CoastSat: A Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery

Kilian Vos, Kristen D. Splinter, Mitchell D. Harley, Joshua A. Simmons, Ian L. Turner

Environmental Modelling & Software · 2019 · 629 citations · 41 references

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Concepts

TL;DR

CoastSat is an open‑source Python toolkit that retrieves time‑series shoreline positions worldwide from over 30 years of publicly available satellite imagery. CoastSat aims to give coastal managers, engineers and scientists a user‑friendly toolkit to monitor and explore coastlines with ~10 m shoreline accuracy. It uses Google Earth Engine to retrieve and pre‑process Landsat and Sentinel‑2 imagery, then applies a supervised classification and sub‑pixel border segmentation algorithm to detect shorelines, with the code and examples hosted on GitHub.

Abstract

CoastSat is an open-source software toolkit written in Python that enables the user to obtain time-series of shoreline position at any sandy coastline worldwide from 30+ years (and growing) of publicly available satellite imagery. The toolkit exploits the capabilities of Google Earth Engine to efficiently retrieve Landsat and Sentinel-2 images cropped to any user-defined region of interest. The resulting images are pre-processed to remove cloudy pixels and enhance spatial resolution, before applying a robust and generic shoreline detection algorithm. This novel shoreline detection technique combines a supervised image classification and a sub-pixel resolution border segmentation to map the position of the shoreline with an accuracy of ~10 m. The purpose of CoastSat is to provide coastal managers, engineers and scientists a user-friendly and practical toolkit to monitor and explore their coastlines. The software is freely-available on GitHub (https://github.com/kvos/CoastSat) and is accompanied by guided examples (Jupyter Notebook) plus step-by-step README documentation.

References

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