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A Tool for Rapid-Repeat High-Resolution Coastal Vegetation Maps to Improve Forecasting of Hurricane Impacts and Coastal Resilience Active

By Community for Data Integration (CDI) April 5, 2023

We will develop and publish a stand-alone Python script to produce high-frequency and high-spatial resolution coastal vegetation maps that leverage new Planet 8-band 3m images, USGS CoNED topo-bathy DEMs, and 3DEP Height Above Ground data. These products will help improve forecasts of hurricane impacts.

Natural coastal vegetated ecosystems provide nature-based solutions to climate-related impacts of hurricanes yet are subject to change each year. Coastal modelers seek simple and fast ways to obtain up-to-date coastal vegetation maps to produce more accurate coastal change forecasts. This project will address this critical need by leveraging Planet Labs 8-band, 3-m daily satellite images, freely available to Federal employees, 3DEP 3-m Height Above Ground raster models and extensive training data. These datasets will be combined with a machine learning classification model to develop a rapid repeat, user friendly, open-source Jupyter Notebook application that delivers high-resolution maps of coastal vegetation showing near real-time conditions. Tutorial notebook code on the use of Planet imagery will build capacity within the USGS remote-sensing community. The project addresses the CDI FY23 theme of climate-related data readiness by making data available for forecasts of hurricane impacts, and that indicate the state of our vegetated ecosystems.