Detecting and tracking reed canarygrass (Phalaris arundinacea) invasion in the Upper Mississippi River floodplain using remote sensing and artifial intelegence.
Description
We have a limited understanding of how the distribution of an invasive grass has changed over time. We seek to use satellite imagery to identify and track annual changes in the distribution of the invasive grass reed canarygrass.
Detailed example
Predictions of reed canarygrass invasion across the Upper Mississippi River system at an annual or subannual time step.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
This will lead to a better understanding of the dynamics (e.g., changes in inundation) that drive changes in the distribution of the invasive grass reed canarygrass to help achieve management objectives.
Controls / human review
ATO: No; PIA: Not published