Next Article in Journal
Numerical Simulations of the Hydraulic Performance of a Breakwater-Integrated Overtopping Wave Energy Converter
Next Article in Special Issue
Low-Cost UAV for High-Resolution and Large-Scale Coastal Dune Change Monitoring Using Photogrammetry
Previous Article in Journal
Oil Slick Characterization Using a Statistical Region-Based Classifier Applied to UAVSAR Data
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle

Continuous Coastal Monitoring with an Automated Terrestrial Lidar Scanner

1
School of Civil & Construction Engineering, Oregon State University, 101 Kearney Hall, Corvallis, OR 97331, USA
2
Coastal and Hydraulics Laboratory, U.S. Army Engineer Research and Development Center, 1261 Duck Road, Duck, NC 27949, USA
3
Department of Civil and Environmental Engineering, Geosensing Systems Engineering and Sciences Program, University of Houston, 5000 Gulf Freeway, Houston, TX 77204, USA
*
Authors to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2019, 7(2), 37; https://doi.org/10.3390/jmse7020037
Received: 3 January 2019 / Revised: 26 January 2019 / Accepted: 29 January 2019 / Published: 7 February 2019
(This article belongs to the Special Issue Application of Remote Sensing Methods to Monitor Coastal Zones)
  |  
PDF [5663 KB, uploaded 20 February 2019]
  |  

Abstract

This paper details the collection, geo-referencing, and data processing algorithms for a fully-automated, permanently deployed terrestrial lidar system for coastal monitoring. The lidar is fixed on a 4-m structure located on a shore-backing dune in Duck, North Carolina. Each hour, the lidar collects a three-dimensional framescan of the nearshore region along with a 30-min two-dimensional linescan time series oriented directly offshore, with a linescan repetition rate of approximately 7 Hz. The data are geo-referenced each hour using a rigorous co-registration process that fits 11 fixed planes to a baseline scan to account for small platform movements, and the residual errors from the fit are used to assess the accuracy of the rectification. This process decreased the mean error (defined as the magnitude of the offset in three planes) over a two-year period by 24.41 cm relative to using a fixed rectification matrix. The automated data processing algorithm then filters and grids the data to generate a dry-beach digital elevation model (DEM) from the framescan along with hourly wave runup, hydrodynamic, and morphologic statistics from the linescan time series. The lidar has collected data semi-continuously since January 2015 (with gaps occurring while the lidar was malfunctioning or being serviced), resulting in an hourly data set spanning four years as of January 2019. Examples of data products and potential applications spanning a range of spatial and temporal scales relevant to coastal processes are discussed. View Full-Text
Keywords: coastal monitoring; terrestrial lidar; continuous remote sensing; coastal morphodynamics coastal monitoring; terrestrial lidar; continuous remote sensing; coastal morphodynamics
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

O’Dea, A.; Brodie, K.L.; Hartzell, P. Continuous Coastal Monitoring with an Automated Terrestrial Lidar Scanner. J. Mar. Sci. Eng. 2019, 7, 37.

Show more citation formats Show less citations formats

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

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
J. Mar. Sci. Eng. EISSN 2077-1312 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top