This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1002/2017JF004302. This is version 1 of this Preprint.
You must log in to post a comment.
Multibeam acoustic backscatter has considerable utility for remote characterization of spatially heterogeneous bed sediment composition over vegetated and unvegetated riverbeds of mixed sand and gravel. However, the use of high-frequency, decimeter-resolution acoustic backscatter for sediment classification in shallow water is hampered by significant topographic contamination of the signal. In mixed sand-gravel riverbeds, changes in the abiotic composition of sediment (such as homogeneous sand to homogeneous gravel) tend to occur over larger spatial scales than is characteristic of small-scale bedform topography (ripples, dunes, and bars) or biota (such as vascular plants and periphyton). A two-stage method is proposed to filter out the morphological contributions to acoustic backscatter. First, the residual supragrain-scale topographic effects in acoustic backscatter with small instantaneous insonified areas, caused by ambiguity in the local (beam-to-beam) bed-sonar geometry, are removed. Then, coherent scales between high-resolution topography and backscatter are identified using cospectra, which are used to design a frequency domain filter that decomposes backscatter into the (unwanted) high-pass component associated with bedform topography (ripples, dunes, and sand waves) and vegetation, and the (desired) low-frequency component associated with the composition of sediment patches superimposed on the topography. This process strengthens relationships between backscatter and sediment composition. A probabilistic framework is presented for classifying vegetated and unvegetated substrates based on acoustic backscatter at decimeter resolution. This capability is demonstrated using data collected from diverse settings within a 386 km reach of a canyon river whose bed varies among sand, gravel, cobbles, boulders, and submerged vegetation.
Civil and Environmental Engineering, Earth Sciences, Engineering, Environmental Monitoring, Environmental Sciences, Geomorphology, Hydraulic Engineering, Physical Sciences and Mathematics, Statistical Models, Statistics and Probability
bedforms, fluvial sedimentology, acoustics, aquatic vegetation, backscatter, multibeam
Published: 2017-11-02 04:46