Coastal environments are some of the most dynamic environments in the world. As they are constantly changing, so are the technologies and techniques we use to map and monitor them. The rapid advancement of sUAS-based remote sensing calls for rigorous field and processing workflows so that more reliable and consistent sUAS projects of coastal environments are carried out. Here, we synthesize the best practices to create sUAS photo-based surveying and processing workflows that can be used and modified by coastal scientists, depending on their project objective. While we aim to simplify the complexity of these workflows, we note that the nature of this work is a craft that carefully combines art, science, and technology. sUAS LiDAR is the next advancement in mapping and monitoring coastal environments. Therefore, future work should consider synthesizing best practices to develop rigorous field and data processing workflows used for sUAS LiDAR-based projects of coastal environments.

This study provides an evaluation of multiple sensors by examining their precision and ability to capture topographic complexity. Five different small unmanned aerial systems (sUAS) were evaluated, each with a different camera, Global Navigation Satellite System (GNSS), and Inertial Measurement Unit (IMU). A lidar was also used on the largest sUAS and as a mobile scanning system. The quality of each of the seven platforms were compared to actual surface measurements gathered with real-time kinematic (RTK)-GNSS and terrestrial laser scanning. Rigorous field and photogrammetric assessment workflows were designed around a combination of structure-from-motion to align images, Monte Carlo simulations to calculate spatially variable error, object-based image analysis to create objects, and MC32-PM algorithm to calculate vertical differences between two dense point clouds. The precision of the sensors ranged 0.115 m (minimum of 0.11 m for MaRS with Sony A7iii camera and maximum of 0.225 m for Mavic2 Pro). In a heterogenous test location with varying slope and high terrain roughness, only three of the seven mobile platforms performed well (MaRS, Inspire 2, and Phantom 4 Pro). All mobile sensors performed better for the homogenous test location, but the sUAS lidar and mobile lidar contained the most noise. The findings presented herein provide insights into cost–benefit of purchasing various sUAS and sensors and their ability to capture high-definition topography.

Barrier islands within sheltered environments are an important natural defense from severe storm impacts for coastal communities worldwide. Despite their importance, these fetch-limited barrier islands remain understudied and their ability to withstand and recover from storms is not well-understood. Here, we present a case study of Sugarloaf Island in North Carolina that demonstrates the operational use of openly accessible LiDAR and aerial imagery data to quantify synoptic habitat, shoreline, and volumetric change between 2014 and 2020, a period that encompasses four hurricanes and a winter storm event. During this time period, our results show: (1) an 11–13% decrease in marsh and shrub habitat, (2) an average landward shoreline migration of 2.9 m yr⁻¹ and up to 5.2 m yr⁻¹ in extreme areas, and (3) a net volume loss of approximately 9800 m³. The results of this study highlight the importance of storms as a driver of morphologic change on Sugarloaf Island and have implications for better understanding the resiliency of fetch-limited barrier islands to storms. This work helps to enhance prerestoration data availability and supports knowledge-based decision-making regarding habitat change, erosional issues, and the efficacy of nature-based solutions to increase the resiliency of a coastal community in North Carolina.

Estuaries provide essential ecosystem services and economic value but are facing widespread degradation due to changing anthropogenic and climatic factors. In North Carolina, coastal structures, like bulkheads and riprap, are widely used by property owners throughout the Albemarle-Pamlico estuary to stop erosion and reclaim lost land following storm events. While coastal development is tightly governed, limited historical and no up-to-date data report on the spatial distribution of coastal structures throughout the Albemarle-Pamlico estuary. Here we describe the development of a dataset that classifies and catalogues 67 km of shoreline type along the Neuse River Estuary (NRE), a large tributary of the Albemarle-Pamlico. We used available LiDAR digital elevation models (DEMs), aerial imagery, and a ground truthing field campaign to determine shoreline type present along the NRE as of 2020. We validated these results using an intensive manual editing procedure that comparatively examines DEMs, LiDAR derived slope, aerial imagery, and ground truth photography of the shoreline. This dataset is available for public download.

Small unmanned aerial systems (UAS) have been increasingly popular in surveying and mapping tasks. While photogrammetry has been the primary UAS sensing technology in other industries, construction activities can also benefit from accurate surveying measurements from airborne LIDAR. This paper discusses a custom-designed low-cost UAS-based LIDAR system that can effectively measure construction excavation and bulk piles. The system is designed with open interfaces that can be easily upgraded and expanded. An error model was developed to predict the horizontal and vertical errors of single point geo-registration for a generic UAS-LIDAR. This model was validated for the proposed UAS-LIDAR system using calibration targets and real-world measurements from different scenarios. The results indicated random errors from LIDAR at approximately 0.1 m and systematic errors at or below centimeter level. Additional pre-processing of the raw point cloud can further reduce the random errors in LIDAR measurements of bulk piles.