Abstract
Cobbles (64–256 mm) are found on beaches throughout the world, influence beach morphology, and can provide shoreline stability. Detailed, frequent, and spatially large-scale quantitative cobble observations at beaches are vital toward a better understanding of sand-cobble beach systems. This study used a truck-mounted mobile terrestrial LiDAR system and a raster-based classification approach to map cobbles automatically. Rasters of LiDAR intensity, intensity deviation, topographic roughness, and slope were utilized for cobble classification. Four machine learning techniques including maximum likelihood, decision tree, support vector machine, and k-nearest neighbors were tested on five raster resolutions ranging from 5–50 cm. The cobble mapping capability varied depending on pixel size, classification technique, surface cobble density, and beach setting. The best performer was a maximum likelihood classification using 20 cm raster resolution. Compared to manual mapping at 15 control sites (size ranging from a few to several hundred square meters), automated mapping errors were <12% (best fit line). This method mapped the spatial location of dense cobble regions more accurately compared to sparse and moderate density cobble areas. The method was applied to a ~40 km section of coast in southern California, and successfully generated temporal and spatial cobble distributions consistent with previous observations.
Highlights
Cobbles (64–256 mm) [1] are found on beaches throughout the world [2] and influence beach shape and morphology (e.g., [3,4])
This study examined four algorithms for automated machine learning-based supervised raster classification including maximum likelihood (MLi), decision tree (DT), support vector machine (SVM), and k-nearest neighbors (KNN) (Figure 5)
This study demonstrates automated cobble mapping using mobile terrestrial Light Detection and Ranging (LiDAR) with relatively low errors in cobble coverage estimations (Figures 7 and 8)
Summary
Cobbles (64–256 mm) [1] are found on beaches throughout the world [2] and influence beach shape and morphology (e.g., [3,4]). Incident wave energy fluctuations and the associated currents influence beach sediment size and distribution [5]. Previous studies (Table 1) have mapped various beach sediment sizes and distributions using photo-based techniques, including labor intensive manual digitization-based mapping from ground-based video cameras (e.g., [26,27]) and Unmanned Aerial Vehicle (UAV) photographs (e.g., [28]). Other studies used automated photo-based techniques to estimate grain sizes based on spatial correlation analysis of (grayscaled) image pixel intensity (i.e., autocorrelation technique, [29]). The automated photo-based techniques are operationally low-cost but provide limited spatial coverage (e.g., at a scale covered by a fixed digital camera) and sometimes require high resolution imagery (image pixel sizes smaller than grain size). Carbonneau et al [33,34] increased spatial coverage by using digital images from airborne surveys (tested in fluvial environments), but errors increased for coarse sediments (>100 mm).
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