Abstract
Side scan sonar in low-cost ‘fishfinder’ systems has become popular in aquatic ecology and sedimentology for imaging submerged riverbed sediment at coverages and resolutions sufficient to relate bed texture to grain-size. Traditional methods to map bed texture (i.e. physical samples) are relatively high-cost and low spatial coverage compared to sonar, which can continuously image several kilometers of channel in a few hours. Towards a goal of automating the classification of bed habitat features, we investigate relationships between substrates and statistical descriptors of bed textures in side scan sonar echograms of alluvial deposits. We develop a method for automated segmentation of bed textures into between two to five grain-size classes. Second-order texture statistics are used in conjunction with a Gaussian Mixture Model to classify the heterogeneous bed into small homogeneous patches of sand, gravel, and boulders with an average accuracy of 80%, 49%, and 61%, respectively. Reach-averaged proportions of these sediment types were within 3% compared to similar maps derived from multibeam sonar.
Highlights
With an expectation that there exists a distribution of each texture metric associated with each substrate type, we considered a Gaussian Mixture Model (GMM) approach to classification
We identified three Grey Level Co-Occurrence Matrix (GLCM) properties, namely Homogeneity, Entropy and GLCM variance, as metrics that can objectively quantify the textures associated with different sediment types
Each classification approach has it own merits, but overall the GMM outperformed the least-squares approach based on its ability to estimate reach-scale proportions of different sediment types
Summary
The grain size of bed sediment is a fundamental attribute of rivers and streams [1], and an important independent variable in studies of river adjustment [2], river classification [3], sediment transport [4], hydraulic roughness [5], and aquatic habitat [6] and is an essential component of habitat suitability models [7,8,9,10]. Riverbeds are often arranged in sediment patch structures or facies of like-sediment [11] providing a diverse range of spatially coherent yet mobile micro-topographies [12]. Field studies have demonstrated how spatial variations in grain size affect the longitudinal organization of benthic community structure [15].
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