The dynamic nature of the nation’s coastlines necessitates frequent shoreline monitoring and mapping. The U.S. Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory (CHL), Field Research Facility (FRF), has collected datasets on the nearshore zone’s changing conditions for over 40 years. During the course of these efforts, CHL has continued to develop different technologies to refine shoreline monitoring techniques, with a particular focus on the application of remote sensing technology to coastal monitoring. Light detection and ranging (lidar) scanners have proven useful for the CHL coastal measurement efforts, providing highly detailed data of coastal change and hydro-dynamic processes.

A Ford F350 pickup truck served as the data collection platform for this research.

Lidar is frequently collected from stationary ground-based platforms, which provide fine detail (100s to 1000s of points per meter) in one location or mobile airborne sampling approaches that provide coverage over large areas but at lower resolution (1 to 10s of points per meter). The U.S. Army Corps of Engineers (USACE), U.S. Naval Oceanographic Office, and National Oceanic and Atmospheric Administration (NOAA) formed the Joint Airborne Lidar Bathymetry Technical Center of eXpertise in 1998 to support coastal mapping requirements and committed to surveying the U.S. coastline every 5 years with airborne lidar. As a result of these and other efforts, coastal monitoring with airborne lidar data has provided a range of insights into coastal change since the late 1990s.

While airborne lidar sampling provides significant coverage, it also demands significant resources for deployment and may lack the temporal and spatial resolution necessary to adequately map the evolution of coastal features at scales relevant to the forcing conditions. To bridge the gap between airborne lidar coverage and terrestrial lidar scanner resolution and ease of deployment, CHL developed the Coastal Lidar and Radar Imaging System (CLARIS), a mobile, truck-based lidar system that can survey 10s of kilometers of coast at high-resolution (100s to 1000s of points per meter) in 1 day.

Mobile, terrestrial-based lidar systems offer the benefits of traditional stationary high-resolution scanning, and given a high-precision inertial navigation system, allow for large regional surveys to be conducted within hours at comparable resolution (Spore and Brodie 2017). CHL utilizes CLARIS to monitor beach elevation throughout the year on seasonal scale as well as before, during, and after extratropical, subtropical, and tropical storms or hurricanes. The regular, frequent collection along the same segments of shoreline reduces the uncertainty that often accompanies more sporadic, interannual beach surveys (Le Mauff et al. 2018).

Frequent high-resolution sub-aerial beach topographic surveys are critical to understanding variability of beach morphological evolution across different time-scales. Continued data collection can support scaling observations between these shorter events, seasonal, or year-length fluctuations to decadal scale change. The observational data can also be used to directly assess the condition of dunes and other natural coastal infrastructure. For example, Brodie and Spore (2015) compared CLARIS data against insight from Hesp (2002) to classify dune health by measuring their slopes, volumes, and curvature. The data will also support the development of better models for wave runup, coastal inundation, and overall sediment transport by providing more data for model input and validation, particularly across larger, regional scales.

This work was done by Nicholas J. Spore, Alexander D. Renaud, Ian W. Conery, and Katherine L. Brodie for the Army Engineer Research and Development Center. For more information, download the Technical Support Package (free white paper) below. (ERDC-0011)

This Brief includes a Technical Support Package (TSP).
Coastal Lidar and Radar Imaging System (CLARIS) Lidar Data Report

(reference ERDC-0011) is currently available for download from the TSP library.

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