A Python toolkit to monitor sandy shoreline change using high-resolution PlanetScope cubesats

This study evaluates an emerging capability to monitor high spatial (pixel size = 3.7 m) and temporal (daily to sub-daily imagery) resolution coastal change using PlanetScope cubesats. A new toolkit (CoastSat.PlanetScope) is presented that enables users to map shorelines from PlanetScope imagery, using a workflow of image coregistration, segmentation, thresholding, shoreline detection and elevation correction. The toolkit is subsequently tested at Narrabeen-Collaroy Beach (SE Australia), evaluating combinations of shoreline detection indices, thresholding approaches and elevation correction. An optimal shoreline accuracy of 3.5 m (RMSE) is found for this coastline using the difference between the near-infrared and blue bands and a weighted peaks thresholding approach. A generic elevation correction model that considers tidal variability and wave setup at the shoreline is then proposed. With a growing archive of high-resolution imagery, PlanetScope presents enormous potential for enhanced coastline mapping of the coast and complements existing approaches using Landsat/ Sentinel-2 imagery.

Automated summary

This study evaluates the use of PlanetScope cubesats for monitoring coastal change with high spatial and temporal resolution. A new toolkit called CoastSat.PlanetScope is introduced for shoreline mapping using PlanetScope imagery. The toolkit includes image coregistration, segmentation, thresholding, shoreline detection, and elevation correction. The study demonstrates an optimal shoreline accuracy of 3.5 meters for the tested coastline using specific indices and thresholding techniques. A generic elevation correction model considering tidal variability and wave setup is proposed. The study highlights the potential of using PlanetScope imagery for enhanced coastline mapping and its complementarity with existing approaches.