An Automated Model to Classify Barrier Island Geomorphology using Lidar Data

Previous research has documented the usefulness of Lidar data to derive a variety of topographic products (e.g. DEM, DTM, canopy and forest structure, and urban infrastructure, to name a few). However, little research exists where Lidar has been used to derive coastal geomorphology. Therefore, the purpose for this project was to build on existing research and develop an automated modeling approach to classify coastal geomorphology and test this at several sites. The study areas were two developed and two undeveloped barrier islands in North Carolina. These sandy linear features protect the mainland from the open ocean. Various coastal processes, such as storms and longshore sediment transport, as well as human influences such as beach nourishment and urban development, shape barrier island geomorphology. This study used four dates of Lidar data from 1998 through 2014 and an automated model was developed in ArcGIS to process and classify Lidar data into ten geomorphic types: intertidal, supratidal, dune, hummock, overwash, channel, swale, upland, road and building. Model results were compared to compute change through time and derived the rate and direction of feature movement. When tropical storms occurred these were the dominant influence on the study areas. On the developed islands, there was less overall influence of storms due to the inability of features to move because of coastal infrastructure. For example, beach nourishment from 2005 to 2010 was the dominant influence on developed beaches because this activity ameliorated the natural tendency for an island to erode and move landward. Understanding how these processes influence barrier island dynamics is critical to predicting an island’s future response to changing environmental factors such as sea-level rise. Policy makers and coastal managers rely on this type of information to make development and conservation decisions.