Next Article in Journal
Quality Assurance Framework Development Based on Six New ECV Data Products to Enhance User Confidence for Climate Applications
Previous Article in Journal
Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping
Article

Automated Cobble Mapping of a Mixed Sand-Cobble Beach Using a Mobile LiDAR System

Scripps Institution of Oceanography, University of California, San Diego, 9500 Gilman Dr., La Jolla, CA 92093, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(8), 1253; https://doi.org/10.3390/rs10081253
Received: 22 June 2018 / Revised: 22 July 2018 / Accepted: 2 August 2018 / Published: 9 August 2018
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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. View Full-Text
Keywords: cobble; mobile terrestrial LiDAR; raster classification; machine learning; beach cobble; mobile terrestrial LiDAR; raster classification; machine learning; beach
Show Figures

Graphical abstract

MDPI and ACS Style

Matsumoto, H.; Young, A.P. Automated Cobble Mapping of a Mixed Sand-Cobble Beach Using a Mobile LiDAR System. Remote Sens. 2018, 10, 1253. https://doi.org/10.3390/rs10081253

AMA Style

Matsumoto H, Young AP. Automated Cobble Mapping of a Mixed Sand-Cobble Beach Using a Mobile LiDAR System. Remote Sensing. 2018; 10(8):1253. https://doi.org/10.3390/rs10081253

Chicago/Turabian Style

Matsumoto, Hironori, and Adam P. Young. 2018. "Automated Cobble Mapping of a Mixed Sand-Cobble Beach Using a Mobile LiDAR System" Remote Sensing 10, no. 8: 1253. https://doi.org/10.3390/rs10081253

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop