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An invasive species of grass known as "buffelgrass" contributes to severewildfires and biodiversity loss in the Southwest United States. We tackle theproblem of predicting buffelgrass "green-ups" (i.e. readiness for herbicidaltreatment). To make our predictions, we explore temporal, visual andmulti-modal models that combine satellite sensing and deep learning. We findthat all of our neural-based approaches improve over conventional buffelgrassgreen-up models, and discuss how neural model deployment promises significantresource savings.

Top-down Green-ups: Satellite Sensing and Deep Models to Predict Buffelgrass Phenology