Automating Shoreline Extraction Workflow Using Normalized Index from UAS Imagery
Being ever-changing and most important feature of coastal landscape, Shoreline reflects the natural processes of erosion as well as deposition, and hence extracting the shoreline is of paramount importance.. However, this extraction process is mostly manual and time-consuming. This process can be improved by developing a custom Geoprocessing tool through automated Python scripts that would help in extracting shoreline as a feature class. The ability of the developed tool is to make use of Red Edge Band to generate Normalized Index and thereby extract the shoreline features in a short span of time without much human intervention. The study area considered for this research is 120-acre Jupiter Inlet Lighthouse Outstanding Natural Area (ONA) that has more than 3500 feet of dynamic shoreline and the required UAS data has been collected using Micasense Multispectral Sensor Package. After performing Drone data processing using ‘Pix4D’, ‘ArcGIS Pro’ and ‘ArcPy’ has been used to automate the extraction process. This presentation will demonstrate the automatically extracted shorelines for study area using the newly developed tool. The extracted shoreline can be further used in performing change detection and in computing the volume of soil erosion for a timeseries of data.