrate Size using Hyperspatial Resolution UAS Imagery and SfM-photogrammetry

The size and distribution of substrate within fluvial environments plays a fundamental role in the availability of aquatic habitats. Remote sensing approaches to substrate size quantification have previously provided coarse grain size outputs (c. 1m) at the catchment scale and very fine resolution outputs (c. 1mm) at the patch scale. Within this paper we assess the potential of a novel approach for rapidly providing hyperspatial resolution (c. 1cm) substrate size outputs at the intermediate mesoscale. This scale is of relevance to rapid habitat assessments within a riverscape style framework. Our approach uses imagery acquired from an unmanned aerial system (UAS) and processed using structure-from-motion (SfM) photogrammetry. We test this method on a 120m reach of a small, shallow river in the English Lake District. We explore the value of SfM point cloud roughness values for developing a predictive relationship with field-measured substrate size. Jack knife analyses indicate that our model is capable of predicting grain sizes with an average residual error of -0.011cm and standard deviation of 1.64cm. We show that our UAS-SfM method offers a rapid, flexible, high spatial resolution, spatially continuous and spatially explicit approach for quantifying fluvial grain size. However, poor precision of grain size estimates suggests that further refinement of our approach is required. With further testing and on-going developments in the capabilities of UAS and associated SfM software, our method may provide a viable method for quantitative, mesoscale river habitat assessments in the future.