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.
Highlights
Collecting data in the nearshore environment is a notoriously difficult task
This paper describes the automated data collection and processing algorithms for a fixed lidar system deployed at the US Army Engineer Research and Development Center’s Field Research Facility, the data products generated by the system, and potential applications of this dataset in coastal science and monitoring efforts
An example of the cross-shore variability in morphodynamic processes can be seen in the elevation time series shown in Figure 8b, taken from a fixed alongshore location (y = 950 m) and from cross-shore locations ranging from x = 55 to 95 m in Field Research Facility (FRF) coordinates, with blue colors located higher on the beach and red colors located lower on the beach
Summary
Collecting data in the nearshore environment is a notoriously difficult task. Breaking waves and strong nearshore currents make nearshore sampling challenging and dangerous, and the relevant hydrodynamic and morphodynamic processes vary over a broad range of spatial and temporal scales. Traditional in situ measurement and survey techniques generally provide spatially sparse datasets, which often fail to capture the spatial variability in wave and current fields, bathymetry, and beach morphology necessary to understand and properly characterize many of these processes [1] These data collection methods are primarily deployed over relatively short time periods (days to months, in the case of fixed instruments) (e.g., [2]) or at a low temporal resolution (monthly to yearly, in the case of surveying) (e.g., [3]) because of financial or logistical constraints. The value of continuous remote sensing is clear, single-location optical systems such as Argus cannot provide highly accurate measurements of water surface and beach elevations, which are important parameters in both coastal science and management
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