Compositional Signatures in Acoustic Backscatter Over Vegetated and Unvegetated Mixed Sand‐Gravel Riverbeds. Issue 10 (13th October 2017)

• Record Type: Journal Article
• Authors: Buscombe, D.; Grams, P. E.; Kaplinski, M. A.
• Is Part Of: Journal of geophysical research. Volume 122:Issue 10(2017)
• Page Start: 1771
• Page End: 1793
• Publication Date: 2017-10-13
• Subjects: acoustic sediment classification -- mixed sand‐gravel riverbed -- acoustic vegetation classification -- fluvial sedimentology -- swath sonar; Geomorphology -- Periodicals; 551.3
• DOI: 10.1002/2017JF004302
• Languages: English
• ISSNs: 2169-9003

Abstract

Abstract : 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. Key Points: Cospectra reveal coherent scales between high‐resolution multibeam topography and backscatter Low‐pass filtering backscatter removes topographic effects: resulting backscatter is better related to sediment composition A probabilistic framework for vegetated and unvegetated riverbed substrate classification is presented