Scaling-up phenological date matching for invasive species mapping: a free opensource workflow
Matching imagery dates to phenological events across large geographic regions for invasive species mapping.
Mapping and forecasting the geographic distributions of invasive plant species is a critical part the USGS National Early Detection and Rapid Response Framework to safeguard our nation from invasive species. Invasive species represent serious threats to our biodiversity and commercial, agricultural, and recreational economies. Ongoing efforts to accurately map and forecast invasive plant species rely on phenological date matching to ensure remote sensing vegetation indices match plant growth stages for a given area. Preliminary date matching work has been done with Google Earth Engine, a planetary scale data catalog and analysis framework, for a landscape scale spatial extent. We propose to develop an open-source and free R workflow for phenological date matching to provide an open-source solution for projects with limited funding to allow users to match imagery scene dates to phenological events for mapping and forecasting geographic distributions of invasive plant species at large spatial extents (for example, continental scales).
Matching imagery dates to phenological events across large geographic regions for invasive species mapping.
Mapping and forecasting the geographic distributions of invasive plant species is a critical part the USGS National Early Detection and Rapid Response Framework to safeguard our nation from invasive species. Invasive species represent serious threats to our biodiversity and commercial, agricultural, and recreational economies. Ongoing efforts to accurately map and forecast invasive plant species rely on phenological date matching to ensure remote sensing vegetation indices match plant growth stages for a given area. Preliminary date matching work has been done with Google Earth Engine, a planetary scale data catalog and analysis framework, for a landscape scale spatial extent. We propose to develop an open-source and free R workflow for phenological date matching to provide an open-source solution for projects with limited funding to allow users to match imagery scene dates to phenological events for mapping and forecasting geographic distributions of invasive plant species at large spatial extents (for example, continental scales).